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Title: Statistical Analysis on Bangla Newspaper Data to Extract Trending Topic and Visualize Its Change Over Time,
Abstract: Trending topic of newspapers is an indicator to understand the situation of a
country and also a way to evaluate the particular newspaper. This paper
represents a model describing few techniques to select trending topics from
Bangla Newspaper. Topics that are discussed more frequently than other in
Bangla newspaper will be marked and how a very famous topic loses its
importance with the change of time and another topic takes its place will be
demonstrated. Data from two popular Bangla Newspaper with date and time were
collected. Statistical analysis was performed after on these data after
preprocessing. Popular and most used keywords were extracted from the stream of
Bangla keyword with this analysis. This model can also cluster category wise
news trend or a list of news trend in daily or weekly basis with enough data. A
pattern can be found on their news trend too. Comparison among past news trend
of Bangla newspapers will give a visualization of the situation of Bangladesh.
This visualization will be helpful to predict future trending topics of Bangla
Newspaper. | [
1,
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] |
Title: Effects of global gas flows on type I migration,
Abstract: Magnetically-driven disk winds would alter the surface density slope of gas
in the inner region of a protoplanetary disk $(r \lesssim 1 {\rm au})$. This in
turn affects planet formation. Recently, the effect of disk wind torque has
been considered with the suggestion that it would carve out the surface density
of the disk from inside and would induce global gas flows (wind-driven
accretion). We aim to investigate effects of global gas flows on type I
migration and also examine planet formation. A simplified approach was taken to
address this issue, and N-body simulations with isolation-mass planets were
also performed. In previous studies, the effect of gas flow induced by
turbulence-driven accretion has been taken into account for its desaturation
effect of the corotation torque. If more rapid gas flows (e.g., wind-driven
accretion) are considered, the desaturation effect can be modified. In
MRI-inactive disks, in which the wind-driven accretion dominates the disk
evolution, the gas flow at the midplane plays an important role. If this flow
is fast, the corotation torque is efficiently desaturated. Then, the fact that
the surface density slope can be positive in the inner region due to the wind
torque can generate an outward migration region extended to super-Earth mass
planets. In this case, we observe that no planets fall onto the central star in
N-body simulations with migration forces imposed to reproduce such migration
pattern. We also see that super-Earth mass planets can undergo outward
migration. Relatively rapid gas flows affects type I migration and thus the
formation of close-in planets. | [
0,
1,
0,
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0,
0
] |
Title: Warp: a method for neural network interpretability applied to gene expression profiles,
Abstract: We show a proof of principle for warping, a method to interpret the inner
working of neural networks in the context of gene expression analysis. Warping
is an efficient way to gain insight to the inner workings of neural nets and
make them more interpretable. We demonstrate the ability of warping to recover
meaningful information for a given class on a samplespecific individual basis.
We found warping works well in both linearly and nonlinearly separable
datasets. These encouraging results show that warping has a potential to be the
answer to neural networks interpretability in computational biology. | [
1,
0,
0,
0,
0,
0
] |
Title: Tilings of convex sets by mutually incongruent equilateral triangles contain arbitrarily small tiles,
Abstract: We show that every tiling of a convex set in the Euclidean plane
$\mathbb{R}^2$ by equilateral triangles of mutually different sizes contains
arbitrarily small tiles. The proof is purely elementary up to the discussion of
one family of tilings of the full plane $\mathbb{R}^2$, which is based on a
surprising connection to a random walk on a directed graph. | [
0,
0,
1,
0,
0,
0
] |
Title: The decomposition of 0-Hecke modules associated to quasisymmetric Schur functions,
Abstract: Recently Tewari and van Willigenburg constructed modules of the 0-Hecke
algebra that are mapped to the quasisymmetric Schur functions by the
quasisymmetric characteristic and decomposed them into a direct sum of certain
submodules. We show that these submodules are indecomposable by determining
their endomorphism rings. | [
0,
0,
1,
0,
0,
0
] |
Title: Bayesian Uncertainty Quantification and Information Fusion in CALPHAD-based Thermodynamic Modeling,
Abstract: Calculation of phase diagrams is one of the fundamental tools in alloy
design---more specifically under the framework of Integrated Computational
Materials Engineering. Uncertainty quantification of phase diagrams is the
first step required to provide confidence for decision making in property- or
performance-based design. As a manner of illustration, a thorough probabilistic
assessment of the CALPHAD model parameters is performed against the available
data for a Hf-Si binary case study using a Markov Chain Monte Carlo sampling
approach. The plausible optimum values and uncertainties of the parameters are
thus obtained, which can be propagated to the resulting phase diagram. Using
the parameter values obtained from deterministic optimization in a
computational thermodynamic assessment tool (in this case Thermo-Calc) as the
prior information for the parameter values and ranges in the sampling process
is often necessary to achieve a reasonable cost for uncertainty quantification.
This brings up the problem of finding an appropriate CALPHAD model with
high-level of confidence which is a very hard and costly task that requires
considerable expert skill. A Bayesian hypothesis testing based on Bayes'
factors is proposed to fulfill the need of model selection in this case, which
is applied to compare four recommended models for the Hf-Si system. However, it
is demonstrated that information fusion approaches, i.e., Bayesian model
averaging and an error correlation-based model fusion, can be used to combine
the useful information existing in all the given models rather than just using
the best selected model, which may lack some information about the system being
modelled. | [
0,
0,
0,
1,
0,
0
] |
Title: Modern Data Formats for Big Bioinformatics Data Analytics,
Abstract: Next Generation Sequencing (NGS) technology has resulted in massive amounts
of proteomics and genomics data. This data is of no use if it is not properly
analyzed. ETL (Extraction, Transformation, Loading) is an important step in
designing data analytics applications. ETL requires proper understanding of
features of data. Data format plays a key role in understanding of data,
representation of data, space required to store data, data I/O during
processing of data, intermediate results of processing, in-memory analysis of
data and overall time required to process data. Different data mining and
machine learning algorithms require input data in specific types and formats.
This paper explores the data formats used by different tools and algorithms and
also presents modern data formats that are used on Big Data Platform. It will
help researchers and developers in choosing appropriate data format to be used
for a particular tool or algorithm. | [
1,
0,
0,
0,
0,
0
] |
Title: Nearest-Neighbor Based Non-Parametric Probabilistic Forecasting with Applications in Photovoltaic Systems,
Abstract: The present contribution offers a simple methodology for the obtainment of
data-driven interval forecasting models by combining pairs of quantile
regressions. Those regressions are created without the usage of the
non-differentiable pinball-loss function, but through a k-nearest-neighbors
based training set transformation and traditional regression approaches. By
leaving the underlying training algorithms of the data mining techniques
unchanged, the presented approach simplifies the creation of quantile
regressions with more complex techniques (e.g. artificial neural networks). The
quality of the presented methodology is tested on the usecase of photovoltaic
power forecasting, for which quantile regressions using polynomial models as
well as artificial neural networks and support vector regressions are created.
From the resulting evaluation values it can be concluded that acceptable
interval forecasting models are created. | [
1,
0,
0,
0,
0,
0
] |
Title: A thermodynamic parallel of the Braess road-network paradox,
Abstract: We provide here a thermodynamic analog of the Braess road-network paradox
with irreversible engines working between reservoirs that are placed at
vertices of the network. Paradoxes of different kinds reappear, emphasizing the
specialty of the network. | [
0,
1,
0,
0,
0,
0
] |
Title: Weighted gevrey class regularity of euler equation in the whole space,
Abstract: In this paper we study the weighted Gevrey class regularity of Euler equation
in the whole space R 3. We first establish the local existence of Euler
equation in weighted Sobolev space, then obtain the weighted Gevrey regularity
of Euler equation. We will use the weighted Sobolev-Gevrey space method to
obtain the results of Gevrey regularity of Euler equation, and the use of the
property of singular operator in the estimate of the pressure term is the
improvement of our work. | [
0,
0,
1,
0,
0,
0
] |
Title: Temporal connectivity in finite networks with non-uniform measures,
Abstract: Soft Random Geometric Graphs (SRGGs) have been widely applied to various
models including those of wireless sensor, communication, social and neural
networks. SRGGs are constructed by randomly placing nodes in some space and
making pairwise links probabilistically using a connection function that is
system specific and usually decays with distance. In this paper we focus on the
application of SRGGs to wireless communication networks where information is
relayed in a multi hop fashion, although the analysis is more general and can
be applied elsewhere by using different distributions of nodes and/or
connection functions. We adopt a general non-uniform density which can model
the stationary distribution of different mobility models, with the interesting
case being when the density goes to zero along the boundaries. The global
connectivity properties of these non-uniform networks are likely to be
determined by highly isolated nodes, where isolation can be caused by the
spatial distribution or the local geometry (boundaries). We extend the analysis
to temporal-spatial networks where we fix the underlying non-uniform
distribution of points and the dynamics are caused by the temporal variations
in the link set, and explore the probability a node near the corner is isolated
at time $T$. This work allows for insight into how non-uniformity (caused by
mobility) and boundaries impact the connectivity features of temporal-spatial
networks. We provide a simple method for approximating these probabilities for
a range of different connection functions and verify them against simulations.
Boundary nodes are numerically shown to dominate the connectivity properties of
these finite networks with non-uniform measure. | [
1,
0,
0,
0,
0,
0
] |
Title: Matching Media Contents with User Profiles by means of the Dempster-Shafer Theory,
Abstract: The media industry is increasingly personalizing the offering of contents in
attempt to better target the audience. This requires to analyze the
relationships that goes established between users and content they enjoy,
looking at one side to the content characteristics and on the other to the user
profile, in order to find the best match between the two. In this paper we
suggest to build that relationship using the Dempster-Shafer's Theory of
Evidence, proposing a reference model and illustrating its properties by means
of a toy example. Finally we suggest possible applications of the model for
tasks that are common in the modern media industry. | [
1,
0,
0,
0,
0,
0
] |
Title: Separability by Piecewise Testable Languages is PTime-Complete,
Abstract: Piecewise testable languages form the first level of the Straubing-Thérien
hierarchy. The membership problem for this level is decidable and testing if
the language of a DFA is piecewise testable is NL-complete. The question has
not yet been addressed for NFAs. We fill in this gap by showing that it is
PSpace-complete. The main result is then the lower-bound complexity of
separability of regular languages by piecewise testable languages. Two regular
languages are separable by a piecewise testable language if the piecewise
testable language includes one of them and is disjoint from the other. For
languages represented by NFAs, separability by piecewise testable languages is
known to be decidable in PTime. We show that it is PTime-hard and that it
remains PTime-hard even for minimal DFAs. | [
1,
0,
0,
0,
0,
0
] |
Title: Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages,
Abstract: We present SuperPivot, an analysis method for low-resource languages that
occur in a superparallel corpus, i.e., in a corpus that contains an order of
magnitude more languages than parallel corpora currently in use. We show that
SuperPivot performs well for the crosslingual analysis of the linguistic
phenomenon of tense. We produce analysis results for more than 1000 languages,
conducting - to the best of our knowledge - the largest crosslingual
computational study performed to date. We extend existing methodology for
leveraging parallel corpora for typological analysis by overcoming a limiting
assumption of earlier work: We only require that a linguistic feature is
overtly marked in a few of thousands of languages as opposed to requiring that
it be marked in all languages under investigation. | [
1,
0,
0,
0,
0,
0
] |
Title: A Stochastic Control Approach to Managed Futures Portfolios,
Abstract: We study a stochastic control approach to managed futures portfolios.
