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Title: State-Space Identification of Unmanned Helicopter Dynamics using Invasive Weed Optimization Algorithm on Flight Data,
Abstract: In order to achieve a good level of autonomy in unmanned helicopters, an
accurate replication of vehicle dynamics is required, which is achievable
through precise mathematical modeling. This paper aims to identify a parametric
state-space system for an unmanned helicopter to a good level of accuracy using
Invasive Weed Optimization (IWO) algorithm. The flight data of Align TREX 550
flybarless helicopter is used in the identification process. The rigid-body
dynamics of the helicopter is modeled in a state-space form that has 40
parameters, which serve as control variables for the IWO algorithm. The results
after 1000 iterations were compared with the traditionally used Prediction
Error Minimization (PEM) method and also with Genetic Algorithm (GA), which
serve as references. Results show a better level of correlation between the
actual and estimated responses of the system identified using IWO to that of
PEM and GA. | [
1,
0,
0,
0,
0,
0
] |
Title: Latent Molecular Optimization for Targeted Therapeutic Design,
Abstract: We devise an approach for targeted molecular design, a problem of interest in
computational drug discovery: given a target protein site, we wish to generate
a chemical with both high binding affinity to the target and satisfactory
pharmacological properties. This problem is made difficult by the enormity and
discreteness of the space of potential therapeutics, as well as the
graph-structured nature of biomolecular surface sites. Using a dataset of
protein-ligand complexes, we surmount these issues by extracting a signature of
the target site with a graph convolutional network and by encoding the discrete
chemical into a continuous latent vector space. The latter embedding permits
gradient-based optimization in molecular space, which we perform using learned
differentiable models of binding affinity and other pharmacological properties.
We show that our approach is able to efficiently optimize these multiple
objectives and discover new molecules with potentially useful binding
properties, validated via docking methods. | [
0,
0,
0,
0,
1,
0
] |
Title: SlimNets: An Exploration of Deep Model Compression and Acceleration,
Abstract: Deep neural networks have achieved increasingly accurate results on a wide
variety of complex tasks. However, much of this improvement is due to the
growing use and availability of computational resources (e.g use of GPUs, more
layers, more parameters, etc). Most state-of-the-art deep networks, despite
performing well, over-parameterize approximate functions and take a significant
amount of time to train. With increased focus on deploying deep neural networks
on resource constrained devices like smart phones, there has been a push to
evaluate why these models are so resource hungry and how they can be made more
efficient. This work evaluates and compares three distinct methods for deep
model compression and acceleration: weight pruning, low rank factorization, and
knowledge distillation. Comparisons on VGG nets trained on CIFAR10 show that
each of the models on their own are effective, but that the true power lies in
combining them. We show that by combining pruning and knowledge distillation
methods we can create a compressed network 85 times smaller than the original,
all while retaining 96% of the original model's accuracy. | [
0,
0,
0,
1,
0,
0
] |
Title: An alternative axiomization of $N$-pseudospaces,
Abstract: We give a new axiomatization of the N-pseudospace, studied in [2]
(Tent(2014)) and [1] (Baudisch,Martin-Pizarro,Ziegler(2014)) based on the
zigzags introduced in [2]. We also present a more detailed account of the
characterization of forking given in [2]. | [
0,
0,
1,
0,
0,
0
] |
Title: Symmetry breaking in linear multipole traps,
Abstract: Radiofrequency multipole traps have been used for some decades in cold
collision experiments, and are gaining interest for precision spectroscopy due
to their low mi-cromotion contribution, and the predicted unusual cold-ion
structures. However, the experimental realisation is not yet fully controlled,
and open questions in the operation of these devices remain. We present
experimental observations of symmetry breaking of the trapping potential in a
macroscopic octupole trap with laser-cooled ions. Numerical simulations have
been performed in order to explain the appearance of additional local potential
minima, and be able to control them in a next step. We characterize these
additional potential minima, in particular with respect to their position,
their potential depth and their probability of population as a function of the
radial and angular displacement of the trapping rods. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep Spatio-temporal Manifold Network for Action Recognition,
Abstract: Visual data such as videos are often sampled from complex manifold. We
propose leveraging the manifold structure to constrain the deep action feature
learning, thereby minimizing the intra-class variations in the feature space
and alleviating the over-fitting problem. Considering that manifold can be
transferred, layer by layer, from the data domain to the deep features, the
manifold priori is posed from the top layer into the back propagation learning
procedure of convolutional neural network (CNN). The resulting algorithm
--Spatio-Temporal Manifold Network-- is solved with the efficient Alternating
Direction Method of Multipliers and Backward Propagation (ADMM-BP). We
theoretically show that STMN recasts the problem as projection over the
manifold via an embedding method. The proposed approach is evaluated on two
benchmark datasets, showing significant improvements to the baselines. | [
1,
0,
0,
0,
0,
0
] |
Title: Gas dynamics in strong centrifugal fields,
Abstract: Dynamics of waves generated by scopes in gas centrifuges (GC) for isotope
separation is considered. The centrifugal acceleration in the GC reaches values
of the order of $10^6$g. The centrifugal and Coriolis forces modify essentially
the conventional sound waves. Three families of the waves with different
polarisation and dispersion exist in these conditions. Dynamics of the flow in
the model GC Iguasu is investigated numerically. Comparison of the results of
the numerical modelling of the wave dynamics with the analytical predictions is
performed. New phenomena of the resonances in the GC is found. The resonances
occur for the waves polarized along the rotational axis having the smallest
dumping due to the viscosity. | [
0,
1,
0,
0,
0,
0
] |
Title: Robust Stackelberg controllability for the Navier--Stokes equations,
Abstract: In this paper we deal with a robust Stackelberg strategy for the
Navier--Stokes system. The scheme is based in considering a robust control
problem for the "follower control" and its associated disturbance function.
Afterwards, we consider the notion of Stackelberg optimization (which is
associated to the "leader control") in order to deduce a local null
controllability result for the Navier--Stokes system. | [
0,
0,
1,
0,
0,
0
] |
Title: Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications,
Abstract: In this paper, five different approaches for reduced-order modeling of
brittle fracture in geomaterials, specifically concrete, are presented and
compared. Four of the five methods rely on machine learning (ML) algorithms to
approximate important aspects of the brittle fracture problem. In addition to
the ML algorithms, each method incorporates different physics-based assumptions
in order to reduce the computational complexity while maintaining the physics
as much as possible. This work specifically focuses on using the ML approaches
to model a 2D concrete sample under low strain rate pure tensile loading
conditions with 20 preexisting cracks present. A high-fidelity finite
element-discrete element model is used to both produce a training dataset of
150 simulations and an additional 35 simulations for validation. Results from
the ML approaches are directly compared against the results from the
high-fidelity model. Strengths and weaknesses of each approach are discussed
and the most important conclusion is that a combination of physics-informed and
data-driven features are necessary for emulating the physics of crack
propagation, interaction and coalescence. All of the models presented here have
runtimes that are orders of magnitude faster than the original high-fidelity
model and pave the path for developing accurate reduced order models that could
be used to inform larger length-scale models with important sub-scale physics
that often cannot be accounted for due to computational cost. | [
0,
0,
0,
1,
0,
0
] |
Title: A ferroelectric quantum phase transition inside the superconducting dome of Sr$_{1-x}$Ca$_{x}$TiO$_{3-δ}$,
Abstract: SrTiO$_{3}$, a quantum paraelectric, becomes a metal with a superconducting
instability after removal of an extremely small number of oxygen atoms. It
turns into a ferroelectric upon substitution of a tiny fraction of strontium
atoms with calcium. The two orders may be accidental neighbors or intimately
connected, as in the picture of quantum critical ferroelectricity. Here, we
show that in Sr$_{1-x}$Ca$_{x}$TiO$_{3-\delta}$ ($0.002<x<0.009$,
$\delta<0.001$) the ferroelectric order coexists with dilute metallicity and
its superconducting instability in a finite window of doping. At a critical
carrier density, which scales with the Ca content, a quantum phase transition
destroys the ferroelectric order. We detect an upturn in the normal-state
scattering and a significant modification of the superconducting dome in the
vicinity of this quantum phase transition. The enhancement of the
superconducting transition temperature with calcium substitution documents the
role played by ferroelectric vicinity in the precocious emergence of
superconductivity in this system, restricting possible theoretical scenarios
for pairing. | [
0,
1,
0,
0,
0,
0
] |
Title: First Detection of Equatorial Dark Dust Lane in a Protostellar Disk at Submillimeter Wavelength,
Abstract: In the earliest (so-called "Class 0") phase of sunlike (low-mass) star
formation, circumstellar disks are expected to form, feeding the protostars.
