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Title: Interference effects of deleterious and beneficial mutations in large asexual populations,
Abstract: Linked beneficial and deleterious mutations are known to decrease the
fixation probability of a favorable mutation in large asexual populations.
While the hindering effect of strongly deleterious mutations on adaptive
evolution has been well studied, how weak deleterious mutations, either in
isolation or with superior beneficial mutations, influence the fixation of a
beneficial mutation has not been fully explored. Here, using a multitype
branching process, we obtain an accurate analytical expression for the fixation
probability when deleterious effects are weak, and exploit this result along
with the clonal interference theory to investigate the joint effect of linked
beneficial and deleterious mutations on the rate of adaptation. We find that
when the mutation rate is increased beyond the beneficial fitness effect, the
fixation probability of the beneficial mutant decreases from Haldane's
classical result towards zero. This has the consequence that above a critical
mutation rate that may depend on the population size, the adaptation rate
decreases exponentially with the mutation rate and is independent of the
population size. In addition, we find that for a range of mutation rates, both
beneficial and deleterious mutations interfere and impede the adaptation
process in large populations. We also study the evolution of mutation rates in
adapting asexual populations, and conclude that linked beneficial mutations
have a stronger influence on mutator fixation than the deleterious mutations. | [
0,
0,
0,
0,
1,
0
] |
Title: Grassmanians and Pseudosphere Arrangements,
Abstract: We extend vector configurations to more general objects that have nicer
combinatorial and topological properties, called weighted pseudosphere
arrangements. These are defined as a weighted variant of arrangements of
pseudospheres, as in the Topological Representation Theorem for oriented
matroids. We show that in rank 3, the real Stiefel manifold, Grassmanian, and
oriented Grassmanian are homotopy equivalent to the analagously defined spaces
of weighted pseudosphere arrangements. We also show for all rank 3 oriented
matroids, that the space of realizations by weighted pseudosphere arrangements
is contractible. This is a sharp contrast with vector configurations, where the
space of realizations can have the homotopy type of any primary semialgebraic
set. | [
0,
0,
1,
0,
0,
0
] |
Title: Rational homotopy theory via Sullivan models: a survey,
Abstract: This survey contains the main results in rational homotopy, from the
beginning to the most recent ones. It makes the status of the art, gives a
short presentation of some areas where rational homotopy has been used, and
contains a lot of important open problems | [
0,
0,
1,
0,
0,
0
] |
Title: Variations on known and recent cardinality bounds,
Abstract: Sapirovskii [18] proved that $|X|\leq\pi\chi(X)^{c(X)\psi(X)}$, for a regular
space $X$. We introduce the $\theta$-pseudocharacter of a Urysohn space $X$,
denoted by $\psi_\theta (X)$, and prove that the previous inequality holds for
Urysohn spaces replacing the bounds on celluarity $c(X)\leq\kappa$ and on
pseudocharacter $\psi(X)\leq\kappa$ with a bound on Urysohn cellularity
$Uc(X)\leq\kappa$ (which is a weaker conditon because $Uc(X)\leq c(X)$) and on
$\theta$-pseudocharacter $\psi_\theta (X)\leq\kappa$ respectivly (note that in
general $\psi(\cdot)\leq\psi_\theta (\cdot)$ and in the class of regular spaces
$\psi(\cdot)=\psi_\theta(\cdot)$). Further, in [6] the authors generalized the
Dissanayake and Willard's inequality: $|X|\leq 2^{aL_{c}(X)\chi(X)}$, for
Hausdorff spaces $X$ [25], in the class of $n$-Hausdorff spaces and de Groot's
result: $|X|\leq 2^{hL(X)}$, for Hausdorff spaces [11], in the class of $T_1$
spaces (see Theorems 2.22 and 2.23 in [6]). In this paper we restate Theorem
2.22 in [6] in the class of $n$-Urysohn spaces and give a variation of Theorem
2.23 in [6] using new cardinal functions, denoted by $UW(X)$, $\psi
w_\theta(X)$, $\theta\hbox{-}aL(X)$, $h\theta\hbox{-}aL(X)$,
$\theta\hbox{-}aL_c(X)$ and $\theta\hbox{-}aL_{\theta}(X)$. In [5] the authors
introduced the Hausdorff point separating weight of a space $X$ denoted by
$Hpsw(X)$ and proved a Hausdorff version of Charlesworth's inequality $|X|\leq
psw(X)^{L(X)\psi(X)}$ [7]. In this paper, we introduce the Urysohn point
separating weight of a space $X$, denoted by $Upsw(X)$, and prove that $|X|\leq
Upsw(X)^{\theta\hbox{-}aL_{c}(X)\psi(X)}$, for a Urysohn space $X$. | [
0,
0,
1,
0,
0,
0
] |
Title: Spatio-temporal intermittency of the turbulent energy cascade,
Abstract: In incompressible and periodic statistically stationary turbulence, exchanges
of turbulent energy across scales and space are characterised by very intense
and intermittent spatio-temporal fluctuations around zero of the
time-derivative term, the spatial turbulent transport of fluctuating energy,
and the pressure-velocity term. These fluctuations are correlated with each
other and with the intense intermittent fluctuations of the interscale energy
transfer rate. These correlations are caused by the sweeping effect, the link
between non-linearity and non-locality, and also relate to geometrical
alignments between the two-point fluctuating pressure force difference and the
two-point fluctuating velocity difference in the case of the correlation
between the interscale transfer rate and the pressure-velocity term. All these
processes are absent from the spatio-temporal average picture of the turbulence
cascade in statistically stationary and homogeneous turbulence. | [
0,
1,
0,
0,
0,
0
] |
Title: Voltage Analytics for Power Distribution Network Topology Verification,
Abstract: Distribution grids constitute complex networks of lines often times
reconfigured to minimize losses, balance loads, alleviate faults, or for
maintenance purposes. Topology monitoring becomes a critical task for optimal
grid scheduling. While synchrophasor installations are limited in low-voltage
grids, utilities have an abundance of smart meter data at their disposal. In
this context, a statistical learning framework is put forth for verifying
single-phase grid structures using non-synchronized voltage data. The related
maximum likelihood task boils down to minimizing a non-convex function over a
non-convex set. The function involves the sample voltage covariance matrix and
the feasible set is relaxed to its convex hull. Asymptotically in the number of
data, the actual topology yields the global minimizer of the original and the
relaxed problems. Under a simplified data model, the function turns out to be
convex, thus offering optimality guarantees. Prior information on line statuses
is also incorporated via a maximum a-posteriori approach. The formulated tasks
are tackled using solvers having complementary strengths. Numerical tests using
real data on benchmark feeders demonstrate that reliable topology estimates can
be acquired even with a few smart meter data, while the non-convex schemes
exhibit superior line verification performance at the expense of additional
computational time. | [
0,
0,
1,
0,
0,
0
] |
Title: The comprehension construction,
Abstract: In this paper we construct an analogue of Lurie's "unstraightening"
construction that we refer to as the "comprehension construction". Its input is
a cocartesian fibration $p \colon E \to B$ between $\infty$-categories together
with a third $\infty$-category $A$. The comprehension construction then defines
a map from the quasi-category of functors from $A$ to $B$ to the large
quasi-category of cocartesian fibrations over $A$ that acts on $f \colon A \to
B$ by forming the pullback of $p$ along $f$. To illustrate the versatility of
this construction, we define the covariant and contravariant Yoneda embeddings
as special cases of the comprehension functor. We then prove that the hom-wise
action of the comprehension functor coincides with an "external action" of the
hom-spaces of $B$ on the fibres of $p$ and use this to prove that the Yoneda
embedding is fully faithful, providing an explicit equivalence between a
quasi-category and the homotopy coherent nerve of a Kan-complex enriched
category. | [
0,
0,
1,
0,
0,
0
] |
Title: Deception Detection in Videos,
Abstract: We present a system for covert automated deception detection in real-life
courtroom trial videos. We study the importance of different modalities like
vision, audio and text for this task. On the vision side, our system uses
classifiers trained on low level video features which predict human
micro-expressions. We show that predictions of high-level micro-expressions can
be used as features for deception prediction. Surprisingly, IDT (Improved Dense
Trajectory) features which have been widely used for action recognition, are
also very good at predicting deception in videos. We fuse the score of
classifiers trained on IDT features and high-level micro-expressions to improve
performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio
domain also provide a significant boost in performance, while information from
transcripts is not very beneficial for our system. Using various classifiers,
our automated system obtains an AUC of 0.877 (10-fold cross-validation) when
evaluated on subjects which were not part of the training set. Even though
state-of-the-art methods use human annotations of micro-expressions for
deception detection, our fully automated approach outperforms them by 5%. When
combined with human annotations of micro-expressions, our AUC improves to
0.922. We also present results of a user-study to analyze how well do average
humans perform on this task, what modalities they use for deception detection
and how they perform if only one modality is accessible. Our project page can
be found at \url{this https URL}. | [
1,
0,
0,
0,
0,
0
] |
Title: Extended degenerate Stirling numbers of the second kind and extended degenerate Bell polynomials,
Abstract: In a recent work, the degenerate Stirling polynomials of the second kind were
studied by T. Kim. In this paper, we investigate the extended degenerate
Stirling numbers of the second kind and the extended degenerate Bell
polynomials associated with them. As results, we give some expressions,
identities and properties about the extended degener- ate Stirling numbers of
the second kind and the extended degenerate Bell polynomials. | [
0,
0,
1,
0,
0,
0
] |
Title: On The Inductive Bias of Words in Acoustics-to-Word Models,
Abstract: Acoustics-to-word models are end-to-end speech recognizers that use words as
targets without relying on pronunciation dictionaries or graphemes. These
models are notoriously difficult to train due to the lack of linguistic
knowledge. It is also unclear how the amount of training data impacts the
optimization and generalization of such models. In this work, we study the
optimization and generalization of acoustics-to-word models under different
amounts of training data. In addition, we study three types of inductive bias,
leveraging a pronunciation dictionary, word boundary annotations, and
constraints on word durations. We find that constraining word durations leads
to the most improvement. Finally, we analyze the word embedding space learned
by the model, and find that the space has a structure dominated by the
pronunciation of words. This suggests that the contexts of words, instead of
their phonetic structure, should be the future focus of inductive bias in
acoustics-to-word models. | [
1,
0,
0,
0,
0,
0
] |
Title: BRAVO - Biased Locking for Reader-Writer Locks,
Abstract: Designers of modern reader-writer locks confront a difficult trade-off
related to reader scalability. Locks that have a compact memory representation
for active readers will typically suffer under high intensity read-dominated
workloads when the "reader indicator"' state is updated frequently by a diverse
set of threads, causing cache invalidation and coherence traffic. Other
designs, such as cohort reader-writer locks, use distributed reader indicators,
one per NUMA node. This improves reader-reader scalability, but also increases
the size of each lock instance.
