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Title: Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction,
Abstract: Machine understanding of complex images is a key goal of artificial
intelligence. One challenge underlying this task is that visual scenes contain
multiple inter-related objects, and that global context plays an important role
in interpreting the scene. A natural modeling framework for capturing such
effects is structured prediction, which optimizes over complex labels, while
modeling within-label interactions. However, it is unclear what principles
should guide the design of a structured prediction model that utilizes the
power of deep learning components. Here we propose a design principle for such
architectures that follows from a natural requirement of permutation
invariance. We prove a necessary and sufficient characterization for
architectures that follow this invariance, and discuss its implication on model
design. Finally, we show that the resulting model achieves new state of the art
results on the Visual Genome scene graph labeling benchmark, outperforming all
recent approaches. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science"
] |
Title: Identifying Similarities in Epileptic Patients for Drug Resistance Prediction,
Abstract: Currently, approximately 30% of epileptic patients treated with antiepileptic
drugs (AEDs) remain resistant to treatment (known as refractory patients). This
project seeks to understand the underlying similarities in refractory patients
vs. other epileptic patients, identify features contributing to drug resistance
across underlying phenotypes for refractory patients, and develop predictive
models for drug resistance in epileptic patients. In this study, epileptic
patient data was examined to attempt to observe discernable similarities or
differences in refractory patients (case) and other non-refractory patients
(control) to map underlying mechanisms in causality. For the first part of the
study, unsupervised algorithms such as Kmeans, Spectral Clustering, and
Gaussian Mixture Models were used to examine patient features projected into a
lower dimensional space. Results from this study showed a high degree of
non-linearity in the underlying feature space. For the second part of this
study, classification algorithms such as Logistic Regression, Gradient Boosted
Decision Trees, and SVMs, were tested on the reduced-dimensionality features,
with accuracy results of 0.83(+/-0.3) testing using 7 fold cross validation.
Observations of test results indicate using a radial basis function kernel PCA
to reduce features ingested by a Gradient Boosted Decision Tree Ensemble lead
to gains in improved accuracy in mapping a binary decision to highly non-linear
features collected from epileptic patients. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics",
"Quantitative Biology"
] |
Title: Designing the color of hot-dip galvanized steel sheet through destructive light interference using a Zn-Ti liquid metallic bath,
Abstract: The color of hot-dip galvanized steel sheet was adjusted in a reproducible
way using a liquid Zn-Ti metallic bath, air atmosphere, and controlling the
bath temperature as the only experimental parameter. Coloring was found only
for sample s cooled in air and dipped into Ti-containing liquid Zn. For samples
dipped into a 0.15 wt pct Ti-containing Zn bath, the color remained metallic
(gray) below a 792 K (519 C) bath temperature; it was yellow at 814 K, violet
at 847 K, and blue at 873 K. With the increasing bath temperature, the
thickness of the adhered Zn-Ti layer gradually decreased from 52 to 32
micrometers, while the thickness of the outer TiO2 layer gradually increased
from 24 to 69 nm. Due to small Al contamination of the Zn bath, a thin (around
2 nm) alumina-rich layer is found between the outer TiO2 layer and the inner
macroscopic Zn layer. It is proven that the color change was governed by the
formation of thin outer TiO2 layer; different colors appear depending on the
thickness of this layer, mostly due to the destructive interference of visible
light on this transparent nano-layer. A complex model was built to explain the
results using known relationships of chemical thermodynamics, adhesion, heat
flow, kinetics of chemical reactions, diffusion, and optics. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Eva-CiM: A System-Level Energy Evaluation Framework for Computing-in-Memory Architectures,
Abstract: Computing-in-Memory (CiM) architectures aim to reduce costly data transfers
by performing arithmetic and logic operations in memory and hence relieve the
pressure due to the memory wall. However, determining whether a given workload
can really benefit from CiM, which memory hierarchy and what device technology
should be adopted by a CiM architecture requires in-depth study that is not
only time consuming but also demands significant expertise in architectures and
compilers. This paper presents an energy evaluation framework, Eva-CiM, for
systems based on CiM architectures. Eva-CiM encompasses a multi-level (from
device to architecture) comprehensive tool chain by leveraging existing
modeling and simulation tools such as GEM5, McPAT [2] and DESTINY [3]. To
support high-confidence prediction, rapid design space exploration and ease of
use, Eva-CiM introduces several novel modeling/analysis approaches including
models for capturing memory access and dependency-aware ISA traces, and for
quantifying interactions between the host CPU and CiM modules. Eva-CiM can
readily produce energy estimates of the entire system for a given program, a
processor architecture, and the CiM array and technology specifications.
Eva-CiM is validated by comparing with DESTINY [3] and [4], and enables
findings including practical contributions from CiM-supported accesses,
CiM-sensitive benchmarking as well as the pros and cons of increased memory
size for CiM. Eva-CiM also enables exploration over different configurations
and device technologies, showing 1.3-6.0X energy improvement for SRAM and
2.0-7.9X for FeFET-RAM, respectively. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Robust estimation of tree structured Gaussian Graphical Model,
Abstract: Consider jointly Gaussian random variables whose conditional independence
structure is specified by a graphical model. If we observe realizations of the
variables, we can compute the covariance matrix, and it is well known that the
support of the inverse covariance matrix corresponds to the edges of the
graphical model. Instead, suppose we only have noisy observations. If the noise
at each node is independent, we can compute the sum of the covariance matrix
and an unknown diagonal. The inverse of this sum is (in general) dense. We ask:
can the original independence structure be recovered? We address this question
for tree structured graphical models. We prove that this problem is
unidentifiable, but show that this unidentifiability is limited to a small
class of candidate trees. We further present additional constraints under which
the problem is identifiable. Finally, we provide an O(n^3) algorithm to find
this equivalence class of trees. | [
1,
0,
0,
1,
0,
0
] | [
"Statistics",
"Mathematics",
"Computer Science"
] |
Title: Monochromatic metrics are generalized Berwald,
Abstract: We show that monochromatic Finsler metrics, i.e., Finsler metrics such that
each two tangent spaces are isomorphic as normed spaces, are generalized
Berwald metrics, i.e., there exists an affine connection, possibly with
torsion, that preserves the Finsler function | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: CTCF Degradation Causes Increased Usage of Upstream Exons in Mouse Embryonic Stem Cells,
Abstract: Transcriptional repressor CTCF is an important regulator of chromatin 3D
structure, facilitating the formation of topologically associating domains
(TADs). However, its direct effects on gene regulation is less well understood.
Here, we utilize previously published ChIP-seq and RNA-seq data to investigate
the effects of CTCF on alternative splicing of genes with CTCF sites. We
compared the amount of RNA-seq signals in exons upstream and downstream of
binding sites following auxin-induced degradation of CTCF in mouse embryonic
stem cells. We found that changes in gene expression following CTCF depletion
were significant, with a general increase in the presence of upstream exons. We
infer that a possible mechanism by which CTCF binding contributes to
alternative splicing is by causing pauses in the transcription mechanism during
which splicing elements are able to concurrently act on upstream exons already
transcribed into RNA. | [
0,
0,
0,
0,
1,
0
] | [
"Quantitative Biology"
] |
Title: The stability of tightly-packed, evenly-spaced systems of Earth-mass planets orbiting a Sun-like star,
Abstract: Many of the multi-planet systems discovered to date have been notable for
their compactness, with neighbouring planets closer together than any in the
Solar System. Interestingly, planet-hosting stars have a wide range of ages,
suggesting that such compact systems can survive for extended periods of time.
We have used numerical simulations to investigate how quickly systems go
unstable in relation to the spacing between planets, focusing on hypothetical
systems of Earth-mass planets on evenly-spaced orbits (in mutual Hill radii).
In general, the further apart the planets are initially, the longer it takes
for a pair of planets to undergo a close encounter. We recover the results of
previous studies, showing a linear trend in the initial planet spacing between
3 and 8 mutual Hill radii and the logarithm of the stability time.
Investigating thousands of simulations with spacings up to 13 mutual Hill radii
reveals distinct modulations superimposed on this relationship in the vicinity
of first and second-order mean motion resonances of adjacent and next-adjacent
planets. We discuss the impact of this structure and the implications on the
stability of compact multi-planet systems. Applying the outcomes of our
simulations, we show that isolated systems of up to five Earth-mass planets can
fit in the habitable zone of a Sun-like star without close encounters for at
least $10^9$ orbits. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Full-Duplex Cooperative Cognitive Radio Networks with Wireless Energy Harvesting,
Abstract: This paper proposes and analyzes a new full-duplex (FD) cooperative cognitive
radio network with wireless energy harvesting (EH). We consider that the
secondary receiver is equipped with a FD radio and acts as a FD hybrid access
point (HAP), which aims to collect information from its associated EH secondary
transmitter (ST) and relay the signals. The ST is assumed to be equipped with
an EH unit and a rechargeable battery such that it can harvest and accumulate
energy from radio frequency (RF) signals transmitted by the primary transmitter
(PT) and the HAP. We develop a novel cooperative spectrum sharing (CSS)
protocol for the considered system. In the proposed protocol, thanks to its FD
capability, the HAP can receive the PT's signals and transmit energy-bearing
signals to charge the ST simultaneously, or forward the PT's signals and
receive the ST's signals at the same time. We derive analytical expressions for
the achievable throughput of both primary and secondary links by characterizing
the dynamic charging/discharging behaviors of the ST battery as a finite-state
Markov chain. We present numerical results to validate our theoretical analysis
and demonstrate the merits of the proposed protocol over its non-cooperative
counterpart. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Analysis of luminosity distributions of strong lensing galaxies: subtraction of diffuse lensed signal,
Abstract: Strong gravitational lensing gives access to the total mass distribution of
galaxies. It can unveil a great deal of information about the lenses dark
matter content when combined with the study of the lenses light profile.
However, gravitational lensing galaxies, by definition, appear surrounded by
point-like and diffuse lensed signal that is irrelevant to the lens flux.
Therefore, the observer is most often restricted to studying the innermost
portions of the galaxy, where classical fitting methods show some
instabilities. We aim at subtracting that lensed signal and at characterising
some lenses light profile by computing their shape parameters. Our objective is
to evaluate the total integrated flux in an aperture the size of the Einstein
ring in order to obtain a robust estimate of the quantity of ordinary matter in
each system. We are expanding the work we started in a previous paper that
consisted in subtracting point-like lensed images and in independently
measuring each shape parameter. We improve it by designing a subtraction of the
diffuse lensed signal, based only on one simple hypothesis of symmetry. This
extra step improves our study of the shape parameters and we refine it even
more by upgrading our half-light radius measurement. We also calculate the
impact of our specific image processing on the error bars. The diffuse lensed
signal subtraction makes it possible to study a larger portion of relevant
galactic flux, as the radius of the fitting region increases by on average
17\%. We retrieve new half-light radii values that are on average 11\% smaller
than in our previous work, although the uncertainties overlap in most cases.