However, such disks are difficult to resolve spatially because of their small
sizes. Moreover, there are theoretical difficulties in producing such disks in
the earliest phase, due to the retarding effects of magnetic fields on the
rotating, collapsing material (so-called "magnetic braking"). With the Atacama
Large Millimeter/submillimeter Array (ALMA), it becomes possible to uncover
such disks and study them in detail. HH 212 is a very young protostellar
system. With ALMA, we not only detect but also spatially resolve its disk in
dust emission at submillimeter wavelength. The disk is nearly edge-on and has a
radius of ~ 60 AU. Interestingly, it shows a prominent equatorial dark lane
sandwiched between two brighter features, due to relatively low temperature and
high optical depth near the disk midplane. For the first time, this dark lane
is seen at submillimeter wavelength, producing a "hamburger"-shaped appearance
that is reminiscent of the scattered-light image of an edge-on disk in optical
and near infrared. Our observations open up an exciting possibility of directly
detecting and characterizing small disks around the youngest protostars through
high-resolution imaging with ALMA, which provides strong constraints on
theories of disk formation. | [
0,
1,
0,
0,
0,
0
] |
Title: General dynamical properties of cosmological models with nonminimal kinetic coupling,
Abstract: We consider cosmological dynamics in the theory of gravity with the scalar
field possessing the nonminimal kinetic coupling to curvature given as $\eta
G^{\mu\nu}\phi_{,\mu}\phi_{,\nu}$, where $\eta$ is an arbitrary coupling
parameter, and the scalar potential $V(\phi)$ which assumed to be as general as
possible. With an appropriate dimensionless parametrization we represent the
field equations as an autonomous dynamical system which contains ultimately
only one arbitrary function $\chi (x)= 8 \pi \vert \eta \vert V(x/\sqrt{8
\pi})$ with $x=\sqrt{8 \pi}\phi$. Then, assuming the rather general properties
of $\chi(x)$, we analyze stationary points and their stability, as well as all
possible asymptotical regimes of the dynamical system. It has been shown that
for a broad class of $\chi(x)$ there exist attractors representing three
accelerated regimes of the Universe evolution, including de Sitter expansion
(or late-time inflation), the Little Rip scenario, and the Big Rip scenario. As
the specific examples, we consider a power-law potential
$V(\phi)=M^4(\phi/\phi_0)^\sigma$, Higgs-like potential
$V(\phi)=\frac{\lambda}{4}(\phi^2-\phi_0^2)^2$, and exponential potential
$V(\phi)=M^4 e^{-\phi/\phi_0}$. | [
0,
1,
0,
0,
0,
0
] |
Title: Extensions of interpolation between the arithmetic-geometric mean inequality for matrices,
Abstract: In this paper, we present some extensions of interpolation between the
arithmetic-geometric means inequality. Among other inequalities, it is shown
that if $A, B, X$ are $n\times n$ matrices, then \begin{align*}
\|AXB^*\|^2\leq\|f_1(A^*A)Xg_1(B^*B)\|\,\|f_2(A^*A)Xg_2(B^*B)\|, \end{align*}
where $f_1,f_2,g_1,g_2$ are non-negative continues functions such that
$f_1(t)f_2(t)=t$ and $g_1(t)g_2(t)=t\,\,(t\geq0)$. We also obtain the
inequality \begin{align*}
\left|\left|\left|AB^*\right|\right|\right|^2\nonumber&\leq
\left|\left|\left|p(A^*A)^{\frac{m}{p}}+
(1-p)(B^*B)^{\frac{s}{1-p}}\right|\right|\right|\,\left|\left|\left|(1-p)(A^*A)^{\frac{n}{1-p}}+
p(B^*B)^{\frac{t}{p}}\right|\right|\right|, \end{align*} in which $m,n,s,t$ are
real numbers such that $m+n=s+t=1$, $|||\cdot|||$ is an arbitrary unitarily
invariant norm and $p\in[0,1]$. | [
0,
0,
1,
0,
0,
0
] |
Title: Machine Translation in Indian Languages: Challenges and Resolution,
Abstract: English to Indian language machine translation poses the challenge of
structural and morphological divergence. This paper describes English to Indian
language statistical machine translation using pre-ordering and suffix
separation. The pre-ordering uses rules to transfer the structure of the source
sentences prior to training and translation. This syntactic restructuring helps
statistical machine translation to tackle the structural divergence and hence
better translation quality. The suffix separation is used to tackle the
morphological divergence between English and highly agglutinative Indian
languages. We demonstrate that the use of pre-ordering and suffix separation
helps in improving the quality of English to Indian Language machine
translation. | [
1,
0,
0,
0,
0,
0
] |
Title: Retrieving the quantitative chemical information at nanoscale from SEM EDX measurements by Machine Learning,
Abstract: The quantitative composition of metal alloy nanowires on InSb(001)
semiconductor surface and gold nanostructures on germanium surface is
determined by blind source separation (BSS) machine learning (ML) method using
non negative matrix factorization (NMF) from energy dispersive X-ray
spectroscopy (EDX) spectrum image maps measured in a scanning electron
microscope (SEM). The BSS method blindly decomposes the collected EDX spectrum
image into three source components, which correspond directly to the X-ray
signals coming from the supported metal nanostructures, bulk semiconductor
signal and carbon background. The recovered quantitative composition is
validated by detailed Monte Carlo simulations and is confirmed by separate
cross-sectional TEM EDX measurements of the nanostructures. This shows that SEM
EDX measurements together with machine learning blind source separation
processing could be successfully used for the nanostructures quantitative
chemical composition determination. | [
0,
1,
0,
0,
0,
0
] |
Title: Decomposition Strategies for Constructive Preference Elicitation,
Abstract: We tackle the problem of constructive preference elicitation, that is the
problem of learning user preferences over very large decision problems,
involving a combinatorial space of possible outcomes. In this setting, the
suggested configuration is synthesized on-the-fly by solving a constrained
optimization problem, while the preferences are learned itera tively by
interacting with the user. Previous work has shown that Coactive Learning is a
suitable method for learning user preferences in constructive scenarios. In
Coactive Learning the user provides feedback to the algorithm in the form of an
improvement to a suggested configuration. When the problem involves many
decision variables and constraints, this type of interaction poses a
significant cognitive burden on the user. We propose a decomposition technique
for large preference-based decision problems relying exclusively on inference
and feedback over partial configurations. This has the clear advantage of
drastically reducing the user cognitive load. Additionally, part-wise inference
can be (up to exponentially) less computationally demanding than inference over
full configurations. We discuss the theoretical implications of working with
parts and present promising empirical results on one synthetic and two
realistic constructive problems. | [
1,
0,
0,
1,
0,
0
] |
Title: Vibrational surface EELS probes confined Fuchs-Kliewer modes,
Abstract: Recently, two reports have demonstrated the amazing possibility to probe
vibrational excitations from nanoparticles with a spatial resolution much
smaller than the corresponding free-space phonon wavelength using electron
energy loss spectroscopy (EELS). While Lagos et al. evidenced a strong spatial
and spectral modulation of the EELS signal over a nanoparticle, Krivanek et al.
did not. Here, we show that discrepancies among different EELS experiments as
well as their relation to optical near- and far-field optical experiments can
be understood by introducing the concept of confined bright and dark
Fuchs-Kliewer modes, whose density of states is probed by EELS. Such a concise
formalism is the vibrational counterpart of the broadly used formalism for
localized surface plasmons; it makes it straightforward to predict or interpret
phenomena already known for localized surface plasmons such as
environment-related energy shifts or the possibility of 3D mapping of the
related surface charge densities. | [
0,
1,
0,
0,
0,
0
] |
Title: The Dependence of the Mass-Metallicity Relation on Large Scale Environment,
Abstract: We examine the relation between gas-phase oxygen abundance and stellar
mass---the MZ relation---as a function of the large scale galaxy environment
parameterized by the local density. The dependence of the MZ relation on the
environment is small. The metallicity where the MZ relation saturates and the
slope of the MZ relation are both independent of the local density. The impact
of the large scale environment is completely parameterized by the
anti-correlation between local density and the turnover stellar mass where the
MZ relation begins to saturate. Analytical modeling suggests that the
anti-correlation between the local density and turnover stellar mass is a
consequence of a variation in the gas content of star-forming galaxies. Across
$\sim1$ order of magnitude in local density, the gas content at a fixed stellar
mass varies by $\sim5\%$. Variation of the specific star formation rate with
environment is consistent with this interpretation. At a fixed stellar mass,
galaxies in low density environments have lower metallicities because they are
slightly more gas-rich than galaxies in high density environments. Modeling the
shape of the mass-metallicity relation thus provides an indirect means to probe
subtle variations in the gas content of star-forming galaxies. | [
0,
1,
0,
0,
0,
0
] |
Title: Proof of Riemann hypothesis, Generalized Riemann hypothesis and Ramanujan $τ$-Dirichlet series hypothesis,
Abstract: We prove Riemann hypothesis, Generalized Riemann hypothesis, and Ramanujan
$\tau$-Dirichlet series hypothesis. Method is to show the convexity of function
which has zeros critical strip the same as zeta function. | [
0,
0,
1,
0,
0,
0
] |
Title: A second order primal-dual method for nonsmooth convex composite optimization,
Abstract: We develop a second order primal-dual method for optimization problems in
which the objective function is given by the sum of a strongly convex twice
differentiable term and a possibly nondifferentiable convex regularizer. After
introducing an auxiliary variable, we utilize the proximal operator of the
nonsmooth regularizer to transform the associated augmented Lagrangian into a
function that is once, but not twice, continuously differentiable. The saddle
point of this function corresponds to the solution of the original optimization
problem. We employ a generalization of the Hessian to define second order
updates on this function and prove global exponential stability of the
corresponding differential inclusion. Furthermore, we develop a globally
convergent customized algorithm that utilizes the primal-dual augmented
Lagrangian as a merit function. We show that the search direction can be
computed efficiently and prove quadratic/superlinear asymptotic convergence. We
use the $\ell_1$-regularized least squares problem and the problem of designing
a distributed controller for a spatially-invariant system to demonstrate the
merits and the effectiveness of our method. | [
1,
1,
0,
0,
0,
0
] |
Title: PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking,
Abstract: Electronic medical records contain multi-format electronic medical data that
consist of an abundance of medical knowledge. Facing with patient's symptoms,
experienced caregivers make right medical decisions based on their professional
knowledge that accurately grasps relationships between symptoms, diagnosis and
corresponding treatments. In this paper, we aim to capture these relationships
by constructing a large and high-quality heterogenous graph linking patients,
diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework
to extract important medical entities from MIMIC-III (Medical Information Mart
for Intensive Care III) and automatically link them with the existing
biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD
graph presented in this paper is accessible on the Web via the SPARQL endpoint,
and provides a pathway for medical discovery and applications, such as
effective treatment recommendations. | [
1,
0,
0,
0,
0,
0
] |
Title: A new algorithm for fast generalized DFTs,
Abstract: We give an new arithmetic algorithm to compute the generalized Discrete
Fourier Transform (DFT) over finite groups $G$. The new algorithm uses
$O(|G|^{\omega/2 + o(1)})$ operations to compute the generalized DFT over
finite groups of Lie type, including the linear, orthogonal, and symplectic
families and their variants, as well as all finite simple groups of Lie type.
Here $\omega$ is the exponent of matrix multiplication, so the exponent
$\omega/2$ is optimal if $\omega = 2$. Previously, "exponent one" algorithms
were known for supersolvable groups and the symmetric and alternating groups.
No exponent one algorithms were known (even under the assumption $\omega = 2$)
for families of linear groups of fixed dimension, and indeed the previous
best-known algorithm for $SL_2(F_q)$ had exponent $4/3$ despite being the focus
of significant effort. We unconditionally achieve exponent at most $1.19$ for
this group, and exponent one if $\omega = 2$. Our algorithm also yields an
improved exponent for computing the generalized DFT over general finite groups
$G$, which beats the longstanding previous best upper bound, for any $\omega$.
In particular, assuming $\omega = 2$, we achieve exponent $\sqrt{2}$, while the
previous best was $3/2$. | [
1,
0,
1,
0,
0,
0
] |
Title: Critical magnetic fields in a superconductor coupled to a superfluid,
Abstract: We study a superconductor that is coupled to a superfluid via density and
derivative couplings. Starting from a Lagrangian for two complex scalar fields,
we derive a temperature-dependent Ginzburg-Landau potential, which is then used
to compute the phase diagram at nonzero temperature and external magnetic
field. This includes the calculation of the critical magnetic fields for the
transition to an array of magnetic flux tubes, based on an approximation for
the interaction between the flux tubes. We find that the transition region
between type-I and type-II superconductivity changes qualitatively due to the
presence of the superfluid: the phase transitions at the upper and lower
critical fields in the type-II regime become first order, opening the
possibility of clustered flux tube phases. These flux tube clusters may be
realized in the core of neutron stars, where superconducting protons are
expected to be coupled to superfluid neutrons. | [
0,
1,
0,
0,
0,
0
] |
Title: The bubble algebras at roots of unity,
Abstract: We introduce multi-colour partition algebras $P_{n,m}(\delta_0, ...,
\delta_{m-1})$, which are generalization of both bubble algebras and partition
algebras, then define the bubble algebra $T_{n,m}(\delta_0, ..., \delta_{m-1})$
as a sub-algebra of the algebra $P_{n,m}(\delta_0, ..., \delta_{m-1})$. We
present general techniques to determine the structure of the bubble algebra
over the complex field in the non-semisimple case. | [
0,
0,
1,
0,
0,
0
] |
Title: Self-Repairing Energy Materials: Sine Qua Non for a Sustainable Future,
Abstract: Materials are central to our way of life and future. Energy and materials as
resources are connected and the obvious connections between them are the energy
cost of materials and the materials cost of energy. For both of these
resilience of the materials is critical; thus a major goal of future chemistry
should be to find materials for energy that can last longer, i.e., design
principles for self-repair in these. | [
0,
1,
0,
0,
0,
0
] |
Title: Design and performance of dual-polarization lumped-element kinetic inductance detectors for millimeter-wave polarimetry,
Abstract: Lumped-element kinetic inductance detectors (LEKIDs) are an attractive
technology for millimeter-wave observations that require large arrays of
extremely low-noise detectors. We designed, fabricated and characterized
64-element (128 LEKID) arrays of horn-coupled, dual-polarization LEKIDs
optimized for ground-based CMB polarimetry. Our devices are sensitive to two
orthogonal polarizations in a single spectral band centered on 150 GHz with
$\Delta\nu/\nu=0.2$. The $65\times 65$ mm square arrays are designed to be
tiled into the focal plane of an optical system. We demonstrate the viability
of these dual-polarization LEKIDs with laboratory measurements. The LEKID
modules are tested with an FPGA-based readout system in a sub-kelvin cryostat
that uses a two-stage adiabatic demagnetization refrigerator. The devices are
characterized using a blackbody and a millimeter-wave source. The polarization
properties are measured with a cryogenic stepped half-wave plate. We measure
the resonator parameters and the detector sensitivity, noise spectrum, dynamic
range, and polarization response. The resonators have internal quality factors
approaching $1\times 10^{6}$. The detectors have uniform response between
orthogonal polarizations and a large dynamic range. The detectors are
photon-noise limited above 1 pW of absorbed power. The noise-equivalent
temperatures under a 3.4 K blackbody load are $<100~\mu\mathrm{K\sqrt{s}}$. The
polarization fractions of detectors sensitive to orthogonal polarizations are
>80%. The entire array is multiplexed on a single readout line, demonstrating a
multiplexing factor of 128. The array and readout meet the requirements for 4
arrays to be read out simultaneously for a multiplexing factor of 512. This
laboratory study demonstrates the first dual-polarization LEKID array optimized
for CMB polarimetry and shows the readiness of the detectors for on-sky
observations. | [
0,
1,
0,
0,
0,
0
] |
Title: An overview of knot Floer homology,
Abstract: Knot Floer homology is an invariant for knots discovered by the authors and,
independently, Jacob Rasmussen. The discovery of this invariant grew naturally
out of studying how a certain three-manifold invariant, Heegaard Floer
homology, changes as the three-manifold undergoes Dehn surgery along a knot.