We propose a simple transformation BRAVO, that augments any existing
reader-writer lock, adding just two integer fields to the lock instance.
Readers make their presence known to writers by hashing their thread's identity
with the lock address, forming an index into a visible readers table. Readers
attempt to install the lock address into that element in the table, making
their existence known to potential writers. All locks and threads in an address
space can share the visible readers table. Updates by readers tend to be
diffused over the table, resulting in a NUMA-friendly design. Crucially,
readers of the same lock tend to write to different locations in the array,
reducing coherence traffic.
Specifically, BRAVO allows a simple compact lock to be augmented so as to
provide scalable concurrent reading but with only a modest increase in
footprint. | [
1,
0,
0,
0,
0,
0
] |
Title: A PCA-based approach for subtracting thermal background emission in high-contrast imaging data,
Abstract: Ground-based observations at thermal infrared wavelengths suffer from large
background radiation due to the sky, telescope and warm surfaces in the
instrument. This significantly limits the sensitivity of ground-based
observations at wavelengths longer than 3 microns. We analyzed this background
emission in infrared high contrast imaging data, show how it can be modelled
and subtracted and demonstrate that it can improve the detection of faint
sources, such as exoplanets. We applied principal component analysis to model
and subtract the thermal background emission in three archival high contrast
angular differential imaging datasets in the M and L filter. We describe how
the algorithm works and explain how it can be applied. The results of the
background subtraction are compared to the results from a conventional mean
background subtraction scheme. Finally, both methods for background subtraction
are also compared by performing complete data reductions. We analyze the
results from the M dataset of HD100546 qualitatively. For the M band dataset of
beta Pic and the L band dataset of HD169142, which was obtained with an annular
groove phase mask vortex vector coronagraph, we also calculate and analyze the
achieved signal to noise (S/N). We show that applying PCA is an effective way
to remove spatially and temporarily varying thermal background emission down to
close to the background limit. The procedure also proves to be very successful
at reconstructing the background that is hidden behind the PSF. In the complete
data reductions, we find at least qualitative improvements for HD100546 and
HD169142, however, we fail to find a significant increase in S/N of beta Pic b.
We discuss these findings and argue that in particular datasets with strongly
varying observing conditions or infrequently sampled sky background will
benefit from the new approach. | [
0,
1,
0,
0,
0,
0
] |
Title: A weak law of large numbers for estimating the correlation in bivariate Brownian semistationary processes,
Abstract: This article presents various weak laws of large numbers for the so-called
realised covariation of a bivariate stationary stochastic process which is not
a semimartingale. More precisely, we consider two cases: Bivariate moving
average processes with stochastic correlation and bivariate Brownian
semistationary processes with stochastic correlation. In both cases, we can
show that the (possibly scaled) realised covariation converges to the
integrated (possibly volatility modulated) stochastic correlation process. | [
0,
0,
1,
1,
0,
0
] |
Title: A Unified Approach to Adaptive Regularization in Online and Stochastic Optimization,
Abstract: We describe a framework for deriving and analyzing online optimization
algorithms that incorporate adaptive, data-dependent regularization, also
termed preconditioning. Such algorithms have been proven useful in stochastic
optimization by reshaping the gradients according to the geometry of the data.
Our framework captures and unifies much of the existing literature on adaptive
online methods, including the AdaGrad and Online Newton Step algorithms as well
as their diagonal versions. As a result, we obtain new convergence proofs for
these algorithms that are substantially simpler than previous analyses. Our
framework also exposes the rationale for the different preconditioned updates
used in common stochastic optimization methods. | [
1,
0,
1,
1,
0,
0
] |
Title: Discretization error cancellation in electronic structure calculation: a quantitative study,
Abstract: It is often claimed that error cancellation plays an essential role in
quantum chemistry and first-principle simulation for condensed matter physics
and materials science. Indeed, while the energy of a large, or even
medium-size, molecular system cannot be estimated numerically within chemical
accuracy (typically 1 kcal/mol or 1 mHa), it is considered that the energy
difference between two configurations of the same system can be computed in
practice within the desired accuracy.
The purpose of this paper is to provide a quantitative study of
discretization error cancellation. The latter is the error component due to the
fact that the model used in the calculation (e.g. Kohn-Sham LDA) must be
discretized in a finite basis set to be solved by a computer. We first report
comprehensive numerical simulations performed with Abinit on two simple
chemical systems, the hydrogen molecule on the one hand, and a system
consisting of two oxygen atoms and four hydrogen atoms on the other hand. We
observe that errors on energy differences are indeed significantly smaller than
errors on energies, but that these two quantities asymptotically converge at
the same rate when the energy cut-off goes to infinity. We then analyze a
simple one-dimensional periodic Schrödinger equation with Dirac potentials,
for which analytic solutions are available. This allows us to explain the
discretization error cancellation phenomenon on this test case with
quantitative mathematical arguments. | [
0,
1,
1,
0,
0,
0
] |
Title: Analysis of spectral clustering algorithms for community detection: the general bipartite setting,
Abstract: We consider spectral clustering algorithms for community detection under a
general bipartite stochastic block model (SBM). A modern spectral clustering
algorithm consists of three steps: (1) regularization of an appropriate
adjacency or Laplacian matrix (2) a form of spectral truncation and (3) a
k-means type algorithm in the reduced spectral domain. We focus on the
adjacency-based spectral clustering and for the first step, propose a new
data-driven regularization that can restore the concentration of the adjacency
matrix even for the sparse networks. This result is based on recent work on
regularization of random binary matrices, but avoids using unknown population
level parameters, and instead estimates the necessary quantities from the data.
We also propose and study a novel variation of the spectral truncation step and
show how this variation changes the nature of the misclassification rate in a
general SBM. We then show how the consistency results can be extended to models
beyond SBMs, such as inhomogeneous random graph models with approximate
clusters, including a graphon clustering problem, as well as general
sub-Gaussian biclustering. A theme of the paper is providing a better
understanding of the analysis of spectral methods for community detection and
establishing consistency results, under fairly general clustering models and
for a wide regime of degree growths, including sparse cases where the average
expected degree grows arbitrarily slowly. | [
1,
0,
0,
1,
0,
0
] |
Title: A computational approach to calculate the heat of transport of aqueous solutions,
Abstract: Thermal gradients induce concentration gradients in alkali halide solutions,
and the salt migrates towards hot or cold regions depending on the average
temperature of the solution. This effect has been interpreted using the heat of
transport, which provides a route to rationalize thermophoretic phenomena.
Early theories provide estimates of the heat of transport at infinite dilution.
