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Title: Wave and Dirac equations on manifolds,
Abstract: We review some recent results on geometric equations on Lorentzian manifolds
such as the wave and Dirac equations. This includes well-posedness and
stability for various initial value problems, as well as results on the
structure of these equations on black-hole spacetimes (in particular, on the
Kerr solution), the index theorem for hyperbolic Dirac operators and properties
of the class of Green-hyperbolic operators. | [
0,
0,
1,
0,
0,
0
] |
Title: The Efimov effect for heteronuclear three-body systems at positive scattering length and finite temperature,
Abstract: We study the recombination process of three atoms scattering into an atom and
diatomic molecule in heteronuclear mixtures of ultracold atomic gases with
large and positive interspecies scattering length at finite temperature. We
calculate the temperature dependence of the three-body recombination rates by
extracting universal scaling functions that parametrize the energy dependence
of the scattering matrix. We compare our results to experimental data for the
40K-87Rb mixture and make a prediction for 6Li-87Rb. We find that contributions
from higher partial wave channels significantly impact the total rate and, in
systems with particularly large mass imbalance, can even obliterate the
recombination minima associated with the Efimov effect. | [
0,
1,
0,
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] |
Title: Mean-Field Controllability and Decentralized Stabilization of Markov Chains, Part II: Asymptotic Controllability and Polynomial Feedbacks,
Abstract: This paper, the second of a two-part series, presents a method for mean-field
feedback stabilization of a swarm of agents on a finite state space whose time
evolution is modeled as a continuous time Markov chain (CTMC). The resulting
(mean-field) control problem is that of controlling a nonlinear system with
desired global stability properties. We first prove that any probability
distribution with a strongly connected support can be stabilized using
time-invariant inputs. Secondly, we show the asymptotic controllability of all
possible probability distributions, including distributions that assign zero
density to some states and which do not necessarily have a strongly connected
support. Lastly, we demonstrate that there always exists a globally
asymptotically stabilizing decentralized density feedback law with the
additional property that the control inputs are zero at equilibrium, whenever
the graph is strongly connected and bidirected. Then the problem of
synthesizing closed-loop polynomial feedback is framed as a optimization
problem using state-of-the-art sum-of-squares optimization tools. The
optimization problem searches for polynomial feedback laws that make the
candidate Lyapunov function a stability certificate for the resulting
closed-loop system. Our methodology is tested for two cases on a five vertex
graph, and the stabilization properties of the constructed control laws are
validated with numerical simulations of the corresponding system of ordinary
differential equations. | [
1,
0,
1,
0,
0,
0
] |
Title: The kinematics of σ-drop bulges from spectral synthesis modelling of a hydrodynamical simulation,
Abstract: A minimum in stellar velocity dispersion is often observed in the central
regions of disc galaxies. To investigate the origin of this feature, known as a
{\sigma}-drop, we analyse the stellar kinematics of a high-resolution N-body +
smooth particle hydrodynamical simulation, which models the secular evolution
of an unbarred disc galaxy. We compared the intrinsic mass-weighted kinematics
to the recovered luminosity-weighted ones. The latter were obtained by
analysing synthetic spectra produced by a new code, SYNTRA, that generates
synthetic spectra by assigning a stellar population synthesis model to each
star particle based on its age and metallicity. The kinematics were derived
from the synthetic spectra as in real spectra to mimic the kinematic analysis
of real galaxies. We found that the recovered luminosity-weighted kinematics in
the centre of the simulated galaxy are biased to higher rotation velocities and
lower velocity dispersions due to the presence of young stars in a thin and
kinematically cool disc, and are ultimately responsible for the {\sigma}-drop. | [
0,
1,
0,
0,
0,
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] |
Title: Algorithms and Architecture for Real-time Recommendations at News UK,
Abstract: Recommendation systems are recognised as being hugely important in industry,
and the area is now well understood. At News UK, there is a requirement to be
able to quickly generate recommendations for users on news items as they are
published. However, little has been published about systems that can generate
recommendations in response to changes in recommendable items and user
behaviour in a very short space of time. In this paper we describe a new
algorithm for updating collaborative filtering models incrementally, and
demonstrate its effectiveness on clickstream data from The Times. We also
describe the architecture that allows recommendations to be generated on the
fly, and how we have made each component scalable. The system is currently
being used in production at News UK. | [
1,
0,
0,
0,
0,
0
] |
Title: A Physics Tragedy,
Abstract: The measurement problem and three other vexing experiments in quantum physics
are described. It is shown how Quantum Field Theory, as formulated by Julian
Schwinger, provides simple solutions for all four experiments. It is also shown
how this theory resolves many other problems of Quantum Mechanics and
Relativity, including a new and simple derivation of E = mc2. | [
0,
1,
0,
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0,
0
] |
Title: Dynamical Mass Generation in Pseudo Quantum Electrodynamics with Four-Fermion Interactions,
Abstract: We describe dynamical symmetry breaking in a system of massless Dirac
fermions with both electromagnetic and four-fermion interactions in (2+1)
dimensions. The former is described by the Pseudo Quantum Electrodynamics
(PQED) and the latter is given by the so-called Gross-Neveu action. We apply
the Hubbard-Stratonovich transformation and the large$-N_f$ expansion in our
model to obtain a Yukawa action. Thereafter, the presence of a symmetry broken
phase is inferred from the non-perturbative Schwinger-Dyson equation for the
electron propagator. This is the physical solution whenever the fine-structure
constant is larger than a critical value $\alpha_c(D N_f)$. In particular, we
obtain the critical coupling constant $\alpha_c\approx 0.36$ for $D N_f=8$.,
where $D=2,4$ corresponds to the SU(2) and SU(4) cases, respectively, and $N_f$
is the flavor number. Our results show a decreasing of the critical coupling
constant in comparison with the case of pure electromagnetic interaction, thus
yielding a more favorable scenario for the occurrence of dynamical symmetry
breaking. For two-dimensional materials,in application in condensed matter
systems, it implies an energy gap at the Dirac points or valleys of the
honeycomb lattice. | [
0,
1,
0,
0,
0,
0
] |
Title: Computational Methods for Path-based Robust Flows,
Abstract: Real world networks are often subject to severe uncertainties which need to
be addressed by any reliable prescriptive model. In the context of the maximum
flow problem subject to arc failure, robust models have gained particular
attention. For a path-based model, the resulting optimization problem is
assumed to be difficult in the literature, yet the complexity status is widely
unknown. We present a computational approach to solve the robust flow problem
to optimality by simultaneous primal and dual separation, the practical
efficacy of which is shown by a computational study.
Furthermore, we introduce a novel model of robust flows which provides a
compromise between stochastic and robust optimization by assigning
probabilities to groups of scenarios. The new model can be solved by the same
computational techniques as the robust model. A bound on the generalization
error is proven for the case that the probabilities are determined empirically.
The suggested model as well as the computational approach extend to linear
optimization problems more general than robust flows. | [
1,
0,
1,
0,
0,
0
] |
Title: A game theoretic approach to a network cloud storage problem,
Abstract: The use of game theory in the design and control of large scale networked
systems is becoming increasingly more important. In this paper, we follow this
approach to efficiently solve a network allocation problem motivated by
peer-to- peer cloud storage models as alternatives to classical centralized
cloud storage services. To this aim, we propose an allocation algorithm that
allows the units to use their neighbors to store a back up of their data. We
prove convergence, characterize the final allocation, and corroborate our
analysis with extensive numerical simulation that shows the good performance of
the algorithm in terms of scalability, complexity and structure of the
solution. | [
1,
0,
1,
0,
0,
0
] |
Title: A Survey of the State-of-the-Art Parallel Multiple Sequence Alignment Algorithms on Multicore Systems,
Abstract: Evolutionary modeling applications are the best way to provide full
information to support in-depth understanding of evaluation of organisms. These
applications mainly depend on identifying the evolutionary history of existing
organisms and understanding the relations between them, which is possible
through the deep analysis of their biological sequences. Multiple Sequence
Alignment (MSA) is considered an important tool in such applications, where it
gives an accurate representation of the relations between different biological
sequences. In literature, many efforts have been put into presenting a new MSA
algorithm or even improving existing ones. However, little efforts on
optimizing parallel MSA algorithms have been done. Nowadays, large datasets
become a reality, and big data become a primary challenge in various fields,
which should be also a new milestone for new bioinformatics algorithms. This
survey presents four of the state-of-the-art parallel MSA algorithms, TCoffee,
MAFFT, MSAProbs, and M2Align. We provide a detailed discussion of each
algorithm including its strengths, weaknesses, and implementation details and
the effectiveness of its parallel implementation compared to the other
algorithms, taking into account the MSA accuracy on two different datasets,
BAliBASE and OXBench. | [
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1,
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] |
Title: Learning Wasserstein Embeddings,
Abstract: The Wasserstein distance received a lot of attention recently in the
community of machine learning, especially for its principled way of comparing
distributions. It has found numerous applications in several hard problems,
such as domain adaptation, dimensionality reduction or generative models.
However, its use is still limited by a heavy computational cost. Our goal is to
alleviate this problem by providing an approximation mechanism that allows to
break its inherent complexity. It relies on the search of an embedding where
the Euclidean distance mimics the Wasserstein distance. We show that such an
embedding can be found with a siamese architecture associated with a decoder
network that allows to move from the embedding space back to the original input
space. Once this embedding has been found, computing optimization problems in
the Wasserstein space (e.g. barycenters, principal directions or even
archetypes) can be conducted extremely fast. Numerical experiments supporting
this idea are conducted on image datasets, and show the wide potential benefits
of our method. | [
1,
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1,
0,
0
] |
Title: Contextual Stochastic Block Models,
Abstract: We provide the first information theoretic tight analysis for inference of
latent community structure given a sparse graph along with high dimensional
node covariates, correlated with the same latent communities. Our work bridges
recent theoretical breakthroughs in the detection of latent community structure
without nodes covariates and a large body of empirical work using diverse
heuristics for combining node covariates with graphs for inference. The
tightness of our analysis implies in particular, the information theoretical
necessity of combining the different sources of information. Our analysis holds
for networks of large degrees as well as for a Gaussian version of the model. | [
1,
0,
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1,
0,
0
] |
Title: Tensor Regression Meets Gaussian Processes,
Abstract: Low-rank tensor regression, a new model class that learns high-order
correlation from data, has recently received considerable attention. At the
same time, Gaussian processes (GP) are well-studied machine learning models for
structure learning. In this paper, we demonstrate interesting connections
between the two, especially for multi-way data analysis. We show that low-rank
tensor regression is essentially learning a multi-linear kernel in Gaussian
processes, and the low-rank assumption translates to the constrained Bayesian
inference problem. We prove the oracle inequality and derive the average case
learning curve for the equivalent GP model. Our finding implies that low-rank
tensor regression, though empirically successful, is highly dependent on the
eigenvalues of covariance functions as well as variable correlations. | [
1,
0,
0,
1,
0,
0
] |
Title: Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition,
Abstract: The Canonical Polyadic decomposition (CPD) is a convenient and intuitive tool
for tensor factorization; however, for higher-order tensors, it often exhibits
high computational cost and permutation of tensor entries, these undesirable
effects grow exponentially with the tensor order. Prior compression of tensor
in-hand can reduce the computational cost of CPD, but this is only applicable
when the rank $R$ of the decomposition does not exceed the tensor dimensions.
To resolve these issues, we present a novel method for CPD of higher-order
tensors, which rests upon a simple tensor network of representative
inter-connected core tensors of orders not higher than 3. For rigour, we
develop an exact conversion scheme from the core tensors to the factor matrices
in CPD, and an iterative algorithm with low complexity to estimate these factor
matrices for the inexact case. Comprehensive simulations over a variety of
scenarios support the approach. | [
1,
0,
0,
0,
0,
0
] |
Title: Social Learning and Diffusion of Pervasive Goods: An Empirical Study of an African App Store,
Abstract: In this study, the authors develop a structural model that combines a macro
diffusion model with a micro choice model to control for the effect of social
influence on the mobile app choices of customers over app stores. Social
influence refers to the density of adopters within the proximity of other
customers. Using a large data set from an African app store and Bayesian
estimation methods, the authors quantify the effect of social influence and
investigate the impact of ignoring this process in estimating customer choices.