Since its original definition, thanks to the contributions of many researchers,
knot Floer homology has emerged as a useful tool for studying knots in its own
right. We give here a few selected highlights of this theory, and then move on
to some new algebraic developments in the computation of knot Floer homology. | [
0,
0,
1,
0,
0,
0
] |
Title: Mathematical model of immune response to hepatitis B,
Abstract: A new detailed mathematical model for dynamics of immune response to
hepatitis B is proposed, which takes into account contributions from innate and
adaptive immune responses, as well as cytokines. Stability analysis of
different steady states is performed to identify parameter regions where the
model exhibits clearance of infection, maintenance of a chronic infection, or
periodic oscillations. Effects of nucleoside analogues and interferon
treatments are analysed, and the critical drug efficiency is determined. | [
0,
0,
0,
0,
1,
0
] |
Title: Controlling Stray Electric Fields on an Atom Chip for Rydberg Experiments,
Abstract: Experiments handling Rydberg atoms near surfaces must necessarily deal with
the high sensitivity of Rydberg atoms to (stray) electric fields that typically
emanate from adsorbates on the surface. We demonstrate a method to modify and
reduce the stray electric field by changing the adsorbates distribution. We use
one of the Rydberg excitation lasers to locally affect the adsorbed dipole
distribution. By adjusting the averaged exposure time we change the strength
(with the minimal value less than $0.2\,\textrm{V/cm}$ at $78\,\mu\textrm{m}$
from the chip) and even the sign of the perpendicular field component. This
technique is a useful tool for experiments handling Ryberg atoms near surfaces,
including atom chips. | [
0,
1,
0,
0,
0,
0
] |
Title: Unseen Progenitors of Luminous High-z Quasars in the R_h=ct Universe,
Abstract: Quasars at high redshift provide direct information on the mass growth of
supermassive black holes and, in turn, yield important clues about how the
Universe evolved since the first (Pop III) stars started forming. Yet even
basic questions regarding the seeds of these objects and their growth mechanism
remain unanswered. The anticipated launch of eROSITA and ATHENA is expected to
facilitate observations of high-redshift quasars needed to resolve these
issues. In this paper, we compare accretion-based supermassive black hole
growth in the concordance LCDM model with that in the alternative
Friedmann-Robertson Walker cosmology known as the R_h=ct universe. Previous
work has shown that the timeline predicted by the latter can account for the
origin and growth of the > 10^9 M_sol highest redshift quasars better than that
of the standard model. Here, we significantly advance this comparison by
determining the soft X-ray flux that would be observed for Eddington-limited
accretion growth as a function of redshift in both cosmologies. Our results
indicate that a clear difference emerges between the two in terms of the number
of detectable quasars at redshift z > 6, raising the expectation that the next
decade will provide the observational data needed to discriminate between these
two models based on the number of detected high-redshift quasar progenitors.
For example, while the upcoming ATHENA mission is expected to detect ~0.16
(i.e., essentially zero) quasars at z ~ 7 in R_h=ct, it should detect ~160 in
LCDM---a quantitatively compelling difference. | [
0,
1,
0,
0,
0,
0
] |
Title: Decoding the spectroscopic features and timescales of aqueous proton defects,
Abstract: Acid solutions exhibit a variety of complex structural and dynamical features
arising from the presence of multiple interacting reactive proton defects and
counterions. However, disentangling the transient structural motifs of proton
defects in the water hydrogen bond network and the mechanisms for their
interconversion remains a formidable challenge. Here, we use simulations
treating the quantum nature of both the electrons and nuclei to show how the
experimentally observed spectroscopic features and relaxation timescales can be
elucidated using a physically transparent coordinate that encodes the overall
asymmetry of the solvation environment of the proton defect. We demonstrate
that this coordinate can be used both to discriminate the extremities of the
features observed in the linear vibrational spectrum and to explain the
molecular motions that give rise to the interconversion timescales observed in
recent nonlinear experiments. This analysis provides a unified condensed-phase
picture of proton structure and dynamics that, at its extrema, encompasses
proton sharing and spectroscopic features resembling the limiting Eigen
[H$_{3}$O(H$_{2}$O)$_{3}$]$^{+}$ and Zundel [H(H$_{2}$O)$_{2}$]$^{+}$ gas-phase
structures, while also describing the rich variety of interconverting
environments in the liquid phase. | [
0,
1,
0,
0,
0,
0
] |
Title: MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment,
Abstract: Generating music has a few notable differences from generating images and
videos. First, music is an art of time, necessitating a temporal model. Second,
music is usually composed of multiple instruments/tracks with their own
temporal dynamics, but collectively they unfold over time interdependently.
Lastly, musical notes are often grouped into chords, arpeggios or melodies in
polyphonic music, and thereby introducing a chronological ordering of notes is
not naturally suitable. In this paper, we propose three models for symbolic
multi-track music generation under the framework of generative adversarial
networks (GANs). The three models, which differ in the underlying assumptions
and accordingly the network architectures, are referred to as the jamming
model, the composer model and the hybrid model. We trained the proposed models
on a dataset of over one hundred thousand bars of rock music and applied them
to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings.
A few intra-track and inter-track objective metrics are also proposed to
evaluate the generative results, in addition to a subjective user study. We
show that our models can generate coherent music of four bars right from
scratch (i.e. without human inputs). We also extend our models to human-AI
cooperative music generation: given a specific track composed by human, we can
generate four additional tracks to accompany it. All code, the dataset and the
rendered audio samples are available at this https URL . | [
1,
0,
0,
0,
0,
0
] |
Title: Geometric Analysis of Synchronization in Neuronal Networks with Global Inhibition and Coupling Delays,
Abstract: We study synaptically coupled neuronal networks to identify the role of
coupling delays in network's synchronized behaviors. We consider a network of
excitable, relaxation oscillator neurons where two distinct populations, one
excitatory and one inhibitory, are coupled and interact with each other. The
excitatory population is uncoupled, while the inhibitory population is tightly
coupled. A geometric singular perturbation analysis yields existence and
stability conditions for synchronization states under different firing patterns
between the two populations, along with formulas for the periods of such
synchronous solutions. Our results demonstrate that the presence of coupling
delays in the network promotes synchronization. Numerical simulations are
conducted to supplement and validate analytical results. We show the results
carry over to a model for spindle sleep rhythms in thalamocortical networks,
one of the biological systems which motivated our study. The analysis helps to
explain how coupling delays in either excitatory or inhibitory synapses
contribute to producing synchronized rhythms. | [
0,
1,
0,
0,
0,
0
] |
Title: Model predictive trajectory optimization and tracking for on-road autonomous vehicles,
Abstract: Motion planning for autonomous vehicles requires spatio-temporal motion plans
(i.e. state trajectories) to account for dynamic obstacles. This requires a
trajectory tracking control process which faithfully tracks planned
trajectories. In this paper, a control scheme is presented which first
optimizes a planned trajectory and then tracks the optimized trajectory using a
feedback-feedforward controller. The feedforward element is calculated in a
model predictive manner with a cost function focusing on driving performance.
Stability of the error dynamic is then guaranteed by the design of the
feedback-feedforward controller. The tracking performance of the control system
is tested in a realistic simulated scenario where the control system must track
an evasive lateral maneuver. The proposed controller performs well in
simulation and can be easily adapted to different dynamic vehicle models. The
uniqueness of the solution to the control synthesis eliminates any
nondeterminism that could arise with switching between numerical solvers for
the underlying mathematical program. | [
1,
0,
0,
0,
0,
0
] |
Title: A Toolbox For Property Checking From Simulation Using Incremental SAT (Extended Abstract),
Abstract: We present a tool that primarily supports the ability to check bounded
properties starting from a sequence of states in a run. The target design is
compiled into an AIGNET which is then selectively and iteratively translated
into an incremental SAT instance in which clauses are added for new terms and
simplified by the assignment of existing literals. Additional applications of
the tool can be derived by the user providing alternative attachments of
constrained functions which guide the iterations and SAT checks performed. Some
Verilog RTL examples are included for reference. | [
1,
0,
0,
0,
0,
0
] |
Title: Machine Learning of Linear Differential Equations using Gaussian Processes,
Abstract: This work leverages recent advances in probabilistic machine learning to
discover conservation laws expressed by parametric linear equations. Such
equations involve, but are not limited to, ordinary and partial differential,
integro-differential, and fractional order operators. Here, Gaussian process
priors are modified according to the particular form of such operators and are
employed to infer parameters of the linear equations from scarce and possibly
noisy observations. Such observations may come from experiments or "black-box"
computer simulations. | [
1,
0,
1,
1,
0,
0
] |
Title: YouTube-8M Video Understanding Challenge Approach and Applications,
Abstract: This paper introduces the YouTube-8M Video Understanding Challenge hosted as
a Kaggle competition and also describes my approach to experimenting with
various models. For each of my experiments, I provide the score result as well
as possible improvements to be made. Towards the end of the paper, I discuss
the various ensemble learning techniques that I applied on the dataset which
significantly boosted my overall competition score. At last, I discuss the
exciting future of video understanding research and also the many applications
that such research could significantly improve. | [
0,
0,
0,
1,
0,
0
] |
Title: Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients,
Abstract: Sepsis is a condition caused by the body's overwhelming and life-threatening
response to infection, which can lead to tissue damage, organ failure, and
finally death. Common signs and symptoms include fever, increased heart rate,
increased breathing rate, and confusion. Sepsis is difficult to predict,
diagnose, and treat. Patients who develop sepsis have an increased risk of
complications and death and face higher health care costs and longer
hospitalization. Today, sepsis is one of the leading causes of mortality among
populations in intensive care units (ICUs). In this paper, we look at the
problem of early detection of sepsis by using temporal data mining. We focus on
the use of knowledge-based temporal abstraction to create meaningful
interval-based abstractions, and on time-interval mining to discover frequent
interval-based patterns. We used 2,560 cases derived from the MIMIC-III
database. We found that the distribution of the temporal patterns whose
frequency is above 10% discovered in the records of septic patients during the
last 6 and 12 hours before onset of sepsis is significantly different from that
distribution within a similar period, during an equivalent time window during
hospitalization, in the records of non-septic patients. This discovery is
encouraging for the purpose of performing an early diagnosis of sepsis using
the discovered patterns as constructed features. | [
1,
0,
0,
1,
0,
0
] |
Title: On the use of the energy probability distribution zeros in the study of phase transitions,
Abstract: This contribution is devoted to cover some technical aspects related to the
use of the recently proposed energy probability distribution zeros in the study
of phase transitions. This method is based on the partial knowledge of the
partition function zeros and has been shown to be extremely efficient to
precisely locate phase transition temperatures. It is based on an iterative
method in such a way that the transition temperature can be approached at will.