This shows that not taking the diffuse lensed signal into account may lead to a
significant overestimate of the half-light radius. We are also able to measure
the flux within the Einstein radius and to compute secure error bars to all of
our results. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Astrophysics"
] |
Title: Non Relativistic Limit of Integrable QFT with fermionic excitations,
Abstract: The aim of this paper is to investigate the non-relativistic limit of
integrable quantum field theories with fermionic fields, such as the O(N)
Gross-Neveu model, the supersymmetric Sinh-Gordon and non-linear sigma models.
The non-relativistic limit of these theories is implemented by a double scaling
limit which consists of sending the speed of light c to infinity and rescaling
at the same time the relevant coupling constant of the model in such a way to
have finite energy excitations. For the general purpose of mapping the space of
continuous non-relativistic integrable models, this paper completes and
integrates the analysis done in Ref.[1] on the non-relativistic limit of purely
bosonic theories. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: Spectral edge behavior for eventually monotone Jacobi and Verblunsky coefficients,
Abstract: We consider Jacobi matrices with eventually increasing sequences of diagonal
and off-diagonal Jacobi parameters. We describe the asymptotic behavior of the
subordinate solution at the top of the essential spectrum, and the asymptotic
behavior of the spectral density at the top of the essential spectrum.
In particular, allowing on both diagonal and off-diagonal Jacobi parameters
perturbations of the free case of the form $- \sum_{j=1}^J c_j n^{-\tau_j} +
o(n^{-\tau_1-1})$ with $0 < \tau_1 < \tau_2 < \dots < \tau_J$ and $c_1>0$, we
find the asymptotic behavior of the $\log$ of spectral density to order
$O(\log(2-x))$ as $x$ approaches $2$.
Apart from its intrinsic interest, the above results also allow us to
describe the asymptotics of the spectral density for orthogonal polynomials on
the unit circle with real-valued Verblunsky coefficients of the same form. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Web Video in Numbers - An Analysis of Web-Video Metadata,
Abstract: Web video is often used as a source of data in various fields of study. While
specialized subsets of web video, mainly earmarked for dedicated purposes, are
often analyzed in detail, there is little information available about the
properties of web video as a whole. In this paper we present insights gained
from the analysis of the metadata associated with more than 120 million videos
harvested from two popular web video platforms, vimeo and YouTube, in 2016 and
compare their properties with the ones found in commonly used video
collections. This comparison has revealed that existing collections do not (or
no longer) properly reflect the properties of web video "in the wild". | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Haantjes Algebras and Diagonalization,
Abstract: We propose the notion of Haantjes algebra, which consists of an assignment of
a family of fields of operators over a differentiable manifold, with vanishing
Haantjes torsion and satisfying suitable compatibility conditions among each
others. Haantjes algebras naturally generalize several known interesting
geometric structures, arising in Riemannian geometry and in the theory of
integrable systems. At the same time, they play a crucial role in the theory of
diagonalization of operators on differentiable manifolds.
Whenever the elements of an Haantjes algebra are semisimple and commute, we
shall prove that there exists a set of local coordinates where all operators
can be diagonalized simultaneously. Moreover, in the non-semisimple case, they
acquire simultaneously a block-diagonal form. | [
0,
1,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Multipair Massive MIMO Relaying Systems with One-Bit ADCs and DACs,
Abstract: This paper considers a multipair amplify-and-forward massive MIMO relaying
system with one-bit ADCs and one-bit DACs at the relay. The channel state
information is estimated via pilot training, and then utilized by the relay to
perform simple maximum-ratio combining/maximum-ratio transmission processing.
Leveraging on the Bussgang decomposition, an exact achievable rate is derived
for the system with correlated quantization noise. Based on this, a closed-form
asymptotic approximation for the achievable rate is presented, thereby enabling
efficient evaluation of the impact of key parameters on the system performance.
Furthermore, power scaling laws are characterized to study the potential energy
efficiency associated with deploying massive one-bit antenna arrays at the
relay. In addition, a power allocation strategy is designed to compensate for
the rate degradation caused by the coarse quantization. Our results suggest
that the quality of the channel estimates depends on the specific orthogonal
pilot sequences that are used, contrary to unquantized systems where any set of
orthogonal pilot sequences gives the same result. Moreover, the sum rate gap
between the double-quantized relay system and an ideal non-quantized system is
a moderate factor of $4/\pi^2$ in the low power regime. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Bayesian Methods for Exoplanet Science,
Abstract: Exoplanet research is carried out at the limits of the capabilities of
current telescopes and instruments. The studied signals are weak, and often
embedded in complex systematics from instrumental, telluric, and astrophysical
sources. Combining repeated observations of periodic events, simultaneous
observations with multiple telescopes, different observation techniques, and
existing information from theory and prior research can help to disentangle the
systematics from the planetary signals, and offers synergistic advantages over
analysing observations separately. Bayesian inference provides a
self-consistent statistical framework that addresses both the necessity for
complex systematics models, and the need to combine prior information and
heterogeneous observations. This chapter offers a brief introduction to
Bayesian inference in the context of exoplanet research, with focus on time
series analysis, and finishes with an overview of a set of freely available
programming libraries. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Statistics"
] |
Title: Feature importance scores and lossless feature pruning using Banzhaf power indices,
Abstract: Understanding the influence of features in machine learning is crucial to
interpreting models and selecting the best features for classification. In this
work we propose the use of principles from coalitional game theory to reason
about importance of features. In particular, we propose the use of the Banzhaf
power index as a measure of influence of features on the outcome of a
classifier. We show that features having Banzhaf power index of zero can be
losslessly pruned without damage to classifier accuracy. Computing the power
indices does not require having access to data samples. However, if samples are
available, the indices can be empirically estimated. We compute Banzhaf power
indices for a neural network classifier on real-life data, and compare the
results with gradient-based feature saliency, and coefficients of a logistic
regression model with $L_1$ regularization. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Critical pairing fluctuations in the normal state of a superconductor: pseudogap and quasi-particle damping,
Abstract: We study the effect of critical pairing fluctuations on the electronic
properties in the normal state of a clean superconductor in three dimensions.
Using a functional renormalization group approach to take the non-Gaussian
nature of critical fluctuations into account, we show microscopically that in
the BCS regime, where the inverse coherence length is much smaller than the
Fermi wavevector, critical pairing fluctuations give rise to a non-analytic
contribution to the quasi-particle damping of order $ T_c \sqrt{Gi} \ln ( 80 /
Gi )$, where the Ginzburg-Levanyuk number $Gi$ is a dimensionless measure for
the width of the critical region. As a consequence, there is a temperature
window above $T_c$ where the quasiparticle damping due to critical pairing
fluctuations can be larger than the usual $T^2$-Fermi liquid damping due to
non-critical scattering processes. On the other hand, in the strong coupling
regime where $Gi$ is of order unity, we find that the quasiparticle damping due
to critical pairing fluctuations is proportional to the temperature. Moreover,
we show that in the vicinity of the critical temperature $T_c$ the electronic
density of states exhibits a fluctuation-induced pseudogap. We also use
functional renormalization group methods to derive and classify various types
of processes induced by the pairing interaction in Fermi systems close to the
superconducting instability. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints,
Abstract: In this paper, we propose a low-rank coordinate descent approach to
structured semidefinite programming with diagonal constraints. The approach,
which we call the Mixing method, is extremely simple to implement, has no free
parameters, and typically attains an order of magnitude or better improvement
in optimization performance over the current state of the art. We show that the
method is strictly decreasing, converges to a critical point, and further that
for sufficient rank all non-optimal critical points are unstable. Moreover, we
prove that with a step size, the Mixing method converges to the global optimum
of the semidefinite program almost surely in a locally linear rate under random
initialization. This is the first low-rank semidefinite programming method that
has been shown to achieve a global optimum on the spherical manifold without
assumption. We apply our algorithm to two related domains: solving the maximum
cut semidefinite relaxation, and solving a maximum satisfiability relaxation
(we also briefly consider additional applications such as learning word
embeddings). In all settings, we demonstrate substantial improvement over the
existing state of the art along various dimensions, and in total, this work
expands the scope and scale of problems that can be solved using semidefinite
programming methods. | [
1,
0,
1,
1,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Stochastic graph Voronoi tessellation reveals community structure,
Abstract: Given a network, the statistical ensemble of its graph-Voronoi diagrams with
randomly chosen cell centers exhibits properties convertible into information
on the network's large scale structures. We define a node-pair level measure
called {\it Voronoi cohesion} which describes the probability for sharing the
same Voronoi cell, when randomly choosing $g$ centers in the network. This
measure provides information based on the global context (the network in its
entirety) a type of information that is not carried by other similarity
measures. We explore the mathematical background of this phenomenon and several
of its potential applications. A special focus is laid on the possibilities and
limitations pertaining to the exploitation of the phenomenon for community
detection purposes. | [
1,
1,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: Recursion for the smallest eigenvalue density of $β$-Wishart-Laguerre ensemble,
Abstract: The statistics of the smallest eigenvalue of Wishart-Laguerre ensemble is
important from several perspectives. The smallest eigenvalue density is
typically expressible in terms of determinants or Pfaffians. These results are
of utmost significance in understanding the spectral behavior of
Wishart-Laguerre ensembles and, among other things, unveil the underlying
universality aspects in the asymptotic limits. However, obtaining exact and
explicit expressions by expanding determinants or Pfaffians becomes impractical
if large dimension matrices are involved. For the real matrices ($\beta=1$)
Edelman has provided an efficient recurrence scheme to work out exact and
explicit results for the smallest eigenvalue density which does not involve
determinants or matrices. Very recently, an analogous recurrence scheme has
been obtained for the complex matrices ($\beta=2$). In the present work we
extend this to $\beta$-Wishart-Laguerre ensembles for the case when exponent
$\alpha$ in the associated Laguerre weight function, $\lambda^\alpha
e^{-\beta\lambda/2}$, is a non-negative integer, while $\beta$ is positive
real. This also gives access to the smallest eigenvalue density of fixed trace
$\beta$-Wishart-Laguerre ensemble, as well as moments for both cases. Moreover,
comparison with earlier results for the smallest eigenvalue density in terms of
certain hypergeometric function of matrix argument results in an effective way
of evaluating these explicitly. Exact evaluations for large values of $n$ (the
matrix dimension) and $\alpha$ also enable us to compare with Tracy-Widom
density and large deviation results of Katzav and Castillo. We also use our
result to obtain the density of the largest of the proper delay times which are
eigenvalues of the Wigner-Smith matrix and are relevant to the problem of
quantum chaotic scattering. | [
0,
0,
0,
1,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds,
Abstract: We propose DeepMapping, a novel registration framework using deep neural
networks (DNNs) as auxiliary functions to align multiple point clouds from
scratch to a globally consistent frame. We use DNNs to model the highly
non-convex mapping process that traditionally involves hand-crafted data
association, sensor pose initialization, and global refinement. Our key novelty
is that properly defining unsupervised losses to "train" these DNNs through
back-propagation is equivalent to solving the underlying registration problem,