The findings show that customer choices in the app store are explained better
by offline than online density of adopters and that ignoring social influence
in estimations results in biased estimates. Furthermore, the findings show that
the mobile app adoption process is similar to adoption of music CDs, among all
other classic economy goods. A counterfactual analysis shows that the app store
can increase its revenue by 13.6% through a viral marketing policy (e.g., a
sharing with friends and family button). | [
1,
0,
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1,
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] |
Title: Dynamic correlations at different time-scales with Empirical Mode Decomposition,
Abstract: The Empirical Mode Decomposition (EMD) provides a tool to characterize time
series in terms of its implicit components oscillating at different
time-scales. We apply this decomposition to intraday time series of the
following three financial indices: the S\&P 500 (USA), the IPC (Mexico) and the
VIX (volatility index USA), obtaining time-varying multidimensional
cross-correlations at different time-scales. The correlations computed over a
rolling window are compared across the three indices, across the components at
different time-scales, at different lags and over time. We uncover a rich
heterogeneity of interactions which depends on the time-scale and has important
led-lag relations which can have practical use for portfolio management, risk
estimation and investments. | [
1,
0,
0,
0,
0,
0
] |
Title: Order-disorder transitions in lattice gases with annealed reactive constraints,
Abstract: We study equilibrium properties of catalytically-activated $A + A \to
\oslash$ reactions taking place on a lattice of adsorption sites. The particles
undergo continuous exchanges with a reservoir maintained at a constant chemical
potential $\mu$ and react when they appear at the neighbouring sites, provided
that some reactive conditions are fulfilled. We model the latter in two
different ways: In the Model I some fraction $p$ of the {\em bonds} connecting
neighbouring sites possesses special catalytic properties such that any two
$A$s appearing on the sites connected by such a bond instantaneously react and
desorb. In the Model II some fraction $p$ of the adsorption {\em sites}
possesses such properties and neighbouring particles react if at least one of
them resides on a catalytic site. For the case of \textit{annealed} disorder in
the distribution of the catalyst, which is tantamount to the situation when the
reaction may take place at any point on the lattice but happens with a finite
probability $p$, we provide an exact solution for both models for the interior
of an infinitely large Cayley tree - the so-called Bethe lattice. We show that
both models exhibit a rich critical behaviour: For the annealed Model I it is
characterised by a transition into an ordered state and a re-entrant transition
into a disordered phase, which both are continuous. For the annealed Model II,
which represents a rather exotic model of statistical mechanics in which
interactions of any particle with its environment have a peculiar Boolean form,
the transition to an ordered state is always continuous, while the re-entrant
transition into the disordered phase may be either continuous or discontinuous,
depending on the value of $p$. | [
0,
1,
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] |
Title: Spherical CNNs,
Abstract: Convolutional Neural Networks (CNNs) have become the method of choice for
learning problems involving 2D planar images. However, a number of problems of
recent interest have created a demand for models that can analyze spherical
images. Examples include omnidirectional vision for drones, robots, and
autonomous cars, molecular regression problems, and global weather and climate
modelling. A naive application of convolutional networks to a planar projection
of the spherical signal is destined to fail, because the space-varying
distortions introduced by such a projection will make translational weight
sharing ineffective.
In this paper we introduce the building blocks for constructing spherical
CNNs. We propose a definition for the spherical cross-correlation that is both
expressive and rotation-equivariant. The spherical correlation satisfies a
generalized Fourier theorem, which allows us to compute it efficiently using a
generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We
demonstrate the computational efficiency, numerical accuracy, and effectiveness
of spherical CNNs applied to 3D model recognition and atomization energy
regression. | [
0,
0,
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1,
0,
0
] |
Title: The Kelvin-Helmholtz instability in the Orion nebula: The effect of radiation pressure,
Abstract: The recent observations of rippled structures on the surface of the Orion
molecular cloud (Berné et al. 2010), have been attributed to the
Kelvin-Helmholtz (KH) instability. The wavelike structures which have mainly
seen near star-forming regions taking place at the interface between the hot
diffuse gas, which is ionized by massive stars, and the cold dense molecular
clouds. The radiation pressure of massive stars and stellar clusters is one of
the important issues that has been considered frequently in the dynamics of
clouds. Here, we investigate the influence of radiation pressure, from
well-known Trapezium cluster in the Orion nebula, on the evolution of KH
instability. The stability of the interface between HII region and molecular
clouds in the presence of the radiation pressure, has been studied using the
linear perturbation analysis for the certain range of the wavelengths. The
linear analysis show that consideration of the radiation pressure intensifies
the growth rate of KH modes and consequently decreases the e-fold time-scale of
the instability. On the other hand the domain of the instability is extended
and includes the more wavelengths, consisting of smaller ones rather than the
case when the effect of the radiation pressure is not considered. Our results
shows that for $\lambda_{\rm KH}>0.15\rm pc$, the growth rate of KH instability
dose not depend to the radiation pressure. Based on our results, the radiation
pressure is a triggering mechanism in development of the KH instability and
subsequently formation of turbulent sub-structures in the molecular clouds near
massive stars. The role of magnetic fields in the presence of the radiation
pressure is also investigated and it is resulted that the magnetic field
suppresses the effects induced by the radiation pressure. | [
0,
1,
0,
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0
] |
Title: Holography and Koszul duality: the example of the $M2$ brane,
Abstract: Si Li and author suggested in that, in some cases, the AdS/CFT correspondence
can be formulated in terms of the algebraic operation of Koszul duality. In
this paper this suggestion is checked explicitly for $M2$ branes in an
$\Omega$-background. The algebra of supersymmetric operators on a stack of $K$
$M2$ branes is shown to be Koszul dual, in large $K$, to the algebra of
supersymmetric operators of $11$-dimensional supergravity in an
$\Omega$-background (using the formulation of supergravity in an
$\Omega$-background presented in arXiv:1610.04144).
The twisted form of supergravity that is used here can be quantized to all
orders in perturbation theory. We find that the Koszul duality result holds to
all orders in perturbation theory, in both the gravitational theory and the
theory on the $M2$. (However, there is a certain non-linear identification of
the coupling constants on each side which I was unable to determine
explicitly).
It is also shown that the algebra of operators on $K$ $M2$ branes, as $K \to
\infty$, is a quantum double-loop algebra (a two-variable analog of the
Yangian). This algebra is also the Koszul dual of the algebra of operators on
the gravitational theory. An explicit presentation for this algebra is
presented, and it is shown that this algebra is the unique quantization of its
classical limit. Some conjectural applications to enumerative geometry of
Calabi-Yau threefolds are also presented. | [
0,
0,
1,
0,
0,
0
] |
Title: Uniqueness of the power of a meromorphic functions with its differential polynomial sharing a set,
Abstract: This paper is devoted to the uniqueness problem of the power of a meromorphic
function with its differential polynomial sharing a set. Our result will extend
a number of results obtained in the theory of normal families. Some questions
are posed for future research. | [
0,
0,
1,
0,
0,
0
] |
Title: Link between the Superconducting Dome and Spin-Orbit Interaction in the (111) LaAlO$_3$/SrTiO$_3$ Interface,
Abstract: We measure the gate voltage ($V_g$) dependence of the superconducting
properties and the spin-orbit interaction in the (111)-oriented
LaAlO$_3$/SrTiO$_3$ interface. Superconductivity is observed in a dome-shaped
region in the carrier density-temperature phase diagram with the maxima of
superconducting transition temperature $T_c$ and the upper critical fields
lying at the same $V_g$. The spin-orbit interaction determined from the
superconducting parameters and confirmed by weak-antilocalization measurements
follows the same gate voltage dependence as $T_c$. The correlation between the
superconductivity and spin-orbit interaction as well as the enhancement of the
parallel upper critical field, well beyond the Chandrasekhar-Clogston limit
suggest that superconductivity and the spin-orbit interaction are linked in a
nontrivial fashion. We propose possible scenarios to explain this
unconventional behavior. | [
0,
1,
0,
0,
0,
0
] |
Title: On the Dynamics of Deterministic Epidemic Propagation over Networks,
Abstract: In this work we review a class of deterministic nonlinear models for the
propagation of infectious diseases over contact networks with
strongly-connected topologies. We consider network models for
susceptible-infected (SI), susceptible-infected-susceptible (SIS), and
susceptible-infected-recovered (SIR) settings. In each setting, we provide a
comprehensive nonlinear analysis of equilibria, stability properties,
convergence, monotonicity, positivity, and threshold conditions. For the
network SI setting, specific contributions include establishing its equilibria,
stability, and positivity properties. For the network SIS setting, we review a
well-known deterministic model, provide novel results on the computation and
characterization of the endemic state (when the system is above the epidemic
threshold), and present alternative proofs for some of its properties. Finally,
for the network SIR setting, we propose novel results for transient behavior,
threshold conditions, stability properties, and asymptotic convergence. These
results are analogous to those well-known for the scalar case. In addition, we
provide a novel iterative algorithm to compute the asymptotic state of the
network SIR system. | [
1,
1,
1,
0,
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] |
Title: Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification,
Abstract: Text-dependent speaker verification is becoming popular in the speaker
recognition society. However, the conventional i-vector framework which has
been successful for speaker identification and other similar tasks works
relatively poorly in this task. Researchers have proposed several new methods
to improve performance, but it is still unclear that which model is the best
choice, especially when the pass-phrases are prompted during enrollment and
test. In this paper, we introduce four modeling methods and compare their
performance on the newly published RedDots dataset. To further explore the
influence of different frame alignments, Viterbi and forward-backward
algorithms are both used in the HMM-based models. Several bottleneck features
are also investigated. Our experiments show that, by explicitly modeling the
lexical content, the HMM-based modeling achieves good results in the
fixed-phrase condition. In the prompted-phrase condition, GMM-HMM and
i-vector/HMM are not as successful. In both conditions, the forward-backward
algorithm brings more benefits to the i-vector/HMM system. Additionally, we
also find that even though bottleneck features perform well for
text-independent speaker verification, they do not outperform MFCCs on the most
challenging Imposter-Correct trials on RedDots. | [
1,
0,
0,
0,
0,
0
] |
Title: Estimation of lactate threshold with machine learning techniques in recreational runners,
Abstract: Lactate threshold is considered an essential parameter when assessing
performance of elite and recreational runners and prescribing training
intensities in endurance sports. However, the measurement of blood lactate
concentration requires expensive equipment and the extraction of blood samples,
which are inconvenient for frequent monitoring. Furthermore, most recreational
runners do not have access to routine assessment of their physical fitness by
the aforementioned equipment so they are not able to calculate the lactate
threshold without resorting to an expensive and specialized centre. Therefore,
the main objective of this study is to create an intelligent system capable of
estimating the lactate threshold of recreational athletes participating in
endurance running sports. The solution here proposed is based on a machine
learning system which models the lactate evolution using recurrent neural
networks and includes the proposal of standardization of the temporal axis as
well as a modification of the stratified sampling method. The results show that
the proposed system accurately estimates the lactate threshold of 89.52% of the
athletes and its correlation with the experimentally measured lactate threshold
is very high (R=0,89). Moreover, its behaviour with the test dataset is as good
as with the training set, meaning that the generalization power of the model is
high. Therefore, in this study a machine learning based system is proposed as
alternative to the traditional invasive lactate threshold measurement tests for
recreational runners. | [
0,
0,
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1,
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] |
Title: A Coin-Tossing Conundrum,
Abstract: It is shown that an equiprobability hypothesis leads to a scenario in which
it is possible to predict the outcome of a single toss of a fair coin with a
success probability greater than 50%. We discuss whether this hypothesis might
be independent of the usual hypotheses governing probability, as well as
whether this hypothesis might be assumed as a result of the Principle of
Indifference. Also discussed are ways to implement or circumvent the
hypothesis. | [
0,
0,
0,
1,
0,
0
] |
Title: Meridian Surfaces on Rotational Hypersurfaces with Lightlike Axis in ${\mathbb E}^4_2$,
Abstract: We construct a special class of Lorentz surfaces in the pseudo-Euclidean
4-space with neutral metric which are one-parameter systems of meridians of
rotational hypersurfaces with lightlike axis and call them meridian surfaces.