The iterative method will be detailed and some convergence issues that has been
observed in its application to the 2D Ising model and to an artificial spin ice
model will be shown, together with ways to circumvent them. | [
0,
1,
0,
0,
0,
0
] |
Title: Visibility-based Power Spectrum Estimation for Low-Frequency Radio Interferometric Observations,
Abstract: We present a visibility based estimator namely, the Tapered Gridded Estimator
(TGE) to estimate the power spectrum of the diffuse sky signal. The TGE has
three novel features. First, the estimator uses gridded visibilities to
estimate the power spectrum which is computationally much faster than
individually correlating the visibilities. Second, a positive noise bias is
removed by subtracting the auto-correlation of the visibilities which is
responsible for the noise bias. Third, the estimator allows us to taper the
field of view so as to suppress the contribution from the sources in the outer
regions and the sidelobes of the telescope's primary beam. We first consider
the two dimensional (2D) TGE to estimate the angular power spectrum $C_{\ell}$.
We have also extended the TGE to estimate the three dimensional (3D) power
spectrum $P({\bf k})$ of the cosmological 21-cm signal. Analytic formulas are
presented for predicting the variance of the binned power spectrum. Both the
estimators and their variance predictions are validated using simulations of
$150 \, {\rm MHz}$ GMRT observations. We have applied the 2D TGE to estimate
$C_{\ell}$ using visibility data for two of the fields observed by TIFR GMRT
Sky Survey (TGSS). We find that the sky signal, after subtracting the point
sources, is likely dominated by the diffuse Galactic synchrotron radiation
across the angular multipole range $240 \le \ell \lesssim 500$. | [
0,
1,
0,
0,
0,
0
] |
Title: Archiving Software Surrogates on the Web for Future Reference,
Abstract: Software has long been established as an essential aspect of the scientific
process in mathematics and other disciplines. However, reliably referencing
software in scientific publications is still challenging for various reasons. A
crucial factor is that software dynamics with temporal versions or states are
difficult to capture over time. We propose to archive and reference surrogates
instead, which can be found on the Web and reflect the actual software to a
remarkable extent. Our study shows that about a half of the webpages of
software are already archived with almost all of them including some kind of
documentation. | [
1,
0,
0,
0,
0,
0
] |
Title: Exact Formulas for the Generalized Sum-of-Divisors Functions,
Abstract: We prove new exact formulas for the generalized sum-of-divisors functions.
The formulas for $\sigma_{\alpha}(x)$ when $\alpha \in \mathbb{C}$ is fixed and
$x \geq 1$ involves a finite sum over all of the prime factors $n \leq x$ and
terms involving the $r$-order harmonic number sequences. The generalized
harmonic number sequences correspond to the partial sums of the Riemann zeta
function when $r > 1$ and are related to the generalized Bernoulli numbers when
$r \leq 0$ is integer-valued. A key part of our expansions of the Lambert
series generating functions for the generalized divisor functions is formed by
taking logarithmic derivatives of the cyclotomic polynomials, $\Phi_n(q)$,
which completely factorize the Lambert series terms $(1-q^n)^{-1}$ into
irreducible polynomials in $q$. We also consider applications of our new
results to asymptotic approximations for sums over these divisor functions and
to the forms of perfect numbers defined by the special case of the divisor
function, $\sigma(n)$, when $\alpha := 1$.
Keywords: divisor function; sum-of-divisors function; Lambert series; perfect
number.
MSC (2010): 30B50; 11N64; 11B83 | [
0,
0,
1,
0,
0,
0
] |
Title: On addition theorems related to elliptic integrals,
Abstract: This paper provides some explicit formulas related to addition theorems for
elliptic integrals $\int_0^x dt/R(t)$, where $R(t)$ is the square root from a
polynomial of degree 4. These integrals are related to complex elliptic genera
and are motivated by Euler's addition theorem for elliptic integrals of the
first kind. | [
0,
0,
1,
0,
0,
0
] |
Title: Manuscripts in Time and Space: Experiments in Scriptometrics on an Old French Corpus,
Abstract: Witnesses of medieval literary texts, preserved in manuscript, are layered
objects , being almost exclusively copies of copies. This results in multiple
and hard to distinguish linguistic strata -- the author's scripta interacting
with the scriptae of the various scribes -- in a context where literary written
language is already a dialectal hybrid. Moreover, no single linguistic
phenomenon allows to distinguish between different scriptae, and only the
combination of multiple characteristics is likely to be significant [9] -- but
which ones? The most common approach is to search for these features in a set
of previously selected texts, that are supposed to be representative of a given
scripta. This can induce a circularity, in which texts are used to select
features that in turn characterise them as belonging to a linguistic area. To
counter this issue, this paper offers an unsupervised and corpus-based
approach, in which clustering methods are applied to an Old French corpus to
identify main divisions and groups. Ultimately, scriptometric profiles are
built for each of them. | [
0,
0,
0,
1,
0,
0
] |
Title: Vertical Bifacial Solar Farms: Physics, Design, and Global Optimization,
Abstract: There have been sustained interest in bifacial solar cell technology since
1980s, with prospects of 30-50% increase in the output power from a stand-alone
single panel. Moreover, a vertical bifacial panel reduces dust accumulation and
provides two output peaks during the day, with the second peak aligned to the
peak electricity demand. Recent commercialization and anticipated growth of
bifacial panel market have encouraged a closer scrutiny of the integrated
power-output and economic viability of bifacial solar farms, where mutual
shading will erode some of the anticipated energy gain associated with an
isolated, single panel. Towards that goal, in this paper we focus on
geography-specific optimizations of ground mounted vertical bifacial solar
farms for the entire world. For local irradiance, we combine the measured
meteorological data with the clear-sky model. In addition, we consider the
detailed effects of direct, diffuse, and albedo light. We assume the panel is
configured into sub-strings with bypass-diodes. Based on calculated light
collection and panel output, we analyze the optimum farm design for maximum
yearly output at any given location in the world. Our results predict that,
regardless of the geographical location, a vertical bifacial farm will yield
10-20% more energy than a traditional monofacial farm for a practical
row-spacing of 2m (1.2m high panels). With the prospect of additional 5-20%
energy gain from reduced soiling and tilt optimization, bifacial solar farm do
offer a viable technology option for large-scale solar energy generation. | [
0,
1,
0,
0,
0,
0
] |
Title: Maximally Correlated Principal Component Analysis,
Abstract: In the era of big data, reducing data dimensionality is critical in many
areas of science. Widely used Principal Component Analysis (PCA) addresses this
problem by computing a low dimensional data embedding that maximally explain
variance of the data. However, PCA has two major weaknesses. Firstly, it only
considers linear correlations among variables (features), and secondly it is
not suitable for categorical data. We resolve these issues by proposing
Maximally Correlated Principal Component Analysis (MCPCA). MCPCA computes
transformations of variables whose covariance matrix has the largest Ky Fan
norm. Variable transformations are unknown, can be nonlinear and are computed
in an optimization. MCPCA can also be viewed as a multivariate extension of
Maximal Correlation. For jointly Gaussian variables we show that the covariance
matrix corresponding to the identity (or the negative of the identity)
transformations majorizes covariance matrices of non-identity functions. Using
this result we characterize global MCPCA optimizers for nonlinear functions of
jointly Gaussian variables for every rank constraint. For categorical variables
we characterize global MCPCA optimizers for the rank one constraint based on
the leading eigenvector of a matrix computed using pairwise joint
distributions. For a general rank constraint we propose a block coordinate
descend algorithm and show its convergence to stationary points of the MCPCA
optimization. We compare MCPCA with PCA and other state-of-the-art
dimensionality reduction methods including Isomap, LLE, multilayer autoencoders
(neural networks), kernel PCA, probabilistic PCA and diffusion maps on several
synthetic and real datasets. We show that MCPCA consistently provides improved
performance compared to other methods. | [
1,
0,
0,
1,
0,
0
] |
Title: $\mathcal{G}$-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space,
Abstract: It is well known that neural networks with rectified linear units (ReLU)
activation functions are positively scale-invariant. Conventional algorithms
like stochastic gradient descent optimize the neural networks in the vector
space of weights, which is, however, not positively scale-invariant. This
mismatch may lead to problems during the optimization process. Then, a natural
question is: \emph{can we construct a new vector space that is positively
scale-invariant and sufficient to represent ReLU neural networks so as to
better facilitate the optimization process }? In this paper, we provide our
positive answer to this question. First, we conduct a formal study on the
positive scaling operators which forms a transformation group, denoted as
$\mathcal{G}$. We show that the value of a path (i.e. the product of the
weights along the path) in the neural network is invariant to positive scaling
and prove that the value vector of all the paths is sufficient to represent the
neural networks under mild conditions. Second, we show that one can identify
some basis paths out of all the paths and prove that the linear span of their
value vectors (denoted as $\mathcal{G}$-space) is an invariant space with lower
dimension under the positive scaling group. Finally, we design stochastic
gradient descent algorithm in $\mathcal{G}$-space (abbreviated as
$\mathcal{G}$-SGD) to optimize the value vector of the basis paths of neural
networks with little extra cost by leveraging back-propagation. Our experiments
show that $\mathcal{G}$-SGD significantly outperforms the conventional SGD
algorithm in optimizing ReLU networks on benchmark datasets. | [
0,
0,
0,
1,
0,
0
] |
Title: Fantastic deductive systems in probability theory on generalizations of fuzzy structures,
Abstract: The aim of this paper is to introduce the notion of fantastic deductive
systems on generalizations of fuzzy structures, and to emphasize their role in
the probability theory on these algebras. We give a characterization of
commutative pseudo-BE algebras and we generalize an axiom system consisting of
four identities to the case of commutative pseudo-BE algebras. We define the
fantastic deductive systems of pseudo-BE algebras and we investigate their
properties. It is proved that, if a pseudo-BE(A) algebra $A$ is commutative,
then all deductive systems of $A$ are fantastic. Moreover, we generalize the
notions of measures, state-measures and measure-morphisms to the case of
pseudo-BE algebras and we also prove that there is a one-to-one correspondence
between the set of all Bosbach states on a bounded pseudo-BE algebra and the
set of its state-measures. The notions of internal states and state-morphism
operators on pseudo-BCK algebras are extended to the case of pseudo-BE algebras
and we also prove that any type II state operator on a pseudo-BE algebra is a
state-morphism operator on it. The notions of pseudo-valuation and commutative
pseudo-valuation on pseudo-BE algebras are defined and investigated. For the
case of commutative pseudo-BE algebras we prove that the two kind of
pseudo-valuations coincide. Characterizations of pseudo-valuations and
commutative pseudo-valuations are given. We show that the kernel of a Bosbach
state (state-morphism, measure, type II state operator, pseudo-valuation) is a
fantastic deductive system. | [
0,
0,
1,
0,
0,
0
] |
Title: Bayesian Learning of Consumer Preferences for Residential Demand Response,
Abstract: In coming years residential consumers will face real-time electricity tariffs
with energy prices varying day to day, and effective energy saving will require
automation - a recommender system, which learns consumer's preferences from her
actions. A consumer chooses a scenario of home appliance use to balance her
comfort level and the energy bill. We propose a Bayesian learning algorithm to
estimate the comfort level function from the history of appliance use. In
numeric experiments with datasets generated from a simulation model of a
consumer interacting with small home appliances the algorithm outperforms
popular regression analysis tools. Our approach can be extended to control an
air heating and conditioning system, which is responsible for up to half of a
household's energy bill. | [
1,
0,
0,
1,
0,
0
] |
Title: Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits,
Abstract: Recent work on follow the perturbed leader (FTPL) algorithms for the
adversarial multi-armed bandit problem has highlighted the role of the hazard
rate of the distribution generating the perturbations. Assuming that the hazard
rate is bounded, it is possible to provide regret analyses for a variety of
FTPL algorithms for the multi-armed bandit problem. This paper pushes the
inquiry into regret bounds for FTPL algorithms beyond the bounded hazard rate
condition. There are good reasons to do so: natural distributions such as the
uniform and Gaussian violate the condition. We give regret bounds for both
bounded support and unbounded support distributions without assuming the hazard
rate condition. We also disprove a conjecture that the Gaussian distribution
cannot lead to a low-regret algorithm. In fact, it turns out that it leads to
near optimal regret, up to logarithmic factors. A key ingredient in our
approach is the introduction of a new notion called the generalized hazard
rate. | [
1,
0,
0,
1,
0,
0
] |
Title: Calculation of the bulk modulus of mixed ionic crystal NH_4Cl_{1-x}Br_x,
Abstract: The ammonium halides present an interesting system for study in view of their
polymorphism and the possible internal rotation of the ammonium ion. The static
properties of the mixed ionic crystal NH$_4$Cl$_{1-x}$Br$_x$ have been recently
investigated, using three-body potential model (TDPM) by the application of
Vegard's law. Here, by using a simple theoretical model, we estimate the bulk
modulus of their ternary alloys NH$_4$Cl$_{1-x}$Br$_x$, in terms of the bulk