yet enables fewer dependencies on good initialization as required by ICP. Our
framework contains two DNNs: a localization network that estimates the poses
for input point clouds, and a map network that models the scene structure by
estimating the occupancy status of global coordinates. This allows us to
convert the registration problem to a binary occupancy classification, which
can be solved efficiently using gradient-based optimization. We further show
that DeepMapping can be readily extended to address the problem of Lidar SLAM
by imposing geometric constraints between consecutive point clouds. Experiments
are conducted on both simulated and real datasets. Qualitative and quantitative
comparisons demonstrate that DeepMapping often enables more robust and accurate
global registration of multiple point clouds than existing techniques. Our code
is available at this http URL. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: The Rice-Shapiro theorem in Computable Topology,
Abstract: We provide requirements on effectively enumerable topological spaces which
guarantee that the Rice-Shapiro theorem holds for the computable elements of
these spaces. We show that the relaxation of these requirements leads to the
classes of effectively enumerable topological spaces where the Rice-Shapiro
theorem does not hold. We propose two constructions that generate effectively
enumerable topological spaces with particular properties from wn--families and
computable trees without computable infinite paths. Using them we propose
examples that give a flavor of this class. | [
1,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications,
Abstract: This paper studies the problem of multivariate linear regression where a
portion of the observations is grossly corrupted or is missing, and the
magnitudes and locations of such occurrences are unknown in priori. To deal
with this problem, we propose a new approach by explicitly consider the error
source as well as its sparseness nature. An interesting property of our
approach lies in its ability of allowing individual regression output elements
or tasks to possess their unique noise levels. Moreover, despite working with a
non-smooth optimization problem, our approach still guarantees to converge to
its optimal solution. Experiments on synthetic data demonstrate the
competitiveness of our approach compared with existing multivariate regression
models. In addition, empirically our approach has been validated with very
promising results on two exemplar real-world applications: The first concerns
the prediction of \textit{Big-Five} personality based on user behaviors at
social network sites (SNSs), while the second is 3D human hand pose estimation
from depth images. The implementation of our approach and comparison methods as
well as the involved datasets are made publicly available in support of the
open-source and reproducible research initiatives. | [
1,
0,
0,
1,
0,
0
] | [
"Statistics",
"Computer Science"
] |
Title: Well-posedness of the Two-dimensional Nonlinear Schrödinger Equation with Concentrated Nonlinearity,
Abstract: We consider a two-dimensional nonlinear Schrödinger equation with
concentrated nonlinearity. In both the focusing and defocusing case we prove
local well-posedness, i.e., existence and uniqueness of the solution for short
times, as well as energy and mass conservation. In addition, we prove that this
implies global existence in the defocusing case, irrespective of the power of
the nonlinearity, while in the focusing case blowing-up solutions may arise. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Solitons in Bose-Einstein Condensates with Helicoidal Spin-Orbit Coupling,
Abstract: We report on the existence and stability of freely moving solitons in a
spatially inhomogeneous Bose- Einstein condensate with helicoidal spin-orbit
(SO) coupling. In spite of the periodically varying parameters, the system
allows for the existence of stable propagating solitons. Such states are found
in the rotating frame, where the helicoidal SO coupling is reduced to a
homogeneous one. In the absence of the Zeeman splitting, the coupled
Gross-Pitaevskii equations describing localized states feature many properties
of the integrable systems. In particular, four-parametric families of solitons
can be obtained in the exact form. Such solitons interact elastically. Zeeman
splitting still allows for the existence of two families of moving solitons,
but makes collisions of solitons inelastic. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Possible heights of graph transformation groups,
Abstract: In the following text we prove that for all finite $p\geq0$ there exists a
topological graph $X$ such that $\{p,p+1,p+2,\ldots\}\cup\{+\infty\}$ is the
collection of all possible heights for transformation groups with phase space
$X$. Moreover for all topological graph $X$ with $p$ as height of
transformation group $(Homeo(X),X)$, $\{p,p+1,p+2,\ldots\}\cup\{+\infty\}$
again is the collection of all possible heights for transformation groups with
phase space $X$. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Dependencies: Formalising Semantic Catenae for Information Retrieval,
Abstract: Building machines that can understand text like humans is an AI-complete
problem. A great deal of research has already gone into this, with astounding
results, allowing everyday people to discuss with their telephones, or have
their reading materials analysed and classified by computers. A prerequisite
for processing text semantics, common to the above examples, is having some
computational representation of text as an abstract object. Operations on this
representation practically correspond to making semantic inferences, and by
extension simulating understanding text. The complexity and granularity of
semantic processing that can be realised is constrained by the mathematical and
computational robustness, expressiveness, and rigour of the tools used.
This dissertation contributes a series of such tools, diverse in their
mathematical formulation, but common in their application to model semantic
inferences when machines process text. These tools are principally expressed in
nine distinct models that capture aspects of semantic dependence in highly
interpretable and non-complex ways. This dissertation further reflects on
present and future problems with the current research paradigm in this area,
and makes recommendations on how to overcome them.
The amalgamation of the body of work presented in this dissertation advances
the complexity and granularity of semantic inferences that can be made
automatically by machines. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Trapped imbalanced fermionic superfluids in one dimension: A variational approach,
Abstract: We propose and analyze a variational wave function for a
population-imbalanced one-dimensional Fermi gas that allows for
Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) type pairing correlations among the two
fermion species, while also accounting for the harmonic confining potential. In
the strongly interacting regime, we find large spatial oscillations of the
order parameter, indicative of an FFLO state. The obtained density profiles
versus imbalance are consistent with recent experimental results as well as
with theoretical calculations based on combining Bethe ansatz with the local
density approximation. Although we find no signature of the FFLO state in the
densities of the two fermion species, we show that the oscillations of the
order parameter appear in density-density correlations, both in-situ and after
free expansion. Furthermore, above a critical polarization, the value of which
depends on the interaction, we find the unpaired Fermi-gas state to be
energetically more favorable. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Modelling and Using Response Times in Online Courses,
Abstract: Each time a learner in a self-paced online course is trying to answer an
assessment question, it takes some time to submit the answer, and if multiple
attempts are allowed and the first answer was incorrect, it takes some time to
submit the second attempt, and so on. Here we study the distribution of such
"response times". We find that the log-normal statistical model for such times,
previously suggested in the literature, holds for online courses qualitatively.
Users who, according to this model, tend to take longer on submits are more
likely to complete the course, have a higher level of engagement and achieve a
higher grade. This finding can be the basis for designing interventions in
online courses, such as MOOCs, which would encourage some users to slow down. | [
1,
0,
0,
0,
0,
0
] | [
"Statistics",
"Quantitative Biology"
] |
Title: Univalent Foundations and the UniMath Library,
Abstract: We give a concise presentation of the Univalent Foundations of mathematics
outlining the main ideas, followed by a discussion of the UniMath library of
formalized mathematics implementing the ideas of the Univalent Foundations
(section 1), and the challenges one faces in attempting to design a large-scale
library of formalized mathematics (section 2). This leads us to a general
discussion about the links between architecture and mathematics where a meeting
of minds is revealed between architects and mathematicians (section 3). On the
way our odyssey from the foundations to the "horizon" of mathematics will lead
us to meet the mathematicians David Hilbert and Nicolas Bourbaki as well as the
architect Christopher Alexander. | [
1,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: Learning Role-based Graph Embeddings,
Abstract: Random walks are at the heart of many existing network embedding methods.
However, such algorithms have many limitations that arise from the use of
random walks, e.g., the features resulting from these methods are unable to
transfer to new nodes and graphs as they are tied to vertex identity. In this
work, we introduce the Role2Vec framework which uses the flexible notion of
attributed random walks, and serves as a basis for generalizing existing
methods such as DeepWalk, node2vec, and many others that leverage random walks.
Our proposed framework enables these methods to be more widely applicable for
both transductive and inductive learning as well as for use on graphs with
attributes (if available). This is achieved by learning functions that
generalize to new nodes and graphs. We show that our proposed framework is
effective with an average AUC improvement of 16.55% while requiring on average
853x less space than existing methods on a variety of graphs. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Quantum Emulation of Extreme Non-equilibrium Phenomena with Trapped Atoms,
Abstract: Ultracold atomic physics experiments offer a nearly ideal context for the
investigation of quantum systems far from equilibrium. We describe three
related emerging directions of research into extreme non-equilibrium phenomena
in atom traps: quantum emulation of ultrafast atom-light interactions, coherent
phasonic spectroscopy in tunable quasicrystals, and realization of Floquet
matter in strongly-driven lattice systems. We show that all three should enable
quantum emulation in parameter regimes inaccessible in solid-state experiments,
facilitating a complementary approach to open problems in non-equilibrium
condensed matter. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Adaptive Multi-Step Prediction based EKF to Power System Dynamic State Estimation,
Abstract: Power system dynamic state estimation is essential to monitoring and
controlling power system stability. Kalman filtering approaches are predominant
in estimation of synchronous machine dynamic states (i.e. rotor angle and rotor
speed). This paper proposes an adaptive multi-step prediction (AMSP) approach
to improve the extended Kalman filter s (EKF) performance in estimating the
dynamic states of a synchronous machine. The proposed approach consists of
three major steps. First, two indexes are defined to quantify the non-linearity
levels of the state transition function and measurement function, respectively.
Second, based on the non-linearity indexes, a multi prediction factor (Mp) is
defined to determine the number of prediction steps. And finally, to mitigate
the non-linearity impact on dynamic state estimation (DSE) accuracy, the
prediction step repeats a few time based on Mp before performing the correction
step. The two-area four-machine system is used to evaluate the effectiveness of
the proposed AMSP approach. It is shown through the Monte-Carlo method that a
good trade-off between estimation accuracy and computational time can be
achieved effectively through the proposed AMSP approach. | [
1,
1,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality,
Abstract: A number of statistical estimation problems can be addressed by semidefinite
programs (SDP). While SDPs are solvable in polynomial time using interior point
methods, in practice generic SDP solvers do not scale well to high-dimensional
problems. In order to cope with this problem, Burer and Monteiro proposed a
non-convex rank-constrained formulation, which has good performance in practice
but is still poorly understood theoretically.
In this paper we study the rank-constrained version of SDPs arising in MaxCut
and in synchronization problems. We establish a Grothendieck-type inequality
that proves that all the local maxima and dangerous saddle points are within a
small multiplicative gap from the global maximum. We use this structural
information to prove that SDPs can be solved within a known accuracy, by
applying the Riemannian trust-region method to this non-convex problem, while
constraining the rank to be of order one. For the MaxCut problem, our
inequality implies that any local maximizer of the rank-constrained SDP
provides a $ (1 - 1/(k-1)) \times 0.878$ approximation of the MaxCut, when the
rank is fixed to $k$.