We give the complete classification of the meridian surfaces with constant
Gauss curvature and prove that there are no meridian surfaces with parallel
mean curvature vector field other than CMC surfaces lying in a hyperplane. We
also classify the meridian surfaces with parallel normalized mean curvature
vector field. We show that in the family of the meridian surfaces there exist
Lorentz surfaces which have parallel normalized mean curvature vector field but
not parallel mean curvature vector. | [
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] |
Title: Hybrid Optimization Method for Reconfiguration of AC/DC Microgrids in All-Electric Ships,
Abstract: Since the limited power capacity, finite inertia, and dynamic loads make the
shipboard power system (SPS) vulnerable, the automatic reconfiguration for
failure recovery in SPS is an extremely significant but still challenging
problem. It is not only required to operate accurately and optimally, but also
to satisfy operating constraints. In this paper, we consider the
reconfiguration optimization for hybrid AC/DC microgrids in all-electric ships.
Firstly, the multi-zone medium voltage DC (MVDC) SPS model is presented. In
this model, the DC power flow for reconfiguration and a generalized AC/DC
converter are modeled for accurate reconfiguration. Secondly, since this
problem is mixed integer nonlinear programming (MINLP), a hybrid method based
on Newton Raphson and Biogeography based Optimization (NRBBO) is designed
according to the characteristics of system, loads, and faults. This method
facilitates to maximize the weighted load restoration while satisfying
operating constraints. Finally, the simulation results demonstrate this method
has advantages in terms of power restoration and convergence speed. | [
1,
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0
] |
Title: Looking at Outfit to Parse Clothing,
Abstract: This paper extends fully-convolutional neural networks (FCN) for the clothing
parsing problem. Clothing parsing requires higher-level knowledge on clothing
semantics and contextual cues to disambiguate fine-grained categories. We
extend FCN architecture with a side-branch network which we refer outfit
encoder to predict a consistent set of clothing labels to encourage
combinatorial preference, and with conditional random field (CRF) to explicitly
consider coherent label assignment to the given image. The empirical results
using Fashionista and CFPD datasets show that our model achieves
state-of-the-art performance in clothing parsing, without additional
supervision during training. We also study the qualitative influence of
annotation on the current clothing parsing benchmarks, with our Web-based tool
for multi-scale pixel-wise annotation and manual refinement effort to the
Fashionista dataset. Finally, we show that the image representation of the
outfit encoder is useful for dress-up image retrieval application. | [
1,
0,
0,
0,
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0
] |
Title: Mermin-Wagner physics, (H,T) phase diagram, and candidate quantum spin-liquid phase in the spin-1/2 triangular-lattice antiferromagnet Ba8CoNb6O24,
Abstract: Ba$_8$CoNb$_6$O$_{24}$ presents a system whose Co$^{2+}$ ions have an
effective spin 1/2 and construct a regular triangular-lattice antiferromagnet
(TLAFM) with a very large interlayer spacing, ensuring purely two-dimensional
character. We exploit this ideal realization to perform a detailed experimental
analysis of the $S = 1/2$ TLAFM, which is one of the keystone models in
frustrated quantum magnetism. We find strong low-energy spin fluctuations and
no magnetic ordering, but a diverging correlation length down to 0.1 K,
indicating a Mermin-Wagner trend towards zero-temperature order. Below 0.1 K,
however, our low-field measurements show an nexpected magnetically disordered
state, which is a candidate quantum spin liquid. We establish the $(H,T)$ phase
diagram, mapping in detail the quantum fluctuation corrections to the available
theoretical analysis. These include a strong upshift in field of the maximum
ordering temperature, qualitative changes to both low- and high-field phase
boundaries, and an ordered regime apparently dominated by the collinear
"up-up-down" state. Ba$_8$CoNb$_6$O$_{24}$ therefore offers fresh input for the
development of theoretical approaches to the field-induced quantum phase
transitions of the $S = 1/2$ Heisenberg TLAFM. | [
0,
1,
0,
0,
0,
0
] |
Title: Most Complex Non-Returning Regular Languages,
Abstract: A regular language $L$ is non-returning if in the minimal deterministic
finite automaton accepting it there are no transitions into the initial state.
Eom, Han and Jirásková derived upper bounds on the state complexity of
boolean operations and Kleene star, and proved that these bounds are tight
using two different binary witnesses. They derived upper bounds for
concatenation and reversal using three different ternary witnesses. These five
witnesses use a total of six different transformations. We show that for each
$n\ge 4$ there exists a ternary witness of state complexity $n$ that meets the
bound for reversal and that at least three letters are needed to meet this
bound. Moreover, the restrictions of this witness to binary alphabets meet the
bounds for product, star, and boolean operations. We also derive tight upper
bounds on the state complexity of binary operations that take arguments with
different alphabets. We prove that the maximal syntactic semigroup of a
non-returning language has $(n-1)^n$ elements and requires at least
$\binom{n}{2}$ generators. We find the maximal state complexities of atoms of
non-returning languages. Finally, we show that there exists a most complex
non-returning language that meets the bounds for all these complexity measures. | [
1,
0,
0,
0,
0,
0
] |
Title: ArtGAN: Artwork Synthesis with Conditional Categorical GANs,
Abstract: This paper proposes an extension to the Generative Adversarial Networks
(GANs), namely as ARTGAN to synthetically generate more challenging and complex
images such as artwork that have abstract characteristics. This is in contrast
to most of the current solutions that focused on generating natural images such
as room interiors, birds, flowers and faces. The key innovation of our work is
to allow back-propagation of the loss function w.r.t. the labels (randomly
assigned to each generated images) to the generator from the discriminator.
With the feedback from the label information, the generator is able to learn
faster and achieve better generated image quality. Empirically, we show that
the proposed ARTGAN is capable to create realistic artwork, as well as generate
compelling real world images that globally look natural with clear shape on
CIFAR-10. | [
1,
0,
0,
0,
0,
0
] |
Title: Polynomial bound for the nilpotency index of finitely generated nil algebras,
Abstract: Working over an infinite field of positive characteristic, an upper bound is
given for the nilpotency index of a finitely generated nil algebra of bounded
nil index $n$ in terms of the maximal degree in a minimal homogenous generating
system of the ring of simultaneous conjugation invariants of tuples of $n$ by
$n$ matrices. This is deduced from a result of Zubkov. As a consequence, a
recent degree bound due to Derksen and Makam for the generators of the ring of
matrix invariants yields an upper bound for the nilpotency index of a finitely
generated nil algebra that is polynomial in the number of generators and the
nil index. Furthermore, a characteristic free treatment is given to Kuzmin's
lower bound for the nilpotency index. | [
0,
0,
1,
0,
0,
0
] |
Title: CFAAR: Control Flow Alteration to Assist Repair,
Abstract: We present CFAAR, a program repair assistance technique that operates by
selectively altering the outcome of suspicious predicates in order to yield
expected behavior. CFAAR is applicable to defects that are repairable by
negating predicates under specific conditions. CFAAR proceeds as follows: 1) it
identifies predicates such that negating them at given instances would make the
failing tests exhibit correct behavior; 2) for each candidate predicate, it
uses the program state information to build a classifier that dictates when the
predicate should be negated; 3) for each classifier, it leverages a Decision
Tree to synthesize a patch to be presented to the developer. We evaluated our
toolset using 149 defects from the IntroClass and Siemens benchmarks. CFAAR
identified 91 potential candidate defects and generated plausible patches for
41 of them. Twelve of the patches are believed to be correct, whereas the rest
provide repair assistance to the developer. | [
1,
0,
0,
0,
0,
0
] |
Title: Cross-Sentence N-ary Relation Extraction with Graph LSTMs,
Abstract: Past work in relation extraction has focused on binary relations in single
sentences. Recent NLP inroads in high-value domains have sparked interest in
the more general setting of extracting n-ary relations that span multiple
sentences. In this paper, we explore a general relation extraction framework
based on graph long short-term memory networks (graph LSTMs) that can be easily
extended to cross-sentence n-ary relation extraction. The graph formulation
provides a unified way of exploring different LSTM approaches and incorporating
various intra-sentential and inter-sentential dependencies, such as sequential,
syntactic, and discourse relations. A robust contextual representation is
learned for the entities, which serves as input to the relation classifier.
This simplifies handling of relations with arbitrary arity, and enables
multi-task learning with related relations. We evaluate this framework in two
important precision medicine settings, demonstrating its effectiveness with
both conventional supervised learning and distant supervision. Cross-sentence
extraction produced larger knowledge bases. and multi-task learning
significantly improved extraction accuracy. A thorough analysis of various LSTM
approaches yielded useful insight the impact of linguistic analysis on
extraction accuracy. | [
1,
0,
0,
0,
0,
0
] |
Title: Community Interaction and Conflict on the Web,
Abstract: Users organize themselves into communities on web platforms. These
communities can interact with one another, often leading to conflicts and toxic
interactions. However, little is known about the mechanisms of interactions
between communities and how they impact users.
Here we study intercommunity interactions across 36,000 communities on
Reddit, examining cases where users of one community are mobilized by negative
sentiment to comment in another community. We show that such conflicts tend to
be initiated by a handful of communities---less than 1% of communities start
74% of conflicts. While conflicts tend to be initiated by highly active
community members, they are carried out by significantly less active members.
We find that conflicts are marked by formation of echo chambers, where users
primarily talk to other users from their own community. In the long-term,
conflicts have adverse effects and reduce the overall activity of users in the
targeted communities.
Our analysis of user interactions also suggests strategies for mitigating the
negative impact of conflicts---such as increasing direct engagement between
attackers and defenders. Further, we accurately predict whether a conflict will
occur by creating a novel LSTM model that combines graph embeddings, user,
community, and text features. This model can be used toreate early-warning
systems for community moderators to prevent conflicts. Altogether, this work
presents a data-driven view of community interactions and conflict, and paves
the way towards healthier online communities. | [
1,
0,
0,
0,
0,
0
] |
Title: Recurrence network measures for hypothesis testing using surrogate data: application to black hole light curves,
Abstract: Recurrence networks and the associated statistical measures have become
important tools in the analysis of time series data. In this work, we test how
effective the recurrence network measures are in analyzing real world data
involving two main types of noise, white noise and colored noise. We use two
prominent network measures as discriminating statistic for hypothesis testing
using surrogate data for a specific null hypothesis that the data is derived
from a linear stochastic process. We show that the characteristic path length
is especially efficient as a discriminating measure with the conclusions
reasonably accurate even with limited number of data points in the time series.
We also highlight an additional advantage of the network approach in
identifying the dimensionality of the system underlying the time series through
a convergence measure derived from the probability distribution of the local
clustering coefficients. As examples of real world data, we use the light
curves from a prominent black hole system and show that a combined analysis
using three primary network measures can provide vital information regarding
the nature of temporal variability of light curves from different spectroscopic
classes. | [
0,
1,
0,
0,
0,
0
] |
Title: Diffraction-limited plenoptic imaging with correlated light,
Abstract: Traditional optical imaging faces an unavoidable trade-off between resolution
and depth of field (DOF). To increase resolution, high numerical apertures (NA)
are needed, but the associated large angular uncertainty results in a limited
range of depths that can be put in sharp focus. Plenoptic imaging was
introduced a few years ago to remedy this trade off. To this aim, plenoptic
imaging reconstructs the path of light rays from the lens to the sensor.