Building on the Schwartz 97 stochastic convenience yield model for commodity
prices, we formulate a utility maximization problem for dynamically trading a
single-maturity futures or multiple futures contracts over a finite horizon. By
analyzing the associated Hamilton-Jacobi-Bellman (HJB) equation, we solve the
investor's utility maximization problem explicitly and derive the optimal
dynamic trading strategies in closed form. We provide numerical examples and
illustrate the optimal trading strategies using WTI crude oil futures data. | [
0,
0,
0,
0,
0,
1
] |
Title: Attention-Set based Metric Learning for Video Face Recognition,
Abstract: Face recognition has made great progress with the development of deep
learning. However, video face recognition (VFR) is still an ongoing task due to
various illumination, low-resolution, pose variations and motion blur. Most
existing CNN-based VFR methods only obtain a feature vector from a single image
and simply aggregate the features in a video, which less consider the
correlations of face images in one video. In this paper, we propose a novel
Attention-Set based Metric Learning (ASML) method to measure the statistical
characteristics of image sets. It is a promising and generalized extension of
Maximum Mean Discrepancy with memory attention weighting. First, we define an
effective distance metric on image sets, which explicitly minimizes the
intra-set distance and maximizes the inter-set distance simultaneously. Second,
inspired by Neural Turing Machine, a Memory Attention Weighting is proposed to
adapt set-aware global contents. Then ASML is naturally integrated into CNNs,
resulting in an end-to-end learning scheme. Our method achieves
state-of-the-art performance for the task of video face recognition on the
three widely used benchmarks including YouTubeFace, YouTube Celebrities and
Celebrity-1000. | [
1,
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0,
0,
0,
0
] |
Title: Variable Annealing Length and Parallelism in Simulated Annealing,
Abstract: In this paper, we propose: (a) a restart schedule for an adaptive simulated
annealer, and (b) parallel simulated annealing, with an adaptive and
parameter-free annealing schedule. The foundation of our approach is the
Modified Lam annealing schedule, which adaptively controls the temperature
parameter to track a theoretically ideal rate of acceptance of neighboring
states. A sequential implementation of Modified Lam simulated annealing is
almost parameter-free. However, it requires prior knowledge of the annealing
length. We eliminate this parameter using restarts, with an exponentially
increasing schedule of annealing lengths. We then extend this restart schedule
to parallel implementation, executing several Modified Lam simulated annealers
in parallel, with varying initial annealing lengths, and our proposed parallel
annealing length schedule. To validate our approach, we conduct experiments on
an NP-Hard scheduling problem with sequence-dependent setup constraints. We
compare our approach to fixed length restarts, both sequentially and in
parallel. Our results show that our approach can achieve substantial
performance gains, throughout the course of the run, demonstrating our approach
to be an effective anytime algorithm. | [
1,
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0,
0,
0,
0
] |
Title: Low-Shapiro hydrostatic reconstruction technique for blood flow simulation in large arteries with varying geometrical and mechanical properties,
Abstract: The purpose of this work is to construct a simple, efficient and accurate
well-balanced numerical scheme for one-dimensional (1D) blood flow in large
arteries with varying geometrical and mechanical properties. As the steady
states at rest are not relevant for blood flow, we construct two well-balanced
hydrostatic reconstruction techniques designed to preserve low-Shapiro number
steady states that may occur in large network simulations. The Shapiro number S
h = u/c is the equivalent of the Froude number for shallow water equations and
the Mach number for compressible Euler equations. The first is the low-Shapiro
hydrostatic reconstruction (HR-LS), which is a simple and efficient method,
inspired from the hydrostatic reconstruction technique (HR). The second is the
subsonic hydrostatic reconstruction (HR-S), adapted here to blood flow and
designed to exactly preserve all subcritical steady states. We systematically
compare HR, HR-LS and HR-S in a series of single artery and arterial network
numerical tests designed to evaluate their well-balanced and wave-capturing
properties. The results indicate that HR is not adapted to compute blood flow
in large arteries as it is unable to capture wave reflections and transmissions
when large variations of the arteries' geometrical and mechanical properties
are considered. On the contrary, HR-S is exactly well-balanced and is the most
accurate hydrostatic reconstruction technique. However, HR-LS is able to
compute low-Shapiro number steady states as well as wave reflections and
transmissions with satisfying accuracy and is simpler and computationally less
expensive than HR-S. We therefore recommend using HR-LS for 1D blood flow
simulations in large arterial network simulations. | [
0,
1,
1,
0,
0,
0
] |
Title: Anisotropic two-gap superconductivity and the absence of a Pauli paramagnetic limit in single-crystalline LaO$_{0.5}$F$_{0.5}$BiS$_2$,
Abstract: Ambient-pressure-grown LaO$_{0.5}$F$_{0.5}$BiS$_2$ with a superconducting
transition temperature $T_{c}\sim$3K possesses a highly anisotropic normal
state. By a series of electrical resistivity measurements with a magnetic field
direction varying between the crystalline $c$-axis and the $ab$-plane, we
present the first datasets displaying the temperature dependence of the
out-of-plane upper critical field $H_{c2}^{\perp}(T)$, the in-plane upper
critical field $H_{c2}^{\parallel}(T)$, as well as the angular dependence of
$H_{c2}$ at fixed temperatures for ambient-pressure-grown
LaO$_{0.5}$F$_{0.5}$BiS$_2$ single crystals. The anisotropy of the
superconductivity, $H_{c2}^{\parallel}/H_{c2}^{\perp}$, reaches $\sim$16 on
approaching 0 K, but it decreases significantly near $T_{c}$. A pronounced
upward curvature of $H_{c2}^{\parallel}(T)$ is observed near $T_{c}$, which we
analyze using a two-gap model. Moreover, $H_{c2}^{\parallel}(0)$ is found to
exceed the Pauli paramagnetic limit, which can be understood by considering the
strong spin-orbit coupling associated with Bi as well as the breaking of the
local inversion symmetry at the electronically active BiS$_2$ bilayers. Hence,
LaO$_{0.5}$F$_{0.5}$BiS$_2$ with a centrosymmetric lattice structure is a
unique platform to explore the physics associated with local parity violation
in the bulk crystal. | [
0,
1,
0,
0,
0,
0
] |
Title: Discrete Invariants of Generically Inconsistent Systems of Laurent Polynomials,
Abstract: Let $ \mathcal{A}_1, \ldots, \mathcal{A}_k $ be finite sets in $ \mathbb{Z}^n
$ and let $ Y \subset (\mathbb{C}^*)^n $ be an algebraic variety defined by a
system of equations \[ f_1 = \ldots = f_k = 0, \] where $ f_1, \ldots, f_k $
are Laurent polynomials with supports in $\mathcal{A}_1, \ldots,
\mathcal{A}_k$. Assuming that $ f_1, \ldots, f_k $ are sufficiently generic,
the Newton polyhedron theory computes discrete invariants of $ Y $ in terms of
the Newton polyhedra of $ f_1, \ldots, f_k $. It may appear that the generic
system with fixed supports $ \mathcal{A}_1, \ldots, \mathcal{A}_k $ is
inconsistent. In this paper, we compute discrete invariants of algebraic
varieties defined by system of equations which are generic in the set of
consistent system with support in $\mathcal{A}_1, \ldots, \mathcal{A}_k$ by
reducing the question to the Newton polyhedra theory. Unlike the classical
situation, not only the Newton polyhedra of $f_1,\dots,f_k$, but also the
supports $\mathcal{A}_1, \ldots, \mathcal{A}_k$ themselves appear in the
answers. | [
0,
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1,
0,
0,
0
] |
Title: Enabling near real-time remote search for fast transient events with lossy data compression,
Abstract: We present a systematic evaluation of JPEG2000 (ISO/IEC 15444) as a transport
data format to enable rapid remote searches for fast transient events as part
of the Deeper Wider Faster program (DWF). DWF uses ~20 telescopes from radio to
gamma-rays to perform simultaneous and rapid-response follow-up searches for
fast transient events on millisecond-to-hours timescales. DWF search demands
have a set of constraints that is becoming common amongst large collaborations.
Here, we focus on the rapid optical data component of DWF led by the Dark
Energy Camera (DECam) at CTIO. Each DECam image has 70 total CCDs saved as a
~1.2 gigabyte FITS file. Near real-time data processing and fast transient
candidate identifications -- in minutes for rapid follow-up triggers on other
telescopes -- requires computational power exceeding what is currently
available on-site at CTIO. In this context, data files need to be transmitted
rapidly to a foreign location for supercomputing post-processing, source
finding, visualization and analysis. This step in the search process poses a
major bottleneck, and reducing the data size helps accommodate faster data
transmission. To maximise our gain in transfer time and still achieve our
science goals, we opt for lossy data compression -- keeping in mind that raw
data is archived and can be evaluated at a later time. We evaluate how lossy
JPEG2000 compression affects the process of finding transients, and find only a
negligible effect for compression ratios up to ~25:1. We also find a linear
relation between compression ratio and the mean estimated data transmission
speed-up factor. Adding highly customized compression and decompression steps
to the science pipeline considerably reduces the transmission time --
validating its introduction to the DWF science pipeline and enabling science
that was otherwise too difficult with current technology. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning,
Abstract: Deep Reinforcement Learning (DRL) has been applied successfully to many
robotic applications. However, the large number of trials needed for training
is a key issue. Most of existing techniques developed to improve training
efficiency (e.g. imitation) target on general tasks rather than being tailored
for robot applications, which have their specific context to benefit from. We
propose a novel framework, Assisted Reinforcement Learning, where a classical
controller (e.g. a PID controller) is used as an alternative, switchable policy
to speed up training of DRL for local planning and navigation problems. The
core idea is that the simple control law allows the robot to rapidly learn
sensible primitives, like driving in a straight line, instead of random
exploration. As the actor network becomes more advanced, it can then take over
to perform more complex actions, like obstacle avoidance. Eventually, the
simple controller can be discarded entirely. We show that not only does this
technique train faster, it also is less sensitive to the structure of the DRL
network and consistently outperforms a standard Deep Deterministic Policy
Gradient network. We demonstrate the results in both simulation and real-world
experiments. | [
1,
0,
0,
0,
0,
0
] |
Title: The Partition Rank of a Tensor and $k$-Right Corners in $\mathbb{F}_{q}^{n}$,
Abstract: Following the breakthrough of Croot, Lev, and Pach, Tao introduced a
symmetrized version of their argument, which is now known as the slice rank
method. In this paper, we introduce a more general version of the slice rank of
a tensor, which we call the Partition Rank. This allows us to extend the slice
rank method to problems that require the variables to be distinct. Using the
partition rank, we generalize a recent result of Ge and Shangguan, and prove
that any set $A\subset\mathbb{F}_{q}^{n}$ of size
\[|A|>(k+1)\cdot\binom{n+(k-1)q}{(k-1)(q-1)}\] contains a $k$-right-corner,
that is distinct vectors $x_{1},\dots,x_{k},x_{k+1}$ where
$x_{1}-x_{k+1},\dots,x_{k}-x_{k+1}$ are mutually orthogonal, for $q=p^{r}$, a
prime power with $p>k$. | [
0,
0,
1,
0,
0,
0
] |
Title: Tunable Optoelectronic Properties of Triply-Bonded Carbon Molecules with Linear and Graphyne Substructures,
Abstract: In this paper we present a detailed computational study of the electronic
structure and optical properties of triply-bonded hydrocarbons with linear, and
graphyne substructures, with the aim of identifying their potential in
opto-electronic device applications. For the purpose, we employed a correlated
electron methodology based upon the Pariser-Parr-Pople model Hamiltonian,
coupled with the configuration interaction (CI) approach, and studied
structures containing up to 42 carbon atoms. Our calculations, based upon
large-scale CI expansions, reveal that the linear structures have intense
optical absorption at the HOMO-LUMO gap, while the graphyne ones have those at
higher energies. Thus, the opto-electronic properties depend on the topology of
the {graphyne substructures, suggesting that they can be tuned by means of
structural modifications. Our results are in very good agreement with the
available experimental data. | [
0,
1,
0,
0,
0,
0
] |
Title: Higher zigzag algebras,
Abstract: Given any Koszul algebra of finite global dimension one can define a new
algebra, which we call a higher zigzag algebra, as a twisted trivial extension
of the Koszul dual of our original algebra. If our original algebra is the path
algebra of a quiver whose underlying graph is a tree, this construction
recovers the zigzag algebras of Huerfano and Khovanov. We study examples of
higher zigzag algebras coming from Iyama's iterative construction of type A
higher representation finite algebras. We give presentations of these algebras
by quivers and relations, and describe relations between spherical twists
acting on their derived categories. We then make a connection to the McKay
correspondence in higher dimensions: if G is a finite abelian subgroup of the
special linear group acting on affine space, then the skew group algebra which
controls the category of G-equivariant sheaves is Koszul dual to a higher
zigzag algebra. Using this, we show that our relations between spherical twists
appear naturally in examples from algebraic geometry. | [
0,
0,
1,
0,
0,
0
] |
Title: Motivic modular forms from equivariant stable homotopy theory,
Abstract: In this paper, we produce a cellular motivic spectrum of motivic modular
forms over $\R$ and $\C$, answering positively to a conjecture of Dan Isaksen.