We then apply our results to data matrices generated according to the
Gaussian ${\mathbb Z}_2$ synchronization problem, and the two-groups stochastic
block model with large bounded degree. We prove that the error achieved by
local maximizers undergoes a phase transition at the same threshold as for
information-theoretically optimal methods. | [
0,
0,
1,
1,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices,
Abstract: In this paper we present a new algorithm for compressive sensing that makes
use of binary measurement matrices and achieves exact recovery of ultra sparse
vectors, in a single pass and without any iterations. Due to its noniterative
nature, our algorithm is hundreds of times faster than $\ell_1$-norm
minimization, and methods based on expander graphs, both of which require
multiple iterations. Our algorithm can accommodate nearly sparse vectors, in
which case it recovers index set of the largest components, and can also
accommodate burst noise measurements. Compared to compressive sensing methods
that are guaranteed to achieve exact recovery of all sparse vectors, our method
requires fewer measurements However, methods that achieve statistical recovery,
that is, recovery of almost all but not all sparse vectors, can require fewer
measurements than our method. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions,
Abstract: An important goal common to domain adaptation and causal inference is to make
accurate predictions when the distributions for the source (or training)
domain(s) and target (or test) domain(s) differ. In many cases, these different
distributions can be modeled as different contexts of a single underlying
system, in which each distribution corresponds to a different perturbation of
the system, or in causal terms, an intervention. We focus on a class of such
causal domain adaptation problems, where data for one or more source domains
are given, and the task is to predict the distribution of a certain target
variable from measurements of other variables in one or more target domains. We
propose an approach for solving these problems that exploits causal inference
and does not rely on prior knowledge of the causal graph, the type of
interventions or the intervention targets. We demonstrate our approach by
evaluating a possible implementation on simulated and real world data. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Approximating Partition Functions in Constant Time,
Abstract: We study approximations of the partition function of dense graphical models.
Partition functions of graphical models play a fundamental role is statistical
physics, in statistics and in machine learning. Two of the main methods for
approximating the partition function are Markov Chain Monte Carlo and
Variational Methods. An impressive body of work in mathematics, physics and
theoretical computer science provides conditions under which Markov Chain Monte
Carlo methods converge in polynomial time. These methods often lead to
polynomial time approximation algorithms for the partition function in cases
where the underlying model exhibits correlation decay. There are very few
theoretical guarantees for the performance of variational methods. One
exception is recent results by Risteski (2016) who considered dense graphical
models and showed that using variational methods, it is possible to find an
$O(\epsilon n)$ additive approximation to the log partition function in time
$n^{O(1/\epsilon^2)}$ even in a regime where correlation decay does not hold.
We show that under essentially the same conditions, an $O(\epsilon n)$
additive approximation of the log partition function can be found in constant
time, independent of $n$. In particular, our results cover dense Ising and
Potts models as well as dense graphical models with $k$-wise interaction. They
also apply for low threshold rank models. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics",
"Physics"
] |
Title: Stability of a Volterra Integral Equation on Time Scales,
Abstract: In this paper, we study Hyers-Ulam stability for integral equation of
Volterra type in time scale setting. Moreover we study the stability of the
considered equation in Hyers-Ulam-Rassias sense. Our technique depends on
successive approximation method, and we use time scale variant of induction
principle to show that equation (1.1) is stable on unbounded domains in
Hyers-Ulam-Rassias sense. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: The Mechanism behind Erosive Bursts in Porous Media,
Abstract: Erosion and deposition during flow through porous media can lead to large
erosive bursts that manifest as jumps in permeability and pressure loss. Here
we reveal that the cause of these bursts is the re-opening of clogged pores
when the pressure difference between two opposite sites of the pore surpasses a
certain threshold. We perform numerical simulations of flow through porous
media and compare our predictions to experimental results, recovering with
excellent agreement shape and power-law distribution of pressure loss jumps,
and the behavior of the permeability jumps as function of particle
concentration. Furthermore, we find that erosive bursts only occur for pressure
gradient thresholds within the range of two critical values, independent on how
the flow is driven. Our findings provide a better understanding of sudden sand
production in oil wells and breakthrough in filtration. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Low Mach number limit of a pressure correction MAC scheme for compressible barotropic flows,
Abstract: We study the incompressible limit of a pressure correction MAC scheme [3] for
the unstationary compressible barotropic Navier-Stokes equations. Provided the
initial data are well-prepared, the solution of the numerical scheme converges,
as the Mach number tends to zero, towards the solution of the classical
pressure correction inf-sup stable MAC scheme for the incompressible
Navier-Stokes equations. | [
0,
1,
1,
0,
0,
0
] | [
"Mathematics",
"Physics",
"Computer Science"
] |
Title: Objective Bayesian Analysis for Change Point Problems,
Abstract: In this paper we present a loss-based approach to change point analysis. In
particular, we look at the problem from two perspectives. The first focuses on
the definition of a prior when the number of change points is known a priori.
The second contribution aims to estimate the number of change points by using a
loss-based approach recently introduced in the literature. The latter considers
change point estimation as a model selection exercise. We show the performance
of the proposed approach on simulated data and real data sets. | [
0,
0,
1,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: Ultrafast Epitaxial Growth of Metre-Sized Single-Crystal Graphene on Industrial Cu Foil,
Abstract: A foundation of the modern technology that uses single-crystal silicon has
been the growth of high-quality single-crystal Si ingots with diameters up to
12 inches or larger. For many applications of graphene, large-area high-quality
(ideally of single-crystal) material will be enabling. Since the first growth
on copper foil a decade ago, inch-sized single-crystal graphene has been
achieved. We present here the growth, in 20 minutes, of a graphene film of 5 x
50 cm2 dimension with > 99% ultra-highly oriented grains. This growth was
achieved by: (i) synthesis of sub-metre-sized single-crystal Cu(111) foil as
substrate; (ii) epitaxial growth of graphene islands on the Cu(111) surface;
(iii) seamless merging of such graphene islands into a graphene film with high
single crystallinity and (iv) the ultrafast growth of graphene film. These
achievements were realized by a temperature-driven annealing technique to
produce single-crystal Cu(111) from industrial polycrystalline Cu foil and the
marvellous effects of a continuous oxygen supply from an adjacent oxide. The
as-synthesized graphene film, with very few misoriented grains (if any), has a
mobility up to ~ 23,000 cm2V-1s-1 at 4 K and room temperature sheet resistance
of ~ 230 ohm/square. It is very likely that this approach can be scaled up to
achieve exceptionally large and high-quality graphene films with single
crystallinity, and thus realize various industrial-level applications at a low
cost. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Electrically driven quantum light emission in electromechanically-tuneable photonic crystal cavities,
Abstract: A single quantum dot deterministically coupled to a photonic crystal
environment constitutes an indispensable elementary unit to both generate and
manipulate single-photons in next-generation quantum photonic circuits. To
date, the scaling of the number of these quantum nodes on a fully-integrated
chip has been prevented by the use of optical pumping strategies that require a
bulky off-chip laser along with the lack of methods to control the energies of
nano-cavities and emitters. Here, we concurrently overcome these limitations by
demonstrating electrical injection of single excitonic lines within a
nano-electro-mechanically tuneable photonic crystal cavity. When an
electrically-driven dot line is brought into resonance with a photonic crystal
mode, its emission rate is enhanced. Anti-bunching experiments reveal the
quantum nature of these on-demand sources emitting in the telecom range. These
results represent an important step forward in the realization of integrated
quantum optics experiments featuring multiple electrically-triggered
Purcell-enhanced single-photon sources embedded in a reconfigurable
semiconductor architecture. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: The Odyssey Approach for Optimizing Federated SPARQL Queries,
Abstract: Answering queries over a federation of SPARQL endpoints requires combining
data from more than one data source. Optimizing queries in such scenarios is
particularly challenging not only because of (i) the large variety of possible
query execution plans that correctly answer the query but also because (ii)
there is only limited access to statistics about schema and instance data of
remote sources. To overcome these challenges, most federated query engines rely
on heuristics to reduce the space of possible query execution plans or on
dynamic programming strategies to produce optimal plans. Nevertheless, these
plans may still exhibit a high number of intermediate results or high execution
times because of heuristics and inaccurate cost estimations. In this paper, we
present Odyssey, an approach that uses statistics that allow for a more
accurate cost estimation for federated queries and therefore enables Odyssey to
produce better query execution plans. Our experimental results show that
Odyssey produces query execution plans that are better in terms of data
transfer and execution time than state-of-the-art optimizers. Our experiments
using the FedBench benchmark show execution time gains of at least 25 times on
average. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: A variant of Gromov's problem on Hölder equivalence of Carnot groups,
Abstract: It is unknown if there exists a locally $\alpha$-Hölder homeomorphism
$f:\mathbb{R}^3\to \mathbb{H}^1$ for any $\frac{1}{2}< \alpha\le \frac{2}{3}$,
although the identity map $\mathbb{R}^3\to \mathbb{H}^1$ is locally
$\frac{1}{2}$-Hölder. More generally, Gromov asked: Given $k$ and a Carnot
group $G$, for which $\alpha$ does there exist a locally $\alpha$-Hölder
homeomorphism $f:\mathbb{R}^k\to G$? Here, we equip a Carnot group $G$ with the
Carnot-Carathéodory metric. In 2014, Balogh, Hajlasz, and Wildrick considered
a variant of this problem. These authors proved that if $k>n$, there does not
exist an injective, $(\frac{1}{2}+)$-Hölder mapping $f:\mathbb{R}^k\to
\mathbb{H}^n$ that is also locally Lipschitz as a mapping into
$\mathbb{R}^{2n+1}$. For their proof, they use the fact that $\mathbb{H}^n$ is
purely $k$-unrectifiable for $k>n$. In this paper, we will extend their result
from the Heisenberg group to model filiform groups and Carnot groups of step at
most three. We will now require that the Carnot group is purely
$k$-unrectifiable. The main key to our proof will be showing that
$(\frac{1}{2}+)$-Hölder maps $f:\mathbb{R}^k\to G$ that are locally Lipschitz
into Euclidean space, are weakly contact. Proving weak contactness in these two
settings requires understanding the relationship between the algebraic and
metric structures of the Carnot group. We will use coordinates of the first and
second kind for Carnot groups. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: The Trees of Hanoi,
Abstract: The game of the Towers of Hanoi is generalized to binary trees. First, a
straightforward solution of the game is discussed. Second, a shorter solution
is presented, which is then shown to be optimal. | [
1,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Benchmarks for Image Classification and Other High-dimensional Pattern Recognition Problems,
Abstract: A good classification method should yield more accurate results than simple
heuristics. But there are classification problems, especially high-dimensional
ones like the ones based on image/video data, for which simple heuristics can
work quite accurately; the structure of the data in such problems is easy to
uncover without any sophisticated or computationally expensive method. On the
other hand, some problems have a structure that can only be found with
sophisticated pattern recognition methods. We are interested in quantifying the
difficulty of a given high-dimensional pattern recognition problem. We consider
the case where the patterns come from two pre-determined classes and where the
objects are represented by points in a high-dimensional vector space. However,
the framework we propose is extendable to an arbitrarily large number of
classes. We propose classification benchmarks based on simple random projection
heuristics. Our benchmarks are 2D curves parameterized by the classification
error and computational cost of these simple heuristics. Each curve divides the
plane into a "positive- gain" and a "negative-gain" region. The latter contains