However, the improvement offered by standard plenoptic imaging is practical and
not fundamental: the increased DOF leads to a proportional reduction of the
resolution well above the diffraction limit imposed by the lens NA. In this
paper, we demonstrate that correlation measurements enable pushing plenoptic
imaging to its fundamental limits of both resolution and DOF. Namely, we
demonstrate to maintain the imaging resolution at the diffraction limit while
increasing the depth of field by a factor of 7. Our results represent the
theoretical and experimental basis for the effective development of the
promising applications of plenoptic imaging. | [
0,
1,
0,
0,
0,
0
] |
Title: Binary Classification with Karmic, Threshold-Quasi-Concave Metrics,
Abstract: Complex performance measures, beyond the popular measure of accuracy, are
increasingly being used in the context of binary classification. These complex
performance measures are typically not even decomposable, that is, the loss
evaluated on a batch of samples cannot typically be expressed as a sum or
average of losses evaluated at individual samples, which in turn requires new
theoretical and methodological developments beyond standard treatments of
supervised learning. In this paper, we advance this understanding of binary
classification for complex performance measures by identifying two key
properties: a so-called Karmic property, and a more technical
threshold-quasi-concavity property, which we show is milder than existing
structural assumptions imposed on performance measures. Under these properties,
we show that the Bayes optimal classifier is a threshold function of the
conditional probability of positive class. We then leverage this result to come
up with a computationally practical plug-in classifier, via a novel threshold
estimator, and further, provide a novel statistical analysis of classification
error with respect to complex performance measures. | [
0,
0,
0,
1,
0,
0
] |
Title: Learning from Noisy Label Distributions,
Abstract: In this paper, we consider a novel machine learning problem, that is,
learning a classifier from noisy label distributions. In this problem, each
instance with a feature vector belongs to at least one group. Then, instead of
the true label of each instance, we observe the label distribution of the
instances associated with a group, where the label distribution is distorted by
an unknown noise. Our goals are to (1) estimate the true label of each
instance, and (2) learn a classifier that predicts the true label of a new
instance. We propose a probabilistic model that considers true label
distributions of groups and parameters that represent the noise as hidden
variables. The model can be learned based on a variational Bayesian method. In
numerical experiments, we show that the proposed model outperforms existing
methods in terms of the estimation of the true labels of instances. | [
1,
0,
0,
1,
0,
0
] |
Title: Commissioning of FLAG: A phased array feed for the GBT,
Abstract: Phased Array Feed (PAF) technology is the next major advancement in radio
astronomy in terms of combining high sensitivity and large field of view. The
Focal L-band Array for the Green Bank Telescope (FLAG) is one of the most
sensitive PAFs developed so far. It consists of 19 dual-polarization elements
mounted on a prime focus dewar resulting in seven beams on the sky. Its
unprecedented system temperature of$\sim$17 K will lead to a 3 fold increase in
pulsar survey speeds as compared to contemporary single pixel feeds. Early
science observations were conducted in a recently concluded commissioning phase
of the FLAG where we clearly demonstrated its science capabilities. We observed
a selection of normal and millisecond pulsars and detected giant pulses from
PSR B1937+21. | [
0,
1,
0,
0,
0,
0
] |
Title: Arithmetic representations of fundamental groups I,
Abstract: Let $X$ be a normal algebraic variety over a finitely generated field $k$ of
characteristic zero, and let $\ell$ be a prime. Say that a continuous
$\ell$-adic representation $\rho$ of $\pi_1^{\text{ét}}(X_{\bar k})$ is
arithmetic if there exists a representation $\tilde \rho$ of a finite index
subgroup of $\pi_1^{\text{ét}}(X)$, with $\rho$ a subquotient of
$\tilde\rho|_{\pi_1(X_{\bar k})}$. We show that there exists an integer $N=N(X,
\ell)$ such that every nontrivial, semisimple arithmetic representation of
$\pi_1^{\text{ét}}(X_{\bar k})$ is nontrivial mod $\ell^N$. As a corollary,
we prove that any nontrivial semisimple representation of
$\pi_1^{\text{ét}}(X_{\bar k})$, which arises from geometry, is nontrivial
mod $\ell^N$. | [
0,
0,
1,
0,
0,
0
] |
Title: Optimization over Degree Sequences,
Abstract: We introduce and study the problem of optimizing arbitrary functions over
degree sequences of hypergraphs and multihypergraphs. We show that over
multihypergraphs the problem can be solved in polynomial time. For hypergraphs,
we show that deciding if a given sequence is the degree sequence of a
3-hypergraph is NP-complete, thereby solving a 30 year long open problem. This
implies that optimization over hypergraphs is hard already for simple concave
functions. In contrast, we show that for graphs, if the functions at vertices
are the same, then the problem is polynomial time solvable. We also provide
positive results for convex optimization over multihypergraphs and graphs and
exploit connections to degree sequence polytopes and threshold graphs. We then
elaborate on connections to the emerging theory of shifted combinatorial
optimization. | [
1,
0,
1,
0,
0,
0
] |
Title: Interferometric Monitoring of Gamma-ray Bright AGNs: S5 0716+714,
Abstract: We present the results of very long baseline interferometry (VLBI)
observations of gamma-ray bright blazar S5 0716+714 using the Korean VLBI
Network (KVN) at the 22, 43, 86, and 129 GHz bands, as part of the
Interferometric Monitoring of Gamma-ray Bright AGNs (iMOGABA) KVN key science
program. Observations were conducted in 29 sessions from January 16, 2013 to
March 1, 2016, with the source being detected and imaged at all available
frequencies. In all epochs, the source was compact on the milliarcsecond (mas)
scale, yielding a compact VLBI core dominating the synchrotron emission on
these scales. Based on the multi-wavelength data between 15 GHz (Owens Valley
Radio Observatory) and 230 GHz (Submillimeter Array), we found that the source
shows multiple prominent enhancements of the flux density at the centimeter
(cm) and millimeter (mm) wavelengths, with mm enhancements leading cm
enhancements by -16$\pm$8 days. The turnover frequency was found to vary
between 21 to 69GHz during our observations. By assuming a synchrotron
self-absorption model for the relativistic jet emission in S5 0716+714, we
found the magnetic field strength in the mas emission region to be $\le$5 mG
during the observing period, yielding a weighted mean of 1.0$\pm$0.6 mG for
higher turnover frequencies (e.g., >45 GHz). | [
0,
1,
0,
0,
0,
0
] |
Title: Parabolic equations with natural growth approximated by nonlocal equations,
Abstract: In this paper we study several aspects related with solutions of nonlocal
problems whose prototype is $$ u_t =\displaystyle \int_{\mathbb{R}^N} J(x-y)
\big( u(y,t) -u(x,t) \big) \mathcal G\big( u(y,t) -u(x,t) \big) dy \qquad
\mbox{ in } \, \Omega \times (0,T)\,, $$ being $ u (x,t)=0 \mbox{ in }
(\mathbb{R}^N\setminus \Omega )\times (0,T)\,$ and $ u(x,0)=u_0 (x) \mbox{ in }
\Omega$. We take, as the most important instance, $\mathcal G (s) \sim 1+
\frac{\mu}{2} \frac{s}{1+\mu^2 s^2 }$ with $\mu\in \mathbb{R}$ as well as $u_0
\in L^1 (\Omega)$, $J$ is a smooth symmetric function with compact support and
$\Omega$ is either a bounded smooth subset of $\mathbb{R}^N$, with nonlocal
Dirichlet boundary condition, or $\mathbb{R}^N$ itself.
The results deal with existence, uniqueness, comparison principle and
asymptotic behavior. Moreover we prove that if the kernel rescales in a
suitable way, the unique solution of the above problem converges to a solution
of the deterministic Kardar-Parisi-Zhang equation. | [
0,
0,
1,
0,
0,
0
] |
Title: Excitonic gap generation in thin-film topological insulators,
Abstract: In this work, we analyze the excitonic gap generation in the strong-coupling
regime of thin films of three-dimensional time-reversal-invariant topological
insulators. We start by writing down the effective gauge theory in
2+1-dimensions from the projection of the 3+1-dimensional quantum
electrodynamics. Within this method, we obtain a short-range interaction, which
has the form of a Thirring-like term, and a long-range one. The interaction
between the two surface states of the material induces an excitonic gap. By
using the large-$N$ approximation in the strong-coupling limit, we find that
there is a dynamical mass generation for the excitonic states that preserves
time-reversal symmetry and is related to the dynamical chiral-symmetry breaking
of our model. This symmetry breaking occurs only for values of the
fermion-flavor number smaller than $N_{c}\approx 11.8$. Our results show that
the inclusion of the full dynamical interaction strongly modifies the critical
number of flavors for the occurrence of exciton condensation, and therefore,
cannot be neglected. | [
0,
1,
0,
0,
0,
0
] |
Title: Constructive Preference Elicitation over Hybrid Combinatorial Spaces,
Abstract: Preference elicitation is the task of suggesting a highly preferred
configuration to a decision maker. The preferences are typically learned by
querying the user for choice feedback over pairs or sets of objects. In its
constructive variant, new objects are synthesized "from scratch" by maximizing
an estimate of the user utility over a combinatorial (possibly infinite) space
of candidates. In the constructive setting, most existing elicitation
techniques fail because they rely on exhaustive enumeration of the candidates.
A previous solution explicitly designed for constructive tasks comes with no
formal performance guarantees, and can be very expensive in (or unapplicable
to) problems with non-Boolean attributes. We propose the Choice Perceptron, a
Perceptron-like algorithm for learning user preferences from set-wise choice
feedback over constructive domains and hybrid Boolean-numeric feature spaces.
We provide a theoretical analysis on the attained regret that holds for a large
class of query selection strategies, and devise a heuristic strategy that aims
at optimizing the regret in practice. Finally, we demonstrate its effectiveness
by empirical evaluation against existing competitors on constructive scenarios
of increasing complexity. | [
1,
0,
0,
0,
0,
0
] |
Title: Ginzburg-Landau equations on Riemann surfaces of higher genus,
Abstract: We study the Ginzburg-Landau equations on Riemann surfaces of arbitrary
genus. In particular: we explicitly construct the (local moduli space of
gauge-equivalent) solutions in a neighbourhood of a constant curvature branch
of solutions; in linearizing the problem, we find a relation with de Rham
cohomology groups of the surface; we classify holomorphic structures on line
bundles arising as solutions to the equations in terms of the degree, the
Abel-Jacobi map, and symmetric products of the surface; we construct explicitly
the automorphy factors and the equivariant connection on the trivial bundle
over the Poincaré upper complex half plane. | [
0,
0,
1,
0,
0,
0
] |
Title: On the Hamming Auto- and Cross-correlation Functions of a Class of Frequency Hopping Sequences of Length $ p^{n} $,
Abstract: In this paper, a new class of frequency hopping sequences (FHSs) of length $
p^{n} $ is constructed by using Ding-Helleseth generalized cyclotomic classes
of order 2, of which the Hamming auto- and cross-correlation functions are
investigated (for the Hamming cross-correlation, only the case $ p\equiv 3\pmod
4 $ is considered). It is shown that the set of the constructed FHSs is optimal
with respect to the average Hamming correlation functions. | [
1,
0,
0,
0,
0,
0
] |
Title: Scikit-Multiflow: A Multi-output Streaming Framework,
Abstract: Scikit-multiflow is a multi-output/multi-label and stream data mining
framework for the Python programming language. Conceived to serve as a platform
to encourage democratization of stream learning research, it provides multiple
state of the art methods for stream learning, stream generators and evaluators.
scikit-multiflow builds upon popular open source frameworks including
scikit-learn, MOA and MEKA. Development follows the FOSS principles and quality
is enforced by complying with PEP8 guidelines and using continuous integration
and automatic testing. The source code is publicly available at
this https URL. | [
0,
0,
0,
1,
0,
0
] |
Title: Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows,
Abstract: Distributed computing platforms provide a robust mechanism to perform
large-scale computations by splitting the task and data among multiple
locations, possibly located thousands of miles apart geographically. Although
such distribution of resources can lead to benefits, it also comes with its
associated problems such as rampant duplication of file transfers increasing
congestion, long job completion times, unexpected site crashing, suboptimal
data transfer rates, unpredictable reliability in a time range, and suboptimal
usage of storage elements. In addition, each sub-system becomes a potential
failure node that can trigger system wide disruptions. In this vision paper, we
outline our approach to leveraging Deep Learning algorithms to discover
solutions to unique problems that arise in a system with computational
infrastructure that is spread over a wide area. The presented vision, motivated
by a real scientific use case from Belle II experiments, is to develop
multilayer neural networks to tackle forecasting, anomaly detection and
optimization challenges in a complex and distributed data movement environment.