This spectrum is constructed to have the appropriate cohomology, as a module
over the relevant motivic Steenrod algebra. We first produce a $\G$-equivariant
version of this spectrum, and then use a machinery to construct a motivic
spectrum from an equivariant one. We believe that this machinery will be of
independent interest. | [
0,
0,
1,
0,
0,
0
] |
Title: Topology and experimental distinguishability,
Abstract: In this work we introduce the idea that the primary application of topology
in experimental sciences is to keep track of what can be distinguished through
experimentation. This link provides understanding and justification as to why
topological spaces and continuous functions are pervasive tools in science. We
first define an experimental observation as a statement that can be verified
using an experimental procedure and show that observations are closed under
finite conjunction and countable disjunction. We then consider observations
that identify elements within a set and show how they induce a Hausdorff and
second-countable topology on that set, thus identifying an open set as one that
can be associated with an experimental observation. We then show that
experimental relationships are continuous functions, as they must preserve
experimental distinguishability, and that they are themselves experimentally
distinguishable by defining a Hausdorff and second-countable topology for this
collection. | [
0,
0,
1,
0,
0,
0
] |
Title: Edge fracture in complex fluids,
Abstract: We study theoretically the edge fracture instability in sheared complex
fluids, by means of linear stability analysis and direct nonlinear simulations.
We derive an exact analytical expression for the onset of edge fracture in
terms of the shear-rate derivative of the fluid's second normal stress
difference, the shear-rate derivative of the shear stress, the jump in shear
stress across the interface between the fluid and the outside medium (usually
air), the surface tension of that interface, and the rheometer gap size. We
provide a full mechanistic understanding of the edge fracture instability,
carefully validated against our simulations. These findings, which are robust
with respect to choice of rheological constitutive model, also suggest a
possible route to mitigating edge fracture, potentially allowing
experimentalists to achieve and accurately measure stronger flows than
hitherto. | [
0,
1,
0,
0,
0,
0
] |
Title: Testing statistical Isotropy in Cosmic Microwave Background Polarization maps,
Abstract: We apply our symmetry based Power tensor technique to test conformity of
PLANCK Polarization maps with statistical isotropy. On a wide range of angular
scales (l=40-150), our preliminary analysis detects many statistically
anisotropic multipoles in foreground cleaned full sky PLANCK polarization maps
viz., COMMANDER and NILC. We also study the effect of residual foregrounds that
may still be present in the galactic plane using both common UPB77 polarization
mask, as well as the individual component separation method specific
polarization masks. However some of the statistically anisotropic modes still
persist, albeit significantly in NILC map. We further probed the data for any
coherent alignments across multipoles in several bins from the chosen multipole
range. | [
0,
1,
0,
0,
0,
0
] |
Title: Generalised Lyapunov Functions and Functionally Generated Trading Strategies,
Abstract: This paper investigates the dependence of functional portfolio generation,
introduced by Fernholz (1999), on an extra finite variation process. The
framework of Karatzas and Ruf (2017) is used to formulate conditions on trading
strategies to be strong arbitrage relative to the market over sufficiently
large time horizons. A mollification argument and Komlos theorem yield a
general class of potential arbitrage strategies. These theoretical results are
complemented by several empirical examples using data from the S&P 500 stocks. | [
0,
0,
0,
0,
0,
1
] |
Title: Local optima of the Sherrington-Kirkpatrick Hamiltonian,
Abstract: We study local optima of the Hamiltonian of the Sherrington-Kirkpatrick
model. We compute the exponent of the expected number of local optima and
determine the "typical" value of the Hamiltonian. | [
1,
0,
0,
0,
0,
0
] |
Title: Joining Extractions of Regular Expressions,
Abstract: Regular expressions with capture variables, also known as "regex formulas,"
extract relations of spans (interval positions) from text. These relations can
be further manipulated via Relational Algebra as studied in the context of
document spanners, Fagin et al.'s formal framework for information extraction.
We investigate the complexity of querying text by Conjunctive Queries (CQs) and
Unions of CQs (UCQs) on top of regex formulas. We show that the lower bounds
(NP-completeness and W[1]-hardness) from the relational world also hold in our
setting; in particular, hardness hits already single-character text! Yet, the
upper bounds from the relational world do not carry over. Unlike the relational
world, acyclic CQs, and even gamma-acyclic CQs, are hard to compute. The source
of hardness is that it may be intractable to instantiate the relation defined
by a regex formula, simply because it has an exponential number of tuples. Yet,
we are able to establish general upper bounds. In particular, UCQs can be
evaluated with polynomial delay, provided that every CQ has a bounded number of
atoms (while unions and projection can be arbitrary). Furthermore, UCQ
evaluation is solvable with FPT (Fixed-Parameter Tractable) delay when the
parameter is the size of the UCQ. | [
1,
0,
0,
0,
0,
0
] |
Title: Learning Generalized Reactive Policies using Deep Neural Networks,
Abstract: We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3. | [
1,
0,
0,
0,
0,
0
] |
Title: Symplectic Coarse-Grained Dynamics: Chalkboard Motion in Classical and Quantum Mechanics,
Abstract: In the usual approaches to mechanics (classical or quantum) the primary
object of interest is the Hamiltonian, from which one tries to deduce the
solutions of the equations of motion (Hamilton or Schrödinger). In the
present work we reverse this paradigm and view the motions themselves as being
the primary objects. This is made possible by studying arbitrary phase space
motions, not of points, but of (small) ellipsoids with the requirement that the
symplectic capacity of these ellipsoids is preserved. This allows us to guide
and control these motions as we like. In the classical case these ellipsoids
correspond to a symplectic coarse graining of phase space, and in the quantum
case they correspond to the "quantum blobs" we defined in previous work, and
which can be viewed as minimum uncertainty phase space cells which are in a
one-to-one correspondence with Gaussian pure states. | [
0,
0,
1,
0,
0,
0
] |
Title: Characteristic functions as bounded multipliers on anisotropic spaces,
Abstract: We show that characteristic functions of domains with boundaries transversal
to stable cones are bounded multipliers on a recently introduced scale
$U^{t,s}_p$ of anisotropic Banach spaces, under the conditions -1+1/p<s<-t<0
and -(r-1)+t<s, with 1<p<infty. (Amended after comments from the referee and M.
Jézéquel, January 10, 2018) | [
0,
1,
0,
0,
0,
0
] |
Title: Pathwise Least Angle Regression and a Significance Test for the Elastic Net,
Abstract: Least angle regression (LARS) by Efron et al. (2004) is a novel method for
constructing the piece-wise linear path of Lasso solutions. For several years,
it remained also as the de facto method for computing the Lasso solution before
more sophisticated optimization algorithms preceded it. LARS method has
recently again increased its popularity due to its ability to find the values
of the penalty parameters, called knots, at which a new parameter enters the
active set of non-zero coefficients. Significance test for the Lasso by
Lockhart et al. (2014), for example, requires solving the knots via the LARS
algorithm. Elastic net (EN), on the other hand, is a highly popular extension
of Lasso that uses a linear combination of Lasso and ridge regression
penalties. In this paper, we propose a new novel algorithm, called pathwise
(PW-)LARS-EN, that is able to compute the EN knots over a grid of EN tuning
parameter {\alpha} values. The developed PW-LARS-EN algorithm decreases the EN
tuning parameter and exploits the previously found knot values and the original
LARS algorithm. A covariance test statistic for the Lasso is then generalized
to the EN for testing the significance of the predictors. Our simulation
studies validate the fact that the test statistic has an asymptotic Exp(1)
distribution. | [
0,
0,
1,
1,
0,
0
] |
Title: 2MTF VI. Measuring the velocity power spectrum,
Abstract: We present measurements of the velocity power spectrum and constraints on the
growth rate of structure $f\sigma_{8}$, at redshift zero, using the peculiar
motions of 2,062 galaxies in the completed 2MASS Tully-Fisher survey (2MTF). To
accomplish this we introduce a model for fitting the velocity power spectrum
including the effects of non-linear Redshift Space Distortions (RSD), allowing
us to recover unbiased fits down to scales $k=0.2\,h\,{\rm Mpc}^{-1}$ without
the need to smooth or grid the data. Our fitting methods are validated using a
set of simulated 2MTF surveys. Using these simulations we also identify that
the Gaussian distributed estimator for peculiar velocities of
\cite{Watkins2015} is suitable for measuring the velocity power spectrum, but
sub-optimal for the 2MTF data compared to using magnitude fluctuations $\delta
m$, and that, whilst our fits are robust to a change in fiducial cosmology,
future peculiar velocity surveys with more constraining power may have to
marginalise over this. We obtain \textit{scale-dependent} constraints on the
growth rate of structure in two bins, finding $f\sigma_{8} =
[0.55^{+0.16}_{-0.13},0.40^{+0.16}_{-0.17}]$ in the ranges $k = [0.007-0.055,
0.55-0.150]\,h\,{\rm Mpc}^{-1}$. We also find consistent results using four
bins. Assuming scale-\textit{independence} we find a value $f\sigma_{8} =
0.51^{+0.09}_{-0.08}$, a $\sim16\%$ measurement of the growth rate. Performing
a consistency check of General Relativity (GR) and combining our results with
CMB data only we find $\gamma = 0.45^{+0.10}_{-0.11}$, a remarkable constraint
considering the small number of galaxies. All of our results are completely
independent of the effects of galaxy bias, and fully consistent with the
predictions of GR (scale-independent $f\sigma_{8}$ and $\gamma\approx0.55$). | [
0,
1,
0,
0,
0,
0
] |
Title: Gradient weighted norm inequalities for very weak solutions of linear parabolic equations with BMO coefficients,
Abstract: In this paper, we prove the Lorentz space $L^{q,p}$-estimates for gradients
of very weak solutions to the linear parabolic equations with
$\mathbf{A}_q$-weights $$u_t-\operatorname{div}(A(x,t)\nabla
u)=\operatorname{div}(F),$$ in a bounded domain $\Omega\times
(0,T)\subset\mathbb{R}^{N+1}$, where $A$ has a small mean oscillation, and
$\Omega$ is a Lipchistz domain with a small Lipschitz constant. | [
0,
0,
1,
0,
0,
0
] |
Title: Local incompressibility estimates for the Laughlin phase,
Abstract: We prove sharp density upper bounds on optimal length-scales for the ground
states of classical 2D Coulomb systems and generalizations thereof. Our method
is new, based on an auxiliary Thomas-Fermi-like variational model. Moreover, we
deduce density upper bounds for the related low-temperature Gibbs states. Our
motivation comes from fractional quantum Hall physics, more precisely, the
perturbation of the Laughlin state by external potentials or impurities. These
give rise to a class of many-body wave-functions that have the form of a
product of the Laughlin state and an analytic function of many variables. This
class is related via Laughlin's plasma analogy to Gibbs states of the
generalized classical Coulomb systems we consider. Our main result shows that
the perturbation of the Laughlin state cannot increase the particle density
anywhere, with implications for the response of FQHE systems to external
perturbations. | [
0,
1,
1,
0,
0,
0
] |
Title: Learning Non-local Image Diffusion for Image Denoising,
Abstract: Image diffusion plays a fundamental role for the task of image denoising.
Recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a
simple but very effective framework for image denoising. However, as the TNRD
model is a local model, the diffusion behavior of which is purely controlled by
information of local patches, it is prone to create artifacts in the homogenous
regions and over-smooth highly textured regions, especially in the case of
strong noise levels. Meanwhile, it is widely known that the non-local
self-similarity (NSS) prior stands as an effective image prior for image
denoising, which has been widely exploited in many non-local methods. In this
work, we are highly motivated to embed the NSS prior into the TNRD model to
tackle its weaknesses. In order to preserve the expected property that
end-to-end training is available, we exploit the NSS prior by a set of
non-local filters, and derive our proposed trainable non-local reaction
diffusion (TNLRD) model for image denoising. Together with the local filters
and influence functions, the non-local filters are learned by employing
loss-specific training. The experimental results show that the trained TNLRD
model produces visually plausible recovered images with more textures and less
artifacts, compared to its local versions. Moreover, the trained TNLRD model
can achieve strongly competitive performance to recent state-of-the-art image
denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural
similarity index (SSIM). | [
1,
0,
0,
0,
0,
0
] |
Title: Deep vs. Diverse Architectures for Classification Problems,
Abstract: This study compares various superlearner and deep learning architectures
(machine-learning-based and neural-network-based) for classification problems
across several simulated and industrial datasets to assess performance and
computational efficiency, as both methods have nice theoretical convergence
properties. Superlearner formulations outperform other methods at small to
moderate sample sizes (500-2500) on nonlinear and mixed linear/nonlinear
predictor relationship datasets, while deep neural networks perform well on
linear predictor relationship datasets of all sizes. This suggests faster
convergence of the superlearner compared to deep neural network architectures
on many messy classification problems for real-world data.
Superlearners also yield interpretable models, allowing users to examine
important signals in the data; in addition, they offer flexible formulation,
where users can retain good performance with low-computational-cost base
algorithms.
K-nearest-neighbor (KNN) regression demonstrates improvements using the
superlearner framework, as well; KNN superlearners consistently outperform deep
architectures and KNN regression, suggesting that superlearners may be better
able to capture local and global geometric features through utilizing a variety
of algorithms to probe the data space. | [
1,
0,
0,
1,
0,
0
] |
Title: Means Moments and Newton's Inequalities,
Abstract: It is shown that Newton's inequalities and the related Maclaurin's
inequalities provide several refinements of the fundamental Arithmetic mean -
Geometric mean - Harmonic mean inequality in terms of the means and variance of
positive real numbers. We also obtain some inequalities involving third and
fourth central moments of real numbers. | [
0,
0,
1,
1,
0,
0
] |
Title: Automatic Exploration of Machine Learning Experiments on OpenML,
Abstract: Understanding the influence of hyperparameters on the performance of a
machine learning algorithm is an important scientific topic in itself and can
help to improve automatic hyperparameter tuning procedures. Unfortunately,
experimental meta data for this purpose is still rare. This paper presents a
large, free and open dataset addressing this problem, containing results on 38
OpenML data sets, six different machine learning algorithms and many different
hyperparameter configurations. Results where generated by an automated random
sampling strategy, termed the OpenML Random Bot. Each algorithm was
cross-validated up to 20.000 times per dataset with different hyperparameters
settings, resulting in a meta dataset of around 2.5 million experiments
overall. | [
0,
0,
0,
1,
0,
0
] |
Title: Generalized Expectation Consistent Signal Recovery for Nonlinear Measurements,
Abstract: In this paper, we propose a generalized expectation consistent signal
recovery algorithm to estimate the signal $\mathbf{x}$ from the nonlinear
measurements of a linear transform output $\mathbf{z}=\mathbf{A}\mathbf{x}$.
This estimation problem has been encountered in many applications, such as
communications with front-end impairments, compressed sensing, and phase
retrieval. The proposed algorithm extends the prior art called generalized
turbo signal recovery from a partial discrete Fourier transform matrix
$\mathbf{A}$ to a class of general matrices. Numerical results show the
excellent agreement of the proposed algorithm with the theoretical
Bayesian-optimal estimator derived using the replica method. | [
1,
0,
1,
0,
0,
0
] |
Title: Proactive Eavesdropping in Relaying Systems,
Abstract: This paper investigates the performance of a legitimate surveillance system,
where a legitimate monitor aims to eavesdrop on a dubious decode-and-forward
relaying communication link. In order to maximize the effective eavesdropping
rate, two strategies are proposed, where the legitimate monitor adaptively acts
as an eavesdropper, a jammer or a helper. In addition, the corresponding
optimal jamming beamformer and jamming power are presented. Numerical results
demonstrate that the proposed strategies attain better performance compared
with intuitive benchmark schemes. Moreover, it is revealed that the position of
the legitimate monitor plays an important role on the eavesdropping performance
for the two strategies. | [
1,
0,
0,
0,
0,
0
] |
Title: Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching,
Abstract: Big data problems frequently require processing datasets in a streaming
fashion, either because all data are available at once but collectively are
larger than available memory or because the data intrinsically arrive one data
point at a time and must be processed online. Here, we introduce a
computationally efficient version of similarity matching, a framework for
online dimensionality reduction that incrementally estimates the top
K-dimensional principal subspace of streamed data while keeping in memory only
the last sample and the current iterate. To assess the performance of our
approach, we construct and make public a test suite containing both a synthetic
data generator and the infrastructure to test online dimensionality reduction
algorithms on real datasets, as well as performant implementations of our
algorithm and competing algorithms with similar aims. Among the algorithms
considered we find our approach to be competitive, performing among the best on
both synthetic and real data. | [
0,
0,
0,
1,
0,
0
] |
Title: Resolving API Mentions in Informal Documents,
Abstract: Developer forums contain opinions and information related to the usage of
APIs. API names in forum posts are often not explicitly linked to their
official resources. Automatic linking of an API mention to its official
resources can be challenging for various reasons, such as, name overloading. We
present a technique, ANACE, to automatically resolve API mentions in the
textual contents of forum posts. Given a database of APIs, we first detect all
words in a forum post that are potential references to an API. We then use a
combination of heuristics and machine learning to eliminate false positives and
to link true positives to the actual APIs and their resources. | [
1,
0,
0,
0,
0,
0
] |
Title: Quantifying tidal stream disruption in a simulated Milky Way,
Abstract: Simulations of tidal streams show that close encounters with dark matter
subhalos induce density gaps and distortions in on-sky path along the streams.
Accordingly, observing disrupted streams in the Galactic halo would
substantiate the hypothesis that dark matter substructure exists there, while
in contrast, observing collimated streams with smoothly varying density
profiles would place strong upper limits on the number density and mass
spectrum of subhalos. Here, we examine several measures of stream "disruption"
and their power to distinguish between halo potentials with and without
substructure and with different global shapes. We create and evolve a
population of 1280 streams on a range of orbits in the Via Lactea II simulation
of a Milky Way-like halo, replete with a full mass range of {\Lambda}CDM
subhalos, and compare it to two control stream populations evolved in smooth
spherical and smooth triaxial potentials, respectively. We find that the number
of gaps observed in a stellar stream is a poor indicator of the halo potential,
but that (i) the thinness of the stream on-sky, (ii) the symmetry of the
leading and trailing tails, and (iii) the deviation of the tails from a
low-order polynomial path on-sky ("path regularity") distinguish between the
three potentials more effectively. We find that globular cluster streams on
low-eccentricity orbits far from the galactic center (apocentric radius ~ 30-80
kpc) are most powerful in distinguishing between the three potentials. If they
exist, such streams will shortly be discoverable and mapped in high dimensions
with near-future photometric and spectroscopic surveys. | [
0,
1,
0,
0,
0,
0
] |
Title: The dehydration of water worlds via atmospheric losses,
Abstract: We present a three-species multi-fluid MHD model (H$^+$, H$_2$O$^+$ and
e$^-$), endowed with the requisite atmospheric chemistry, that is capable of
accurately quantifying the magnitude of water ion losses from exoplanets. We
apply this model to a water world with Earth-like parameters orbiting a
Sun-like star for three cases: (i) current normal solar wind conditions, (ii)
ancient normal solar wind conditions, and (iii) one extreme "Carrington-type"
space weather event. We demonstrate that the ion escape rate for (ii), with a
value of 6.0$\times$10$^{26}$ s$^{-1}$, is about an order of magnitude higher
than the corresponding value of 6.7$\times$10$^{25}$ s$^{-1}$ for (i). Studies
of ion losses induced by space weather events, where the ion escape rates can
reach $\sim$ 10$^{28}$ s$^{-1}$, are crucial for understanding how an active,
early solar-type star (e.g., with frequent coronal mass ejections) could have
accelerated the depletion of the exoplanet's atmosphere. We briefly explore the
ramifications arising from the loss of water ions, especially for planets
orbiting M-dwarfs where such effects are likely to be significant. | [
0,
1,
0,
0,
0,
0
] |
Title: Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets,
Abstract: With the ever-growing amounts of textual data from a large variety of
languages, domains, and genres, it has become standard to evaluate NLP
algorithms on multiple datasets in order to ensure consistent performance
across heterogeneous setups. However, such multiple comparisons pose
significant challenges to traditional statistical analysis methods in NLP and
can lead to erroneous conclusions. In this paper, we propose a Replicability
Analysis framework for a statistically sound analysis of multiple comparisons
between algorithms for NLP tasks. We discuss the theoretical advantages of this
framework over the current, statistically unjustified, practice in the NLP
literature, and demonstrate its empirical value across four applications:
multi-domain dependency parsing, multilingual POS tagging, cross-domain
sentiment classification and word similarity prediction. | [
1,
0,
0,
0,
0,
0
] |
Title: Reflections on Cyberethics Education for Millennial Software Engineers,
Abstract: Software is a key component of solutions for 21st Century problems. These
problems are often "wicked", complex, and unpredictable. To provide the best
possible solution, millennial software engineers must be prepared to make
ethical decisions, thinking critically, and acting systematically. This reality
demands continuous changes in educational systems and curricula delivery, as
misjudgment might have serious social impact. This study aims to investigate
and reflect on Software Engineering (SE) Programs, proposing a conceptual
framework for analyzing cyberethics education and a set of suggestions on how
to integrate it into the SE undergraduate curriculum. | [
1,
0,
0,
0,
0,
0
] |
Title: The effect of an offset polar cap dipolar magnetic field on the modeling of the Vela pulsar's $γ$-ray light curves,
Abstract: We performed geometric pulsar light curve modeling using static, retarded
vacuum, and offset polar cap (PC) dipole $B$-fields (the latter is
characterized by a parameter $\epsilon$), in conjunction with standard two-pole
caustic (TPC) and outer gap (OG) emission geometries. The offset-PC dipole
$B$-field mimics deviations from the static dipole (which corresponds to
$\epsilon=0$). In addition to constant-emissivity geometric models, we also
considered a slot gap (SG) $E$-field associated with the offset-PC dipole
$B$-field and found that its inclusion leads to qualitatively different light
curves. Solving the particle transport equation shows that the particle energy
only becomes large enough to yield significant curvature radiation at large
altitudes above the stellar surface, given this relatively low $E$-field.