modulus of the end members alone. The calculated values are comparable to those
deduced from the three-body potential model (TDPM) by the application of
Vegard's law. | [
0,
1,
0,
0,
0,
0
] |
Title: Hyers-Ulam stability of elliptic Möbius difference equation,
Abstract: The linear fractional map $ f(z) = \frac{az+ b}{cz + d} $ on the Riemann
sphere with complex coefficients $ ad-bc \neq 0 $ is called Möbius map. If $
f $ satisfies $ ad-bc=1 $ and $ -2<a+d<2 $, then $ f $ is called
$\textit{elliptic}$ Möbius map. Let $ \{ b_n \}_{n \in \mathbb{N}_0} $ be the
solution of the elliptic Möbius difference equation $ b_{n+1} = f(b_n) $ for
every $ n \in \mathbb{N}_0 $. Then the sequence $ \{ b_n \}_{n \in
\mathbb{N}_0} $ has no Hyers-Ulam stability. | [
0,
0,
1,
0,
0,
0
] |
Title: Diffeological, Frölicher, and Differential Spaces,
Abstract: Differential calculus on Euclidean spaces has many generalisations. In
particular, on a set $X$, a diffeological structure is given by maps from open
subsets of Euclidean spaces to $X$, a differential structure is given by maps
from $X$ to $\mathbb{R}$, and a Frölicher structure is given by maps from
$\mathbb{R}$ to $X$ as well as maps from $X$ to $\mathbb{R}$. We illustrate the
relations between these structures through examples. | [
0,
0,
1,
0,
0,
0
] |
Title: Elementary abelian subgroups in some special p-groups,
Abstract: Let $P$ be a finite $p$-group and $p$ be an odd prime. Let
$\mathcal{A}_p(P)_{\geq2}$ be a poset consisting of elementary abelian
subgroups of rank at least 2. If the derived subgroup $P'\cong C_p\times C_p$,
then the spheres occurring in $\mathcal{A}_p(P)_{\geq2}$ all have the same
dimension. | [
0,
0,
1,
0,
0,
0
] |
Title: Learning Plannable Representations with Causal InfoGAN,
Abstract: In recent years, deep generative models have been shown to 'imagine'
convincing high-dimensional observations such as images, audio, and even video,
learning directly from raw data. In this work, we ask how to imagine
goal-directed visual plans -- a plausible sequence of observations that
transition a dynamical system from its current configuration to a desired goal
state, which can later be used as a reference trajectory for control. We focus
on systems with high-dimensional observations, such as images, and propose an
approach that naturally combines representation learning and planning. Our
framework learns a generative model of sequential observations, where the
generative process is induced by a transition in a low-dimensional planning
model, and an additional noise. By maximizing the mutual information between
the generated observations and the transition in the planning model, we obtain
a low-dimensional representation that best explains the causal nature of the
data. We structure the planning model to be compatible with efficient planning
algorithms, and we propose several such models based on either discrete or
continuous states. Finally, to generate a visual plan, we project the current
and goal observations onto their respective states in the planning model, plan
a trajectory, and then use the generative model to transform the trajectory to
a sequence of observations. We demonstrate our method on imagining plausible
visual plans of rope manipulation. | [
1,
0,
0,
1,
0,
0
] |
Title: SGD Learns the Conjugate Kernel Class of the Network,
Abstract: We show that the standard stochastic gradient decent (SGD) algorithm is
guaranteed to learn, in polynomial time, a function that is competitive with
the best function in the conjugate kernel space of the network, as defined in
Daniely, Frostig and Singer. The result holds for log-depth networks from a
rich family of architectures. To the best of our knowledge, it is the first
polynomial-time guarantee for the standard neural network learning algorithm
for networks of depth more that two.
As corollaries, it follows that for neural networks of any depth between $2$
and $\log(n)$, SGD is guaranteed to learn, in polynomial time, constant degree
polynomials with polynomially bounded coefficients. Likewise, it follows that
SGD on large enough networks can learn any continuous function (not in
polynomial time), complementing classical expressivity results. | [
1,
0,
0,
1,
0,
0
] |
Title: Towards information optimal simulation of partial differential equations,
Abstract: Most simulation schemes for partial differential equations (PDEs) focus on
minimizing a simple error norm of a discretized version of a field. This paper
takes a fundamentally different approach; the discretized field is interpreted
as data providing information about a real physical field that is unknown. This
information is sought to be conserved by the scheme as the field evolves in
time. Such an information theoretic approach to simulation was pursued before
by information field dynamics (IFD). In this paper we work out the theory of
IFD for nonlinear PDEs in a noiseless Gaussian approximation. The result is an
action that can be minimized to obtain an informationally optimal simulation
scheme. It can be brought into a closed form using field operators to calculate
the appearing Gaussian integrals. The resulting simulation schemes are tested
numerically in two instances for the Burgers equation. Their accuracy surpasses
finite-difference schemes on the same resolution. The IFD scheme, however, has
to be correctly informed on the subgrid correlation structure. In certain
limiting cases we recover well-known simulation schemes like spectral Fourier
Galerkin methods. We discuss implications of the approximations made. | [
0,
1,
0,
1,
0,
0
] |
Title: Verifying Quantum Programs: From Quipper to QPMC,
Abstract: In this paper we present a translation from the quantum programming language
Quipper to the QPMC model checker, with the main aim of verifying Quipper
programs. Quipper is an embedded functional programming language for quantum
computation. It is above all a circuit description language, for this reason it
uses the vector state formalism and its main purpose is to make circuit
implementation easy providing high level operations for circuit manipulation.
Quipper provides both an high-level circuit building interface and a simulator.
QPMC is a model checker for quantum protocols based on the density matrix
formalism. QPMC extends the probabilistic model checker IscasMC allowing to
formally verify properties specified in the temporal logic QCTL on Quantum
Markov Chains. We implemented and tested our translation on several quantum
algorithms, including Grover's quantum search. | [
1,
0,
0,
0,
0,
0
] |
Title: Topological semimetal state and field-induced Fermi surface reconstruction in antiferromagnetic monopnictide NdSb,
Abstract: We report the experimental realization of Dirac semimetal state in NdSb, a
material with antiferromagnetic ground state. The occurrence of topological
semimetal state has been well supported by our band structure calculations and
the experimental observation of chiral anomaly induced negative
magnetoresistance. A field-induced Fermi surface reconstruction is observed, in
response to the change of spin polarization. The observation of topological
semimetal state in a magnetic material provides an opportunity to investigate
the magneto-topological phenomena. | [
0,
1,
0,
0,
0,
0
] |
Title: Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs,
Abstract: Understanding the 3D structure of a scene is of vital importance, when it
comes to developing fully autonomous robots. To this end, we present a novel
deep learning based framework that estimates depth, surface normals and surface
curvature by only using a single RGB image. To the best of our knowledge this
is the first work to estimate surface curvature from colour using a machine
learning approach. Additionally, we demonstrate that by tuning the network to
infer well designed features, such as surface curvature, we can achieve
improved performance at estimating depth and normals.This indicates that
network guidance is still a useful aspect of designing and training a neural
network. We run extensive experiments where the network is trained to infer
different tasks while the model capacity is kept constant resulting in
different feature maps based on the tasks at hand. We outperform the previous
state-of-the-art benchmarks which jointly estimate depths and surface normals
while predicting surface curvature in parallel. | [
1,
0,
0,
0,
0,
0
] |
Title: Motif and Hypergraph Correlation Clustering,
Abstract: Motivated by applications in social and biological network analysis, we
introduce a new form of agnostic clustering termed~\emph{motif correlation
clustering}, which aims to minimize the cost of clustering errors associated
with both edges and higher-order network structures. The problem may be
succinctly described as follows: Given a complete graph $G$, partition the
vertices of the graph so that certain predetermined `important' subgraphs
mostly lie within the same cluster, while `less relevant' subgraphs are allowed
to lie across clusters. Our contributions are as follows: We first introduce
several variants of motif correlation clustering and then show that these
clustering problems are NP-hard. We then proceed to describe polynomial-time
clustering algorithms that provide constant approximation guarantees for the
problems at hand. Despite following the frequently used LP relaxation and
rounding procedure, the algorithms involve a sophisticated and carefully
designed neighborhood growing step that combines information about both edge
and motif structures. We conclude with several examples illustrating the
performance of the developed algorithms on synthetic and real networks. | [
1,
0,
0,
0,
0,
0
] |
Title: Adversarial Imitation via Variational Inverse Reinforcement Learning,
Abstract: We consider a problem of learning the reward and policy from expert examples
under unknown dynamics in high-dimensional scenarios. Our proposed method
builds on the framework of generative adversarial networks and introduces the
empowerment-regularized maximum-entropy inverse reinforcement learning to learn
near-optimal rewards and policies. Empowerment-based regularization prevents
the policy from overfitting expert demonstration, thus leads to a generalized
behavior which results in learning near-optimal rewards. Our method
simultaneously learns empowerment through variational information maximization
along with the reward and policy under the adversarial learning formulation. We
evaluate our approach on various high-dimensional complex control tasks. We
also test our learned rewards in challenging transfer learning problems where
training and testing environments are made to be different from each other in
terms of dynamics or structure. The results show that our proposed method not
only learns near-optimal rewards and policies that are matching expert behavior
but also performs significantly better than state-of-the-art inverse
reinforcement learning algorithms. | [
1,
0,
0,
1,
0,
0
] |
Title: Intrinsically motivated reinforcement learning for human-robot interaction in the real-world,
Abstract: For a natural social human-robot interaction, it is essential for a robot to
learn the human-like social skills. However, learning such skills is
notoriously hard due to the limited availability of direct instructions from
people to teach a robot. In this paper, we propose an intrinsically motivated
reinforcement learning framework in which an agent gets the intrinsic
motivation-based rewards through the action-conditional predictive model. By
using the proposed method, the robot learned the social skills from the
human-robot interaction experiences gathered in the real uncontrolled
environments. The results indicate that the robot not only acquired human-like
social skills but also took more human-like decisions, on a test dataset, than
a robot which received direct rewards for the task achievement. | [
1,
0,
0,
0,
0,
0
] |
Title: Beyond Backprop: Online Alternating Minimization with Auxiliary Variables,
Abstract: We propose a novel online alternating minimization (AltMin) algorithm for
training deep neural networks, provide theoretical convergence guarantees and
demonstrate its advantages on several classification tasks as compared both to
standard backpropagation with stochastic gradient descent (backprop-SGD) and to
offline alternating minimization. The key difference from backpropagation is an
explicit optimization over hidden activations, which eliminates gradient chain
computation in backprop, and breaks the weight training problem into
independent, local optimization subproblems; this allows to avoid vanishing
gradient issues, simplify handling non-differentiable nonlinearities, and
perform parallel weight updates across the layers. Moreover, parallel local
synaptic weight optimization with explicit activation propagation is a step
closer to a more biologically plausible learning model than backpropagation,
whose biological implausibility has been frequently criticized. Finally, the
online nature of our approach allows to handle very large datasets, as well as
continual, lifelong learning, which is our key contribution on top of recently
proposed offline alternating minimization schemes (e.g., (Carreira-Perpinan
andWang 2014), (Taylor et al. 2016)). | [
0,
0,
0,
1,
0,
0
] |
Title: Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering,
Abstract: Sensing in complex systems requires large-scale information exchange and
on-the-go communications over heterogeneous networks and integrated processing
platforms. Many networked cyber-physical systems exhibit hierarchical
infrastructures of information flows, which naturally leads to a multi-level
tree-like information structure in which each level corresponds to a particular
scale of representation. This work focuses on the multiscale fusion of data
collected at multiple levels of the system. We propose a multiscale state-space
model to represent multi-resolution data over the hierarchical information
system and formulate a multi-stage dynamic zero-sum game to design a
multi-scale $H_{\infty}$ robust filter. We present numerical experiments for
one and two-dimensional signals and provide a comparative analysis of the
minimax filter with the standard Kalman filter to show the improvement in
signal-to-noise ratio (SNR). | [
1,
0,
0,
0,
0,
0
] |
Title: Topology of irrationally indifferent attractors,
Abstract: We study the attractors of a class of holomorphic systems with an
irrationally indifferent fixed point. We prove a trichotomy for the topology of
the attractor based on the arithmetic of the rotation number at the fixed
point. That is, the attractor is either a Jordan curve, a one-sided hairy
circle, or a Cantor bouquet. This has a number of remarkable corollaries on a
conjecture of M. Herman about the optimal arithmetic condition for the
existence of a critical point on the boundary of the Siegel disk, and a
conjecture of A. Douady on the topology of the boundary of Siegel disks.