Through this vision based on Deep Learning principles, we aim to achieve
reduced congestion events, faster file transfer rates, and enhanced site
reliability. | [
1,
0,
0,
0,
0,
0
] |
Title: The Rational Sectional Category of Certain Universal Fibrations,
Abstract: We prove that the sectional category of the universal fibration with fibre X,
for X any space that satisfies a well-known conjecture of Halperin, equals one
after rationalization. | [
0,
0,
1,
0,
0,
0
] |
Title: Characterising exo-ringsystems around fast-rotating stars using the Rossiter-McLaughlin effect,
Abstract: Planetary rings produce a distinct shape distortion in transit lightcurves.
However, to accurately model such lightcurves the observations need to cover
the entire transit, especially ingress and egress, as well as an out-of-transit
baseline. Such observations can be challenging for long period planets, where
the transits may last for over a day. Planetary rings will also impact the
shape of absorption lines in the stellar spectrum, as the planet and rings
cover different parts of the rotating star (the Rossiter-McLaughlin effect).
These line-profile distortions depend on the size, structure, opacity,
obliquity and sky projected angle of the ring system. For slow rotating stars,
this mainly impacts the amplitude of the induced velocity shift, however, for
fast rotating stars the large velocity gradient across the star allows the line
distortion to be resolved, enabling direct determination of the ring
parameters. We demonstrate that by modeling these distortions we can recover
ring system parameters (sky-projected angle, obliquity and size) using only a
small part of the transit. Substructure in the rings, e.g. gaps, can be
recovered if the width of the features ($\delta W$) relative to the size of the
star is similar to the intrinsic velocity resolution (set by the width of the
local stellar profile, $\gamma$) relative to the stellar rotation velocity ($v$
sin$i$, i.e. $\delta W / R_* \gtrsim v$sin$i$/$\gamma$). This opens up a new
way to study the ring systems around planets with long orbital periods, where
observations of the full transit, covering the ingress and egress, are not
always feasible. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning a Generative Model of Cancer Metastasis,
Abstract: We introduce a Unified Disentanglement Network (UFDN) trained on The Cancer
Genome Atlas (TCGA). We demonstrate that the UFDN learns a biologically
relevant latent space of gene expression data by applying our network to two
classification tasks of cancer status and cancer type. Our UFDN specific
algorithms perform comparably to random forest methods. The UFDN allows for
continuous, partial interpolation between distinct cancer types. Furthermore,
we perform an analysis of differentially expressed genes between skin cutaneous
melanoma(SKCM) samples and the same samples interpolated into glioblastoma
(GBM). We demonstrate that our interpolations learn relevant metagenes that
recapitulate known glioblastoma mechanisms and suggest possible starting points
for investigations into the metastasis of SKCM into GBM. | [
0,
0,
0,
0,
1,
0
] |
Title: Joint User Selection and Energy Minimization for Ultra-Dense Multi-channel C-RAN with Incomplete CSI,
Abstract: This paper provides a unified framework to deal with the challenges arising
in dense cloud radio access networks (C-RAN), which include huge power
consumption, limited fronthaul capacity, heavy computational complexity,
unavailability of full channel state information (CSI), etc. Specifically, we
aim to jointly optimize the remote radio head (RRH) selection, user equipment
(UE)-RRH associations and beam-vectors to minimize the total network power
consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI
subject to per-RRH power constraints, each UE's total rate requirement, and
fronthaul link capacity constraints. This optimization problem is NP-hard. In
addition, due to the incomplete CSI, the exact expression of UEs' rate
expression is intractable. We first conservatively replace UEs' rate expression
with its lower-bound. Then, based on the successive convex approximation (SCA)
technique and the relationship between the data rate and the mean square error
(MSE), we propose a single-layer iterative algorithm to solve the NPC
minimization problem with convergence guarantee. In each iteration of the
algorithm, the Lagrange dual decomposition method is used to derive the
structure of the optimal beam-vectors, which facilitates the parallel
computations at the Baseband unit (BBU) pool. Furthermore, a bisection UE
selection algorithm is proposed to guarantee the feasibility of the problem.
Simulation results show the benefits of the proposed algorithms and the fact
that a limited amount of CSI is sufficient to achieve performance close to that
obtained when perfect CSI is possessed. | [
1,
0,
0,
0,
0,
0
] |
Title: On Sidorenko's conjecture for determinants and Gaussian Markov random fields,
Abstract: We study a class of determinant inequalities that are closely related to
Sidorenko's famous conjecture (Also conjectured by Erd\H os and Simonovits in a
different form). Our results can also be interpreted as entropy inequalities
for Gaussian Markov random fields (GMRF). We call a GMRF on a finite graph $G$
homogeneous if the marginal distributions on the edges are all identical. We
show that if $G$ satisfies Sidorenko's conjecture then the differential entropy
of any homogeneous GMRF on $G$ is at least $|E(G)|$ times the edge entropy plus
$|V(G)|-2|E(G)|$ times the point entropy. We also prove this inequality in a
large class of graphs for which Sidorenko's conjecture is not verified
including the so-called Möbius ladder: $K_{5,5}\setminus C_{10}$. The
connection between Sidorenko's conjecture and GMRF's is established via a large
deviation principle on high dimensional spheres combined with graph limit
theory. | [
0,
0,
1,
0,
0,
0
] |
Title: Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations,
Abstract: Power grids are critical infrastructure assets that face non-technical losses
(NTL) such as electricity theft or faulty meters. NTL may range up to 40% of
the total electricity distributed in emerging countries. Industrial NTL
detection systems are still largely based on expert knowledge when deciding
whether to carry out costly on-site inspections of customers. Electricity
providers are reluctant to move to large-scale deployments of automated systems
that learn NTL profiles from data due to the latter's propensity to suggest a
large number of unnecessary inspections. In this paper, we propose a novel
system that combines automated statistical decision making with expert
knowledge. First, we propose a machine learning framework that classifies
customers into NTL or non-NTL using a variety of features derived from the
customers' consumption data. The methodology used is specifically tailored to
the level of noise in the data. Second, in order to allow human experts to feed
their knowledge in the decision loop, we propose a method for visualizing
prediction results at various granularity levels in a spatial hologram. Our
approach allows domain experts to put the classification results into the
context of the data and to incorporate their knowledge for making the final
decisions of which customers to inspect. This work has resulted in appreciable
results on a real-world data set of 3.6M customers. Our system is being
deployed in a commercial NTL detection software. | [
1,
0,
0,
0,
0,
0
] |
Title: A model of reward-modulated motor learning with parallelcortical and basal ganglia pathways,
Abstract: Many recent studies of the motor system are divided into two distinct
approaches: Those that investigate how motor responses are encoded in cortical
neurons' firing rate dynamics and those that study the learning rules by which
mammals and songbirds develop reliable motor responses. Computationally, the
first approach is encapsulated by reservoir computing models, which can learn
intricate motor tasks and produce internal dynamics strikingly similar to those
of motor cortical neurons, but rely on biologically unrealistic learning rules.
The more realistic learning rules developed by the second approach are often
derived for simplified, discrete tasks in contrast to the intricate dynamics
that characterize real motor responses. We bridge these two approaches to
develop a biologically realistic learning rule for reservoir computing. Our
algorithm learns simulated motor tasks on which previous reservoir computing
algorithms fail, and reproduces experimental findings including those that
relate motor learning to Parkinson's disease and its treatment. | [
0,
0,
0,
0,
1,
0
] |
Title: A de Sitter limit analysis for dark energy and modified gravity models,
Abstract: The effective field theory of dark energy and modified gravity is supposed to
well describe, at low energies, the behaviour of the gravity modifications due
to one extra scalar degree of freedom. The usual curvature perturbation is very
useful when studying the conditions for the avoidance of ghost instabilities as
well as the positivity of the squared speeds of propagation for both the scalar
and tensor modes, or the Stückelberg field performs perfectly when
investigating the evolution of linear perturbations. We show that the viable
parameters space identified by requiring no-ghost instabilities and positive
squared speeds of propagation does not change by performing a field
redefinition, while the requirement of the avoidance of tachyonic instability
might instead be different. Therefore, we find interesting to associate to the
general modified gravity theory described in the effective field theory
framework, a perturbation field which will inherit the whole properties of the
theory. In the present paper we address the following questions: 1) how can we
define such a field? and 2) what is the mass of such a field as the background
approaches a final de Sitter state? We define a gauge invariant quantity which
identifies the density of the dark energy perturbation field valid for any
background. We derive the mass associated to the gauge invariant dark energy
field on a de Sitter background, which we retain to be still a good
approximation also at very low redshift ($z\simeq 0$). On this background we
also investigate the value of the speed of propagation and we find that there
exist classes of theories which admit a non-vanishing speed of propagation,
even among the Horndeski model, for which in literature it has previously been
found a zero speed. We finally apply our results to specific well known models. | [
0,
1,
0,
0,
0,
0
] |
Title: Empirical Analysis on Comparing the Performance of Alpha Miner Algorithm in SQL Query Language and NoSQL Column-Oriented Databases Using Apache Phoenix,
Abstract: Process-Aware Information Systems (PAIS) is an IT system that support
business processes and generate large amounts of event logs from the execution
of business processes. An event log is represented as a tuple of CaseID,
Timestamp, Activity and Actor. Process Mining is a new and emerging field that
aims at analyzing the event logs to discover, enhance and improve business
processes and check conformance between run time and design time business
processes. The large volume of event logs generated are stored in the
databases. Relational databases perform well for a certain class of
applications. However, there are a certain class of applications for which
relational databases are not able to scale. To handle such class of
applications, NoSQL database systems emerged. Discovering a process model
(workflow model) from event logs is one of the most challenging and important
Process Mining task. The $\alpha$-miner algorithm is one of the first and most
widely used Process Discovery technique. Our objective is to investigate which
of the databases (Relational or NoSQL) performs better for a Process Discovery
application under Process Mining. We implement the $\alpha$-miner algorithm on
relational (row-oriented) and NoSQL (column-oriented) databases in database
query languages so that our algorithm is tightly coupled to the database. We
present a performance benchmarking and comparison of the $\alpha$-miner
algorithm on row-oriented database and NoSQL column-oriented database so that
we can compare which database can efficiently store massive event logs and
analyze it in seconds to discover a process model. | [
1,
0,
0,
0,
0,
0
] |
Title: Cross-modal Deep Metric Learning with Multi-task Regularization,
Abstract: DNN-based cross-modal retrieval has become a research hotspot, by which users
can search results across various modalities like image and text. However,
existing methods mainly focus on the pairwise correlation and reconstruction
error of labeled data. They ignore the semantically similar and dissimilar
constraints between different modalities, and cannot take advantage of
unlabeled data. This paper proposes Cross-modal Deep Metric Learning with
Multi-task Regularization (CDMLMR), which integrates quadruplet ranking loss
and semi-supervised contrastive loss for modeling cross-modal semantic
similarity in a unified multi-task learning architecture. The quadruplet
ranking loss can model the semantically similar and dissimilar constraints to
preserve cross-modal relative similarity ranking information. The
semi-supervised contrastive loss is able to maximize the semantic similarity on
both labeled and unlabeled data. Compared to the existing methods, CDMLMR
exploits not only the similarity ranking information but also unlabeled
cross-modal data, and thus boosts cross-modal retrieval accuracy. | [
1,
0,
0,
1,
0,
0
] |
Title: $W$-entropy formulas on super Ricci flows and Langevin deformation on Wasserstein space over Riemannian manifolds,
Abstract: In this survey paper, we give an overview of our recent works on the study of
the $W$-entropy for the heat equation associated with the Witten Laplacian on
super-Ricci flows and the Langevin deformation on Wasserstein space over
Riemannian manifolds. Inspired by Perelman's seminal work on the entropy
formula for the Ricci flow, we prove the $W$-entropy formula for the heat
equation associated with the Witten Laplacian on $n$-dimensional complete
Riemannian manifolds with the $CD(K, m)$-condition, and the $W$-entropy formula
for the heat equation associated with the time dependent Witten Laplacian on
$n$-dimensional compact manifolds equipped with a $(K, m)$-super Ricci flow,
where $K\in \mathbb{R}$ and $m\in [n, \infty]$. Furthermore, we prove an
analogue of the $W$-entropy formula for the geodesic flow on the Wasserstein
space over Riemannian manifolds. Our result recaptures an important result due
to Lott and Villani on the displacement convexity of the Boltzmann-Shannon
entropy on Riemannian manifolds with non-negative Ricci curvature. To better
understand the similarity between above two $W$-entropy formulas, we introduce
the Langevin deformation of geometric flows on the cotangent bundle over the
Wasserstein space and prove an extension of the $W$-entropy formula for the
Langevin deformation. Finally, we make a discussion on the $W$-entropy for the
Ricci flow from the point of view of statistical mechanics and probability
theory. | [
0,
0,
1,
0,
0,
0
] |
Title: Cancellable elements of the lattice of semigroup varieties,
Abstract: We completely determine all commutative semigroup varieties that are
cancellable elements of the lattice SEM of all semigroup varieties. In
particular, we prove that, for commutative varieties, the properties of being
cancellable and modular elements of SEM are equivalent. | [
0,
0,
1,
0,
0,
0
] |
Title: Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs,
Abstract: The availability of large scale event data with time stamps has given rise to
dynamically evolving knowledge graphs that contain temporal information for
each edge. Reasoning over time in such dynamic knowledge graphs is not yet well
understood. To this end, we present Know-Evolve, a novel deep evolutionary
knowledge network that learns non-linearly evolving entity representations over
time. The occurrence of a fact (edge) is modeled as a multivariate point
process whose intensity function is modulated by the score for that fact
computed based on the learned entity embeddings. We demonstrate significantly
improved performance over various relational learning approaches on two large
scale real-world datasets. Further, our method effectively predicts occurrence
or recurrence time of a fact which is novel compared to prior reasoning
approaches in multi-relational setting. | [
1,
0,
0,
0,
0,
0
] |
Title: Mechanisms of dimensionality reduction and decorrelation in deep neural networks,
Abstract: Deep neural networks are widely used in various domains. However, the nature
of computations at each layer of the deep networks is far from being well
understood. Increasing the interpretability of deep neural networks is thus
important. Here, we construct a mean-field framework to understand how compact
representations are developed across layers, not only in deterministic deep
networks with random weights but also in generative deep networks where an
unsupervised learning is carried out. Our theory shows that the deep
computation implements a dimensionality reduction while maintaining a finite
level of weak correlations between neurons for possible feature extraction.