These values are used to interpret thermodiffusion (Soret) and thermoelectric
(Seebeck) effects. However, accessing heats of transport of individual ions at
finite concentration remains an outstanding question both theoretically and
experimentally. Here we discuss a computational approach to calculate heats of
transport of aqueous solutions at finite concentrations, and apply our method
to study lithium chloride solutions at concentrations $>0.5$~M. The heats of
transport are significantly different for Li$^+$ and Cl$^-$ ions, unlike what
is expected at infinite dilution. We find theoretical evidence for the
existence of minima in the Soret coefficient of LiCl, where the magnitude of
the heat of transport is maximized. The Seebeck coefficient obtained from the
ionic heats of transport varies significantly with temperature and
concentration. We identify thermodynamic conditions leading to a maximization
of the thermoelectric response of aqueous solutions. | [
0,
1,
0,
0,
0,
0
] |
Title: The p-convolution forest: a method for solving graphical models with additive probabilistic equations,
Abstract: Convolution trees, loopy belief propagation, and fast numerical p-convolution
are combined for the first time to efficiently solve networks with several
additive constraints between random variables. An implementation of this
"convolution forest" approach is constructed from scratch, including an
improved trimmed convolution tree algorithm and engineering details that permit
fast inference in practice, and improve the ability of scientists to prototype
models with additive relationships between discrete variables. The utility of
this approach is demonstrated using several examples: these include
illustrations on special cases of some classic NP-complete problems (subset sum
and knapsack), identification of GC-rich genomic regions with a large hidden
Markov model, inference of molecular composition from summary statistics of the
intact molecule, and estimation of elemental abundance in the presence of
overlapping isotope peaks. | [
0,
0,
0,
1,
0,
0
] |
Title: Statistical Timing Analysis for Latch-Controlled Circuits with Reduced Iterations and Graph Transformations,
Abstract: Level-sensitive latches are widely used in high- performance designs. For
such circuits efficient statistical timing analysis algorithms are needed to
take increasing process vari- ations into account. But existing methods solving
this problem are still computationally expensive and can only provide the yield
at a given clock period. In this paper we propose a method combining reduced
iterations and graph transformations. The reduced iterations extract setup time
constraints and identify a subgraph for the following graph transformations
handling the constraints from nonpositive loops. The combined algorithms are
very efficient, more than 10 times faster than other existing methods, and
result in a parametric minimum clock period, which together with the hold time
constraints can be used to compute the yield at any given clock period very
easily. | [
1,
0,
0,
0,
0,
0
] |
Title: A moment-angle manifold whose cohomology is not torsion free,
Abstract: In this paper we give a method to construct moment-angle manifolds whose
cohomologies are not torsion free. We also give method to describe the
corresponding simplicial sphere by its non-faces. | [
0,
0,
1,
0,
0,
0
] |
Title: The committee machine: Computational to statistical gaps in learning a two-layers neural network,
Abstract: Heuristic tools from statistical physics have been used in the past to locate
the phase transitions and compute the optimal learning and generalization
errors in the teacher-student scenario in multi-layer neural networks. In this
contribution, we provide a rigorous justification of these approaches for a
two-layers neural network model called the committee machine. We also introduce
a version of the approximate message passing (AMP) algorithm for the committee
machine that allows to perform optimal learning in polynomial time for a large
set of parameters. We find that there are regimes in which a low generalization
error is information-theoretically achievable while the AMP algorithm fails to
deliver it, strongly suggesting that no efficient algorithm exists for those
cases, and unveiling a large computational gap. | [
0,
0,
0,
1,
0,
0
] |
Title: On the anomalous {changes of seismicity and} geomagnetic field prior to the 2011 $M_w$ 9.0 Tohoku earthquake,
Abstract: Xu et al. [J. Asian Earth Sci. {\bf 77}, 59-65 (2013)] It has just been
reported that approximately 2 months prior to the $M_w$9.0 Tohoku earthquake
that occurred in Japan on 11 March 2011 anomalous variations of the geomagnetic
field have been observed in the vertical component at a measuring station about
135 km from the epicenter for about 10 days (4 to 14 January 2011). Here, we
show that this observation is in striking agreement with independent recent
results obtained from natural time analysis of seismicity in Japan. In
particular, this analysis has revealed that an unprecedented minimum of the
order parameter fluctuations of seismicity was observed around 5 January 2011,
thus pointing to the initiation at that date of a strong precursory Seismic
Electric Signals activity accompanied by the anomalous geomagnetic field
variations. Starting from this date, natural time analysis of the subsequent
seismicity indicates that a strong mainshock was expected in a few days to one
week after 08:40 LT on 10 March 2011. | [
0,
1,
0,
0,
0,
0
] |
Title: Exponentially small splitting of separatrices near a period-doubling bifurcation in area-preserving maps,
Abstract: We consider the conservative Hénon family at the period-doubling
bifurcation of its fixed point and demonstrate that the separatrices of the
fixed saddle point nearing the bifurcation split exponentially: given that
$\lambda_+$ is the smaller of the eigenvalues of the saddle point, the angle
between the separatrices along the homoclinic orbit satisfies $$\sin \alpha =
O(e^{-{\pi^2 \over \log |\lambda_+|}})+ O\left( e^{-2 (1-\kappa) {\pi^2 \over
\log |\lambda_+|}} \right),$$ for any positive $\kappa<1$. | [
0,
0,
1,
0,
0,
0
] |
Title: Deep Neural Networks for Multiple Speaker Detection and Localization,
Abstract: We propose to use neural networks for simultaneous detection and localization
of multiple sound sources in human-robot interaction. In contrast to
conventional signal processing techniques, neural network-based sound source
localization methods require fewer strong assumptions about the environment.
Previous neural network-based methods have been focusing on localizing a single
sound source, which do not extend to multiple sources in terms of detection and
localization. In this paper, we thus propose a likelihood-based encoding of the
network output, which naturally allows the detection of an arbitrary number of
sources. In addition, we investigate the use of sub-band cross-correlation
information as features for better localization in sound mixtures, as well as
three different network architectures based on different motivations.
Experiments on real data recorded from a robot show that our proposed methods
significantly outperform the popular spatial spectrum-based approaches. | [
1,
0,
0,
0,
0,
0
] |
Title: Active Tolerant Testing,
Abstract: In this work, we give the first algorithms for tolerant testing of nontrivial
classes in the active model: estimating the distance of a target function to a
hypothesis class C with respect to some arbitrary distribution D, using only a
small number of label queries to a polynomial-sized pool of unlabeled examples
drawn from D. Specifically, we show that for the class D of unions of d
intervals on the line, we can estimate the error rate of the best hypothesis in
the class to an additive error epsilon from only $O(\frac{1}{\epsilon^6}\log
\frac{1}{\epsilon})$ label queries to an unlabeled pool of size
$O(\frac{d}{\epsilon^2}\log \frac{1}{\epsilon})$. The key point here is the
number of labels needed is independent of the VC-dimension of the class. This
extends the work of Balcan et al. [2012] who solved the non-tolerant testing
problem for this class (distinguishing the zero-error case from the case that
the best hypothesis in the class has error greater than epsilon).
We also consider the related problem of estimating the performance of a given
learning algorithm A in this setting. That is, given a large pool of unlabeled
examples drawn from distribution D, can we, from only a few label queries,
estimate how well A would perform if the entire dataset were labeled? We focus
on k-Nearest Neighbor style algorithms, and also show how our results can be
applied to the problem of hyperparameter tuning (selecting the best value of k
for the given learning problem). | [
1,
0,
0,
1,
0,
0
] |
Title: Dirac Line-nodes and Effect of Spin-orbit Coupling in Non-symmorphic Critical Semimetal MSiS (M=Hf, Zr),
Abstract: Topological Dirac semimetals (TDSs) represent a new state of quantum matter
recently discovered that offers a platform for realizing many exotic physical
phenomena. A TDS is characterized by the linear touching of bulk (conduction
and valance) bands at discrete points in the momentum space (i.e. 3D Dirac
points), such as in Na3Bi and Cd3As2. More recently, new types of Dirac
semimetals with robust Dirac line-nodes (with non-trivial topology or near the
critical point between topological phase transitions) have been proposed that
extends the bulk linear touching from discrete points to 1D lines. In this
work, using angle-resolved photoemission spectroscopy (ARPES), we explored the
electronic structure of the non-symmorphic crystals MSiS (M=Hf, Zr).