Therefore, particles do not always attain the radiation-reaction limit. Our
overall optimal light curve fit is for the retarded vacuum dipole field and OG
model, at an inclination angle $\alpha=78{_{-1}^{+1}}^{\circ}$ and observer
angle $\zeta=69{_{-1}^{+2}}^{\circ}$. For this $B$-field, the TPC model is
statistically disfavored compared to the OG model. For the static dipole field,
neither model is significantly preferred. We found that smaller values of
$\epsilon$ are favored for the offset-PC dipole field when assuming constant
emissivity, and larger $\epsilon$ values favored for variable emissivity, but
not significantly so. When multiplying the SG $E$-field by a factor of 100, we
found improved light curve fits, with $\alpha$ and $\zeta$ being closer to best
fits from independent studies, as well as curvature radiation reaction at lower
altitudes. | [
0,
1,
0,
0,
0,
0
] |
Title: Survey on Models and Techniques for Root-Cause Analysis,
Abstract: Automation and computer intelligence to support complex human decisions
becomes essential to manage large and distributed systems in the Cloud and IoT
era. Understanding the root cause of an observed symptom in a complex system
has been a major problem for decades. As industry dives into the IoT world and
the amount of data generated per year grows at an amazing speed, an important
question is how to find appropriate mechanisms to determine root causes that
can handle huge amounts of data or may provide valuable feedback in real-time.
While many survey papers aim at summarizing the landscape of techniques for
modelling system behavior and infering the root cause of a problem based in the
resulting models, none of those focuses on analyzing how the different
techniques in the literature fit growing requirements in terms of performance
and scalability. In this survey, we provide a review of root-cause analysis,
focusing on these particular aspects. We also provide guidance to choose the
best root-cause analysis strategy depending on the requirements of a particular
system and application. | [
1,
0,
0,
0,
0,
0
] |
Title: Test Prioritization in Continuous Integration Environments,
Abstract: Two heuristics namely diversity-based (DBTP) and history-based test
prioritization (HBTP) have been separately proposed in the literature. Yet,
their combination has not been widely studied in continuous integration (CI)
environments. The objective of this study is to catch regression faults
earlier, allowing developers to integrate and verify their changes more
frequently and continuously. To achieve this, we investigated six open-source
projects, each of which included several builds over a large time period.
Findings indicate that previous failure knowledge seems to have strong
predictive power in CI environments and can be used to effectively prioritize
tests. HBTP does not necessarily need to have large data, and its effectiveness
improves to a certain degree with larger history interval. DBTP can be used
effectively during the early stages, when no historical data is available, and
also combined with HBTP to improve its effectiveness. Among the investigated
techniques, we found that history-based diversity using NCD Multiset is
superior in terms of effectiveness but comes with relatively higher overhead in
terms of method execution time. Test prioritization in CI environments can be
effectively performed with negligible investment using previous failure
knowledge, and its effectiveness can be further improved by considering
dissimilarities among the tests. | [
1,
0,
0,
0,
0,
0
] |
Title: Weighted integral Hankel operators with continuous spectrum,
Abstract: Using the Kato-Rosenblum theorem, we describe the absolutely continuous
spectrum of a class of weighted integral Hankel operators in $L^2(\mathbb
R_+)$. These self-adjoint operators generalise the explicitly diagonalisable
operator with the integral kernel $s^\alpha t^\alpha(s+t)^{-1-2\alpha}$, where
$\alpha>-1/2$. Our analysis can be considered as an extension of J.Howland's
1992 paper which dealt with the unweighted case, corresponding to $\alpha=0$. | [
0,
0,
1,
0,
0,
0
] |
Title: Local properties of Riesz minimal energy configurations and equilibrium measures,
Abstract: We investigate separation properties of $N$-point configurations that
minimize discrete Riesz $s$-energy on a compact set $A\subset \mathbb{R}^p$.
When $A$ is a smooth $(p-1)$-dimensional manifold without boundary and $s\in
[p-2, p-1)$, we prove that the order of separation (as $N\to \infty$) is the
best possible. The same conclusions hold for the points that are a fixed
positive distance from the boundary of $A$ whenever $A$ is any $p$-dimensional
set. These estimates extend a result of Dahlberg for certain smooth
$(p-1)$-dimensional surfaces when $s=p-2$ (the harmonic case). Furthermore, we
obtain the same separation results for `greedy' $s$-energy points. We deduce
our results from an upper regularity property of the $s$-equilibrium measure
(i.e., the measure that solves the continuous minimal Riesz $s$-energy
problem), and we show that this property holds under a local smoothness
assumption on the set $A$. | [
0,
0,
1,
0,
0,
0
] |
Title: Cascaded Segmentation-Detection Networks for Word-Level Text Spotting,
Abstract: We introduce an algorithm for word-level text spotting that is able to
accurately and reliably determine the bounding regions of individual words of
text "in the wild". Our system is formed by the cascade of two convolutional
neural networks. The first network is fully convolutional and is in charge of
detecting areas containing text. This results in a very reliable but possibly
inaccurate segmentation of the input image. The second network (inspired by the
popular YOLO architecture) analyzes each segment produced in the first stage,
and predicts oriented rectangular regions containing individual words. No
post-processing (e.g. text line grouping) is necessary. With execution time of
450 ms for a 1000-by-560 image on a Titan X GPU, our system achieves the
highest score to date among published algorithms on the ICDAR 2015 Incidental
Scene Text dataset benchmark. | [
1,
0,
0,
0,
0,
0
] |
Title: Hilbert $C^*$-modules over $Σ^*$-algebras II: $Σ^*$-Morita equivalence,
Abstract: In previous work, we defined and studied $\Sigma^*$-modules, a class of
Hilbert $C^*$-modules over $\Sigma^*$-algebras (the latter are $C^*$-algebras
that are sequentially closed in the weak operator topology). The present work
continues this study by developing the appropriate $\Sigma^*$-algebraic
analogue of the notion of strong Morita equivalence for $C^*$-algebras. We
define strong $\Sigma^*$-Morita equivalence, prove a few characterizations,
look at the relationship with equivalence of categories of a certain type of
Hilbert space representation, study $\Sigma^*$-versions of the interior and
exterior tensor products, and prove a $\Sigma^*$-version of the
Brown-Green-Rieffel stable isomorphism theorem. | [
0,
0,
1,
0,
0,
0
] |
Title: Topological degeneracy and pairing in a one-dimensional gas of spinless Fermions,
Abstract: We revisit the low energy physics of one dimensional spinless fermion
liquids, showing that with sufficiently strong interactions the conventional
Luttinger liquid can give way to a strong pairing phase. While the density
fluctuations in both phases are described by a gapless Luttinger liquid, single
fermion excitations are gapped only in the strong pairing phase. Smooth spatial
Interfaces between the two phases lead to topological degeneracies in the
ground state and low energy phonon spectrum. Using a concrete microscopic
model, with both single particle and pair hopping, we show that the strong
pairing state is established through emergence of a new low energy fermionic
mode. We characterize the two phases with numerical calculations using the
density matrix renormalization group. In particular we find enhancement of the
central charge from $c=1$ in the two Luttinger liquid phases to $c=3/2$ at the
critical point, which gives direct evidence for an emergent critical Majorana
mode. Finally, we confirm the existence of topological degeneracies in the low
energy phonon spectrum, associated with spatial interfaces between the two
phases. | [
0,
1,
0,
0,
0,
0
] |
Title: Rejecting inadmissible rules in reduced normal forms in S4,
Abstract: Several methods for checking admissibility of rules in the modal logic $S4$
are presented in [1], [15]. These methods determine admissibility of rules in
$S4$, but they don't determine or give substitutions rejecting inadmissible
rules. In this paper, we investigate some relations between one of the above
methods, based on the reduced normal form rules, and sets of substitutions
which reject them. We also generalize the method in [1], [15] for one rule to
admissibility of a set of rules. | [
1,
0,
1,
0,
0,
0
] |
Title: An Executable Sequential Specification for Spark Aggregation,
Abstract: Spark is a new promising platform for scalable data-parallel computation. It
provides several high-level application programming interfaces (APIs) to
perform parallel data aggregation. Since execution of parallel aggregation in
Spark is inherently non-deterministic, a natural requirement for Spark programs
is to give the same result for any execution on the same data set. We present
PureSpark, an executable formal Haskell specification for Spark aggregate
combinators. Our specification allows us to deduce the precise condition for
deterministic outcomes from Spark aggregation. We report case studies analyzing
deterministic outcomes and correctness of Spark programs. | [
1,
0,
0,
0,
0,
0
] |
Title: Obstructions to a small hyperbolicity in Helly graphs,
Abstract: It is known that for every graph $G$ there exists the smallest Helly graph
$\cal H(G)$ into which $G$ isometrically embeds ($\cal H(G)$ is called the
injective hull of $G$) such that the hyperbolicity of $\cal H(G)$ is equal to
the hyperbolicity of $G$. Motivated by this, we investigate structural
properties of Helly graphs that govern their hyperbolicity and identify three
isometric subgraphs of the King-grid as structural obstructions to a small
hyperbolicity in Helly graphs. | [
1,
0,
0,
0,
0,
0
] |
Title: Calculation of time resolution of the J-PET tomograph using the Kernel Density Estimation,
Abstract: In this paper we estimate the time resolution of the J-PET scanner built from
plastic scintillators. We incorporate the method of signal processing using the
Tikhonov regularization framework and the Kernel Density Estimation method. We
obtain simple, closed-form analytical formulas for time resolutions. The
proposed method is validated using signals registered by means of the single
detection unit of the J-PET tomograph built out from 30 cm long plastic
scintillator strip. It is shown that the experimental and theoretical results,
obtained for the J-PET scanner equipped with vacuum tube photomultipliers, are
consistent. | [
0,
1,
0,
0,
0,
0
] |
Title: Writer Independent Offline Signature Recognition Using Ensemble Learning,
Abstract: The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. In offline (static)
signature verification, the dynamic information of the signature writing
process is lost, and it is difficult to design good feature extractors that can
distinguish genuine signatures and skilled forgeries. This verification task is
even harder in writer independent scenarios which is undeniably fiscal for
realistic cases. In this paper, we have proposed an Ensemble model for offline
writer, independent signature verification task with Deep learning. We have
used two CNNs for feature extraction, after that RGBT for classification &
Stacking to generate final prediction vector. We have done extensive
experiments on various datasets from various sources to maintain a variance in
the dataset. We have achieved the state of the art performance on various
datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Automated Directed Fairness Testing,
Abstract: Fairness is a critical trait in decision making. As machine-learning models
are increasingly being used in sensitive application domains (e.g. education
and employment) for decision making, it is crucial that the decisions computed
by such models are free of unintended bias. But how can we automatically
validate the fairness of arbitrary machine-learning models? For a given
machine-learning model and a set of sensitive input parameters, our AEQUITAS
approach automatically discovers discriminatory inputs that highlight fairness
violation. At the core of AEQUITAS are three novel strategies to employ
probabilistic search over the input space with the objective of uncovering
fairness violation. Our AEQUITAS approach leverages inherent robustness
property in common machine-learning models to design and implement scalable
test generation methodologies. An appealing feature of our generated test
inputs is that they can be systematically added to the training set of the
underlying model and improve its fairness. To this end, we design a fully
automated module that guarantees to improve the fairness of the underlying
model.
We implemented AEQUITAS and we have evaluated it on six state-of-the-art
classifiers, including a classifier that was designed with fairness
constraints. We show that AEQUITAS effectively generates inputs to uncover
fairness violation in all the subject classifiers and systematically improves
the fairness of the respective models using the generated test inputs. In our
evaluation, AEQUITAS generates up to 70% discriminatory inputs (w.r.t. the
total number of inputs generated) and leverages these inputs to improve the
fairness up to 94%. | [
1,
0,
0,
1,
0,
0
] |
Title: Intel MPX Explained: An Empirical Study of Intel MPX and Software-based Bounds Checking Approaches,
Abstract: Memory-safety violations are a prevalent cause of both reliability and
security vulnerabilities in systems software written in unsafe languages like
C/C++. Unfortunately, all the existing software-based solutions to this problem
exhibit high performance overheads preventing them from wide adoption in
production runs. To address this issue, Intel recently released a new ISA
extension - Memory Protection Extensions (Intel MPX), a hardware-assisted
full-stack solution to protect against memory safety violations. In this work,
we perform an exhaustive study of the Intel MPX architecture to understand its
advantages and caveats. We base our study along three dimensions: (a)
performance overheads, (b) security guarantees, and (c) usability issues. To
put our results in perspective, we compare Intel MPX with three prominent
software-based approaches: (1) trip-wire - AddressSanitizer, (2) object-based -
SAFECode, and (3) pointer-based - SoftBound. Our main conclusion is that Intel
MPX is a promising technique that is not yet practical for widespread adoption.