Mechanisms of dimensionality reduction and decorrelation are unified in the
same framework. This work may pave the way for understanding how a sensory
hierarchy works. | [
1,
0,
0,
1,
0,
0
] |
Title: Detecting Strong Ties Using Network Motifs,
Abstract: Detecting strong ties among users in social and information networks is a
fundamental operation that can improve performance on a multitude of
personalization and ranking tasks. Strong-tie edges are often readily obtained
from the social network as users often participate in multiple overlapping
networks via features such as following and messaging. These networks may vary
greatly in size, density and the information they carry. This setting leads to
a natural strong tie detection task: given a small set of labeled strong tie
edges, how well can one detect unlabeled strong ties in the remainder of the
network?
This task becomes particularly daunting for the Twitter network due to scant
availability of pairwise relationship attribute data, and sparsity of strong
tie networks such as phone contacts. Given these challenges, a natural approach
is to instead use structural network features for the task, produced by {\em
combining} the strong and "weak" edges. In this work, we demonstrate via
experiments on Twitter data that using only such structural network features is
sufficient for detecting strong ties with high precision. These structural
network features are obtained from the presence and frequency of small network
motifs on combined strong and weak ties. We observe that using motifs larger
than triads alleviate sparsity problems that arise for smaller motifs, both due
to increased combinatorial possibilities as well as benefiting strongly from
searching beyond the ego network. Empirically, we observe that not all motifs
are equally useful, and need to be carefully constructed from the combined
edges in order to be effective for strong tie detection. Finally, we reinforce
our experimental findings with providing theoretical justification that
suggests why incorporating these larger sized motifs as features could lead to
increased performance in planted graph models. | [
1,
1,
0,
0,
0,
0
] |
Title: Streaming Weak Submodularity: Interpreting Neural Networks on the Fly,
Abstract: In many machine learning applications, it is important to explain the
predictions of a black-box classifier. For example, why does a deep neural
network assign an image to a particular class? We cast interpretability of
black-box classifiers as a combinatorial maximization problem and propose an
efficient streaming algorithm to solve it subject to cardinality constraints.
By extending ideas from Badanidiyuru et al. [2014], we provide a constant
factor approximation guarantee for our algorithm in the case of random stream
order and a weakly submodular objective function. This is the first such
theoretical guarantee for this general class of functions, and we also show
that no such algorithm exists for a worst case stream order. Our algorithm
obtains similar explanations of Inception V3 predictions $10$ times faster than
the state-of-the-art LIME framework of Ribeiro et al. [2016]. | [
1,
0,
0,
1,
0,
0
] |
Title: Staging superstructures in high-$T_c$ Sr/O co-doped La$_{2-x}$Sr$_x$CuO$_{4+y}$,
Abstract: We present high energy X-ray diffraction studies on the structural phases of
an optimal high-$T_c$ superconductor La$_{2-x}$Sr$_x$CuO$_{4+y}$ tailored by
co-hole-doping. This is specifically done by varying the content of two very
different chemical species, Sr and O, respectively, in order to study the
influence of each. A superstructure known as staging is observed in all
samples, with the staging number $n$ increasing for higher Sr dopings $x$. We
find that the staging phases emerge abruptly with temperature, and can be
described as a second order phase transition with transition temperatures
slightly depending on the Sr doping. The Sr appears to correlate the
interstitial oxygen in a way that stabilises the reproducibility of the staging
phase both in terms of staging period and volume fraction in a specific sample.
The structural details as investigated in this letter appear to have no direct
bearing on the electronic phase separation previously observed in the same
samples. This provides new evidence that the electronic phase separation is
determined by the overall hole concentration rather than specific Sr/O content
and concommittant structural details. | [
0,
1,
0,
0,
0,
0
] |
Title: Interface magnetism and electronic structure: ZnO(0001)/Co3O4(111),
Abstract: We have studied the structural, electronic and magnetic properties of spinel
$\rm Co_3O_4$(111) surfaces and their interfaces with ZnO (0001) using density
functional theory (DFT) within the Generalized Gradient Approximation with
on-site Coulomb repulsion term (GGA+U). Two possible forms of spinel surface,
containing $\rm Co^{2+} $ and $\rm Co^{3+} $ ions and terminated with either
cobalt or oxygen ions were considered, as well as their interface with zinc
oxide. Our calculations demonstrate that $\rm Co^{3+} $ ions attain non-zero
magnetic moments at the surface and interface, in contrast to the bulk, where
they are not magnetic, leading to the ferromagnetic ordering. Since heavily
Co-doped ZnO samples can contain $\rm Co_3O_4 $ secondary phase, such a
magnetic ordering at the interface might explain the origin of the magnetism in
these diluted magnetic semiconductors (DMS). | [
0,
1,
0,
0,
0,
0
] |
Title: Bloch-type spaces and extended Cesàro operators in the unit ball of a complex Banach space,
Abstract: Let $\mathbb{B}$ be the unit ball of a complex Banach space $X$. In this
paper, we will generalize the Bloch-type spaces and the little Bloch-type
spaces to the open unit ball $\mathbb{B}$ by using the radial derivative. Next,
we define an extended Cesàro operator $T_{\varphi}$ with holomorphic symbol
$\varphi$ and characterize those $\varphi$ for which $T_{\varphi}$ is bounded
between the Bloch-type spaces and the little Bloch-type spaces. We also
characterize those $\varphi$ for which $T_{\varphi}$ is compact between the
Bloch-type spaces and the little Bloch-type spaces under some additional
assumption on the symbol $\varphi$. When $\mathbb{B}$ is the open unit ball of
a finite dimensional complex Banach space $X$, this additional assumption is
automatically satisfied. | [
0,
0,
1,
0,
0,
0
] |
Title: Alchemist: An Apache Spark <=> MPI Interface,
Abstract: The Apache Spark framework for distributed computation is popular in the data
analytics community due to its ease of use, but its MapReduce-style programming
model can incur significant overheads when performing computations that do not
map directly onto this model. One way to mitigate these costs is to off-load
computations onto MPI codes. In recent work, we introduced Alchemist, a system
for the analysis of large-scale data sets. Alchemist calls MPI-based libraries
from within Spark applications, and it has minimal coding, communication, and
memory overheads. In particular, Alchemist allows users to retain the
productivity benefits of working within the Spark software ecosystem without
sacrificing performance efficiency in linear algebra, machine learning, and
other related computations.
In this paper, we discuss the motivation behind the development of Alchemist,
and we provide a detailed overview its design and usage. We also demonstrate
the efficiency of our approach on medium-to-large data sets, using some
standard linear algebra operations, namely matrix multiplication and the
truncated singular value decomposition of a dense matrix, and we compare the
performance of Spark with that of Spark+Alchemist. These computations are run
on the NERSC supercomputer Cori Phase 1, a Cray XC40. | [
0,
0,
0,
1,
0,
0
] |
Title: Compositional (In)Finite Abstractions for Large-Scale Interconnected Stochastic Systems,
Abstract: This paper is concerned with a compositional approach for constructing both
infinite (reduced-order models) and finite abstractions (a.k.a. finite Markov
decision processes) of large-scale interconnected discrete-time stochastic
control systems. The proposed framework is based on the notion of stochastic
simulation functions enabling us to use an abstract system as a substitution of
the original one in the controller design process with guaranteed error bounds.
In the first part of the paper, we derive sufficient small-gain type conditions
for the compositional quantification of the probabilistic distance between the
interconnection of stochastic control subsystems and that of their infinite
abstractions. We then construct infinite abstractions together with their
corresponding stochastic simulation functions for a class of discrete-time
nonlinear stochastic control systems. In the second part of the paper, we
leverage small-gain type conditions for the compositional construction of
finite abstractions. We propose an approach to construct finite Markov decision
processes (MDPs) of the concrete models (or their reduced-order versions)
satisfying an incremental input-to-state stability property. We also show that
for a particular class of nonlinear stochastic control systems, the
aforementioned property can be readily checked by matrix inequalities. We
demonstrate the effectiveness of the proposed results by applying our
approaches to the temperature regulation in a circular building and
constructing compositionally a finite abstraction of a network containing 1000
rooms. We also apply our proposed techniques to a fully connected network of 20
nonlinear subsystems (totally 100 dimensions) and construct finite MDPs from
their reduced-order versions (together 20 dimensions) with guaranteed error
bounds on their output trajectories. | [
1,
0,
0,
0,
0,
0
] |
Title: Infinitely generated symbolic Rees algebras over finite fields,
Abstract: For the polynomial ring over an arbitrary field with twelve variables, there
exists a prime ideal whose symbolic Rees algebra is not finitely generated. | [
0,
0,
1,
0,
0,
0
] |
Title: Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models,
Abstract: Deep generative neural networks have proven effective at both conditional and
unconditional modeling of complex data distributions. Conditional generation
enables interactive control, but creating new controls often requires expensive
retraining. In this paper, we develop a method to condition generation without
retraining the model. By post-hoc learning latent constraints, value functions
that identify regions in latent space that generate outputs with desired
attributes, we can conditionally sample from these regions with gradient-based
optimization or amortized actor functions. Combining attribute constraints with
a universal "realism" constraint, which enforces similarity to the data
distribution, we generate realistic conditional images from an unconditional
variational autoencoder. Further, using gradient-based optimization, we
demonstrate identity-preserving transformations that make the minimal
adjustment in latent space to modify the attributes of an image. Finally, with
discrete sequences of musical notes, we demonstrate zero-shot conditional
generation, learning latent constraints in the absence of labeled data or a
differentiable reward function. Code with dedicated cloud instance has been
made publicly available (this https URL). | [
1,
0,
0,
1,
0,
0
] |
Title: Characterization of Zinc oxide & Aluminum Ferrite and Simulation studies of M-H plots of Cobalt/Cobaltoxide,
Abstract: Zinc oxide and Aluminum Ferrite were prepared Chemical route. The samples
were characterized by XRD and VSM. Simulation of M-H plots of Co/CoO thin films
were performed. Effect of parameters was observed on saturation magnetization. | [
0,
1,
0,
0,
0,
0
] |
Title: Poisson Structures and Potentials,
Abstract: We introduce a notion of weakly log-canonical Poisson structures on positive
varieties with potentials. Such a Poisson structure is log-canonical up to
terms dominated by the potential. To a compatible real form of a weakly
log-canonical Poisson variety we assign an integrable system on the product of
a certain real convex polyhedral cone (the tropicalization of the variety) and
a compact torus.