Remarkably, by mapping out the band structure in the full 3D Brillouin Zone
(BZ), we observed two sets of Dirac line-nodes in parallel with the kz-axis and
their dispersions. Interestingly, along directions other than the line-nodes in
the 3D BZ, the bulk degeneracy is lifted by spin-orbit coupling (SOC) in both
compounds with larger magnitude in HfSiS. Our work not only experimentally
confirms a new Dirac line-node semimetal family protected by non-symmorphic
symmetry, but also helps understanding and further exploring the exotic
properties as well as practical applications of the MSiS family of compounds. | [
0,
1,
0,
0,
0,
0
] |
Title: An Arcsine Law for Markov Random Walks,
Abstract: The classic arcsine law for the number
$N_{n}^{>}:=n^{-1}\sum_{k=1}^{n}\mathbf{1}_{\{S_{k}>0\}}$ of positive terms, as
$n\to\infty$, in an ordinary random walk $(S_{n})_{n\ge 0}$ is extended to the
case when this random walk is governed by a positive recurrent Markov chain
$(M_{n})_{n\ge 0}$ on a countable state space $\mathcal{S}$, that is, for a
Markov random walk $(M_{n},S_{n})_{n\ge 0}$ with positive recurrent discrete
driving chain. More precisely, it is shown that $n^{-1}N_{n}^{>}$ converges in
distribution to a generalized arcsine law with parameter $\rho\in [0,1]$ (the
classic arcsine law if $\rho=1/2$) iff the Spitzer condition $$
\lim_{n\to\infty}\frac{1}{n}\sum_{k=1}^{n}\mathbb{P}_{i}(S_{n}>0)\ =\ \rho $$
holds true for some and then all $i\in\mathcal{S}$, where
$\mathbb{P}_{i}:=\mathbb{P}(\cdot|M_{0}=i)$ for $i\in\mathcal{S}$. It is also
proved, under an extra assumption on the driving chain if $0<\rho<1$, that this
condition is equivalent to the stronger variant $$
\lim_{n\to\infty}\mathbb{P}_{i}(S_{n}>0)\ =\ \rho. $$ For an ordinary random
walk, this was shown by Doney for $0<\rho<1$ and by Bertoin and Doney for
$\rho\in\{0,1\}$. | [
0,
0,
1,
0,
0,
0
] |
Title: Linear Estimation of Treatment Effects in Demand Response: An Experimental Design Approach,
Abstract: Demand response aims to stimulate electricity consumers to modify their loads
at critical time periods. In this paper, we consider signals in demand response
programs as a binary treatment to the customers and estimate the average
treatment effect, which is the average change in consumption under the demand
response signals. More specifically, we propose to estimate this effect by
linear regression models and derive several estimators based on the different
models. From both synthetic and real data, we show that including more
information about the customers does not always improve estimation accuracy:
the interaction between the side information and the demand response signal
must be carefully modeled. In addition, we compare the traditional linear
regression model with the modified covariate method which models the
interaction between treatment effect and covariates. We analyze the variances
of these estimators and discuss different cases where each respective estimator
works the best. The purpose of these comparisons is not to claim the
superiority of the different methods, rather we aim to provide practical
guidance on the most suitable estimator to use under different settings. Our
results are validated using data collected by Pecan Street and EnergyPlus. | [
1,
0,
1,
0,
0,
0
] |
Title: Private Learning on Networks: Part II,
Abstract: This paper considers a distributed multi-agent optimization problem, with the
global objective consisting of the sum of local objective functions of the
agents. The agents solve the optimization problem using local computation and
communication between adjacent agents in the network. We present two randomized
iterative algorithms for distributed optimization. To improve privacy, our
algorithms add "structured" randomization to the information exchanged between
the agents. We prove deterministic correctness (in every execution) of the
proposed algorithms despite the information being perturbed by noise with
non-zero mean. We prove that a special case of a proposed algorithm (called
function sharing) preserves privacy of individual polynomial objective
functions under a suitable connectivity condition on the network topology. | [
1,
0,
1,
0,
0,
0
] |
Title: The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification,
Abstract: Annotation of training data is the major bottleneck in the creation of text
classification systems. Active learning is a commonly used technique to reduce
the amount of training data one needs to label. A crucial aspect of active
learning is determining when to stop labeling data. Three potential sources for
informing when to stop active learning are an additional labeled set of data,
an unlabeled set of data, and the training data that is labeled during the
process of active learning. To date, no one has compared and contrasted the
advantages and disadvantages of stopping methods based on these three
information sources. We find that stopping methods that use unlabeled data are
more effective than methods that use labeled data. | [
1,
0,
0,
1,
0,
0
] |
Title: RAFP-Pred: Robust Prediction of Antifreeze Proteins using Localized Analysis of n-Peptide Compositions,
Abstract: In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs)
to counter the otherwise lethal intracellular formation of ice. Structures and
sequences of various AFPs exhibit a high degree of heterogeneity, consequently
the prediction of the AFPs is considered to be a challenging task. In this
research, we propose to handle this arduous manifold learning task using the
notion of localized processing. In particular an AFP sequence is segmented into
two sub-segments each of which is analyzed for amino acid and di-peptide
compositions. We propose to use only the most significant features using the
concept of information gain (IG) followed by a random forest classification
approach. The proposed RAFP-Pred achieved an excellent performance on a number
of standard datasets. We report a high Youden's index
(sensitivity+specificity-1) value of 0.75 on the standard independent test data
set outperforming the AFP-PseAAC, AFP\_PSSM, AFP-Pred and iAFP by a margin of
0.05, 0.06, 0.14 and 0.68 respectively. The verification rate on the UniProKB
dataset is found to be 83.19\% which is substantially superior to the 57.18\%
reported for the iAFP method. | [
0,
0,
0,
0,
1,
0
] |
Title: Second descent and rational points on Kummer varieties,
Abstract: A powerful method pioneered by Swinnerton-Dyer allows one to study rational
points on pencils of curves of genus 1 by combining the fibration method with a
sophisticated form of descent. A variant of this method, first used by
Skorobogatov and Swinnerton-Dyer in 2005, can be applied to study rational
points on Kummer varieties. In this paper we extend the method to include an
additional step of second descent. Assuming finiteness of the relevant
Tate-Shafarevich groups, we use the extended method to show that the
Brauer-Manin obstruction is the only obstruction to the Hasse principle on
Kummer varieties associated to abelian varieties with all rational 2-torsion,
under mild additional hypotheses. | [
0,
0,
1,
0,
0,
0
] |
Title: When Can Neural Networks Learn Connected Decision Regions?,
Abstract: Previous work has questioned the conditions under which the decision regions
of a neural network are connected and further showed the implications of the
corresponding theory to the problem of adversarial manipulation of classifiers.
It has been proven that for a class of activation functions including leaky
ReLU, neural networks having a pyramidal structure, that is no layer has more
hidden units than the input dimension, produce necessarily connected decision
regions. In this paper, we advance this important result by further developing
the sufficient and necessary conditions under which the decision regions of a
neural network are connected. We then apply our framework to overcome the
limits of existing work and further study the capacity to learn connected
regions of neural networks for a much wider class of activation functions
including those widely used, namely ReLU, sigmoid, tanh, softlus, and
exponential linear function. | [
1,
0,
0,
1,
0,
0
] |
Title: Note on the backwards uniqueness of mean curvature flow,
Abstract: In this note, we will show a backwards uniqueness theorem of the mean
curvature flow with bounded second fundamental form in arbitrary codimension. | [
0,
0,
1,
0,
0,
0
] |
Title: A Volcanic Hydrogen Habitable Zone,
Abstract: The classical habitable zone is the circular region around a star in which
liquid water could exist on the surface of a rocky planet. The outer edge of
the traditional N2-CO2-H2O habitable zone (HZ) extends out to nearly 1.7 AU in
our Solar System, beyond which condensation and scattering by CO2 outstrips its
greenhouse capacity. Here, we show that volcanic outgassing of atmospheric H2
on a planet near the outer edge can extend the habitable zone out to ~2.4 AU in
our solar system. This wider volcanic hydrogen habitable zone (N2-CO2-H2O-H2)
can be sustained as long as volcanic H2 output offsets its escape from the top
of the atmosphere. We use a single-column radiative-convective climate model to
compute the HZ limits of this volcanic hydrogen habitable zone for hydrogen
concentrations between 1% and 50%, assuming diffusion-limited atmospheric
escape. At a hydrogen concentration of 50%, the effective stellar flux required
to support the outer edge decreases by ~35% to 60% for M to A stars. The
corresponding orbital distances increase by ~30% to 60%. The inner edge of this
HZ only moves out by ~0.1 to 4% relative to the classical HZ because H2 warming
is reduced in dense H2O atmospheres. The atmospheric scale heights of such
volcanic H2 atmospheres near the outer edge of the HZ also increase,
facilitating remote detection of atmospheric signatures. | [
0,
1,
0,
0,
0,
0
] |
Title: A Penrose type inequaltiy for graphs over Reissner-Nordström-anti-deSitter manifold,
Abstract: In this paper, we use the inverse mean curvature flow to establish an optimal
Minkowski type inquality, weighted Alexandrov-Fenchel inequality for the mean
convex star shaped hypersurfaces in Reissner-Nordström-anti-deSitter manifold
and Penrose type inequality for asymptotically locally hyperbolic manifolds in
which can be realized as graphs over Reissner-Nordström-anti-deSitter
manifold. | [
0,
0,
1,
0,
0,
0
] |
Title: A Novel Approach for Fast and Accurate Mean Error Distance Computation in Approximate Adders,
Abstract: In error-tolerant applications, approximate adders have been exploited
extensively to achieve energy efficient system designs. Mean error distance is
one of the important error metrics used as a performance measure of approximate
adders. In this work, a fast and efficient methodology is proposed to determine
the exact mean error distance in approximate lower significant bit adders. A
detailed description of the proposed algorithm along with an example has been
demonstrated in this paper. Experimental analysis shows that the proposed
method performs better than existing Monte Carlo simulation approach both in
terms of accuracy and execution time. | [
1,
0,
0,
0,
0,
0
] |
Title: Fluorescent Troffer-powered Internet of Things: An Experimental Study of Electric-field Energy Harvesting,
Abstract: A totally new energy harvesting architecture that exploits ambient
electric-field (E-field) emitting from fluorescent light fixtures is presented.