Intel MPX's performance overheads are still high (roughly 50% on average), and
the supporting infrastructure has bugs which may cause compilation or runtime
errors. Moreover, we showcase the design limitations of Intel MPX: it cannot
detect temporal errors, may have false positives and false negatives in
multithreaded code, and its restrictions on memory layout require substantial
code changes for some programs. | [
1,
0,
0,
0,
0,
0
] |
Title: Generating Long-term Trajectories Using Deep Hierarchical Networks,
Abstract: We study the problem of modeling spatiotemporal trajectories over long time
horizons using expert demonstrations. For instance, in sports, agents often
choose action sequences with long-term goals in mind, such as achieving a
certain strategic position. Conventional policy learning approaches, such as
those based on Markov decision processes, generally fail at learning cohesive
long-term behavior in such high-dimensional state spaces, and are only
effective when myopic modeling lead to the desired behavior. The key difficulty
is that conventional approaches are "shallow" models that only learn a single
state-action policy. We instead propose a hierarchical policy class that
automatically reasons about both long-term and short-term goals, which we
instantiate as a hierarchical neural network. We showcase our approach in a
case study on learning to imitate demonstrated basketball trajectories, and
show that it generates significantly more realistic trajectories compared to
non-hierarchical baselines as judged by professional sports analysts. | [
1,
0,
0,
0,
0,
0
] |
Title: The usefulness of Poynting's theorem in magnetic turbulence,
Abstract: We rewrite Poynting's theorem, already used in a previous publication
(Treumann & Baumjohann 2017) to derive relations between the turbulent magnetic
and electric power spectral densities, to make explicit where the mechanical
contributions enter. We then make explicit use of the relativistic
transformation of the turbulent electric fluctuations to obtain expressions
which depend only on the magnetic and velocity fluctuations. Any electric
fluctuations play just an intermediate role. Equations are constructed for the
turbulent conductivity spectrum in Alfvénic and non-Alfvénic turbulence in
extension of the results in the above citation. An observation-based discussion
of their use in application to solar wind turbulence is given. The inertial
range solar wind turbulence exhibits signs of chaos and self-organisation. | [
0,
1,
0,
0,
0,
0
] |
Title: The {\it victory} project v1.0: an efficient parquet equations solver,
Abstract: {\it Victory}, i.e. \underline{vi}enna \underline{c}omputational
\underline{to}ol deposito\underline{ry}, is a collection of numerical tools for
solving the parquet equations for the Hubbard model and similar many body
problems. The parquet formalism is a self-consistent theory at both the single-
and two-particle levels, and can thus describe individual fermions as well as
their collective behavior on equal footing. This is essential for the
understanding of various emergent phases and their transitions in many-body
systems, in particular for cases in which a single-particle description fails.
Our implementation of {\it victory} is in modern Fortran and it fully respects
the structure of various vertex functions in both momentum and Matsubara
frequency space. We found the latter to be crucial for the convergence of the
parquet equations, as well as for the correct determination of various physical
observables. In this release, we thoroughly explain the program structure and
the controlled approximations to efficiently solve the parquet equations, i.e.
the two-level kernel approximation and the high-frequency regulation. | [
0,
1,
0,
0,
0,
0
] |
Title: An In Vitro Vascularized Tumor Platform for Modeling Breast Tumor Stromal Interactions and Characterizing the Subsequent Response,
Abstract: Tumor stromal interactions have been shown to be the driving force behind the
poor prognosis associated with aggressive breast tumors. These interactions,
specifically between tumor and the surrounding ECM, and tumor and vascular
endothelium, promote tumor formation, angiogenesis, and metastasis. In this
study, we develop an in vitro vascularized tumor platform that allows for
investigation of tumor-stromal interactions in three breast tumor derived cell
lines of varying aggressiveness: MDA-IBC3, SUM149, and MDA-MB-231. The platform
recreates key features of breast tumors, including increased vascular
permeability, vessel sprouting, and ECM remodeling. Morphological and
quantitative analysis reveals differential effects from each tumor cell type on
endothelial coverage, permeability, expression of VEGF, and collagen
remodeling. Triple negative tumors, SUM149 and MDA-MB-321, resulted in a
significantly (p<0.05) higher endothelial permeability and decreased
endothelial coverage compared to the control TIME only platform. SUM149/TIME
platforms were 1.3 fold lower (p<0.05), and MDA-MB-231/TIME platforms were 1.5
fold lower (p<0.01) in endothelial coverage compared to the control TIME only
platform. HER2+ MDA-IBC3 tumor cells expressed high levels of VEGF (p<0.01) and
induced vessel sprouting. Vessels sprouting was tracked for 3 weeks and with
increasing time exhibited formation of multiple vessel sprouts that invaded
into the ECM and surrounded clusters of MDA-IBC3 cells. Both IBC cell lines,
SUM149 and MDA-IBC3, resulted in a collagen ECM with significantly greater
porosity with 1.6 and 1.1 fold higher compared to control, p<0.01. The breast
cancer in vitro vascularized platforms introduced in this paper are an
adaptable, high throughout tool for unearthing tumor-stromal mechanisms and
dynamics behind tumor progression and may prove essential in developing
effective targeted therapeutics. | [
0,
0,
0,
0,
1,
0
] |
Title: Testing for Principal Component Directions under Weak Identifiability,
Abstract: We consider the problem of testing, on the basis of a $p$-variate Gaussian
random sample, the null hypothesis ${\cal H}_0: {\pmb \theta}_1= {\pmb
\theta}_1^0$ against the alternative ${\cal H}_1: {\pmb \theta}_1 \neq {\pmb
\theta}_1^0$, where ${\pmb \theta}_1$ is the "first" eigenvector of the
underlying covariance matrix and ${\pmb \theta}_1^0$ is a fixed unit
$p$-vector. In the classical setup where eigenvalues $\lambda_1>\lambda_2\geq
\ldots\geq \lambda_p$ are fixed, the Anderson (1963) likelihood ratio test
(LRT) and the Hallin, Paindaveine and Verdebout (2010) Le Cam optimal test for
this problem are asymptotically equivalent under the null hypothesis, hence
also under sequences of contiguous alternatives. We show that this equivalence
does not survive asymptotic scenarios where
$\lambda_{n1}/\lambda_{n2}=1+O(r_n)$ with $r_n=O(1/\sqrt{n})$. For such
scenarios, the Le Cam optimal test still asymptotically meets the nominal level
constraint, whereas the LRT severely overrejects the null hypothesis.
Consequently, the former test should be favored over the latter one whenever
the two largest sample eigenvalues are close to each other. By relying on the
Le Cam's asymptotic theory of statistical experiments, we study the non-null
and optimality properties of the Le Cam optimal test in the aforementioned
asymptotic scenarios and show that the null robustness of this test is not
obtained at the expense of power. Our asymptotic investigation is extensive in
the sense that it allows $r_n$ to converge to zero at an arbitrary rate. While
we restrict to single-spiked spectra of the form
$\lambda_{n1}>\lambda_{n2}=\ldots=\lambda_{np}$ to make our results as striking
as possible, we extend our results to the more general elliptical case.
Finally, we present an illustrative real data example. | [
0,
0,
1,
1,
0,
0
] |
Title: Locally-adaptive Bayesian nonparametric inference for phylodynamics,
Abstract: Phylodynamics is an area of population genetics that uses genetic sequence
data to estimate past population dynamics. Modern state-of-the-art Bayesian
nonparametric methods for phylodynamics use either change-point models or
Gaussian process priors to recover population size trajectories of unknown
form. Change-point models suffer from computational issues when the number of
change-points is unknown and needs to be estimated. Gaussian process-based
methods lack local adaptivity and cannot accurately recover trajectories that
exhibit features such as abrupt changes in trend or varying levels of
smoothness. We propose a novel, locally-adaptive approach to Bayesian
nonparametric phylodynamic inference that has the flexibility to accommodate a
large class of functional behaviors. Local adaptivity results from modeling the
log-transformed effective population size a priori as a horseshoe Markov random
field, a recently proposed statistical model that blends together the best
properties of the change-point and Gaussian process modeling paradigms. We use
simulated data to assess model performance, and find that our proposed method
results in reduced bias and increased precision when compared to contemporary
methods. We also use our models to reconstruct past changes in genetic
diversity of human hepatitis C virus in Egypt and to estimate population size
changes of ancient and modern steppe bison. These analyses show that our new
method captures features of the population size trajectories that were missed
by the state-of-the-art phylodynamic methods. | [
0,
0,
0,
0,
1,
0
] |
Title: Out of sight out of mind: Perceived physical distance between the observer and someone in pain shapes observer's neural empathic reactions,
Abstract: Social and affective relations may shape empathy to others' affective states.