We apply this theory to the dual Poisson-Lie group $G^*$ of a
simply-connected semisimple complex Lie group $G$. We define a positive
structure and potential on $G^*$ and show that the natural Poisson-Lie
structure on $G^*$ is weakly log-canonical with respect to this positive
structure and potential.
For $K \subset G$ the compact real form, we show that the real form $K^*
\subset G^*$ is compatible and prove that the corresponding integrable system
is defined on the product of the decorated string cone and the compact torus of
dimension $\frac{1}{2}({\rm dim} \, G - {\rm rank} \, G)$. | [
0,
0,
1,
0,
0,
0
] |
Title: The closure of ideals of $\boldsymbol{\ell^1(Σ)}$ in its enveloping $\boldsymbol{\mathrm{C}^\ast}$-algebra,
Abstract: If $X$ is a compact Hausdorff space and $\sigma$ is a homeomorphism of $X$,
then an involutive Banach algebra $\ell^1(\Sigma)$ of crossed product type is
naturally associated with the topological dynamical system $\Sigma=(X,\sigma)$.
We initiate the study of the relation between two-sided ideals of
$\ell^1(\Sigma)$ and ${\mathrm C}^\ast(\Sigma)$, the enveloping
$\mathrm{C}^\ast$-algebra ${\mathrm C}(X)\rtimes_\sigma \mathbb Z$ of
$\ell^1(\Sigma)$. Among others, we prove that the closure of a proper two-sided
ideal of $\ell^1(\Sigma)$ in ${\mathrm C}^\ast(\Sigma)$ is again a proper
two-sided ideal of ${\mathrm C}^\ast(\Sigma)$. | [
0,
0,
1,
0,
0,
0
] |
Title: An alternative quadratic formula,
Abstract: The classical quadratic formula and some of its lesser known variants for
solving the quadratic equation are reviewed. Then, a new formula for the roots
of a quadratic polynomial is presented. | [
0,
0,
1,
0,
0,
0
] |
Title: Sound event detection using weakly-labeled semi-supervised data with GCRNNS, VAT and Self-Adaptive Label Refinement,
Abstract: In this paper, we present a gated convolutional recurrent neural network
based approach to solve task 4, large-scale weakly labelled semi-supervised
sound event detection in domestic environments, of the DCASE 2018 challenge.
Gated linear units and a temporal attention layer are used to predict the onset
and offset of sound events in 10s long audio clips. Whereby for training only
weakly-labelled data is used. Virtual adversarial training is used for
regularization, utilizing both labelled and unlabeled data. Furthermore, we
introduce self-adaptive label refinement, a method which allows unsupervised
adaption of our trained system to refine the accuracy of frame-level class
predictions. The proposed system reaches an overall macro averaged event-based
F-score of 34.6%, resulting in a relative improvement of 20.5% over the
baseline system. | [
1,
0,
0,
0,
0,
0
] |
Title: Inflationary $α$-attractor cosmology: A global dynamical systems perspective,
Abstract: We study flat FLRW $\alpha$-attractor $\mathrm{E}$- and $\mathrm{T}$-models
by introducing a dynamical systems framework that yields regularized
unconstrained field equations on two-dimensional compact state spaces. This
results in both illustrative figures and a complete description of the entire
solution spaces of these models, including asymptotics. In particular, it is
shown that observational viability, which requires a sufficient number of
$e$-folds, is associated with a solution given by a one-dimensional center
manifold of a past asymptotic de Sitter state, where the center manifold
structure also explains why nearby solutions are attracted to this
`inflationary attractor solution.' A center manifold expansion yields a
description of the inflationary regime with arbitrary analytic accuracy, where
the slow-roll approximation asymptotically describes the tangency condition of
the center manifold at the asymptotic de Sitter state. | [
0,
1,
0,
0,
0,
0
] |
Title: Symmetric Convex Sets with Minimal Gaussian Surface Area,
Abstract: Let $\Omega\subset\mathbb{R}^{n+1}$ have minimal Gaussian surface area among
all sets satisfying $\Omega=-\Omega$ with fixed Gaussian volume. Let $A=A_{x}$
be the second fundamental form of $\partial\Omega$ at $x$, i.e. $A$ is the
matrix of first order partial derivatives of the unit normal vector at
$x\in\partial\Omega$. For any $x=(x_{1},\ldots,x_{n+1})\in\mathbb{R}^{n+1}$,
let $\gamma_{n}(x)=(2\pi)^{-n/2}e^{-(x_{1}^{2}+\cdots+x_{n+1}^{2})/2}$. Let
$\|A\|^{2}$ be the sum of the squares of the entries of $A$, and let
$\|A\|_{2\to 2}$ denote the $\ell_{2}$ operator norm of $A$.
It is shown that if $\Omega$ or $\Omega^{c}$ is convex, and if either
$$\int_{\partial\Omega}(\|A_{x}\|^{2}-1)\gamma_{n}(x)dx>0\qquad\mbox{or}\qquad
\int_{\partial\Omega}\Big(\|A_{x}\|^{2}-1+2\sup_{y\in\partial\Omega}\|A_{y}\|_{2\to
2}^{2}\Big)\gamma_{n}(x)dx<0,$$ then $\partial\Omega$ must be a round cylinder.
That is, except for the case that the average value of $\|A\|^{2}$ is slightly
less than $1$, we resolve the convex case of a question of Barthe from 2001.
The main tool is the Colding-Minicozzi theory for Gaussian minimal surfaces,
which studies eigenfunctions of the Ornstein-Uhlenbeck type operator $L=
\Delta-\langle x,\nabla \rangle+\|A\|^{2}+1$ associated to the surface
$\partial\Omega$. A key new ingredient is the use of a randomly chosen degree 2
polynomial in the second variation formula for the Gaussian surface area. Our
actual results are a bit more general than the above statement. Also, some of
our results hold without the assumption of convexity. | [
1,
0,
1,
0,
0,
0
] |
Title: Advancements in Continuum Approximation Models for Logistics and Transportation Systems: 1996 - 2016,
Abstract: Continuum Approximation (CA) is an efficient and parsimonious technique for
modeling complex logistics problems. In this paper,we review recent studies
that develop CA models for transportation, distribution and logistics problems
with the aim of synthesizing recent advancements and identifying current
research gaps. This survey focuses on important principles and key results from
CA models. In particular, we consider how these studies fill the gaps
identified by the most recent literature reviews in this field. We observe that
CA models are used in a wider range of applications, especially in the areas of
facility location and integrated supply chain management. Most studies use CA
as an alternative to exact solution approaches; however, CA can also be used in
combination with exact approaches. We also conclude with promising areas of
future work. | [
0,
0,
1,
0,
0,
0
] |
Title: On a free boundary problem and minimal surfaces,
Abstract: From minimal surfaces such as Simons' cone and catenoids, using refined
Lyapunov-Schmidt reduction method, we construct new solutions for a free
boundary problem whose free boundary has two components. In dimension $8$,
using variational arguments, we also obtain solutions which are global
minimizers of the corresponding energy functional. This shows that Savin's
theorem is optimal. | [
0,
0,
1,
0,
0,
0
] |
Title: Storing and retrieving long-term memories: cooperation and competition in synaptic dynamics,
Abstract: We first review traditional approaches to memory storage and formation,
drawing on the literature of quantitative neuroscience as well as statistical
physics. These have generally focused on the fast dynamics of neurons; however,
there is now an increasing emphasis on the slow dynamics of synapses, whose
weight changes are held to be responsible for memory storage. An important
first step in this direction was taken in the context of Fusi's cascade model,
where complex synaptic architectures were invoked, in particular, to store
long-term memories. No explicit synaptic dynamics were, however, invoked in
that work. These were recently incorporated theoretically using the techniques
used in agent-based modelling, and subsequently, models of competing and
cooperating synapses were formulated. It was found that the key to the storage
of long-term memories lay in the competitive dynamics of synapses. In this
review, we focus on models of synaptic competition and cooperation, and look at
the outstanding challenges that remain. | [
0,
0,
0,
0,
1,
0
] |
Title: An algorithm for removing sensitive information: application to race-independent recidivism prediction,
Abstract: Predictive modeling is increasingly being employed to assist human
decision-makers. One purported advantage of replacing or augmenting human
judgment with computer models in high stakes settings-- such as sentencing,
hiring, policing, college admissions, and parole decisions-- is the perceived
"neutrality" of computers. It is argued that because computer models do not
hold personal prejudice, the predictions they produce will be equally free from
prejudice. There is growing recognition that employing algorithms does not
remove the potential for bias, and can even amplify it if the training data
were generated by a process that is itself biased. In this paper, we provide a
probabilistic notion of algorithmic bias. We propose a method to eliminate bias
from predictive models by removing all information regarding protected
variables from the data to which the models will ultimately be trained. Unlike
previous work in this area, our framework is general enough to accommodate data
on any measurement scale. Motivated by models currently in use in the criminal
justice system that inform decisions on pre-trial release and parole, we apply
our proposed method to a dataset on the criminal histories of individuals at
the time of sentencing to produce "race-neutral" predictions of re-arrest. In
the process, we demonstrate that a common approach to creating "race-neutral"
models-- omitting race as a covariate-- still results in racially disparate
predictions. We then demonstrate that the application of our proposed method to
these data removes racial disparities from predictions with minimal impact on
predictive accuracy. | [
0,
0,
0,
1,
0,
0
] |
Title: A Question Answering Approach to Emotion Cause Extraction,
Abstract: Emotion cause extraction aims to identify the reasons behind a certain
emotion expressed in text. It is a much more difficult task compared to emotion
classification. Inspired by recent advances in using deep memory networks for
question answering (QA), we propose a new approach which considers emotion
cause identification as a reading comprehension task in QA. Inspired by
convolutional neural networks, we propose a new mechanism to store relevant
context in different memory slots to model context information. Our proposed
approach can extract both word level sequence features and lexical features.
Performance evaluation shows that our method achieves the state-of-the-art
performance on a recently released emotion cause dataset, outperforming a
number of competitive baselines by at least 3.01% in F-measure. | [
1,
0,
0,
0,
0,
0
] |
Title: Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting,
Abstract: Machine Learning on graph-structured data is an important and omnipresent
task for a vast variety of applications including anomaly detection and dynamic
network analysis. In this paper, a deep generative model is introduced to
capture continuous probability densities corresponding to the nodes of an
arbitrary graph. In contrast to all learning formulations in the area of
discriminative pattern recognition, we propose a scalable generative
optimization/algorithm theoretically proved to capture distributions at the
nodes of a graph. Our model is able to generate samples from the probability
densities learned at each node. This probabilistic data generation model, i.e.
convolutional graph auto-encoder (CGAE), is devised based on the localized
first-order approximation of spectral graph convolutions, deep learning, and
the variational Bayesian inference. We apply our CGAE to a new problem, the
spatio-temporal probabilistic solar irradiance prediction. Multiple solar
radiation measurement sites in a wide area in northern states of the US are
modeled as an undirected graph. Using our proposed model, the distribution of
future irradiance given historical radiation observations is estimated for
every site/node. Numerical results on the National Solar Radiation Database
show state-of-the-art performance for probabilistic radiation prediction on
geographically distributed irradiance data in terms of reliability, sharpness,
and continuous ranked probability score. | [
0,
0,
0,
1,
0,
0
] |
Title: Quasisymmetrically co-Hopfian Sierpiński Spaces and Menger Curve,
Abstract: A metric space $X$ is quasisymmetrically co-Hopfian if every quasisymmetric
embedding of $X$ into itself is onto. We construct the first examples of metric
spaces homeomorphic to the universal Menger curve and higher dimensional
Sierpiński spaces, which are quasisymmetrically co-Hopfian. We also show that
the collection of quasisymmetric equivalence classes of spaces homeomorphic to
the Menger curve is uncountable. These results answer a problem and generalize
results of Merenkov from \cite{Mer:coHopf}. | [
0,
0,
1,
0,
0,
0
] |
Title: Localization properties and high-fidelity state transfer in electronic hopping models with correlated disorder,
Abstract: We investigate a tight-binding electronic chain featuring diagonal and
off-diagonal disorder, these being modelled through the long-range-correlated
fractional Brownian motion. Particularly, by employing exact diagonalization
methods, we evaluate how the eigenstate spectrum of the system and its related
single-particle dynamics respond to both competing sources of disorder.