A copper plate, 50 x 50 cm in size, is placed in between the ambient field to
extract energy by capacitive coupling. A low voltage prototype is designed,
structured and tested on a conventional ceiling-type 4-light fluorescent
troffer operating in 50 Hz 220 V AC power grid. It is examined that the
harvester is able to collect roughly 1.25 J of energy in 25 min when a 0.1 F of
super-capacitor is employed. The equivalent circuit and the physical model of
the proposed harvesting paradigm are provided, and the attainable power is
evaluated in both theoretical and experimental manner. The scavenged energy is
planned to be utilized for building battery-less Internet of Things (IoT)
networks that are obliged to sense environmental parameters, analyze the
gathered data, and remotely inform a higher authority within predefined
periods. | [
1,
1,
0,
0,
0,
0
] |
Title: Parallel Simultaneous Perturbation Optimization,
Abstract: Stochastic computer simulations enable users to gain new insights into
complex physical systems. Optimization is a common problem in this context:
users seek to find model inputs that maximize the expected value of an
objective function. The objective function, however, is time-intensive to
evaluate, and cannot be directly measured. Instead, the stochastic nature of
the model means that individual realizations are corrupted by noise. More
formally, we consider the problem of optimizing the expected value of an
expensive black-box function with continuously-differentiable mean, from which
observations are corrupted by Gaussian noise. We present Parallel Simultaneous
Perturbation Optimization (PSPO), which extends a well-known stochastic
optimization algorithm, simultaneous perturbation stochastic approximation, in
several important ways. Our modifications allow the algorithm to fully take
advantage of parallel computing resources, like high-performance cloud
computing. The resulting PSPO algorithm takes fewer time-consuming iterations
to converge, automatically chooses the step size, and can vary the error
tolerance by step. Theoretical results are supported by a numerical example. To
demonstrate the performance of the algorithm, we implemented the algorithm to
maximize the pseudo-likelihood of a stochastic epidemiological model to data of
a measles outbreak. | [
0,
0,
1,
0,
0,
0
] |
Title: Agent-based model for the origins of scaling in human language,
Abstract: Background/Introduction: The Zipf's law establishes that if the words of a
(large) text are ordered by decreasing frequency, the frequency versus the rank
decreases as a power law with exponent close to -1. Previous work has stressed
that this pattern arises from a conflict of interests of the participants of
communication: speakers and hearers. Methods: The challenge here is to define a
computational language game on a population of agents, playing games mainly
based on a parameter that measures the relative participant's interests.
Results: Numerical simulations suggest that at critical values of the parameter
a human-like vocabulary, exhibiting scaling properties, seems to appear.
Conclusions: The appearance of an intermediate distribution of frequencies at
some critical values of the parameter suggests that on a population of
artificial agents the emergence of scaling partly arises as a self-organized
process only from local interactions between agents. | [
1,
1,
0,
0,
0,
0
] |
Title: Credible Review Detection with Limited Information using Consistency Analysis,
Abstract: Online reviews provide viewpoints on the strengths and shortcomings of
products/services, influencing potential customers' purchasing decisions.
However, the proliferation of non-credible reviews -- either fake (promoting/
demoting an item), incompetent (involving irrelevant aspects), or biased --
entails the problem of identifying credible reviews. Prior works involve
classifiers harnessing rich information about items/users -- which might not be
readily available in several domains -- that provide only limited
interpretability as to why a review is deemed non-credible. This paper presents
a novel approach to address the above issues. We utilize latent topic models
leveraging review texts, item ratings, and timestamps to derive consistency
features without relying on item/user histories, unavailable for "long-tail"
items/users. We develop models, for computing review credibility scores to
provide interpretable evidence for non-credible reviews, that are also
transferable to other domains -- addressing the scarcity of labeled data.
Experiments on real-world datasets demonstrate improvements over
state-of-the-art baselines. | [
1,
0,
0,
1,
0,
0
] |
Title: The Asymptotically Self-Similar Regime for the Einstein Vacuum Equations,
Abstract: We develop a local theory for the construction of singular spacetimes in all
spacetime dimensions which become asymptotically self-similar as the
singularity is approached. The techniques developed also allow us to construct
and classify exact self-similar solutions which correspond to the formal
asymptotic expansions of Fefferman and Graham's ambient metric. | [
0,
0,
1,
0,
0,
0
] |
Title: Kinematics and dynamics of an egg-shaped robot with a gyro driven inertia actuator,
Abstract: The manuscript discusses still preliminary considerations with regard to the
dynamics and kinematics of an egg shaped robot with an gyro driven inertia
actuator. The method of calculation follows the idea that we would like to
express the entire dynamic equations in terms of moments instead of forces.
Also we avoid to derive the equations from a Lagrange function with
constraints. The result of the calculations is meant to be applicable to two
robot prototypes that have been build at the AES\&R Laboratory at the National
Chung Cheng University in Taiwan. | [
1,
0,
0,
0,
0,
0
] |
Title: Cloaking and anamorphism for light and mass diffusion,
Abstract: We first review classical results on cloaking and mirage effects for
electromagnetic waves. We then show that transformation optics allows the
masking of objects or produces mirages in diffusive regimes. In order to
achieve this, we consider the equation for diffusive photon density in
transformed coordinates, which is valid for diffusive light in scattering
media. More precisely, generalizing transformations for star domains introduced
in [Diatta and Guenneau, J. Opt. 13, 024012, 2011] for matter waves, we
numerically demonstrate that infinite conducting objects of different shapes
scatter diffusive light in exactly the same way. We also propose a design of
external light-diffusion cloak with spatially varying sign-shifting parameters
that hides a finite size scatterer outside the cloak. We next analyse
non-physical parameter in the transformed Fick's equation derived in [Guenneau
and Puvirajesinghe, R. Soc. Interface 10, 20130106, 2013], and propose to use a
non-linear transform that overcomes this problem. We finally investigate other
form invariant transformed diffusion-like equations in the time domain, and
touch upon conformal mappings and non-Euclidean cloaking applied to diffusion
processes. | [
0,
1,
1,
0,
0,
0
] |
Title: A statistical physics approach to learning curves for the Inverse Ising problem,
Abstract: Using methods of statistical physics, we analyse the error of learning
couplings in large Ising models from independent data (the inverse Ising
problem). We concentrate on learning based on local cost functions, such as the
pseudo-likelihood method for which the couplings are inferred independently for
each spin. Assuming that the data are generated from a true Ising model, we
compute the reconstruction error of the couplings using a combination of the
replica method with the cavity approach for densely connected systems. We show
that an explicit estimator based on a quadratic cost function achieves minimal
reconstruction error, but requires the length of the true coupling vector as
prior knowledge. A simple mean field estimator of the couplings which does not
need such knowledge is asymptotically optimal, i.e. when the number of
observations is much large than the number of spins. Comparison of the theory
with numerical simulations shows excellent agreement for data generated from
two models with random couplings in the high temperature region: a model with
independent couplings (Sherrington-Kirkpatrick model), and a model where the
matrix of couplings has a Wishart distribution. | [
0,
1,
0,
1,
0,
0
] |
Title: Fast Kinetic Scheme : efficient MPI parallelization strategy for 3D Boltzmann equation,
Abstract: In this paper we present a parallelization strategy on distributed memory
systems for the Fast Kinetic Scheme --- a semi-Lagrangian scheme developed in
[J. Comput. Phys., Vol. 255, 2013, pp 680-698] for solving kinetic equations.