Previous studies also revealed that people tend to form very different mental
representations of stimuli on the basis of their physical distance. In this
regard, embodied cognition proposes that different physical distances between
individuals activate different interpersonal processing modes, such that close
physical distance tends to activate the interpersonal processing mode typical
of socially and affectively close relationships. In Experiment 1, two groups of
participants were administered a pain decision task involving upright and
inverted face stimuli painfully or neutrally stimulated, and we monitored their
neural empathic reactions by means of event-related potentials (ERPs)
technique. Crucially, participants were presented with face stimuli of one of
two possible sizes in order to manipulate retinal size and perceived physical
distance, roughly corresponding to the close and far portions of social
distance. ERPs modulations compatible with an empathic reaction were observed
only for the group exposed to face stimuli appearing to be at a close social
distance from the participants. This reaction was absent in the group exposed
to smaller stimuli corresponding to face stimuli observed from a far social
distance. In Experiment 2, one different group of participants was engaged in a
match-to-sample task involving the two-size upright face stimuli of Experiment
1 to test whether the modulation of neural empathic reaction observed in
Experiment 1 could be ascribable to differences in the ability to identify
faces of the two different sizes. Results suggested that face stimuli of the
two sizes could be equally identifiable. In line with the Construal Level and
Embodied Simulation theoretical frameworks, we conclude that perceived physical
distance may shape empathy as well as social and affective distance. | [
0,
0,
0,
0,
1,
0
] |
Title: Dynamic Difficulty Adjustment on MOBA Games,
Abstract: This paper addresses the dynamic difficulty adjustment on MOBA games as a way
to improve the player's entertainment. Although MOBA is currently one of the
most played genres around the world, it is known as a game that offer less
autonomy, more challenges and consequently more frustration. Due to these
characteristics, the use of a mechanism that performs the difficulty balance
dynamically seems to be an interesting alternative to minimize and/or avoid
that players experience such frustrations. In this sense, this paper presents a
dynamic difficulty adjustment mechanism for MOBA games. The main idea is to
create a computer controlled opponent that adapts dynamically to the player
performance, trying to offer to the player a better game experience. This is
done by evaluating the performance of the player using a metric based on some
game features and switching the difficulty of the opponent's artificial
intelligence behavior accordingly. Quantitative and qualitative experiments
were performed and the results showed that the system is capable of adapting
dynamically to the opponent's skills. In spite of that, the qualitative
experiments with users showed that the player's expertise has a greater
influence on the perception of the difficulty level and dynamic adaptation. | [
1,
0,
0,
0,
0,
0
] |
Title: On iteration of Cox rings,
Abstract: We characterize all varieties with a torus action of complexity one that
admit iteration of Cox rings. | [
0,
0,
1,
0,
0,
0
] |
Title: Stability Conditions and Lagrangian Cobordisms,
Abstract: In this paper we study the interplay between Lagrangian cobordisms and
stability conditions. We show that any stability condition on the derived
Fukaya category $D\mathcal{F}uk(M)$ of a symplectic manifold $(M,\omega)$
induces a stability condition on the derived Fukaya category of Lagrangian
cobordisms $D\mathcal{F}uk(\mathbb{C} \times M)$. In addition, using stability
conditions, we provide general conditions under which the homomorphism $\Theta:
\Omega_{Lag}(M)\to K_0(D\mathcal{F}uk(M))$, introduced by Biran and Cornea, is
an isomorphism. This yields a better understanding of how stability conditions
affect $\Theta$ and it allows us to elucidate Haug's result, that the
Lagrangian cobordism group of $T^2$ is isomorphic to
$K_0(D\mathcal{F}uk(T^2))$. | [
0,
0,
1,
0,
0,
0
] |
Title: Sentiment Analysis of Citations Using Word2vec,
Abstract: Citation sentiment analysis is an important task in scientific paper
analysis. Existing machine learning techniques for citation sentiment analysis
are focusing on labor-intensive feature engineering, which requires large
annotated corpus. As an automatic feature extraction tool, word2vec has been
successfully applied to sentiment analysis of short texts. In this work, I
conducted empirical research with the question: how well does word2vec work on
the sentiment analysis of citations? The proposed method constructed sentence
vectors (sent2vec) by averaging the word embeddings, which were learned from
Anthology Collections (ACL-Embeddings). I also investigated polarity-specific
word embeddings (PS-Embeddings) for classifying positive and negative
citations. The sentence vectors formed a feature space, to which the examined
citation sentence was mapped to. Those features were input into classifiers
(support vector machines) for supervised classification. Using
10-cross-validation scheme, evaluation was conducted on a set of annotated
citations. The results showed that word embeddings are effective on classifying
positive and negative citations. However, hand-crafted features performed
better for the overall classification. | [
1,
0,
0,
0,
0,
0
] |
Title: Hybrid Clustering based on Content and Connection Structure using Joint Nonnegative Matrix Factorization,
Abstract: We present a hybrid method for latent information discovery on the data sets
containing both text content and connection structure based on constrained low
rank approximation. The new method jointly optimizes the Nonnegative Matrix
Factorization (NMF) objective function for text clustering and the Symmetric
NMF (SymNMF) objective function for graph clustering. We propose an effective
algorithm for the joint NMF objective function, based on a block coordinate
descent (BCD) framework. The proposed hybrid method discovers content
associations via latent connections found using SymNMF. The method can also be
applied with a natural conversion of the problem when a hypergraph formulation
is used or the content is associated with hypergraph edges.
Experimental results show that by simultaneously utilizing both content and
connection structure, our hybrid method produces higher quality clustering
results compared to the other NMF clustering methods that uses content alone
(standard NMF) or connection structure alone (SymNMF). We also present some
interesting applications to several types of real world data such as citation
recommendations of papers. The hybrid method proposed in this paper can also be
applied to general data expressed with both feature space vectors and pairwise
similarities and can be extended to the case with multiple feature spaces or
multiple similarity measures. | [
1,
0,
0,
1,
0,
0
] |
Title: Embedding dimension and codimension of tensor products of algebras over a field,
Abstract: Let k be a field. This paper investigates the embedding dimension and
codimension of Noetherian local rings arising as localizations of tensor
products of k-algebras. We use results and techniques from prime spectra and
dimension theory to establish an analogue of the "special chain theorem" for
the embedding dimension of tensor products, with effective consequence on the
transfer or defect of regularity as exhibited by the (embedding) codimension. | [
0,
0,
1,
0,
0,
0
] |
Title: The Cosmic Axion Spin Precession Experiment (CASPEr): a dark-matter search with nuclear magnetic resonance,
Abstract: The Cosmic Axion Spin Precession Experiment (CASPEr) is a nuclear magnetic
resonance experiment (NMR) seeking to detect axion and axion-like particles
which could make up the dark matter present in the universe. We review the
predicted couplings of axions and axion-like particles with baryonic matter
that enable their detection via NMR. We then describe two measurement schemes
being implemented in CASPEr. The first method, presented in the original CASPEr
proposal, consists of a resonant search via continuous-wave NMR spectroscopy.
This method offers the highest sensitivity for frequencies ranging from a few
Hz to hundreds of MHz, corresponding to masses $ m_{\rm a} \sim
10^{-14}$--$10^{-6}$ eV. Sub-Hz frequencies are typically difficult to probe
with NMR due to the diminishing sensitivity of magnetometers in this region. To
circumvent this limitation, we suggest new detection and data processing
modalities. We describe a non-resonant frequency-modulation detection scheme,
enabling searches from mHz to Hz frequencies ($m_{\rm a} \sim
10^{-17}$--$10^{-14} $ eV), extending the detection bandwidth by three decades. | [
0,
1,
0,
0,
0,
0
] |
Title: On eccentricity version of Laplacian energy of a graph,
Abstract: The energy of a graph G is equal to the sum of absolute values of the
eigenvalues of the adjacency matrix of G, whereas the Laplacian energy of a
graph G is equal to the sum of the absolute value of the difference between the
eigenvalues of the Laplacian matrix of G and average degree of the vertices of
G. Motivated by the work from Sharafdini et al. [R. Sharafdini, H. Panahbar,
Vertex weighted Laplacian graph energy and other topological indices. J. Math.
Nanosci. 2016, 6, 49-57.], in this paper we investigate the eccentricity
version of Laplacian energy of a graph G. | [
1,
0,
1,
0,
0,
0
] |
Title: LARNN: Linear Attention Recurrent Neural Network,
Abstract: The Linear Attention Recurrent Neural Network (LARNN) is a recurrent
attention module derived from the Long Short-Term Memory (LSTM) cell and ideas
from the consciousness Recurrent Neural Network (RNN). Yes, it LARNNs. The
LARNN uses attention on its past cell state values for a limited window size
$k$. The formulas are also derived from the Batch Normalized LSTM (BN-LSTM)
cell and the Transformer Network for its Multi-Head Attention Mechanism. The
Multi-Head Attention Mechanism is used inside the cell such that it can query
its own $k$ past values with the attention window. This has the effect of
augmenting the rank of the tensor with the attention mechanism, such that the
cell can perform complex queries to question its previous inner memories, which
should augment the long short-term effect of the memory. With a clever trick,
the LARNN cell with attention can be easily used inside a loop on the cell
state, just like how any other Recurrent Neural Network (RNN) cell can be
looped linearly through time series. This is due to the fact that its state,
which is looped upon throughout time steps within time series, stores the inner
states in a "first in, first out" queue which contains the $k$ most recent
states and on which it is easily possible to add static positional encoding
when the queue is represented as a tensor. This neural architecture yields
better results than the vanilla LSTM cells. It can obtain results of 91.92% for
the test accuracy, compared to the previously attained 91.65% using vanilla
LSTM cells. Note that this is not to compare to other research, where up to
93.35% is obtained, but costly using 18 LSTM cells rather than with 2 to 3
cells as analyzed here. Finally, an interesting discovery is made, such that
adding activation within the multi-head attention mechanism's linear layers can
yield better results in the context researched hereto. | [
0,
0,
0,
1,
0,
0
] |
Title: Improved nonparametric estimation of the drift in diffusion processes,
Abstract: In this paper, we consider the robust adaptive non parametric estimation
problem for the drift coefficient in diffusion processes. An adaptive model
selection procedure, based on the improved weighted least square estimates, is
proposed. Sharp oracle inequalities for the robust risk have been obtained. | [
0,
0,
1,
1,
0,
0
] |
Title: Energy-efficient Hybrid CMOS-NEMS LIF Neuron Circuit in 28 nm CMOS Process,
Abstract: Designing analog sub-threshold neuromorphic circuits in deep sub-micron
technologies e.g. 28 nm can be a daunting task due to the problem of excessive
leakage current. We propose novel energy-efficient hybrid CMOS-nano
electro-mechanical switches (NEMS) Leaky Integrate and Fire (LIF) neuron and
synapse circuits and investigate the impact of NEM switches on the leakage
power and overall energy consumption. We analyze the performance of
biologically-inspired neuron circuit in terms of leakage power consumption and
present new energy-efficient neural circuits that operate with biologically
plausible firing rates. Our results show the proposed CMOS-NEMS neuron circuit
is, on average, 35% more energy-efficient than its CMOS counterpart with same
complexity in 28 nm process. Moreover, we discuss how NEM switches can be
utilized to further improve the scalability of mixed-signal neuromorphic
circuits. | [
1,
0,
0,
0,
0,
0
] |
Title: Quasar Rain: the Broad Emission Line Region as Condensations in the Warm Accretion Disk Wind,
Abstract: The origin of the broad emission line region (BELR) in quasars and active
galactic nuclei is still unclear. I propose that condensations form in the
warm, radiation pressure driven, accretion disk wind of quasars creating the
BEL clouds and uniting them with the other two manifestations of cool, 10,000
K, gas in quasars, the low ionization phase of the warm absorbers (WAs) and the
clouds causing X-ray eclipses. The cool clouds will condense quickly (days to
years), before the WA outflows reach escape velocity (which takes months to
centuries). Cool clouds form in equilibrium with the warm phase of the wind
because the rapidly varying X-ray quasar continuum changes the force
multiplier, causing pressure waves to move gas into stable locations in
pressure-temperature space. The narrow range of 2-phase equilibrium densities
may explain the scaling of the BELR size with the square root of luminosity,
while the scaling of cloud formation timescales could produce the Baldwin
effect. These dense clouds have force multipliers of order unity and so cannot
be accelerated to escape velocity. They fall back on a dynamical timescale
(months to centuries), producing an inflow that rains down toward the central
black hole. As they soon move at Mach ~40 with respect to the WA outflow, these
'raindrops' will be rapidly destroyed within months. This rain of clouds may
produce the elliptical BELR orbits implied by velocity resolved reverberation
mapping in some objects, and can explain the opening angle and destruction
timescale of the narrow 'cometary' tails of the clouds seen in X-ray eclipse
observations. Some consequences and challenges of this 'quasar rain' model are
presented along with several avenues for theoretical investigation. | [
0,
1,
0,
0,
0,
0
] |
Title: Consistent polynomial-time unseeded graph matching for Lipschitz graphons,
Abstract: We propose a consistent polynomial-time method for the unseeded node matching
problem for networks with smooth underlying structures. Despite widely
conjectured by the research community that the structured graph matching
problem to be significantly easier than its worst case counterpart, well-known
to be NP-hard, the statistical version of the problem has stood a challenge
that resisted any solution both provable and polynomial-time. The closest
existing work requires quasi-polynomial time. Our method is based on the latest
advances in graphon estimation techniques and analysis on the concentration of
empirical Wasserstein distances. Its core is a simple yet unconventional
sampling-and-matching scheme that reduces the problem from unseeded to seeded.