Combined with earlier results on the topic, this completes the topological
description of the behaviors of typical orbits near such fixed points, when the
rotation number is of high type. | [
0,
0,
1,
0,
0,
0
] |
Title: Crystalline Soda Can Metamaterial exhibiting Graphene-like Dispersion at subwavelength scale,
Abstract: Graphene, a honeycomb lattice of carbon atoms ruled by tight-binding
interaction, exhibits extraordinary electronic properties due to the presence
of Dirac cones within its band structure. These intriguing singularities have
naturally motivated the discovery of their classical analogues. In this work,
we present a general and direct procedure to reproduce the peculiar physics of
graphene within a very simple acoustic metamaterial: a double lattice of soda
cans resonant at two different frequencies. The first triangular sub-lattice
generates a bandgap at low frequency, which induces a tight-binding coupling
between the resonant defects of the second Honeycomb one, hence allowing us to
obtain a graphene-like band structure. We prove the relevance of this approach
by showing that both numerical and experimental dispersion relations exhibit
the requested Dirac cone. We also demonstrate the straightforward monitoring of
the coupling strength within the crystal of resonant defects. This work shows
that crystalline metamaterials are very promising candidates to investigate
tantalizing solid-state physics phenomena with classical waves. | [
0,
1,
0,
0,
0,
0
] |
Title: Modeling Retinal Ganglion Cell Population Activity with Restricted Boltzmann Machines,
Abstract: The retina is a complex nervous system which encodes visual stimuli before
higher order processing occurs in the visual cortex. In this study we evaluated
whether information about the stimuli received by the retina can be retrieved
from the firing rate distribution of Retinal Ganglion Cells (RGCs), exploiting
High-Density 64x64 MEA technology. To this end, we modeled the RGC population
activity using mean-covariance Restricted Boltzmann Machines, latent variable
models capable of learning the joint distribution of a set of continuous
observed random variables and a set of binary unobserved random units. The idea
was to figure out if binary latent states encode the regularities associated to
different visual stimuli, as modes in the joint distribution. We measured the
goodness of mcRBM encoding by calculating the Mutual Information between the
latent states and the stimuli shown to the retina. Results show that binary
states can encode the regularities associated to different stimuli, using both
gratings and natural scenes as stimuli. We also discovered that hidden
variables encode interesting properties of retinal activity, interpreted as
population receptive fields. We further investigated the ability of the model
to learn different modes in population activity by comparing results associated
to a retina in normal conditions and after pharmacologically blocking GABA
receptors (GABAC at first, and then also GABAA and GABAB). As expected, Mutual
Information tends to decrease if we pharmacologically block receptors. We
finally stress that the computational method described in this work could
potentially be applied to any kind of neural data obtained through MEA
technology, though different techniques should be applied to interpret the
results. | [
1,
0,
0,
0,
0,
0
] |
Title: Stochastic partial differential fluid equations as a diffusive limit of deterministic Lagrangian multi-time dynamics,
Abstract: In {\em{Holm}, Proc. Roy. Soc. A 471 (2015)} stochastic fluid equations were
derived by employing a variational principle with an assumed stochastic
Lagrangian particle dynamics. Here we show that the same stochastic Lagrangian
dynamics naturally arises in a multi-scale decomposition of the deterministic
Lagrangian flow map into a slow large-scale mean and a rapidly fluctuating
small scale map. We employ homogenization theory to derive effective slow
stochastic particle dynamics for the resolved mean part, thereby justifying
stochastic fluid partial equations in the Eulerian formulation. To justify the
application of rigorous homogenization theory, we assume mildly chaotic fast
small-scale dynamics, as well as a centering condition. The latter requires
that the mean of the fluctuating deviations is small, when pulled back to the
mean flow. | [
0,
1,
1,
0,
0,
0
] |
Title: A general framework for solving convex optimization problems involving the sum of three convex functions,
Abstract: In this paper, we consider solving a class of convex optimization problem
which minimizes the sum of three convex functions $f(x)+g(x)+h(Bx)$, where
$f(x)$ is differentiable with a Lipschitz continuous gradient, $g(x)$ and
$h(x)$ have a closed-form expression of their proximity operators and $B$ is a
bounded linear operator. This type of optimization problem has wide application
in signal recovery and image processing. To make full use of the
differentiability function in the optimization problem, we take advantage of
two operator splitting methods: the forward-backward splitting method and the
three operator splitting method. In the iteration scheme derived from the two
operator splitting methods, we need to compute the proximity operator of $g+h
\circ B$ and $h \circ B$, respectively. Although these proximity operators do
not have a closed-form solution in general, they can be solved very
efficiently. We mainly employ two different approaches to solve these proximity
operators: one is dual and the other is primal-dual. Following this way, we
fortunately find that three existing iterative algorithms including Condat and
Vu algorithm, primal-dual fixed point (PDFP) algorithm and primal-dual three
operator (PD3O) algorithm are a special case of our proposed iterative
algorithms. Moreover, we discover a new kind of iterative algorithm to solve
the considered optimization problem, which is not covered by the existing ones.
Under mild conditions, we prove the convergence of the proposed iterative
algorithms. Numerical experiments applied on fused Lasso problem, constrained
total variation regularization in computed tomography (CT) image reconstruction
and low-rank total variation image super-resolution problem demonstrate the
effectiveness and efficiency of the proposed iterative algorithms. | [
0,
0,
1,
0,
0,
0
] |
Title: Testing isotropy in the Two Micron All-Sky redshift survey with information entropy,
Abstract: We use information entropy to test the isotropy in the nearby galaxy
distribution mapped by the Two Micron All-Sky redshift survey (2MRS). We find
that the galaxy distribution is highly anisotropic on small scales. The radial
anisotropy gradually decreases with increasing length scales and the observed
anisotropy is consistent with that expected for an isotropic Poisson
distribution beyond a length scale of $90 \, h^{-1}\, {\rm Mpc}$. Using mock
catalogues from N-body simulations, we find that the galaxy distribution in the
2MRS exhibits a degree of anisotropy compatible with that of the $\Lambda$CDM
model after accounting for the clustering bias of the 2MRS galaxies. We also
quantify the polar and azimuthal anisotropies and identify two directions
$(l,b)=(150^{\circ}, -15^{\circ})$, $(l,b)=(310^{\circ},-15^{\circ})$ which are
significantly anisotropic compared to the other directions in the sky. We
suggest that their preferential orientations on the sky may indicate a possible
alignment of the Local Group with two nearby large scale structures. Despite
the differences in the degree of anisotropy on small scales, we find that the
galaxy distributions in both the 2MRS and the $\Lambda$CDM model are isotropic
on a scale of $90 \, h^{-1}\, {\rm Mpc}$. | [
0,
1,
0,
0,
0,
0
] |
Title: VEGAS: A VST Early-type GAlaxy Survey. II. Photometric study of giant ellipticals and their stellar halos,
Abstract: Observations of diffuse starlight in the outskirts of galaxies are thought to
be a fundamental source of constraints on the cosmological context of galaxy
assembly in the $\Lambda$CDM model. Such observations are not trivial because
of the extreme faintness of such regions. In this work, we investigate the
photometric properties of six massive early type galaxies (ETGs) in the VEGAS
sample (NGC 1399, NGC 3923, NGC 4365, NGC 4472, NGC 5044, and NGC 5846) out to
extremely low surface brightness levels, with the goal of characterizing the
global structure of their light profiles for comparison to state-of-the-art
galaxy formation models. We carry out deep and detailed photometric mapping of
our ETG sample taking advantage of deep imaging with VST/OmegaCAM in the g and
i bands. By fitting the light profiles, and comparing the results to
simulations of elliptical galaxy assembly, we identify signatures of a
transition between "relaxed" and "unrelaxed" accreted components and can
constrain the balance between in situ and accreted stars. The very good
agreement of our results with predictions from theoretical simulations
demonstrates that the full VEGAS sample of $\sim 100$ ETGs will allow us to use
the distribution of diffuse light as a robust statistical probe of the
hierarchical assembly of massive galaxies. | [
0,
1,
0,
0,
0,
0
] |
Title: Homological dimension formulas for trivial extension algebras,
Abstract: Let $A= \Lambda \oplus C$ be a trivial extension algebra. The aim of this
paper is to establish formulas for the projective dimension and the injective
dimension for a certain class of $A$-modules which is expressed by using the
derived functors $- \otimes^{\mathbb{L}}_{\Lambda}C$ and
$\mathbb{R}\text{Hom}_{\Lambda}(C, -)$. Consequently, we obtain formulas for
the global dimension of $A$, which gives a modern expression of the classical
formula for the global dimension by Palmer-Roos and Löfwall that is written
in complicated classical derived functors.
The main application of the formulas is to give a necessary and sufficient
condition for $A$ to be an Iwanaga-Gorenstein algebra.