The original algorithm was proposed for the BGK approximation of the collision
kernel. In this work we deal with its extension to the full Boltzmann equation
in six dimensions, where the collision operator is resolved by means of fast
spectral method. We present close to ideal scalability of the proposed
algorithm on tera- and peta-scale systems. | [
0,
1,
1,
0,
0,
0
] |
Title: Finding a Feasible Initial Solution for Flatness-Based Multi-Link Manipulator Motion Planning under State and Control Constraints,
Abstract: In this paper, we present a method to initialize at a feasible point and
unfailingly solve a non-convex optimization problem in which a set-point motion
is planned for a multi-link manipulator under state and control constraints. We
construct an initial feasible solution by analyzing the final time effect for
feasibility problems of flatness based motion planning problems. More
specifically, we first find a feasible time-optimal trajectory under state
constraints without a control constraint by solving a linear programming
problem. Then, we find a feasible trajectory under control constraints by
scaling the trajectory. To evaluate the practical applicability of the proposed
method, we did numerical experiments to solve a multi-link manipulator motion
planning problem by combining the method with recursive inverse dynamics
algorithms. | [
1,
0,
1,
0,
0,
0
] |
Title: Techniques for visualizing LSTMs applied to electrocardiograms,
Abstract: This paper explores four different visualization techniques for long
short-term memory (LSTM) networks applied to continuous-valued time series. On
the datasets analysed, we find that the best visualization technique is to
learn an input deletion mask that optimally reduces the true class score. With
a specific focus on single-lead electrocardiograms from the MIT-BIH arrhythmia
dataset, we show that salient input features for the LSTM classifier align well
with medical theory. | [
1,
0,
0,
1,
0,
0
] |
Title: Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration,
Abstract: Discovery of an accurate causal Bayesian network structure from observational
data can be useful in many areas of science. Often the discoveries are made
under uncertainty, which can be expressed as probabilities. To guide the use of
such discoveries, including directing further investigation, it is important
that those probabilities be well-calibrated. In this paper, we introduce a
novel framework to derive calibrated probabilities of causal relationships from
observational data. The framework consists of three components: (1) an
approximate method for generating initial probability estimates of the edge
types for each pair of variables, (2) the availability of a relatively small
number of the causal relationships in the network for which the truth status is
known, which we call a calibration training set, and (3) a calibration method
for using the approximate probability estimates and the calibration training
set to generate calibrated probabilities for the many remaining pairs of
variables. We also introduce a new calibration method based on a shallow neural
network. Our experiments on simulated data support that the proposed approach
improves the calibration of causal edge predictions. The results also support
that the approach often improves the precision and recall of predictions. | [
1,
0,
0,
1,
0,
0
] |
Title: Faster Bounding Box Annotation for Object Detection in Indoor Scenes,
Abstract: This paper proposes an approach for rapid bounding box annotation for object
detection datasets. The procedure consists of two stages: The first step is to
annotate a part of the dataset manually, and the second step proposes
annotations for the remaining samples using a model trained with the first
stage annotations. We experimentally study which first/second stage split
minimizes to total workload. In addition, we introduce a new fully labeled
object detection dataset collected from indoor scenes. Compared to other indoor
datasets, our collection has more class categories, different backgrounds,
lighting conditions, occlusion and high intra-class differences. We train deep
learning based object detectors with a number of state-of-the-art models and
compare them in terms of speed and accuracy. The fully annotated dataset is
released freely available for the research community. | [
0,
0,
0,
1,
0,
0
] |
Title: The SeaQuest Spectrometer at Fermilab,
Abstract: The SeaQuest spectrometer at Fermilab was designed to detect
oppositely-charged pairs of muons (dimuons) produced by interactions between a
120 GeV proton beam and liquid hydrogen, liquid deuterium and solid nuclear
targets. The primary physics program uses the Drell-Yan process to probe
antiquark distributions in the target nucleon. The spectrometer consists of a
target system, two dipole magnets and four detector stations. The upstream
magnet is a closed-aperture solid iron magnet which also serves as the beam
dump, while the second magnet is an open aperture magnet. Each of the detector
stations consists of scintillator hodoscopes and a high-resolution tracking
device. The FPGA-based trigger compares the hodoscope signals to a set of
pre-programmed roads to determine if the event contains oppositely-signed,
high-mass muon pairs. | [
0,
1,
0,
0,
0,
0
] |
Title: Direct-Manipulation Visualization of Deep Networks,
Abstract: The recent successes of deep learning have led to a wave of interest from
non-experts. Gaining an understanding of this technology, however, is
difficult. While the theory is important, it is also helpful for novices to
develop an intuitive feel for the effect of different hyperparameters and
structural variations. We describe TensorFlow Playground, an interactive, open
sourced visualization that allows users to experiment via direct manipulation
rather than coding, enabling them to quickly build an intuition about neural
nets. | [
1,
0,
0,
1,
0,
0
] |
Title: How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices?,
Abstract: How many samples are sufficient to guarantee that the eigenvectors and
eigenvalues of the sample covariance matrix are close to those of the actual
covariance matrix? For a wide family of distributions, including distributions
with finite second moment and distributions supported in a centered Euclidean
ball, we prove that the inner product between eigenvectors of the sample and
actual covariance matrices decreases proportionally to the respective
eigenvalue distance. Our findings imply non-asymptotic concentration bounds for
eigenvectors, eigenspaces, and eigenvalues. They also provide conditions for
distinguishing principal components based on a constant number of samples. | [
0,
0,
1,
1,
0,
0
] |
Title: Wavefronts for a nonlinear nonlocal bistable reaction-diffusion equation in population dynamics,
Abstract: The wavefronts of a nonlinear nonlocal bistable reaction-diffusion equation,
\begin{align*} \frac{\partial u}{\partial t}=\frac{\partial^2u}{\partial
x^2}+u^2(1-J_\sigma*u)-du,\quad(t,x)\in(0,\infty)\times\mathbb R, \end{align*}
with $J_\sigma(x)=(1/\sigma)= J(x/\sigma)$ and $ \int_{\mathbb R} J(x)dx=1 $
are investigated in this article. It is proven that there exists a
$c_*(\sigma)$ such that for all $c\geq c_*(\sigma)$, a monotone wavefront
$(c,\omega)$ can be connected by the two positive equilibrium points. On the
other hand, there exists a $c^*(\sigma)$ such that the model admits a
semi-wavefront $(c^*(\sigma),\omega)$ with $\omega(-\infty)=0$. Furthermore, it
is shown that for sufficiently small $\sigma$, the semi-wavefronts are in fact
wavefronts connecting $0$ to the largest equilibrium. In addition, the
wavefronts converge to those of the local problem as $\sigma\to0$. | [
0,
0,
1,
0,
0,
0
] |
Title: Kafnets: kernel-based non-parametric activation functions for neural networks,
Abstract: Neural networks are generally built by interleaving (adaptable) linear layers
with (fixed) nonlinear activation functions. To increase their flexibility,
several authors have proposed methods for adapting the activation functions
themselves, endowing them with varying degrees of flexibility. None of these
approaches, however, have gained wide acceptance in practice, and research in
this topic remains open. In this paper, we introduce a novel family of flexible
activation functions that are based on an inexpensive kernel expansion at every
neuron. Leveraging over several properties of kernel-based models, we propose
multiple variations for designing and initializing these kernel activation
functions (KAFs), including a multidimensional scheme allowing to nonlinearly
combine information from different paths in the network. The resulting KAFs can
approximate any mapping defined over a subset of the real line, either convex
or nonconvex. Furthermore, they are smooth over their entire domain, linear in
their parameters, and they can be regularized using any known scheme, including
the use of $\ell_1$ penalties to enforce sparseness. To the best of our
knowledge, no other known model satisfies all these properties simultaneously.
In addition, we provide a relatively complete overview on alternative
techniques for adapting the activation functions, which is currently lacking in
the literature. A large set of experiments validates our proposal. | [
1,
0,
0,
1,
0,
0
] |
Title: Modeling rooted in-trees by finite p-groups,
Abstract: The aim of this chapter is to provide an adequate graph theoretic framework
for the description of periodic bifurcations which have recently been
discovered in descendant trees of finite p-groups. The graph theoretic concepts
of rooted in-trees with weighted vertices and edges perfectly admit an abstract
formulation of the group theoretic notions of successive extensions, nuclear
rank, multifurcation, and step size. Since all graphs in this chapter are
infinite and dense, we use methods of pattern recognition and independent
component analysis to reduce the complex structure to periodically repeating
finite patterns. The method of group cohomology yields subgraph isomorphisms
required for proving the periodicity of branches along mainlines. Finally the
mainlines are glued together with the aid of infinite limit groups whose finite
quotients form the vertices of mainlines. The skeleton of the infinite graph is
a countable union of infinite mainlines, connected by periodic bifurcations.
Each mainline is the backbone of a minimal subtree consisting of a periodically
repeating finite pattern of branches with bounded depth. A second periodicity
is caused by isomorphisms between all minimal subtrees which make up the
complete infinite graph. Only the members of the first minimal tree are
metabelian and the bifurcations, which were unknown up to now, open the long
desired door to non-metabelian extensions whose second derived quotients are
isomorphic to the metabelian groups. An application of this key result to
algebraic number theory solves the problem of p-class field towers of exact
length three. | [
0,
0,
1,
0,
0,
0
] |
Title: Towards Algorithmic Typing for DOT,
Abstract: The Dependent Object Types (DOT) calculus formalizes key features of Scala.
The D$_{<: }$ calculus is the core of DOT. To date, presentations of D$_{<: }$
have used declarative typing and subtyping rules, as opposed to algorithmic.
Unfortunately, algorithmic typing for full D$_{<: }$ is known to be an
undecidable problem.
We explore the design space for a restricted version of D$_{<: }$ that has
decidable typechecking. Even in this simplified D$_{<: }$ , algorithmic typing
and subtyping are tricky, due to the "bad bounds" problem. The Scala compiler
bypasses bad bounds at the cost of a loss in expressiveness in its type system.