Moreover, we report the possibility of carrying out efficient end-to-end
quantum-state transfer protocols even in the presence of such generalized
disorder due to the appearance of extended states around the middle of the band
in the limit of strong correlations. | [
0,
1,
0,
0,
0,
0
] |
Title: On consistency of optimal pricing algorithms in repeated posted-price auctions with strategic buyer,
Abstract: We study revenue optimization learning algorithms for repeated posted-price
auctions where a seller interacts with a single strategic buyer that holds a
fixed private valuation for a good and seeks to maximize his cumulative
discounted surplus. For this setting, first, we propose a novel algorithm that
never decreases offered prices and has a tight strategic regret bound in
$\Theta(\log\log T)$ under some mild assumptions on the buyer surplus
discounting. This result closes the open research question on the existence of
a no-regret horizon-independent weakly consistent pricing. The proposed
algorithm is inspired by our observation that a double decrease of offered
prices in a weakly consistent algorithm is enough to cause a linear regret.
This motivates us to construct a novel transformation that maps a
right-consistent algorithm to a weakly consistent one that never decreases
offered prices.
Second, we outperform the previously known strategic regret upper bound of
the algorithm PRRFES, where the improvement is achieved by means of a finer
constant factor $C$ of the principal term $C\log\log T$ in this upper bound.
Finally, we generalize results on strategic regret previously known for
geometric discounting of the buyer's surplus to discounting of other types,
namely: the optimality of the pricing PRRFES to the case of geometrically
concave decreasing discounting; and linear lower bound on the strategic regret
of a wide range of horizon-independent weakly consistent algorithms to the case
of arbitrary discounts. | [
1,
0,
0,
1,
0,
0
] |
Title: City-wide Analysis of Electronic Health Records Reveals Gender and Age Biases in the Administration of Known Drug-Drug Interactions,
Abstract: The occurrence of drug-drug-interactions (DDI) from multiple drug
prescriptions is a serious problem, both for individuals and health-care
systems, since patients with complications due to DDI are likely to re-enter
the system at a costlier level. We present a large-scale longitudinal study of
the DDI phenomenon at the primary- and secondary-care level using electronic
health records from the city of Blumenau in Southern Brazil (pop. ~340,000).
This is the first study of DDI we are aware of that follows an entire city
longitudinally for 18 months. We found that 181 distinct drug pairs known to
interact were dispensed concomitantly to 12% of the patients in the city's
public health-care system. Further, 4% of the patients were dispensed major DDI
combinations, likely to result in very serious adverse reactions and costs we
estimate to be larger than previously reported. DDI results are integrated into
associative networks for inference and visualization, revealing key medications
and interactions. Analysis reveals that women have a 60% increased risk of DDI
as compared to men; the increase becomes 90% when only major DDI are
considered. Furthermore, DDI risk increases substantially with age. Patients
aged 70-79 years have a 34% risk of DDI when they are prescribed two or more
drugs concomitantly. Interestingly, a null model demonstrates that age and
women-specific risks from increased polypharmacy far exceed expectations in
those populations. This suggests that social and biological factors are at
play. Finally, we demonstrate that machine learning classifiers accurately
predict patients likely to be administered DDI given their history of
prescribed drugs, gender, and age (MCC=.7,AUC=.97). These results demonstrate
that accurate warning systems for known DDI can be devised for health-care
systems leading to substantial reduction of DDI-related adverse reactions and
health-care savings. | [
1,
0,
0,
1,
1,
0
] |
Title: A remark on the disorienting of species due to the fluctuating environment,
Abstract: In this article we study the stabilizing of a primitive pattern of behaviour
for the two-species community with chemotaxis due to the short-wavelength
external signal. We use a system of Patlak-Keller-Segel type as a model of the
community. It is well-known that such systems can produce complex unsteady
patterns of behaviour which are usually explained mathematically by
bifurcations of some basic solutions that describe simpler patterns. As far as
we aware, all such bifurcations in the models of the Patlak-Keller-Segel type
had been found for homogeneous (i.e. translationally invariant) systems where
the basic solutions are equilibria with homogeneous distributions of all
species. The model considered in the present paper does not possess the
translational invariance: one of species (the predators) is assumed to be
capable of moving in response to a signal produced externally in addition to
the signal emitted by another species (the prey). For instance, the external
signal may arise from the inhomogeneity of the distribution of an environmental
characteristic such as temperature, salinity, terrain relief, etc. Our goal is
to examine the effect of short-wavelength inhomogeneity. To do this, we employ
a certain homogenization procedure. We separate the short-wavelength and smooth
components of the system response and derive a slow system governing the latter
one. Analysing the slow system and comparing it with the case of homogeneous
environment shows that, generically, a short-wavelength inhomogeneity results
in an exponential decrease in the motility of the predators. The loss of
motility prevents, to a great extent, the occurrence of complex unsteady
patterns and dramatically stabilizes the primitive basic solution. In some
sense, the necessity of dealing with intensive small-scale changes of the
environment makes the system unable to respond to other challenges. | [
0,
0,
0,
0,
1,
0
] |
Title: Fourier optimization and prime gaps,
Abstract: We investigate some extremal problems in Fourier analysis and their
connection to a problem in prime number theory. In particular, we improve the
current bounds for the largest possible gap between consecutive primes assuming
the Riemann hypothesis. | [
0,
0,
1,
0,
0,
0
] |
Title: An Interpretable Knowledge Transfer Model for Knowledge Base Completion,
Abstract: Knowledge bases are important resources for a variety of natural language
processing tasks but suffer from incompleteness. We propose a novel embedding
model, \emph{ITransF}, to perform knowledge base completion. Equipped with a
sparse attention mechanism, ITransF discovers hidden concepts of relations and
transfer statistical strength through the sharing of concepts. Moreover, the
learned associations between relations and concepts, which are represented by
sparse attention vectors, can be interpreted easily. We evaluate ITransF on two
benchmark datasets---WN18 and FB15k for knowledge base completion and obtains
improvements on both the mean rank and Hits@10 metrics, over all baselines that
do not use additional information. | [
1,
0,
0,
0,
0,
0
] |
Title: Vecchia approximations of Gaussian-process predictions,
Abstract: Gaussian processes (GPs) are highly flexible function estimators used for
geospatial analysis, nonparametric regression, and machine learning, but they
are computationally infeasible for large datasets. Vecchia approximations of
GPs have been used to enable fast evaluation of the likelihood for parameter
inference. Here, we study Vecchia approximations of spatial predictions at
observed and unobserved locations, including obtaining joint predictive
distributions at large sets of locations. We propose a general Vecchia
framework for GP predictions, which contains some novel and some existing
special cases. We study the accuracy and computational properties of these
approaches theoretically and numerically. We show that our new approaches
exhibit linear computational complexity in the total number of spatial
locations. We also apply our methods to a satellite dataset of chlorophyll
fluorescence. | [
0,
0,
0,
1,
0,
0
] |
Title: Long-Term Sequential Prediction Using Expert Advice,
Abstract: For the prediction with experts' advice setting, we consider some methods to
construct forecasting algorithms that suffer loss not much more than any expert
in the pool. In contrast to the standard approach, we investigate the case of
long-term forecasting of time series. This approach implies that each expert
issues a forecast for a time point ahead (or a time interval), and then the
master algorithm combines these forecasts into one aggregated forecast
(sequence of forecasts). We introduce two new approaches to aggregating
experts' long-term interval predictions. Both are based on Vovk's aggregating
algorithm. The first approach applies the method of Mixing Past Posteriors
method to the long-term prediction. The second approach is used for the
interval forecasting and considers overlapping experts. The upper bounds for
regret of these algorithms for adversarial case are obtained. We also present
the results of numerical experiments on time series long-term prediction. | [
1,
0,
0,
1,
0,
0
] |
Title: AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control,
Abstract: In this paper, a new adaptive multi-batch experience replay scheme is
proposed for proximal policy optimization (PPO) for continuous action control.
On the contrary to original PPO, the proposed scheme uses the batch samples of
past policies as well as the current policy for the update for the next policy,
where the number of the used past batches is adaptively determined based on the
oldness of the past batches measured by the average importance sampling (IS)
weight. The new algorithm constructed by combining PPO with the proposed
multi-batch experience replay scheme maintains the advantages of original PPO
such as random mini-batch sampling and small bias due to low IS weights by
storing the pre-computed advantages and values and adaptively determining the
mini-batch size. Numerical results show that the proposed method significantly
increases the speed and stability of convergence on various continuous control
tasks compared to original PPO. | [
1,
0,
0,
0,
0,
0
] |
Title: Magnetic states of MnP: muon-spin rotation studies,
Abstract: Muon-spin rotation data collected at ambient pressure ($p$) and at $p=2.42$
GPa in MnP were analyzed to check their consistency with various low- and
high-pressure magnetic structures reported in the literature. Our analysis
confirms that in MnP the low-temperature and low-pressure helimagnetic phase is
characterised by an increased value of the average magnetic moment compared to
the high-temperature ferromagnetic phase. An elliptical double-helical
structure with a propagation vector ${\bf Q}=(0,0,0.117)$, an $a-$axis moment
elongated by approximately 18% and an additional tilt of the rotation plane
towards $c-$direction by $\simeq 4-8^{\rm o}$ leads to a good agreement between
the theory and the experiment. The analysis of the high-pressure $\mu$SR data
reveals that the new magnetic order appearing for pressures exceeding $1.5$ GPa
can not be described by keeping the propagation vector ${\bf Q} \parallel c$.
Even the extreme case -- decoupling the double-helical structure into four
individual helices -- remains inconsistent with the experiment. It is shown
that the high-pressure magnetic phase which is a precursor of superconductivity
is an incommensurate helical state with ${\bf Q} \parallel b$. | [
0,
1,
0,
0,
0,
0
] |
Title: The Hyper Suprime-Cam Software Pipeline,
Abstract: In this paper, we describe the optical imaging data processing pipeline
developed for the Subaru Telescope's Hyper Suprime-Cam (HSC) instrument. The
HSC Pipeline builds on the prototype pipeline being developed by the Large
Synoptic Survey Telescope's Data Management system, adding customizations for
HSC, large-scale processing capabilities, and novel algorithms that have since
been reincorporated into the LSST codebase. While designed primarily to reduce
HSC Subaru Strategic Program (SSP) data, it is also the recommended pipeline
for reducing general-observer HSC data. The HSC pipeline includes high level
processing steps that generate coadded images and science-ready catalogs as
well as low-level detrending and image characterizations. | [
0,
1,
0,
0,
0,
0
] |
Title: Plasma-based wakefield accelerators as sources of axion-like particles,
Abstract: We estimate the average flux density of minimally-coupled axion-like
particles generated by a laser-driven plasma wakefield propagating along a
constant strong magnetic field. Our calculations suggest that a terrestrial
source based on this approach could generate a pulse of axion-like particles
whose flux density is comparable to that of solar axion-like particles at
Earth. This mechanism is optimal for axion-like particles with mass in the
range of interest of contemporary experiments designed to detect dark matter
using microwave cavities. | [
0,
1,
0,
0,
0,
0
] |
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