We also give a description of the kernel $\text{Ker} \varpi$ of the canonical
functor $\varpi: \mathsf{D}^{\mathrm{b}}(\text{mod} \Lambda) \to
\text{Sing}^{\mathbb{Z}} A$ in the case $\text{pd} C < \infty$. | [
0,
0,
1,
0,
0,
0
] |
Title: Spread of entanglement in a Sachdev-Ye-Kitaev chain,
Abstract: We study the spread of Rényi entropy between two halves of a
Sachdev-Ye-Kitaev (SYK) chain of Majorana fermions, prepared in a thermofield
double (TFD) state. The SYK chain model is a model of chaotic many-body
systems, which describes a one-dimensional lattice of Majorana fermions, with
spatially local random quartic interaction. We find that for integer Rényi
index $n>1$, the Rényi entanglement entropy saturates at a parametrically
smaller value than expected. This implies that the TFD state of the SYK chain
does not rapidly thermalize, despite being maximally chaotic: instead, it
rapidly approaches a prethermal state. We compare our results to the signatures
of thermalization observed in other quenches in the SYK model, and to intuition
from nearly-$\mathrm{AdS}_2$ gravity. | [
0,
1,
0,
0,
0,
0
] |
Title: The weak order on integer posets,
Abstract: We explore lattice structures on integer binary relations (i.e. binary
relations on the set $\{1, 2, \dots, n\}$ for a fixed integer $n$) and on
integer posets (i.e. partial orders on the set $\{1, 2, \dots, n\}$ for a fixed
integer $n$). We first observe that the weak order on the symmetric group
naturally extends to a lattice structure on all integer binary relations. We
then show that the subposet of this weak order induced by integer posets
defines as well a lattice. We finally study the subposets of this weak order
induced by specific families of integer posets corresponding to the elements,
the intervals, and the faces of the permutahedron, the associahedron, and some
recent generalizations of those. | [
0,
0,
1,
0,
0,
0
] |
Title: Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization,
Abstract: Undetected overfitting can occur when there are significant redundancies
between training and validation data. We describe AVE, a new measure of
training-validation redundancy for ligand-based classification problems that
accounts for the similarity amongst inactive molecules as well as active. We
investigated seven widely-used benchmarks for virtual screening and
classification, and show that the amount of AVE bias strongly correlates with
the performance of ligand-based predictive methods irrespective of the
predicted property, chemical fingerprint, similarity measure, or
previously-applied unbiasing techniques. Therefore, it may be that the
previously-reported performance of most ligand-based methods can be explained
by overfitting to benchmarks rather than good prospective accuracy. | [
1,
0,
0,
1,
0,
0
] |
Title: Inverse dispersion method for calculation of complex photonic band diagram and $\cal{PT}$-symmetry,
Abstract: We suggest an inverse dispersion method for calculating photonic band diagram
for materials with arbitrary frequency-dependent dielectric functions. The
method is able to calculate the complex wave vector for a given frequency by
solving the eigenvalue problem with a non-Hermitian operator. The analogy with
$\cal{PT}$-symmetric Hamiltonians reveals that the operator corresponds to the
momentum as a physical quantity and the singularities at the band edges are
related to the branch points and responses for the features on the band edges.
The method is realized using plane wave expansion technique for two-dimensional
periodical structure in the case of TE- and TM-polarization. We illustrate the
applicability of the method by calculation of the photonic band diagrams of an
infinite two-dimension square lattice composed of dielectric cylinders using
the measured frequency dependent dielectric functions of different materials
(amorphous hydrogenated carbon, silicon, and chalcogenide glass). We show that
the method allows to distinguish unambiguously between Bragg and Mie gaps in
the spectra. | [
0,
1,
0,
0,
0,
0
] |
Title: MmWave vehicle-to-infrastructure communication: Analysis of urban microcellular networks,
Abstract: Vehicle-to-infrastructure (V2I) communication may provide high data rates to
vehicles via millimeter-wave (mmWave) microcellular networks. This paper uses
stochastic geometry to analyze the coverage of urban mmWave microcellular
networks. Prior work used a pathloss model with a line-of-sight probability
function based on randomly oriented buildings, to determine whether a link was
line-of-sight or non-line-of-sight. In this paper, we use a pathloss model
inspired by measurements, which uses a Manhattan distance pathloss model and
accounts for differences in pathloss exponents and losses when turning corners.
In our model, streets are randomly located as a Manhattan Poisson line process
(MPLP) and the base stations (BSs) are distributed according to a Poisson point
process. Our model is well suited for urban microcellular networks where the
BSs are deployed at street level. Based on this new approach, we derive the
coverage probability under certain BS association rules to obtain closed-form
solutions without much complexity. In addition, we draw two main conclusions
from our work. First, non-line-of-sight BSs are not a major benefit for
association or source of interference most of the time. Second, there is an
ultra-dense regime where deploying active BSs does not enhance coverage. | [
1,
0,
0,
0,
0,
0
] |
Title: Resolvent estimates on asymptotically cylindrical manifolds and on the half line,
Abstract: Manifolds with infinite cylindrical ends have continuous spectrum of
increasing multiplicity as energy grows, and in general embedded resonances and
eigenvalues can accumulate at infinity. However, we prove that if geodesic
trapping is sufficiently mild, then such an accumulation is ruled out, and
moreover the cutoff resolvent is uniformly bounded at high energies. We obtain
as a corollary the existence of resonance free regions near the continuous
spectrum.
We also obtain improved estimates when the resolvent is cut off away from
part of the trapping, and along the way we prove some resolvent estimates for
repulsive potentials on the half line which may be of independent interest. | [
0,
0,
1,
0,
0,
0
] |
Title: On absolutely normal and continued fraction normal numbers,
Abstract: We give a construction of a real number that is normal to all integer bases
and continued fraction normal. The computation of the first n digits of its
continued fraction expansion performs in the order of n^4 mathematical
operations. The construction works by defining successive refinements of
appropriate subintervals to achieve, in the limit, simple normality to all
integer bases and continued fraction normality. The main diffculty is to
control the length of these subintervals. To achieve this we adapt and combine
known metric theorems on continued fractions and on expansions in integers
bases. | [
0,
0,
1,
0,
0,
0
] |
Title: Combinatorial properties of the G-degree,
Abstract: A strong interaction is known to exist between edge-colored graphs (which
encode PL pseudo-manifolds of arbitrary dimension) and random tensor models (as
a possible approach to the study of Quantum Gravity). The key tool is the {\it
G-degree} of the involved graphs, which drives the {\it $1/N$ expansion} in the
tensor models context. In the present paper - by making use of combinatorial
properties concerning Hamiltonian decompositions of the complete graph - we
prove that, in any even dimension $d\ge 4$, the G-degree of all bipartite
graphs, as well as of all (bipartite or non-bipartite) graphs representing
singular manifolds, is an integer multiple of $(d-1)!$. As a consequence, in
even dimension, the terms of the $1/N$ expansion corresponding to odd powers of
$1/N$ are null in the complex context, and do not involve colored graphs
representing singular manifolds in the real context.
In particular, in the 4-dimensional case, where the G-degree is shown to
depend only on the regular genera with respect to an arbitrary pair of
"associated" cyclic permutations, several results are obtained, relating the
G-degree or the regular genus of 5-colored graphs and the Euler characteristic
of the associated PL 4-manifolds. | [
0,
0,
1,
0,
0,
0
] |
Title: Quasi-random Agents for Image Transition and Animation,
Abstract: Quasi-random walks show similar features as standard random walks, but with
much less randomness. We utilize this established model from discrete
mathematics and show how agents carrying out quasi-random walks can be used for
image transition and animation. The key idea is to generalize the notion of
quasi-random walks and let a set of autonomous agents perform quasi-random
walks painting an image. Each agent has one particular target image that they
paint when following a sequence of directions for their quasi-random walk. The
sequence can easily be chosen by an artist and allows them to produce a wide
range of different transition patterns and animations. | [
1,
0,
0,
0,
0,
0
] |
Title: Deep Speaker Verification: Do We Need End to End?,
Abstract: End-to-end learning treats the entire system as a whole adaptable black box,
which, if sufficient data are available, may learn a system that works very
well for the target task. This principle has recently been applied to several
prototype research on speaker verification (SV), where the feature learning and
classifier are learned together with an objective function that is consistent
with the evaluation metric. An opposite approach to end-to-end is feature
learning, which firstly trains a feature learning model, and then constructs a
back-end classifier separately to perform SV. Recently, both approaches
achieved significant performance gains on SV, mainly attributed to the smart
utilization of deep neural networks. However, the two approaches have not been
carefully compared, and their respective advantages have not been well
discussed. In this paper, we compare the end-to-end and feature learning
approaches on a text-independent SV task. Our experiments on a dataset sampled
from the Fisher database and involving 5,000 speakers demonstrated that the
feature learning approach outperformed the end-to-end approach. This is a
strong support for the feature learning approach, at least with data and
computation resources similar to ours. | [
1,
0,
0,
0,
0,
0
] |
Title: Neural Models for Documents with Metadata,
Abstract: Most real-world document collections involve various types of metadata, such
as author, source, and date, and yet the most commonly-used approaches to
modeling text corpora ignore this information. While specialized models have
been developed for particular applications, few are widely used in practice, as
customization typically requires derivation of a custom inference algorithm. In
this paper, we build on recent advances in variational inference methods and
propose a general neural framework, based on topic models, to enable flexible
incorporation of metadata and allow for rapid exploration of alternative
models. Our approach achieves strong performance, with a manageable tradeoff
between perplexity, coherence, and sparsity. Finally, we demonstrate the
potential of our framework through an exploration of a corpus of articles about
US immigration. | [
1,
0,
0,
1,
0,
0
] |
Title: Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network,
Abstract: Recent years have witnessed great success of convolutional neural network
(CNN) for various problems both in low and high level visions. Especially
noteworthy is the residual network which was originally proposed to handle
high-level vision problems and enjoys several merits. This paper aims to extend
the merits of residual network, such as skip connection induced fast training,
for a typical low-level vision problem, i.e., single image super-resolution. In
general, the two main challenges of existing deep CNN for supper-resolution lie
in the gradient exploding/vanishing problem and large numbers of parameters or
computational cost as CNN goes deeper. Correspondingly, the skip connections or
identity mapping shortcuts are utilized to avoid gradient exploding/vanishing
problem. In addition, the skip connections have naturally centered the
activation which led to better performance. To tackle with the second problem,
a lightweight CNN architecture which has carefully designed width, depth and
skip connections was proposed. In particular, a strategy of gradually varying
the shape of network has been proposed for residual network. Different residual
architectures for image super-resolution have also been compared. Experimental
results have demonstrated that the proposed CNN model can not only achieve
state-of-the-art PSNR and SSIM results for single image super-resolution but
also produce visually pleasant results. This paper has extended the mmm 2017
oral conference paper with a considerable new analyses and more experiments
especially from the perspective of centering activations and ensemble behaviors
of residual network. | [
1,
0,
0,
0,
0,
0
] |
Title: Variations of BPS structure and a large rank limit,
Abstract: We study a class of flat bundles, of finite rank $N$, which arise naturally
from the Donaldson-Thomas theory of a Calabi-Yau threefold $X$ via the notion
of a variation of BPS structure. We prove that in a large $N$ limit their flat
sections converge to the solutions to certain infinite dimensional
Riemann-Hilbert problems recently found by Bridgeland. In particular this
implies an expression for the positive degree, genus $0$ Gopakumar-Vafa
contribution to the Gromov-Witten partition function of $X$ in terms of
solutions to confluent hypergeometric differential equations. | [
0,
0,
1,
0,
0,
0
] |
Title: Strong correlations between the exponent $α$ and the particle number for a Renyi-monoatomic gas in Gibbs' statistical mechanics,
Abstract: Appealing to the 1902 Gibbs' formalism for classical statistical mechanics
(SM), the first SM axiomatic theory ever that successfully explained
equilibrium thermodynamics, we will here show that already at the classical
level there is a strong correlation between the Renyi's exponent $\alpha$ and
the number of particles for very simple systems. No reference to heat baths is
needed for such a purpose. | [
0,
1,
0,
0,
0,
0
] |
Title: Smooth backfitting of proportional hazards -- A new approach projecting survival data,
Abstract: Smooth backfitting has proven to have a number of theoretical and practical
advantages in structured regression. Smooth backfitting projects the data down
onto the structured space of interest providing a direct link between data and
estimator. This paper introduces the ideas of smooth backfitting to survival
analysis in a proportional hazard model, where we assume an underlying
conditional hazard with multiplicative components. We develop asymptotic theory
for the estimator and we use the smooth backfitter in a practical application,
where we extend recent advances of in-sample forecasting methodology by
allowing more information to be incorporated, while still obeying the
structured requirements of in-sample forecasting. | [
0,
0,
1,
1,
0,
0
] |
Title: Trajectory Tracking Using Motion Primitives for the Purcell's Swimmer,
Abstract: Locomotion at low Reynolds numbers is a topic of growing interest, spurred by
its various engineering and medical applications. This paper presents a novel
prototype and a locomotion algorithm for the 3-link planar Purcell's swimmer
based on Lie algebraic notions. The kinematic model based on Cox theory of the
prototype swimmer is a driftless control-affine system. Using the existing
strong controllability and related results, the existence of motion primitives
is initially shown. The Lie algebra of the control vector fields is then used
to synthesize control profiles to generate motions along the basis of the Lie
algebra associated with the structure group of the system. An open loop control
system with vision-based positioning is successfully implemented which allows
tracking any given continuous trajectory of the position and orientation of the
swimmer's base link. Alongside, the paper also provides a theoretical
interpretation of the symmetry arguments presented in the existing literature
to generate the control profiles of the swimmer. | [
1,
0,
0,
0,
0,
0
] |
Title: Data Analysis in Multimedia Quality Assessment: Revisiting the Statistical Tests,
Abstract: Assessment of multimedia quality relies heavily on subjective assessment, and
is typically done by human subjects in the form of preferences or continuous
ratings. Such data is crucial for analysis of different multimedia processing
algorithms as well as validation of objective (computational) methods for the
said purpose. To that end, statistical testing provides a theoretical framework
towards drawing meaningful inferences, and making well grounded conclusions and
recommendations. While parametric tests (such as t test, ANOVA, and error
estimates like confidence intervals) are popular and widely used in the
community, there appears to be a certain degree of confusion in the application
of such tests. Specifically, the assumption of normality and homogeneity of
variance is often not well understood. Therefore, the main goal of this paper
is to revisit them from a theoretical perspective and in the process provide
useful insights into their practical implications. Experimental results on both
simulated and real data are presented to support the arguments made. A software
implementing the said recommendations is also made publicly available, in order
to achieve the goal of reproducible research. | [
1,
0,
0,
1,
0,
0
] |
Title: Removing Isolated Zeroes by Homotopy,
Abstract: Suppose that the inverse image of the zero vector by a continuous map
$f:{\mathbb R}^n\to{\mathbb R}^q$ has an isolated point $P$. There is a local
obstruction to removing this isolated zero by a small perturbation,
generalizing the notion of index for vector fields, the $q=n$ case. The
existence of a continuous map $g$ which approximates $f$ but is nonvanishing
near $P$ is equivalent to a topological property we call "locally inessential,"
and for dimensions $n$, $q$ where $\pi_{n-1}(S^{q-1})$ is trivial, every
isolated zero is locally inessential. We consider the problem of constructing
such an approximation $g$, and show that there exists a continuous homotopy
from $f$ to $g$ through locally nonvanishing maps. If $f$ is a semialgebraic
map, then there exists such a homotopy which is also semialgebraic. For $q=2$
and $f$ real analytic with a locally inessential isolated zero, there exists a
Hölder continuous homotopy $F(x,t)$ which, for $(x,t)\ne(P,0)$, is real
analytic and nonvanishing. The existence of a smooth homotopy, given a smooth
map $f$, is stated as an open question. | [
0,
0,
1,
0,
0,
0
] |
Title: Continuous cocycle superrigidity for coinduced actions and relative ends,
Abstract: We prove that certain coinduced actions for an inclusion of finitely
generated commensurated subgroups with relative one end are continuous cocycle
superrigid actions. We also show the necessity for the relative end assumption. | [
0,
0,
1,
0,
0,
0
] |
Title: Brownian ratchets: How stronger thermal noise can reduce diffusion,
Abstract: We study diffusion properties of an inertial Brownian motor moving on a
ratchet substrate, i.e. a periodic structure with broken reflection symmetry.