Based on the approach taken in the Scala compiler, we present the Step Typing
and Step Subtyping relations for D$_{<: }$. We prove these relations sound and
decidable. They are not complete with respect to the original D$_{<: }$ rules. | [
1,
0,
0,
0,
0,
0
] |
Title: A new statistical method for characterizing the atmospheres of extrasolar planets,
Abstract: By detecting light from extrasolar planets,we can measure their compositions
and bulk physical properties. The technologies used to make these measurements
are still in their infancy, and a lack of self-consistency suggests that
previous observations have underestimated their systemic errors.We demonstrate
a statistical method, newly applied to exoplanet characterization, which uses a
Bayesian formalism to account for underestimated errorbars. We use this method
to compare photometry of a substellar companion, GJ 758b, with custom
atmospheric models. Our method produces a probability distribution of
atmospheric model parameters including temperature, gravity, cloud model
(fsed), and chemical abundance for GJ 758b. This distribution is less sensitive
to highly variant data, and appropriately reflects a greater uncertainty on
parameter fits. | [
0,
1,
0,
0,
0,
0
] |
Title: Prioritizing network communities,
Abstract: Uncovering modular structure in networks is fundamental for systems in
biology, physics, and engineering. Community detection identifies candidate
modules as hypotheses, which then need to be validated through experiments,
such as mutagenesis in a biological laboratory. Only a few communities can
typically be validated, and it is thus important to prioritize which
communities to select for downstream experimentation. Here we develop CRank, a
mathematically principled approach for prioritizing network communities. CRank
efficiently evaluates robustness and magnitude of structural features of each
community and then combines these features into the community prioritization.
CRank can be used with any community detection method. It needs only
information provided by the network structure and does not require any
additional metadata or labels. However, when available, CRank can incorporate
domain-specific information to further boost performance. Experiments on many
large networks show that CRank effectively prioritizes communities, yielding a
nearly 50-fold improvement in community prioritization. | [
0,
0,
0,
1,
1,
0
] |
Title: On Structured Prediction Theory with Calibrated Convex Surrogate Losses,
Abstract: We provide novel theoretical insights on structured prediction in the context
of efficient convex surrogate loss minimization with consistency guarantees.
For any task loss, we construct a convex surrogate that can be optimized via
stochastic gradient descent and we prove tight bounds on the so-called
"calibration function" relating the excess surrogate risk to the actual risk.
In contrast to prior related work, we carefully monitor the effect of the
exponential number of classes in the learning guarantees as well as on the
optimization complexity. As an interesting consequence, we formalize the
intuition that some task losses make learning harder than others, and that the
classical 0-1 loss is ill-suited for general structured prediction. | [
1,
0,
0,
1,
0,
0
] |
Title: Improving Sharir and Welzl's bound on crossing-free matchings through solving a stronger recurrence,
Abstract: Sharir and Welzl [1] derived a bound on crossing-free matchings primarily
based on solving a recurrence based on the size of the matchings. We show that
the recurrence given in Lemma 2.3 in Sharir and Welzl can be improve to
$(2n-6s)\textbf{Ma}_{m}(P)\leq\frac{68}{3}(s+2)\textbf{Ma}_{m-1}(P)$ and
$(3n-7s)\textbf{Ma}_{m}(P)\leq44.5(s+2)\textbf{Ma}_{m-1}(P)$, thereby improving
the upper bound for crossing-free matchings. | [
0,
0,
1,
0,
0,
0
] |
Title: Computing eigenfunctions and eigenvalues of boundary value problems with the orthogonal spectral renormalization method,
Abstract: The spectral renormalization method was introduced in 2005 as an effective
way to compute ground states of nonlinear Schrödinger and Gross-Pitaevskii
type equations. In this paper, we introduce an orthogonal spectral
renormalization (OSR) method to compute ground and excited states (and their
respective eigenvalues) of linear and nonlinear eigenvalue problems. The
implementation of the algorithm follows four simple steps: (i) reformulate the
underlying eigenvalue problem as a fixed point equation, (ii) introduce a
renormalization factor that controls the convergence properties of the
iteration, (iii) perform a Gram-Schmidt orthogonalization process in order to
prevent the iteration from converging to an unwanted mode; and (iv) compute the
solution sought using a fixed-point iteration. The advantages of the OSR scheme
over other known methods (such as Newton's and self-consistency) are: (i) it
allows the flexibility to choose large varieties of initial guesses without
diverging, (ii) easy to implement especially at higher dimensions and (iii) it
can easily handle problems with complex and random potentials. The OSR method
is implemented on benchmark Hermitian linear and nonlinear eigenvalue problems
as well as linear and nonlinear non-Hermitian $\mathcal{PT}$-symmetric models. | [
0,
1,
0,
0,
0,
0
] |
Title: A Discontinuity Adjustment for Subdistribution Function Confidence Bands Applied to Right-Censored Competing Risks Data,
Abstract: The wild bootstrap is the resampling method of choice in survival analytic
applications. Theoretic justifications rely on the assumption of existing
intensity functions which is equivalent to an exclusion of ties among the event
times. However, such ties are omnipresent in practical studies. It turns out
that the wild bootstrap should only be applied in a modified manner that
corrects for altered limit variances and emerging dependencies. This again
ensures the asymptotic exactness of inferential procedures. An analogous
necessity is the use of the Greenwood-type variance estimator for Nelson-Aalen
estimators which is particularly preferred in tied data regimes. All theoretic
arguments are transferred to bootstrapping Aalen-Johansen estimators for
cumulative incidence functions in competing risks. An extensive simulation
study as well as an application to real competing risks data of male intensive
care unit patients suffering from pneumonia illustrate the practicability of
the proposed technique. | [
0,
0,
1,
1,
0,
0
] |
Title: Estimation of block sparsity in compressive sensing,
Abstract: In this paper, we consider a soft measure of block sparsity,
$k_\alpha(\mathbf{x})=\left(\lVert\mathbf{x}\rVert_{2,\alpha}/\lVert\mathbf{x}\rVert_{2,1}\right)^{\frac{\alpha}{1-\alpha}},\alpha\in[0,\infty]$
and propose a procedure to estimate it by using multivariate isotropic
symmetric $\alpha$-stable random projections without sparsity or block sparsity
assumptions. The limiting distribution of the estimator is given. Some
simulations are conducted to illustrate our theoretical results. | [
1,
0,
0,
1,
0,
0
] |
Title: Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP,
Abstract: A fundamental question in reinforcement learning is whether model-free
algorithms are sample efficient. Recently, Jin et al. \cite{jin2018q} proposed
a Q-learning algorithm with UCB exploration policy, and proved it has nearly
optimal regret bound for finite-horizon episodic MDP. In this paper, we adapt
Q-learning with UCB-exploration bonus to infinite-horizon MDP with discounted
rewards \emph{without} accessing a generative model. We show that the
\textit{sample complexity of exploration} of our algorithm is bounded by
$\tilde{O}({\frac{SA}{\epsilon^2(1-\gamma)^7}})$. This improves the previously
best known result of $\tilde{O}({\frac{SA}{\epsilon^4(1-\gamma)^8}})$ in this
setting achieved by delayed Q-learning \cite{strehl2006pac}, and matches the
lower bound in terms of $\epsilon$ as well as $S$ and $A$ except for
logarithmic factors. | [
1,
0,
0,
1,
0,
0
] |
Title: You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems,
Abstract: Properly benchmarking Automated Program Repair (APR) systems should
contribute to the development and adoption of the research outputs by
practitioners. To that end, the research community must ensure that it reaches
significant milestones by reliably comparing state-of-the-art tools for a
better understanding of their strengths and weaknesses. In this work, we
identify and investigate a practical bias caused by the fault localization (FL)
step in a repair pipeline. We propose to highlight the different fault
localization configurations used in the literature, and their impact on APR
systems when applied to the Defects4J benchmark. Then, we explore the
performance variations that can be achieved by `tweaking' the FL step.
Eventually, we expect to create a new momentum for (1) full disclosure of APR
experimental procedures with respect to FL, (2) realistic expectations of
repairing bugs in Defects4J, as well as (3) reliable performance comparison
among the state-of-the-art APR systems, and against the baseline performance
results of our thoroughly assessed kPAR repair tool. Our main findings include:
(a) only a subset of Defects4J bugs can be currently localized by commonly-used
FL techniques; (b) current practice of comparing state-of-the-art APR systems
(i.e., counting the number of fixed bugs) is potentially misleading due to the
bias of FL configurations; and (c) APR authors do not properly qualify their
performance achievement with respect to the different tuning parameters
implemented in APR systems. | [
1,
0,
0,
0,
0,
0
] |
Title: Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates,
Abstract: Spatial understanding is a fundamental problem with wide-reaching real-world
applications. The representation of spatial knowledge is often modeled with
spatial templates, i.e., regions of acceptability of two objects under an
explicit spatial relationship (e.g., "on", "below", etc.). In contrast with
prior work that restricts spatial templates to explicit spatial prepositions
(e.g., "glass on table"), here we extend this concept to implicit spatial
language, i.e., those relationships (generally actions) for which the spatial
arrangement of the objects is only implicitly implied (e.g., "man riding
horse"). In contrast with explicit relationships, predicting spatial
arrangements from implicit spatial language requires significant common sense
spatial understanding. Here, we introduce the task of predicting spatial
templates for two objects under a relationship, which can be seen as a spatial
question-answering task with a (2D) continuous output ("where is the man w.r.t.
a horse when the man is walking the horse?"). We present two simple
neural-based models that leverage annotated images and structured text to learn
this task. The good performance of these models reveals that spatial locations
are to a large extent predictable from implicit spatial language. Crucially,
the models attain similar performance in a challenging generalized setting,
where the object-relation-object combinations (e.g.,"man walking dog") have
never been seen before. Next, we go one step further by presenting the models
with unseen objects (e.g., "dog"). In this scenario, we show that leveraging
word embeddings enables the models to output accurate spatial predictions,
proving that the models acquire solid common sense spatial knowledge allowing
for such generalization. | [
1,
0,
0,
1,
0,
0
] |
Title: Evaporating pure, binary and ternary droplets: thermal effects and axial symmetry breaking,
Abstract: The Greek aperitif Ouzo is not only famous for its specific anise-flavored
taste, but also for its ability to turn from a transparent miscible liquid to a
milky-white colored emulsion when water is added. Recently, it has been shown
that this so-called Ouzo effect, i.e. the spontaneous emulsification of oil
microdroplets, can also be triggered by the preferential evaporation of ethanol
in an evaporating sessile Ouzo drop, leading to an amazingly rich drying
process with multiple phase transitions [H. Tan et al., Proc. Natl. Acad. Sci.