Our method allows flexible efficiencies, is convenient to analyze and
potentially can be extended to more general settings. Our work enables a rich
variety of subsequent estimations and inferences. | [
0,
0,
0,
1,
0,
0
] |
Title: Photoinduced filling of near nodal gap in Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$,
Abstract: We report time and angle resolved spectroscopic measurements in optimally
doped Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$. The spectral function is monitored as
a function of temperature, photoexcitation density and delay time from the pump
pulse. According to our data, the superconducting gap becomes slightly stiffer
when moving off the nodal direction. The nodal quasiparticles develop a faster
dynamics when pumping the superconductor with a fluence that is large enough to
induce the total collapse of the gap. We discuss the observed relaxation in
terms of a dynamical reformation of Cooper pairs. | [
0,
1,
0,
0,
0,
0
] |
Title: Globally convergent Jacobi-type algorithms for simultaneous orthogonal symmetric tensor diagonalization,
Abstract: In this paper, we consider a family of Jacobi-type algorithms for
simultaneous orthogonal diagonalization problem of symmetric tensors. For the
Jacobi-based algorithm of [SIAM J. Matrix Anal. Appl., 2(34):651--672, 2013],
we prove its global convergence for simultaneous orthogonal diagonalization of
symmetric matrices and 3rd-order tensors. We also propose a new Jacobi-based
algorithm in the general setting and prove its global convergence for
sufficiently smooth functions. | [
1,
0,
1,
0,
0,
0
] |
Title: Representing the Deligne-Hinich-Getzler $\infty$-groupoid,
Abstract: The goal of the present paper is to introduce a smaller, but equivalent
version of the Deligne-Hinich-Getzler $\infty$-groupoid associated to a
homotopy Lie algebra. In the case of differential graded Lie algebras, we
represent it by a universal cosimplicial object. | [
0,
0,
1,
0,
0,
0
] |
Title: Sentence-level quality estimation by predicting HTER as a multi-component metric,
Abstract: This submission investigates alternative machine learning models for
predicting the HTER score on the sentence level. Instead of directly predicting
the HTER score, we suggest a model that jointly predicts the amount of the 4
distinct post-editing operations, which are then used to calculate the HTER
score. This also gives the possibility to correct invalid (e.g. negative)
predicted values prior to the calculation of the HTER score. Without any
feature exploration, a multi-layer perceptron with 4 outputs yields small but
significant improvements over the baseline. | [
1,
0,
0,
0,
0,
0
] |
Title: Machine Assisted Analysis of Vowel Length Contrasts in Wolof,
Abstract: Growing digital archives and improving algorithms for automatic analysis of
text and speech create new research opportunities for fundamental research in
phonetics. Such empirical approaches allow statistical evaluation of a much
larger set of hypothesis about phonetic variation and its conditioning factors
(among them geographical / dialectal variants). This paper illustrates this
vision and proposes to challenge automatic methods for the analysis of a not
easily observable phenomenon: vowel length contrast. We focus on Wolof, an
under-resourced language from Sub-Saharan Africa. In particular, we propose
multiple features to make a fine evaluation of the degree of length contrast
under different factors such as: read vs semi spontaneous speech ; standard vs
dialectal Wolof. Our measures made fully automatically on more than 20k vowel
tokens show that our proposed features can highlight different degrees of
contrast for each vowel considered. We notably show that contrast is weaker in
semi-spontaneous speech and in a non standard semi-spontaneous dialect. | [
1,
0,
0,
0,
0,
0
] |
Title: Precision of Evaluation Methods in White Light Interferometry: Correlogram Correlation Method,
Abstract: In this paper we promote a method for the evaluation of a surface topography
which we call the correlogram correlation method. Employing a theoretical
analysis as well as numerical simulations the method is proven to be the most
accurate among available evaluation algorithms in the common case of
uncorrelated noise. Examples illustrate the superiority of the correlogram
correlation method over the common envelope and phase methods. | [
0,
1,
0,
0,
0,
0
] |
Title: Non-wetting drops at liquid interfaces: From liquid marbles to Leidenfrost drops,
Abstract: We consider the flotation of deformable, non-wetting drops on a liquid
interface. We consider the deflection of both the liquid interface and the
droplet itself in response to the buoyancy forces, density difference and the
various surface tensions within the system. Our results suggest new insight
into a range of phenomena in which such drops occur, including Leidenfrost
droplets and floating liquid marbles. In particular, we show that the floating
state of liquid marbles is very sensitive to the tension of the
particle-covered interface and suggest that this sensitivity may make such
experiments a useful assay of the properties of these complex interfaces. | [
0,
1,
0,
0,
0,
0
] |
Title: Kinetics of the Crystalline Nuclei Growth in Glassy Systems,
Abstract: In this work, we study the crystalline nuclei growth in glassy systems
focusing primarily on the early stages of the process, at which the size of a
growing nucleus is still comparable with the critical size. On the basis of
molecular dynamics simulation results for two crystallizing glassy systems, we
evaluate the growth laws of the crystalline nuclei and the parameters of the
growth kinetics at the temperatures corresponding to deep supercoolings;
herein, the statistical treatment of the simulation results is done within the
mean-first-passage-time method. It is found for the considered systems at
different temperatures that the crystal growth laws rescaled onto the waiting
times of the critically-sized nucleus follow the unified dependence, that can
simplify significantly theoretical description of the post-nucleation growth of
crystalline nuclei. The evaluated size-dependent growth rates are characterized
by transition to the steady-state growth regime, which depends on the
temperature and occurs in the glassy systems when the size of a growing nucleus
becomes two-three times larger than a critical size. It is suggested to
consider the temperature dependencies of the crystal growth rate
characteristics by using the reduced temperature scale $\widetilde{T}$. Thus,
it is revealed that the scaled values of the crystal growth rate
characteristics (namely, the steady-state growth rate and the attachment rate
for the critically-sized nucleus) as functions of the reduced temperature
$\widetilde{T}$ for glassy systems follow the unified power-law dependencies.
This finding is supported by available simulation results; the correspondence
with the experimental data for the crystal growth rate in glassy systems at the
temperatures near the glass transition is also discussed. | [
0,
1,
0,
0,
0,
0
] |
Title: Electron Cloud Trapping In Recycler Combined Function Dipole Magnets,
Abstract: Electron cloud can lead to a fast instability in intense proton and positron
beams in circular accelerators. In the Fermilab Recycler the electron cloud is
confined within its combined function magnets. We show that the field of
combined function magnets traps the electron cloud, present the results of
analytical estimates of trapping, and compare them to numerical simulations of
electron cloud formation. The electron cloud is located at the beam center and
up to 1% of the particles can be trapped by the magnetic field. Since the
process of electron cloud build-up is exponential, once trapped this amount of
electrons significantly increases the density of the cloud on the next
revolution. In a Recycler combined function dipole this multi-turn accumulation
allows the electron cloud reaching final intensities orders of magnitude
greater than in a pure dipole. The multi-turn build-up can be stopped by
injection of a clearing bunch of $10^{10}$ p at any position in the ring. | [
0,
1,
0,
0,
0,
0
] |
Title: Causal Inference Under Network Interference: A Framework for Experiments on Social Networks,
Abstract: No man is an island, as individuals interact and influence one another daily
in our society. When social influence takes place in experiments on a
population of interconnected individuals, the treatment on a unit may affect
the outcomes of other units, a phenomenon known as interference. This thesis
develops a causal framework and inference methodology for experiments where
interference takes place on a network of influence (i.e. network interference).
In this framework, the network potential outcomes serve as the key quantity and
flexible building blocks for causal estimands that represent a variety of
primary, peer, and total treatment effects. These causal estimands are
estimated via principled Bayesian imputation of missing outcomes. The theory on
the unconfoundedness assumptions leading to simplified imputation highlights
the importance of including relevant network covariates in the potential
outcome model. Additionally, experimental designs that result in balanced
covariates and sizes across treatment exposure groups further improve the
causal estimate, especially by mitigating potential outcome model
mis-specification. The true potential outcome model is not typically known in
real-world experiments, so the best practice is to account for interference and
confounding network covariates through both balanced designs and model-based
imputation. A full factorial simulated experiment is formulated to demonstrate
this principle by comparing performance across different randomization schemes
during the design phase and estimators during the analysis phase, under varying
network topology and true potential outcome models. Overall, this thesis
asserts that interference is not just a nuisance for analysis but rather an
opportunity for quantifying and leveraging peer effects in real-world
experiments. | [
0,
0,
1,
1,
0,
0
] |
Title: Polynomial configurations in sets of positive upper density over local fields,
Abstract: Let $F(x)=(f_1(x), \dots, f_m(x))$ be such that $1, f_1, \dots, f_m$ are
linearly independent polynomials with real coefficients. Based on ideas of
Bachoc, DeCorte, Oliveira and Vallentin in combination with estimating certain
oscillatory integrals with polynomial phase we will show that the independence
ratio of the Cayley graph of $\mathbb{R}^m$ with respect to the portion of the
graph of $F$ defined by $a\leq \log |s| \leq T$ is at most $O(1/(T-a))$. We
conclude that if $I \subseteq \mathbb{R}^m$ has positive upper density, then
the difference set $I-I$ contains vectors of the form $F(s)$ for an unbounded
set of values $s \in \mathbb{R}$. It follows that the Borel chromatic number of
the Cayley graph of $\mathbb{R}^m$ with respect to the set $\{ \pm F(s): s \in
\mathbb{R} \}$ is infinite. Analogous results are also proven when $\mathbb{R}$
is replaced by the field of $p$-adic numbers $\mathbb{Q}_p$. At the end, we
will also the existence of real analytic functions $f_1, \dots, f_m$, for which
the analogous statements no longer hold. | [
0,
0,
1,
0,
0,
0
] |
Title: Deterministic Genericity for Polynomial Ideals,
Abstract: We consider several notions of genericity appearing in algebraic geometry and
commutative algebra. Special emphasis is put on various stability notions which
are defined in a combinatorial manner and for which a number of equivalent
algebraic characterisations are provided. It is shown that in characteristic
zero the corresponding generic positions can be obtained with a simple
deterministic algorithm. In positive characteristic, only adapted stable
positions are reachable except for quasi-stability which is obtainable in any
characteristic. | [
1,
0,
1,
0,
0,
0
] |
Title: Precise Pointing of Cubesat Telescopes: Comparison Between Heat and Light Induced Attitude Control Methods,
Abstract: CubeSats are emerging as low-cost tools to perform astronomy, exoplanet
searches and earth observation. These satellites can target an object for
science observation for weeks on end. This is typically not possible on larger
missions where usage time is shared. The problem of designing an attitude
control system for CubeSat telescopes is very challenging because current
choice of actuators such as reaction-wheels and magnetorquers can induce jitter
on the spacecraft due to moving mechanical parts and due to external
disturbances. These telescopes may contain cryo-pumps and servos that introduce
additional vibrations. A better solution is required. In our paper, we analyze
the feasibility of utilizing solar radiation pressure (SRP) and radiometric
force to achieve precise attitude control. Our studies show radiometric
actuators to be a viable method to achieve precise pointing. The device uses 8
thin vanes of different temperatures placed in a near-vacuum chamber. These
chambers contain trace quantities of lightweight, inert gasses like argon. The
temperature gradient across the vanes causes the gas molecules to strike the
vanes differently and thus inducing a force. By controlling these forces, it's
possible to produce a torque to precisely point or spin a spacecraft. We
present a conceptual design of a CubeSat that is equipped with these actuators.
We then analyze the potential slew maneuver and slew rates possible with these
actuators by simulating their performance. Our analytical and simulation
results point towards a promising pathway for laboratory testing of this
technology and demonstration of this technology in space. | [
0,
1,
0,
0,
0,
0
] |
Title: Cyclicity in weighted $\ell^p$ spaces,
Abstract: We study the cyclicity in weighted $\ell^p(\mathbb{Z})$ spaces. For $p \geq
1$ and $\beta \geq 0$, let $\ell^p\_\beta(\mathbb{Z})$ be the space of
sequences $u=(u\_n)\_{n\in \mathbb{Z}}$ such that $(u\_n |n|^{\beta})\in
\ell^p(\mathbb{Z}) $. We obtain both necessary conditions and sufficient
conditions for $u$ to be cyclic in $\ell^p\_\beta(\mathbb{Z})$, in other words,
for $ \{(u\_{n+k})\_{n \in \mathbb{Z}},~ k \in \mathbb{Z} \}$ to span a dense
subspace of $\ell^p\_\beta(\mathbb{Z})$. The conditions are given in terms of
the Hausdorff dimension and the capacity of the zero set of the Fourier
transform of $u$. | [
0,
0,
1,
0,
0,
0
] |
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