methods that are ill-suited for the given classification problem. The former is
divided into two by the curve asymptote; methods that lie in the small region
under the curve but right of the asymptote merely provide a computational gain
but no structural advantage over the random heuristics. We prove that the curve
asymptotes are optimal (i.e. at Bayes error) in some cases, and thus no
sophisticated method can provide a structural advantage over the random
heuristics. Such classification problems, an example of which we present in our
numerical experiments, provide poor ground for testing new pattern
classification methods. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Efficient Correlated Topic Modeling with Topic Embedding,
Abstract: Correlated topic modeling has been limited to small model and problem sizes
due to their high computational cost and poor scaling. In this paper, we
propose a new model which learns compact topic embeddings and captures topic
correlations through the closeness between the topic vectors. Our method
enables efficient inference in the low-dimensional embedding space, reducing
previous cubic or quadratic time complexity to linear w.r.t the topic size. We
further speedup variational inference with a fast sampler to exploit sparsity
of topic occurrence. Extensive experiments show that our approach is capable of
handling model and data scales which are several orders of magnitude larger
than existing correlation results, without sacrificing modeling quality by
providing competitive or superior performance in document classification and
retrieval. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Structure-aware error bounds for linear classification with the zero-one loss,
Abstract: We prove risk bounds for binary classification in high-dimensional settings
when the sample size is allowed to be smaller than the dimensionality of the
training set observations. In particular, we prove upper bounds for both
'compressive learning' by empirical risk minimization (ERM) (that is when the
ERM classifier is learned from data that have been projected from
high-dimensions onto a randomly selected low-dimensional subspace) as well as
uniform upper bounds in the full high-dimensional space. A novel tool we employ
in both settings is the 'flipping probability' of Durrant and Kaban (ICML 2013)
which we use to capture benign geometric structures that make a classification
problem 'easy' in the sense of demanding a relatively low sample size for
guarantees of good generalization. Furthermore our bounds also enable us to
explain or draw connections between several existing successful classification
algorithms. Finally we show empirically that our bounds are informative enough
in practice to serve as the objective function for learning a classifier (by
using them to do so). | [
0,
0,
1,
1,
0,
0
] | [
"Computer Science",
"Statistics",
"Mathematics"
] |
Title: Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions,
Abstract: We present a practical approach for processing mobile sensor time series data
for continual deep learning predictions. The approach comprises data cleaning,
normalization, capping, time-based compression, and finally classification with
a recurrent neural network. We demonstrate the effectiveness of the approach in
a case study with 279 participants. On the basis of sparse sensor events, the
network continually predicts whether the participants would attend to a
notification within 10 minutes. Compared to a random baseline, the classifier
achieves a 40% performance increase (AUC of 0.702) on a withheld test set. This
approach allows to forgo resource-intensive, domain-specific, error-prone
feature engineering, which may drastically increase the applicability of
machine learning to mobile phone sensor data. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Electron affinities of water clusters from density-functional and many-body-perturbation theory,
Abstract: In this work, we assess the accuracy of dielectric-dependent hybrid density
functionals and many-body perturbation theory methods for the calculation of
electron affinities of small water clusters, including hydrogen-bonded water
dimer and water hexamer isomers. We show that many-body perturbation theory in
the G$_0$W$_0$ approximation starting with the dielectric-dependent hybrid
functionals predicts electron affinities of clusters within 0.1 eV of the
coupled-cluster results with single, double, and perturbative triple
excitations. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Exact zero modes in twisted Kitaev chains,
Abstract: We study the Kitaev chain under generalized twisted boundary conditions, for
which both the amplitudes and the phases of the boundary couplings can be tuned
at will. We explicitly show the presence of exact zero modes for large chains
belonging to the topological phase in the most general case, in spite of the
absence of "edges" in the system. For specific values of the phase parameters,
we rigorously obtain the condition for the presence of the exact zero modes in
finite chains, and show that the zero modes obtained are indeed localized. The
full spectrum of the twisted chains with zero chemical potential is
analytically presented. Finally, we demonstrate the persistence of zero modes
(level crossing) even in the presence of disorder or interactions. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors,
Abstract: Pairwise association measure is an important operation in data analytics.
Kendall's tau coefficient is one widely used correlation coefficient
identifying non-linear relationships between ordinal variables. In this paper,
we investigated a parallel algorithm accelerating all-pairs Kendall's tau
coefficient computation via single instruction multiple data (SIMD) vectorized
sorting on Intel Xeon Phis by taking advantage of many processing cores and
512-bit SIMD vector instructions. To facilitate workload balancing and overcome
on-chip memory limitation, we proposed a generic framework for symmetric
all-pairs computation by building provable bijective functions between job
identifier and coordinate space. Performance evaluation demonstrated that our
algorithm on one 5110P Phi achieves two orders-of-magnitude speedups over
16-threaded MATLAB and three orders-of-magnitude speedups over sequential R,
both running on high-end CPUs. Besides, our algorithm exhibited rather good
distributed computing scalability with respect to number of Phis. Source code
and datasets are publicly available at this http URL. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Rational motivic path spaces and Kim's relative unipotent section conjecture,
Abstract: We initiate a study of path spaces in the nascent context of "motivic dga's",
under development in doctoral work by Gabriella Guzman. This enables us to
reconstruct the unipotent fundamental group of a pointed scheme from the
associated augmented motivic dga, and provides us with a factorization of Kim's
relative unipotent section conjecture into several smaller conjectures with a
homotopical flavor. Based on a conversation with Joseph Ayoub, we prove that
the path spaces of the punctured projective line over a number field are
concentrated in degree zero with respect to Levine's t-structure for mixed Tate
motives. This constitutes a step in the direction of Kim's conjecture. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Convergence Rates of Latent Topic Models Under Relaxed Identifiability Conditions,
Abstract: In this paper we study the frequentist convergence rate for the Latent
Dirichlet Allocation (Blei et al., 2003) topic models. We show that the maximum
likelihood estimator converges to one of the finitely many equivalent
parameters in Wasserstein's distance metric at a rate of $n^{-1/4}$ without
assuming separability or non-degeneracy of the underlying topics and/or the
existence of more than three words per document, thus generalizing the previous
works of Anandkumar et al. (2012, 2014) from an information-theoretical
perspective. We also show that the $n^{-1/4}$ convergence rate is optimal in
the worst case. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: 21 cm Angular Power Spectrum from Minihalos as a Probe of Primordial Spectral Runnings,
Abstract: Measurements of 21 cm line fluctuations from minihalos have been discussed as
a powerful probe of a wide range of cosmological models. However, previous
studies have taken into account only the pixel variance, where contributions
from different scales are integrated. In order to sort out information from
different scales, we formulate the angular power spectrum of 21 cm line
fluctuations from minihalos at different redshifts, which can enhance the
constraining power enormously. By adopting this formalism, we investigate
expected constraints on parameters characterizing the primordial power
spectrum, particularly focusing on the spectral index $n_s$ and its runnings
$\alpha_s$ and $\beta_s$. We show that future observations of 21 cm line
fluctuations from minihalos, in combination with cosmic microwave background,
can potentially probe these runnings as $\alpha_s \sim {\cal O}(10^{-3})$ and
$\beta_s \sim {\cal O}(10^{-4})$. Its implications to the test of inflationary
models are also discussed. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Astrophysics"
] |
Title: Anonymous Variables in Imperative Languages,
Abstract: In this paper, we bring anonymous variables into imperative languages.
Anonymous variables represent don't-care values and have proven useful in logic
programming. To bring the same level of benefits into imperative languages, we
describe an extension to C wth anonymous variables. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Simultaneously constraining the astrophysics of reionisation and the epoch of heating with 21CMMC,
Abstract: The cosmic 21 cm signal is set to revolutionise our understanding of the
early Universe, allowing us to probe the 3D temperature and ionisation
structure of the intergalactic medium (IGM). It will open a window onto the
unseen first galaxies, showing us how their UV and X-ray photons drove the
cosmic milestones of the epoch of reionisation (EoR) and epoch of heating
(EoH). To facilitate parameter inference from the 21 cm signal, we previously
developed 21CMMC: a Monte Carlo Markov Chain sampler of 3D EoR simulations.
Here we extend 21CMMC to include simultaneous modelling of the EoH, resulting
in a complete Bayesian inference framework for the astrophysics dominating the
observable epochs of the cosmic 21 cm signal. We demonstrate that second
generation interferometers, the Hydrogen Epoch of Reionisation Array (HERA) and
Square Kilometre Array (SKA) will be able to constrain ionising and X-ray
source properties of the first galaxies with a fractional precision of order
$\sim1$-10 per cent (1$\sigma$). The ionisation history of the Universe can be
constrained to within a few percent. Using our extended framework, we quantify
the bias in EoR parameter recovery incurred by the common simplification of a
saturated spin temperature in the IGM. Depending on the extent of overlap
between the EoR and EoH, the recovered astrophysical parameters can be biased
by $\sim3-10\sigma$. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Astrophysics"
] |
Title: Non-penalized variable selection in high-dimensional linear model settings via generalized fiducial inference,
Abstract: Standard penalized methods of variable selection and parameter estimation
rely on the magnitude of coefficient estimates to decide which variables to
include in the final model. However, coefficient estimates are unreliable when
the design matrix is collinear. To overcome this challenge an entirely new
perspective on variable selection is presented within a generalized fiducial
inference framework. This new procedure is able to effectively account for
linear dependencies among subsets of covariates in a high-dimensional setting
where $p$ can grow almost exponentially in $n$, as well as in the classical
setting where $p \le n$. It is shown that the procedure very naturally assigns
small probabilities to subsets of covariates which include redundancies by way
of explicit $L_{0}$ minimization. Furthermore, with a typical sparsity
assumption, it is shown that the proposed method is consistent in the sense
that the probability of the true sparse subset of covariates converges in
probability to 1 as $n \to \infty$, or as $n \to \infty$ and $p \to \infty$.
Very reasonable conditions are needed, and little restriction is placed on the
class of possible subsets of covariates to achieve this consistency result. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: The existence of positive least energy solutions for a class of Schrodinger-Poisson systems involving critical nonlocal term with general nonlinearity,
Abstract: The present study is concerned with the following Schrödinger-Poisson
system involving critical nonlocal term with general nonlinearity: $$ \left\{
\begin{array}{ll} -\Delta u+V(x)u- \phi |u|^3u= f(u), & x\in\mathbb{R}^3,
-\Delta \phi= |u|^5, & x\in\mathbb{R}^3,\\ \end{array} \right. $$ Under certain
assumptions on non-constant $V(x)$, the existence of a positive least energy
solution is obtained by using some new analytical skills and Pohožaev type
manifold. In particular, the Ambrosetti-Rabinowitz type condition or
monotonicity assumption on the nonlinearity is not necessary. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Comparison of SMT and RBMT; The Requirement of Hybridization for Marathi-Hindi MT,
Abstract: We present in this paper our work on comparison between Statistical Machine
Translation (SMT) and Rule-based machine translation for translation from
Marathi to Hindi. Rule Based systems although robust take lots of time to
build. On the other hand statistical machine translation systems are easier to
create, maintain and improve upon. We describe the development of a basic
Marathi-Hindi SMT system and evaluate its performance. Through a detailed error
analysis, we, point out the relative strengths and weaknesses of both systems.