The motor is driven by an unbiased time-periodic symmetric force which takes
the system out of thermal equilibrium. For selected parameter sets, the system
is in a non-chaotic regime in which we can identify a non-monotonic dependence
of the diffusion coefficient on temperature: for low temperature, it initially
increases as temperature grows, passes through its local maximum, next starts
to diminish reaching its local minimum and finally it monotonically increases
in accordance with the Einstein linear relation. Particularly interesting is
the temperature interval in which diffusion is suppressed by thermal noise and
we explain this effect in terms of transition rates of a three-state stochastic
model. | [
0,
1,
0,
0,
0,
0
] |
Title: Wadge Degrees of $ω$-Languages of Petri Nets,
Abstract: We prove that $\omega$-languages of (non-deterministic) Petri nets and
$\omega$-languages of (non-deterministic) Turing machines have the same
topological complexity: the Borel and Wadge hierarchies of the class of
$\omega$-languages of (non-deterministic) Petri nets are equal to the Borel and
Wadge hierarchies of the class of $\omega$-languages of (non-deterministic)
Turing machines which also form the class of effective analytic sets. In
particular, for each non-null recursive ordinal $\alpha < \omega\_1^{\rm CK}
$ there exist some ${\bf \Sigma}^0\_\alpha$-complete and some ${\bf
\Pi}^0\_\alpha$-complete $\omega$-languages of Petri nets, and the supremum of
the set of Borel ranks of $\omega$-languages of Petri nets is the ordinal
$\gamma\_2^1$, which is strictly greater than the first non-recursive ordinal
$\omega\_1^{\rm CK}$. We also prove that there are some ${\bf
\Sigma}\_1^1$-complete, hence non-Borel, $\omega$-languages of Petri nets, and
that it is consistent with ZFC that there exist some $\omega$-languages of
Petri nets which are neither Borel nor ${\bf \Sigma}\_1^1$-complete. This
answers the question of the topological complexity of $\omega$-languages of
(non-deterministic) Petri nets which was left open in [DFR14,FS14]. | [
1,
0,
1,
0,
0,
0
] |
Title: Is there agreement on the prestige of scholarly book publishers in the Humanities? DELPHI over survey results,
Abstract: Despite having an important role supporting assessment processes, criticism
towards evaluation systems and the categorizations used are frequent.
Considering the acceptance by the scientific community as an essential issue
for using rankings or categorizations in research evaluation, the aim of this
paper is testing the results of rankings of scholarly book publishers'
prestige, Scholarly Publishers Indicators (SPI hereafter). SPI is a public,
survey-based ranking of scholarly publishers' prestige (among other
indicators). The latest version of the ranking (2014) was based on an expert
consultation with a large number of respondents. In order to validate and
refine the results for Humanities' fields as proposed by the assessment
agencies, a Delphi technique was applied with a panel of randomly selected
experts over the initial rankings. The results show an equalizing effect of the
technique over the initial rankings as well as a high degree of concordance
between its theoretical aim (consensus among experts) and its empirical results
(summarized with Gini Index). The resulting categorization is understood as
more conclusive and susceptible of being accepted by those under evaluation. | [
1,
0,
0,
1,
0,
0
] |
Title: Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths,
Abstract: Zero-shot recognition aims to accurately recognize objects of unseen classes
by using a shared visual-semantic mapping between the image feature space and
the semantic embedding space. This mapping is learned on training data of seen
classes and is expected to have transfer ability to unseen classes. In this
paper, we tackle this problem by exploiting the intrinsic relationship between
the semantic space manifold and the transfer ability of visual-semantic
mapping. We formalize their connection and cast zero-shot recognition as a
joint optimization problem. Motivated by this, we propose a novel framework for
zero-shot recognition, which contains dual visual-semantic mapping paths. Our
analysis shows this framework can not only apply prior semantic knowledge to
infer underlying semantic manifold in the image feature space, but also
generate optimized semantic embedding space, which can enhance the transfer
ability of the visual-semantic mapping to unseen classes. The proposed method
is evaluated for zero-shot recognition on four benchmark datasets, achieving
outstanding results. | [
1,
0,
0,
0,
0,
0
] |
Title: Spoken Language Biomarkers for Detecting Cognitive Impairment,
Abstract: In this study we developed an automated system that evaluates speech and
language features from audio recordings of neuropsychological examinations of
92 subjects in the Framingham Heart Study. A total of 265 features were used in
an elastic-net regularized binomial logistic regression model to classify the
presence of cognitive impairment, and to select the most predictive features.
We compared performance with a demographic model from 6,258 subjects in the
greater study cohort (0.79 AUC), and found that a system that incorporated both
audio and text features performed the best (0.92 AUC), with a True Positive
Rate of 29% (at 0% False Positive Rate) and a good model fit (Hosmer-Lemeshow
test > 0.05). We also found that decreasing pitch and jitter, shorter segments
of speech, and responses phrased as questions were positively associated with
cognitive impairment. | [
1,
0,
0,
0,
0,
0
] |
Title: Performance Analysis of Low-Density Parity-Check Codes over 2D Interference Channels via Density Evolution,
Abstract: The theoretical analysis of detection and decoding of low-density
parity-check (LDPC) codes transmitted over channels with two-dimensional (2D)
interference and additive white Gaussian noise (AWGN) is provided in this
paper. The detection and decoding system adopts the joint iterative detection
and decoding scheme (JIDDS) in which the log-domain sum-product algorithm is
adopted to decode the LDPC codes. The graph representations of the JIDDS are
explained. Using the graph representations, we prove that the message-flow
neighborhood of the detection and decoding system will be tree-like for a
sufficiently long code length. We further confirm that the performance of the
JIDDS will concentrate around the performance in which message-flow
neighborhood is tree-like. Based on the tree-like message-flow neighborhood, we
employ a modified density evolution algorithm to track the message densities
during the iterations. A threshold is calculated using the density evolution
algorithm which can be considered as the theoretical performance limit of the
system. Simulation results demonstrate that the modified density evolution is
effective in analyzing the performance of 2D interference systems. | [
1,
0,
1,
0,
0,
0
] |
Title: Novel market approach for locally balancing renewable energy production and flexible demand,
Abstract: Future electricity distribution grids will host a considerable share of
variable renewable energy sources and local storage resources. Moreover, they
will face new load structures due for example to the growth of the electric
vehicle market. These trends raise the need for new paradigms for distribution
grids operation, in which Distribution System Operators will increasingly rely
on demand side flexibility and households will progressively become prosumers
playing an active role on smart grid energy management. However, in present
energy management architectures, the lack of coordination among actors limits
the capability of the grid to enable the mentioned trends. In this paper we
tackle this problem by proposing an architecture that enables households to
autonomously exchange energy blocks and flexibility services with neighbors,
operators and market actors. The solution is based on a blockchain transactive
platform. We focus on a market application, where households can trade energy
with their neighbors, aimed to locally balancing renewable energy production.
We propose a market mechanism and dynamic transport prices that provide an
incentive for households to locally manage energy resources in a way that
responds to both pro-sumer and operator needs. We evaluate the impact of such
markets through comprehensive simulations using power flow analysis and
realistic load profiles, providing valuable insight for the design of
appropriate mechanisms and incentives. | [
1,
0,
0,
0,
0,
0
] |
Title: Atomic and electronic structures of stable linear carbon chains on Ag-nanoparticles,
Abstract: In this work, we report X-ray photoelectron (XPS) and valence band (VB)
spectroscopy measurements of surfactant-free silver nanoparticles and
silver/linear carbon chains (Ag@LCC) structures prepared by pulse laser
ablation (PLA) in water. Our measurements demonstrate significant oxidation
only on the surfaces of the silver nanoparticles with many covalent
carbon-silver bonds but only negligible traces of carbon-oxygen bonds.
Theoretical modeling also provides evidence of the formation of robust
carbon-silver bonds between linear carbon chains and pure and partially
oxidized silver surfaces. A comparison of theoretical and experimental
electronic structures also provides evidence of the presence of non-oxidized
linear carbon chains on silver surfaces. To evaluate the chemical stability, we
investigated the energetics of the physical adsorption of oxidative species
(water and oxygen) and found that this adsorption is much preferrable on
oxidized or pristine silver surfaces than the adsorption of linear carbon
chains, which makes the initial step in the oxidation of LCC energetically
unfavorable. | [
0,
1,
0,
0,
0,
0
] |
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