Effectively, we shall see that even with a small amount of training corpus a
statistical machine translation system has many advantages for high quality
domain specific machine translation over that of a rule-based counterpart. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Personalized and Private Peer-to-Peer Machine Learning,
Abstract: The rise of connected personal devices together with privacy concerns call
for machine learning algorithms capable of leveraging the data of a large
number of agents to learn personalized models under strong privacy
requirements. In this paper, we introduce an efficient algorithm to address the
above problem in a fully decentralized (peer-to-peer) and asynchronous fashion,
with provable convergence rate. We show how to make the algorithm
differentially private to protect against the disclosure of information about
the personal datasets, and formally analyze the trade-off between utility and
privacy. Our experiments show that our approach dramatically outperforms
previous work in the non-private case, and that under privacy constraints, we
can significantly improve over models learned in isolation. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations,
Abstract: We present GALARIO, a computational library that exploits the power of modern
graphical processing units (GPUs) to accelerate the analysis of observations
from radio interferometers like ALMA or the VLA. GALARIO speeds up the
computation of synthetic visibilities from a generic 2D model image or a radial
brightness profile (for axisymmetric sources). On a GPU, GALARIO is 150 faster
than standard Python and 10 times faster than serial C++ code on a CPU. Highly
modular, easy to use and to adopt in existing code, GALARIO comes as two
compiled libraries, one for Nvidia GPUs and one for multicore CPUs, where both
have the same functions with identical interfaces. GALARIO comes with Python
bindings but can also be directly used in C or C++. The versatility and the
speed of GALARIO open new analysis pathways that otherwise would be
prohibitively time consuming, e.g. fitting high resolution observations of
large number of objects, or entire spectral cubes of molecular gas emission. It
is a general tool that can be applied to any field that uses radio
interferometer observations. The source code is available online at
this https URL under the open source GNU Lesser General
Public License v3. | [
0,
1,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Spectral Norm Regularization for Improving the Generalizability of Deep Learning,
Abstract: We investigate the generalizability of deep learning based on the sensitivity
to input perturbation. We hypothesize that the high sensitivity to the
perturbation of data degrades the performance on it. To reduce the sensitivity
to perturbation, we propose a simple and effective regularization method,
referred to as spectral norm regularization, which penalizes the high spectral
norm of weight matrices in neural networks. We provide supportive evidence for
the abovementioned hypothesis by experimentally confirming that the models
trained using spectral norm regularization exhibit better generalizability than
other baseline methods. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Estimating the chromospheric magnetic field from a revised NLTE modeling: the case of HR7428,
Abstract: In this work we use the semi-empirical atmospheric modeling method to obtain
the chro-mospheric temperature, pressure, density and magnetic field
distribution versus height in the K2 primary component of the RS CVn binary
system HR 7428. While temperature, pressure, density are the standard output of
the semi-empirical modeling technique, the chromospheric magnetic field
estimation versus height comes from considering the possibility of not
im-posing hydrostatic equilibrium in the semi-empirical computation. The
stability of the best non-hydrostatic equilibrium model, implies the presence
of an additive (toward the center of the star) pressure, that decreases in
strength from the base of the chromosphere toward the outer layers.
Interpreting the additive pressure as magnetic pressure we estimated a magnetic
field intensity of about 500 gauss at the base of the chromosphere. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: An accurate approximation formula for gamma function,
Abstract: In this paper, we present a very accurate approximation for gamma function:
\begin{equation*} \Gamma \left( x+1\right) \thicksim \sqrt{2\pi x}\left(
\dfrac{x}{e}\right) ^{x}\left( x\sinh \frac{1}{x}\right) ^{x/2}\exp \left(
\frac{7}{324}\frac{1}{ x^{3}\left( 35x^{2}+33\right) }\right) =W_{2}\left(
x\right) \end{equation*} as $x\rightarrow \infty $, and prove that the function
$x\mapsto \ln \Gamma \left( x+1\right) -\ln W_{2}\left( x\right) $ is strictly
decreasing and convex from $\left( 1,\infty \right) $ onto $\left( 0,\beta
\right) $, where \begin{equation*} \beta =\frac{22\,025}{22\,032}-\ln
\sqrt{2\pi \sinh 1}\approx 0.00002407. \end{equation*} | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Reconciling Bayesian and Total Variation Methods for Binary Inversion,
Abstract: A central theme in classical algorithms for the reconstruction of
discontinuous functions from observational data is perimeter regularization. On
the other hand, sparse or noisy data often demands a probabilistic approach to
the reconstruction of images, to enable uncertainty quantification; the
Bayesian approach to inversion is a natural framework in which to carry this
out. The link between Bayesian inversion methods and perimeter regularization,
however, is not fully understood. In this paper two links are studied: (i) the
MAP objective function of a suitably chosen phase-field Bayesian approach is
shown to be closely related to a least squares plus perimeter regularization
objective; (ii) sample paths of a suitably chosen Bayesian level set
formulation are shown to possess finite perimeter and to have the ability to
learn about the true perimeter. Furthermore, the level set approach is shown to
lead to faster algorithms for uncertainty quantification than the phase field
approach. | [
0,
0,
1,
1,
0,
0
] | [
"Mathematics",
"Statistics"
] |
Title: Perception Driven Texture Generation,
Abstract: This paper investigates a novel task of generating texture images from
perceptual descriptions. Previous work on texture generation focused on either
synthesis from examples or generation from procedural models. Generating
textures from perceptual attributes have not been well studied yet. Meanwhile,
perceptual attributes, such as directionality, regularity and roughness are
important factors for human observers to describe a texture. In this paper, we
propose a joint deep network model that combines adversarial training and
perceptual feature regression for texture generation, while only random noise
and user-defined perceptual attributes are required as input. In this model, a
preliminary trained convolutional neural network is essentially integrated with
the adversarial framework, which can drive the generated textures to possess
given perceptual attributes. An important aspect of the proposed model is that,
if we change one of the input perceptual features, the corresponding appearance
of the generated textures will also be changed. We design several experiments
to validate the effectiveness of the proposed method. The results show that the
proposed method can produce high quality texture images with desired perceptual
properties. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Computational modeling approaches in gonadotropin signaling,
Abstract: Follicle-stimulating hormone (FSH) and luteinizing hormone (LH) play
essential roles in animal reproduction. They exert their function through
binding to their cognate receptors, which belong to the large family of G
protein-coupled receptors (GPCRs). This recognition at the plasma membrane
triggers a plethora of cellular events, whose processing and integration
ultimately lead to an adapted biological response. Understanding the nature and
the kinetics of these events is essential for innovative approaches in drug
discovery. The study and manipulation of such complex systems requires the use
of computational modeling approaches combined with robust in vitro functional
assays for calibration and validation. Modeling brings a detailed understanding
of the system and can also be used to understand why existing drugs do not work
as well as expected, and how to design more efficient ones. | [
0,
0,
0,
0,
1,
0
] | [
"Quantitative Biology",
"Computer Science"
] |
Title: Shape and Positional Geometry of Multi-Object Configurations,
Abstract: In previous work, we introduced a method for modeling a configuration of
objects in 2D and 3D images using a mathematical "medial/skeletal linking
structure." In this paper, we show how these structures allow us to capture
positional properties of a multi-object configuration in addition to the shape
properties of the individual objects. In particular, we introduce numerical
invariants for positional properties which measure the closeness of neighboring
objects, including identifying the parts of the objects which are close, and
the "relative significance" of objects compared with the other objects in the
configuration. Using these numerical measures, we introduce a hierarchical
ordering and relations between the individual objects, and quantitative
criteria for identifying subconfigurations. In addition, the invariants provide
a "proximity matrix" which yields a unique set of weightings measuring overall
proximity of objects in the configuration. Furthermore, we show that these
invariants, which are volumetrically defined and involve external regions, may
be computed via integral formulas in terms of "skeletal linking integrals"
defined on the internal skeletal structures of the objects. | [
1,
0,
0,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: Interface mediated mechanisms of plastic strain recovery in AgCu alloy,
Abstract: Through the combination of transmission electron microscopy analysis of the
deformed microstructure and molecular dynamics computer simulations of the
deformation processes, the mechanisms of plastic strain recovery in bulk AgCu
eutectic with either incoherent twin or cube-on-cube interfaces between the Ag
and Cu layers and a bilayer thickness of 500 nm have been revealed. The
character of the incoherent twin interfaces changed uniquely after dynamic
compressive loading for samples that exhibited plastic strain recovery and was
found to drive the recovery, which is due to dislocation retraction and
rearrangement of the interfaces. The magnitude of the recovery decreased with
increasing strain as dislocation tangles and dislocation cell structures
formed. No change in the orientation relationship was found at cube-on-cube
interfaces and these exhibited a lesser amount of plastic strain recovery in
the simulations and none experimentally in samples with larger layer
thicknesses with predominantly cube-on-cube interfaces. Molecular dynamics
computer simulations verified the importance of the change in the incoherent
twin interface structure as the driving force for dislocation annihilation at
the interfaces and the plastic strain recovery. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Materials Science"
] |
Title: A criterion related to the Riemann Hypothesis,
Abstract: A crucial role in the Nyman-Beurling-Báez-Duarte approach to the Riemann
Hypothesis is played by the distance \[
d_N^2:=\inf_{A_N}\frac{1}{2\pi}\int_{-\infty}^\infty\left|1-\zeta
A_N\left(\frac{1}{2}+it\right)\right|^2\frac{dt}{\frac{1}{4}+t^2}\:, \] where
the infimum is over all Dirichlet polynomials
$$A_N(s)=\sum_{n=1}^{N}\frac{a_n}{n^s}$$ of length $N$. In this paper we
investigate $d_N^2$ under the assumption that the Riemann zeta function has
four non-trivial zeros off the critical line. Thus we obtain a criterion for
the non validity of the Riemann Hypothesis. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Non-Stationary Spectral Kernels,
Abstract: We propose non-stationary spectral kernels for Gaussian process regression.
We propose to model the spectral density of a non-stationary kernel function as
a mixture of input-dependent Gaussian process frequency density surfaces. We
solve the generalised Fourier transform with such a model, and present a family
of non-stationary and non-monotonic kernels that can learn input-dependent and
potentially long-range, non-monotonic covariances between inputs. We derive
efficient inference using model whitening and marginalized posterior, and show
with case studies that these kernels are necessary when modelling even rather
simple time series, image or geospatial data with non-stationary
characteristics. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: The ANTARES Collaboration: Contributions to ICRC 2017 Part II: The multi-messenger program,
Abstract: Papers on the ANTARES multi-messenger program, prepared for the 35th
International Cosmic Ray Conference (ICRC 2017, Busan, South Korea) by the
ANTARES Collaboration | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Orbital-dependent correlations in PuCoGa$_5$,
Abstract: We investigate the normal state of the superconducting compound PuCoGa$_5$
using the combination of density functional theory (DFT) and dynamical mean
field theory (DMFT), with the continuous time quantum Monte Carlo (CTQMC) and
the vertex-corrected one-crossing approximation (OCA) as the impurity solvers.
Our DFT+DMFT(CTQMC) calculations suggest a strong tendency of Pu-5$f$ orbitals
to differentiate at low temperatures. The renormalized 5$f_{5/2}$ states
exhibit a Fermi-liquid behavior whereas one electron in the 5$f_{7/2}$ states
is at the edge of a Mott localization. We find that the orbital differentiation
is manifested as the removing of 5$f_{7/2}$ spectral weight from the Fermi
level relative to DFT. We corroborate these conclusions with DFT+DMFT(OCA)
calculations which demonstrate that 5$f_{5/2}$ electrons have a much larger
Kondo scale than the 5$f_{7/2}$. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Employee turnover prediction and retention policies design: a case study,
Abstract: This paper illustrates the similarities between the problems of customer
churn and employee turnover. An example of employee turnover prediction model
leveraging classical machine learning techniques is developed. Model outputs
are then discussed to design \& test employee retention policies. This type of
retention discussion is, to our knowledge, innovative and constitutes the main
value of this paper. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Do You Want Your Autonomous Car To Drive Like You?,
Abstract: With progress in enabling autonomous cars to drive safely on the road, it is
time to start asking how they should be driving. A common answer is that they
should be adopting their users' driving style. This makes the assumption that
users want their autonomous cars to drive like they drive - aggressive drivers
want aggressive cars, defensive drivers want defensive cars. In this paper, we
put that assumption to the test. We find that users tend to prefer a
significantly more defensive driving style than their own. Interestingly, they
prefer the style they think is their own, even though their actual driving
style tends to be more aggressive. We also find that preferences do depend on
the specific driving scenario, opening the door for new ways of learning
driving style preference. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: COLA: Decentralized Linear Learning,
Abstract: Decentralized machine learning is a promising emerging paradigm in view of
global challenges of data ownership and privacy. We consider learning of linear
classification and regression models, in the setting where the training data is
decentralized over many user devices, and the learning algorithm must run
on-device, on an arbitrary communication network, without a central
coordinator. We propose COLA, a new decentralized training algorithm with
strong theoretical guarantees and superior practical performance. Our framework
overcomes many limitations of existing methods, and achieves communication
efficiency, scalability, elasticity as well as resilience to changes in data
and participating devices. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Performance Limits of Solutions to Network Utility Maximization Problems,
Abstract: We study performance limits of solutions to utility maximization problems
(e.g., max-min problems) in wireless networks as a function of the power budget
$\bar{p}$ available to transmitters. Special focus is devoted to the utility
and the transmit energy efficiency (i.e., utility over transmit power) of the
solution. Briefly, we show tight bounds for the general class of network
utility optimization problems that can be solved by computing conditional
eigenvalues of standard interference mappings. The proposed bounds, which are
based on the concept of asymptotic functions, are simple to compute, provide us
with good estimates of the performance of networks for any value of $\bar{p}$
in many real-world applications, and enable us to determine points in which
networks move from a noise limited regime to an interference limited regime.
Furthermore, they also show that the utility and the transmit energy efficiency
scales as $\Theta(1)$ and $\Theta(1/\bar{p})$, respectively, as
$\bar{p}\to\infty$. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Toeplitz Order,
Abstract: A new approach to problems of the Uncertainty Principle in Harmonic Analysis,
based on the use of Toeplitz operators, has brought progress to some of the
classical problems in the area. The goal of this paper is to develop and
systematize the function theoretic component of the Toeplitz approach by
introducing a partial order on the set of inner functions induced by the action
of Toeplitz operators. We study connections of the new order with some of the
classical problems and known results. We discuss remaining problems and
possible directions for further research. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Explicit cocycle formulas on finite abelian groups with applications to braided linear Gr-categories and Dijkgraaf-Witten invariants,
Abstract: We provide explicit and unified formulas for the cocycles of all degrees on
the normalized bar resolutions of finite abelian groups. This is achieved by
constructing a chain map from the normalized bar resolution to a Koszul-like
resolution for any given finite abelian group. With a help of the obtained
cocycle formulas, we determine all the braided linear Gr-categories and compute
the Dijkgraaf-Witten Invariants of the $n$-torus for all $n$. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Classical affine W-superalgebras via generalized Drinfeld-Sokolov reductions and related integrable systems,
Abstract: The purpose of this article is to investigate relations between
W-superalgebras and integrable super-Hamiltonian systems. To this end, we
introduce the generalized Drinfel'd-Sokolov (D-S) reduction associated to a Lie
superalgebra $g$ and its even nilpotent element $f$, and we find a new
definition of the classical affine W-superalgebra $W(g,f,k)$ via the D-S
reduction. This new construction allows us to find free generators of
$W(g,f,k)$, as a differential superalgebra, and two independent Lie brackets on
$W(g,f,k)/\partial W(g,f,k).$ Moreover, we describe super-Hamiltonian systems
with the Poisson vertex algebras theory. A W-superalgebra with certain
properties can be understood as an underlying differential superalgebra of a
series of integrable super-Hamiltonian systems. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Temporally Identity-Aware SSD with Attentional LSTM,
Abstract: Temporal object detection has attracted significant attention, but most
popular detection methods can not leverage the rich temporal information in
videos. Very recently, many different algorithms have been developed for video
detection task, but real-time online approaches are frequently deficient. In
this paper, based on attention mechanism and convolutional long short-term
memory (ConvLSTM), we propose a temporal signal-shot detector (TSSD) for
real-world detection. Distinct from previous methods, we take aim at temporally
integrating pyramidal feature hierarchy using ConvLSTM, and design a novel
structure including a low-level temporal unit as well as a high-level one
(HL-TU) for multi-scale feature maps. Moreover, we develop a creative temporal
analysis unit, namely, attentional ConvLSTM (AC-LSTM), in which a temporal
attention module is specially tailored for background suppression and scale
suppression while a ConvLSTM integrates attention-aware features through time.
An association loss is designed for temporal coherence. Besides, online tubelet
analysis (OTA) is exploited for identification. Finally, our method is
evaluated on ImageNet VID dataset and 2DMOT15 dataset. Extensive comparisons on
the detection and tracking capability validate the superiority of the proposed
approach. Consequently, the developed TSSD-OTA is fairly faster and achieves an
overall competitive performance in terms of detection and tracking. The source
code will be made available. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Cubical Covers of Sets in $\mathbb{R}^n$,
Abstract: Wild sets in $\mathbb{R}^n$ can be tamed through the use of various
representations though sometimes this taming removes features considered
important. Finding the wildest sets for which it is still true that the
representations faithfully inform us about the original set is the focus of
this rather playful, expository paper that we hope will stimulate interest in
cubical coverings as well as the other two ideas we explore briefly: Jones'
$\beta$ numbers and varifolds from geometric measure theory. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Mitochondrial network fragmentation modulates mutant mtDNA accumulation independently of absolute fission-fusion rates,
Abstract: Mitochondrial DNA (mtDNA) mutations cause severe congenital diseases but may
also be associated with healthy aging. MtDNA is stochastically replicated and
degraded, and exists within organelles which undergo dynamic fusion and
fission. The role of the resulting mitochondrial networks in determining the
time evolution of the cellular proportion of mutated mtDNA molecules
(heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy
variance), remains incompletely understood. Heteroplasmy variance is
particularly important since it modulates the number of pathological cells in a
tissue. Here, we provide the first wide-reaching mathematical treatment which
bridges mitochondrial network and genetic states. We show that, for a range of
models, the rate of increase in heteroplasmy variance, and the rate of
\textit{de novo} mutation, is proportionately modulated by the fraction of
unfused mitochondria, independently of the absolute fission-fusion rate. In the
context of selective fusion, we show that intermediate fusion/fission ratios
are optimal for the clearance of mtDNA mutants. Our findings imply that
modulating network state, mitophagy rate and copy number to slow down
heteroplasmy dynamics when mean heteroplasmy is low, could have therapeutic
advantages for mitochondrial disease and healthy aging. | [
0,
0,
0,
0,
1,
0
] | [
"Quantitative Biology",
"Mathematics"
] |
Title: Depth Separation for Neural Networks,
Abstract: Let $f:\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}\to\mathbb{S}$ be a function of
the form $f(\mathbf{x},\mathbf{x}') = g(\langle\mathbf{x},\mathbf{x}'\rangle)$
for $g:[-1,1]\to \mathbb{R}$. We give a simple proof that shows that poly-size
depth two neural networks with (exponentially) bounded weights cannot
approximate $f$ whenever $g$ cannot be approximated by a low degree polynomial.
Moreover, for many $g$'s, such as $g(x)=\sin(\pi d^3x)$, the number of neurons
must be $2^{\Omega\left(d\log(d)\right)}$. Furthermore, the result holds
w.r.t.\ the uniform distribution on $\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$.
As many functions of the above form can be well approximated by poly-size depth
three networks with poly-bounded weights, this establishes a separation between
depth two and depth three networks w.r.t.\ the uniform distribution on
$\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: An Accurate Interconnect Test Structure for Parasitic Validation in On-Chip Machine Learning Accelerators,
Abstract: For nanotechnology nodes, the feature size is shrunk rapidly, the wire
becomes narrow and thin, it leads to high RC parasitic, especially for
resistance. The overall system performance are dominated by interconnect rather
than device. As such, it is imperative to accurately measure and model
interconnect parasitic in order to predict interconnect performance on silicon.
Despite many test structures developed in the past to characterize device
models and layout effects, only few of them are available for interconnects.
Nevertheless, they are either not suitable for real chip implementation or too
complicated to be embedded. A compact yet comprehensive test structure to
capture all interconnect parasitic in a real chip is needed. To address this
problem, this paper describes a set of test structures that can be used to
study the timing performance (i.e. propagation delay and crosstalk) of various
interconnect configurations. Moreover, an empirical model is developed to
estimate the actual RC parasitic. Compared with the state-of-the-art
interconnect test structures, the new structure is compact in size and can be
easily embedded on die as a parasitic variation monitor. We have validated the
proposed structure on a test chip in TSMC 28nm HPM process. Recently, the test
structure is further modified to identify the serious interconnect process
issues for critical path design using TSMC 7nm FF process. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Possible spin gapless semiconductor type behaviour in CoFeMnSi epitaxial thin films,
Abstract: Spin-gapless semiconductors with their unique band structures have recently
attracted much attention due to their interesting transport properties that can
be utilized in spintronics applications. We have successfully deposited the
thin films of quaternary spin-gapless semiconductor CoFeMnSi Heusler alloy on
MgO (001) substrates using a pulsed laser deposition system. These films show
epitaxial growth along (001) direction and display uniform and smooth
crystalline surface. The magnetic properties reveal that the film is
ferromagnetically soft along the in-plane direction and its Curie temperature
is well above 400 K. The electrical conductivity of the film is low and
exhibits a nearly temperature independent semiconducting behaviour. The
estimated temperature coefficient of resistivity for the film is -7x10^-10
Ohm.m/K, which is comparable to the values reported for spin-gapless
semiconductors. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: The general linear 2-groupoid,
Abstract: We deal with the symmetries of a (2-term) graded vector space or bundle. Our
first theorem shows that they define a (strict) Lie 2-groupoid in a natural
way. Our second theorem explores the construction of nerves for Lie
2-categories, showing that it yields simplicial manifolds if the 2-cells are
invertible. Finally, our third and main theorem shows that smooth
pseudofunctors into our general linear 2-groupoid classify 2-term
representations up to homotopy of Lie groupoids. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Non-equilibrium Optical Conductivity: General Theory and Application to Transient Phases,
Abstract: A non-equilibrium theory of optical conductivity of dirty-limit
superconductors and commensurate charge density wave is presented. We discuss
the current response to different experimentally relevant light-field probe
pulses and show that a single frequency definition of the optical conductivity
$\sigma(\omega)\equiv j(\omega)/E(\omega)$ is difficult to interpret out of the
adiabatic limit. We identify characteristic time domain signatures
distinguishing between superconducting, normal metal and charge density wave
states. We also suggest a route to directly address the instantaneous
superfluid stiffness of a superconductor by shaping the probe light field. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Semantic Code Repair using Neuro-Symbolic Transformation Networks,
Abstract: We study the problem of semantic code repair, which can be broadly defined as
automatically fixing non-syntactic bugs in source code. The majority of past
work in semantic code repair assumed access to unit tests against which
candidate repairs could be validated. In contrast, the goal here is to develop
a strong statistical model to accurately predict both bug locations and exact
fixes without access to information about the intended correct behavior of the
program. Achieving such a goal requires a robust contextual repair model, which
we train on a large corpus of real-world source code that has been augmented
with synthetically injected bugs. Our framework adopts a two-stage approach
where first a large set of repair candidates are generated by rule-based
processors, and then these candidates are scored by a statistical model using a
novel neural network architecture which we refer to as Share, Specialize, and
Compete. Specifically, the architecture (1) generates a shared encoding of the
source code using an RNN over the abstract syntax tree, (2) scores each
candidate repair using specialized network modules, and (3) then normalizes
these scores together so they can compete against one another in comparable
probability space. We evaluate our model on a real-world test set gathered from
GitHub containing four common categories of bugs. Our model is able to predict
the exact correct repair 41\% of the time with a single guess, compared to 13\%
accuracy for an attentional sequence-to-sequence model. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Hints on the gradual re-sizing of the torus in AGN by decomposing IRS/Spitzer spectra,
Abstract: Several authors have claimed that the less luminous active galactic nuclei
(AGN) are not capable of sustaining the dusty torus structure. Thus, a gradual
re-sizing of the torus is expected when the AGN luminosity decreases. Our aim
is to confront mid-infrared observations of local AGN of different luminosities
with this scenario. We decomposed about ~100 IRS/Spitzer spectra of LLAGN and
powerful Seyferts in order to decontaminate the torus component from other
contributors. We have used the affinity propagation (AP) method to cluster the
data into five groups within the sample according to torus contribution to the
5-15 um range (Ctorus) and bolometric luminosity. The AP groups show a
progressively higher torus contribution and an increase of the bolometric
luminosity, from Group 1 (Ctorus~ 0% and logLbol ~ 41) and up to Group 5
(Ctorus ~80% and log(Lbol) ~44). We have fitted the average spectra of each of
the AP groups to clumpy models. The torus is no longer present in Group 1,
supporting the disappearance at low-luminosities. We were able to fit the
average spectra for the torus component in Groups 3 (Ctorus~ 40% and log(Lbol)~
42.6), 4 (Ctorus~ 60% and log(Lbol)~ 43.7), and 5 to Clumpy torus models. We
did not find a good fitting to Clumpy torus models for Group 2 (Ctorus~ 18% and
log(Lbol)~ 42). This might suggest a different configuration and/or composition
of the clouds for Group 2, which is consistent with a different gas content
seen in Groups 1, 2, and 3, according to the detections of H2 molecular lines.
Groups 3, 4, and 5 show a trend to decrease of the width of the torus (which
yields to a likely decrease of the geometrical covering factor), although we
cannot confirm it with the present data. Finally, Groups 3, 4, and 5 show an
increase on the outer radius of the torus for higher luminosities, consistent
with a re-sizing of the torus according to the AGN luminosity. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time,
Abstract: Dynamic topic modeling facilitates the identification of topical trends over
time in temporal collections of unstructured documents. We introduce a novel
unsupervised neural dynamic topic model named as Recurrent Neural
Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each
time influence the topic discovery in the subsequent time steps. We account for
the temporal ordering of documents by explicitly modeling a joint distribution
of latent topical dependencies over time, using distributional estimators with
temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP
research, we demonstrate that compared to state-of-the art topic models, RNNRSM
shows better generalization, topic interpretation, evolution and trends. We
also introduce a metric (named as SPAN) to quantify the capability of dynamic
topic model to capture word evolution in topics over time. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Bootstrapping for multivariate linear regression models,
Abstract: The multivariate linear regression model is an important tool for
investigating relationships between several response variables and several
predictor variables. The primary interest is in inference about the unknown
regression coefficient matrix. We propose multivariate bootstrap techniques as
a means for making inferences about the unknown regression coefficient matrix.
These bootstrapping techniques are extensions of those developed in Freedman
(1981), which are only appropriate for univariate responses. Extensions to the
multivariate linear regression model are made without proof. We formalize this
extension and prove its validity. A real data example and two simulated data
examples which offer some finite sample verification of our theoretical results
are provided. | [
0,
0,
1,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: Long coherence times for edge spins,
Abstract: We show that in certain one-dimensional spin chains with open boundary
conditions, the edge spins retain memory of their initial state for very long
times. The long coherence times do not require disorder, only an ordered phase.
In the integrable Ising and XYZ chains, the presence of a strong zero mode
means the coherence time is infinite, even at infinite temperature. When Ising
is perturbed by interactions breaking the integrability, the coherence time
remains exponentially long in the perturbing couplings. We show that this is a
consequence of an edge "almost" strong zero mode that almost commutes with the
Hamiltonian. We compute this operator explicitly, allowing us to estimate
accurately the plateau value of edge spin autocorrelator. | [
0,
1,
1,
0,
0,
0
] | [
"Physics"
] |
Title: DR/DZ equivalence conjecture and tautological relations,
Abstract: In this paper we present a family of conjectural relations in the
tautological ring of the moduli spaces of stable curves which implies the
strong double ramification/Dubrovin-Zhang equivalence conjecture. Our
tautological relations have the form of an equality between two different
families of tautological classes, only one of which involves the double
ramification cycle. We prove that both families behave the same way upon
pullback and pushforward with respect to forgetting a marked point. We also
prove that our conjectural relations are true in genus $0$ and $1$ and also
when first pushed forward from $\overline{\mathcal{M}}_{g,n+m}$ to
$\overline{\mathcal{M}}_{g,n}$ and then restricted to $\mathcal{M}_{g,n}$, for
any $g,n,m\geq 0$. Finally we show that, for semisimple CohFTs, the DR/DZ
equivalence only depends on a subset of our relations, finite in each genus,
which we prove for $g\leq 2$. As an application we find a new formula for the
class $\lambda_g$ as a linear combination of dual trees intersected with kappa
and psi classes, and we check it for $g \leq 3$. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Forward Flux Sampling Calculation of Homogeneous Nucleation Rates from Aqueous NaCl Solutions,
Abstract: We used molecular dynamics simulations and the path sampling technique known
as forward flux sampling to study homogeneous nucleation of NaCl crystals from
supersaturated aqueous solutions at 298 K and 1 bar. Nucleation rates were
obtained for a range of salt concentrations for the Joung-Cheatham NaCl force
field combined with the SPC/E water model. The calculated nucleation rates are
significantly lower than available experimental measurements. The estimates for
the nucleation rates in this work do not rely on classical nucleation theory,
but the pathways observed in the simulations suggest that the nucleation
process is better described by classical nucleation theory than an alternative
interpretation based on Ostwald's step rule, in contrast to some prior
simulations of related models. In addition to the size of NaCl nucleus, we find
that the crystallinity of a nascent cluster plays an important role in the
nucleation process. Nuclei with high crystallinity were found to have higher
growth probability and longer lifetimes, possibly because they are less exposed
to hydration water. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Chemistry"
] |
Title: Statistical Implications of the Revenue Transfer Methodology in the Affordable Care Act,
Abstract: The Affordable Care Act (ACA) includes a permanent revenue transfer
methodology which provides financial incentives to health insurance plans that
have higher than average actuarial risk. In this paper, we derive some
statistical implications of the revenue transfer methodology in the ACA. We
treat as random variables the revenue transfers between individual insurance
plans in a given marketplace, where each plan's revenue transfer amount is
measured as a percentage of the plan's total premium. We analyze the means and
variances of those random variables, and deduce from the zero sum nature of the
revenue transfers that there is no limit to the magnitude of revenue transfer
payments relative to plans' total premiums. Using data provided by the American
Academy of Actuaries and by the Centers for Medicare and Medicaid Services, we
obtain an explanation for empirical phenomena that revenue transfers were more
variable and can be substantially greater for insurance plans with smaller
market shares. We show that it is often the case that an insurer which has
decreasing market share will also have increased volatility in its revenue
transfers. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Quantitative Finance"
] |
Title: Chance-Constrained Combinatorial Optimization with a Probability Oracle and Its Application to Probabilistic Partial Set Covering,
Abstract: We investigate a class of chance-constrained combinatorial optimization
problems. Given a pre-specified risk level $\epsilon \in [0,1]$, the
chance-constrained program aims to find the minimum cost selection of a vector
of binary decisions $x$ such that a desirable event $\mathcal{B}(x)$ occurs
with probability at least $ 1-\epsilon$. In this paper, we assume that we have
an oracle that computes $\mathbb P( \mathcal{B}(x))$ exactly. Using this
oracle, we propose a general exact method for solving the chance-constrained
problem. In addition, we show that if the chance-constrained program is solved
approximately by a sampling-based approach, then the oracle can be used as a
tool for checking and fixing the feasibility of the optimal solution given by
this approach. We demonstrate the effectiveness of our proposed methods on a
variant of the probabilistic set covering problem (PSC), which admits an
efficient probability oracle. We give a compact mixed-integer program that
solves PSC optimally (without sampling) for a special case. For large-scale
instances for which the exact methods exhibit slow convergence, we propose a
sampling-based approach that exploits the special structure of PSC. In
particular, we introduce a new class of facet-defining inequalities for a
submodular substructure of PSC, and show that a sampling-based algorithm
coupled with the probability oracle solves the large-scale test instances
effectively. | [
0,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
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