USA 113(31) (2016) 8642]. Due to the enhanced evaporation near the contact
line, the nucleation of oil droplets starts at the rim which results in an oil
ring encircling the drop. Furthermore, the oil droplets are advected through
the Ouzo drop by a fast solutal Marangoni flow. In this article, we investigate
the evaporation of mixture droplets in more detail, by successively increasing
the mixture complexity from pure water over a binary water-ethanol mixture to
the ternary Ouzo mixture (water, ethanol and anise oil). In particular,
axisymmetric and full three-dimensional finite element method simulations have
been performed on these droplets to discuss thermal effects and the complicated
flow in the droplet driven by an interplay of preferential evaporation,
evaporative cooling and solutal and thermal Marangoni flow. By using image
analysis techniques and micro-PIV measurements, we are able to compare the
numerically predicted volume evolutions and velocity fields with experimental
data. The Ouzo droplet is furthermore investigated by confocal microscopy. It
is shown that the oil ring predominantly emerges due to coalescence. | [
0,
1,
0,
0,
0,
0
] |
Title: Gaussian Processes for Demand Unconstraining,
Abstract: One of the key challenges in revenue management is unconstraining demand
data. Existing state of the art single-class unconstraining methods make
restrictive assumptions about the form of the underlying demand and can perform
poorly when applied to data which breaks these assumptions. In this paper, we
propose a novel unconstraining method that uses Gaussian process (GP)
regression. We develop a novel GP model by constructing and implementing a new
non-stationary covariance function for the GP which enables it to learn and
extrapolate the underlying demand trend. We show that this method can cope with
important features of realistic demand data, including nonlinear demand trends,
variations in total demand, lengthy periods of constraining, non-exponential
inter-arrival times, and discontinuities/changepoints in demand data. In all
such circumstances, our results indicate that GPs outperform existing
single-class unconstraining methods. | [
0,
0,
0,
1,
0,
0
] |
Title: Generalization of two Bonnet's Theorems to the relative Differential Geometry of the 3-dimensional Euclidean space,
Abstract: This paper is devoted to the 3-dimensional relative differential geometry of
surfaces. In the Euclidean space $\R{E} ^3 $ we consider a surface $\varPhi
%\colon \vect{x} = \vect{x}(u^1,u^2) $ with position vector field $\vect{x}$,
which is relatively normalized by a relative normalization $\vect{y}% (u^1,u^2)
$. A surface $\varPhi^*% \colon \vect{x}^* = \vect{x}^*(u^1,u^2) $ with
position vector field $\vect{x}^* = \vect{x} + \mu \, \vect{y}$, where $\mu$ is
a real constant, is called a relatively parallel surface to $\varPhi$. Then
$\vect{y}$ is also a relative normalization of $\varPhi^*$. The aim of this
paper is to formulate and prove the relative analogues of two well known
theorems of O.~Bonnet which concern the parallel surfaces (see~\cite{oB1853}). | [
0,
0,
1,
0,
0,
0
] |
Title: Some Aspects of Uniqueness Theory of Entire and Meromorphic Functions (Ph.D. thesis),
Abstract: The subject of our thesis is the uniqueness theory of meromorphic functions
and it is devoted to problems concerning Bruck conjecture, set sharing and
related topics. The tool, we used in our discussions is classical Nevanlinna
theory of meromorphic functions. In 1996, in order to find the relation between
an entire function with its derivative, counterpart sharing one value CM, a
famous conjecture was proposed by R. Bruck. Since then the conjecture and its
analogous results have been investigated by many researchers and continuous
efforts have been put on by them. In our thesis, we have obtained similar types
of conclusions as that of Bruck for two differential polynomials which in turn
improve several existing results under different sharing environment. A number
of examples have been exhibited to justify the necessity or sharpness of some
conditions, hypothesis used in the thesis. As a variation of value sharing, F.
Gross first introduced the idea of set sharing, by proposing a problem, which
has later became popular as Gross Problem. Inspired by the Gross' Problem, the
set sharing problems were started which was later shifted towards the
characterization of the polynomial backbone of different unique range sets. In
our study, we introduced some new type of unique range sets and at the same
time, we further explored the anatomy of these unique range sets generating
polynomials as well as connected Bruck conjecture with Gross' Problem. | [
0,
0,
1,
0,
0,
0
] |
Title: Transfer Regression via Pairwise Similarity Regularization,
Abstract: Transfer learning methods address the situation where little labeled training
data from the "target" problem exists, but much training data from a related
"source" domain is available. However, the overwhelming majority of transfer
learning methods are designed for simple settings where the source and target
predictive functions are almost identical, limiting the applicability of
transfer learning methods to real world data. We propose a novel, weaker,
property of the source domain that can be transferred even when the source and
target predictive functions diverge. Our method assumes the source and target
functions share a Pairwise Similarity property, where if the source function
makes similar predictions on a pair of instances, then so will the target
function. We propose Pairwise Similarity Regularization Transfer, a flexible
graph-based regularization framework which can incorporate this modeling
assumption into standard supervised learning algorithms. We show how users can
encode domain knowledge into our regularizer in the form of spatial continuity,
pairwise "similarity constraints" and how our method can be scaled to large
data sets using the Nystrom approximation. Finally, we present positive and
negative results on real and synthetic data sets and discuss when our Pairwise
Similarity transfer assumption seems to hold in practice. | [
1,
0,
0,
0,
0,
0
] |
Title: Star formation in a galactic outflow,
Abstract: Recent observations have revealed massive galactic molecular outflows that
may have physical conditions (high gas densities) required to form stars.
Indeed, several recent models predict that such massive galactic outflows may
ignite star formation within the outflow itself. This star-formation mode, in
which stars form with high radial velocities, could contribute to the
morphological evolution of galaxies, to the evolution in size and velocity
dispersion of the spheroidal component of galaxies, and would contribute to the
population of high-velocity stars, which could even escape the galaxy. Such
star formation could provide in-situ chemical enrichment of the circumgalactic
and intergalactic medium (through supernova explosions of young stars on large
orbits), and some models also predict that it may contribute substantially to
the global star formation rate observed in distant galaxies. Although there
exists observational evidence for star formation triggered by outflows or jets
into their host galaxy, as a consequence of gas compression, evidence for star
formation occurring within galactic outflows is still missing. Here we report
new spectroscopic observations that unambiguously reveal star formation
occurring in a galactic outflow at a redshift of 0.0448. The inferred star
formation rate in the outflow is larger than 15 Msun/yr. Star formation may
also be occurring in other galactic outflows, but may have been missed by
previous observations owing to the lack of adequate diagnostics. | [
0,
1,
0,
0,
0,
0
] |
Title: Representation theoretic realization of non-symmetric Macdonald polynomials at infinity,
Abstract: We study the nonsymmetric Macdonald polynomials specialized at infinity from
various points of view. First, we define a family of modules of the Iwahori
algebra whose characters are equal to the nonsymmetric Macdonald polynomials
specialized at infinity. Second, we show that these modules are isomorphic to
the dual spaces of sections of certain sheaves on the semi-infinite Schubert
varieties. Third, we prove that the global versions of these modules are
homologically dual to the level one affine Demazure modules. | [
0,
0,
1,
0,
0,
0
] |
Title: Complex spectrogram enhancement by convolutional neural network with multi-metrics learning,
Abstract: This paper aims to address two issues existing in the current speech
enhancement methods: 1) the difficulty of phase estimations; 2) a single
objective function cannot consider multiple metrics simultaneously. To solve
the first problem, we propose a novel convolutional neural network (CNN) model
for complex spectrogram enhancement, namely estimating clean real and imaginary
(RI) spectrograms from noisy ones. The reconstructed RI spectrograms are
directly used to synthesize enhanced speech waveforms. In addition, since
log-power spectrogram (LPS) can be represented as a function of RI
spectrograms, its reconstruction is also considered as another target. Thus a
unified objective function, which combines these two targets (reconstruction of
RI spectrograms and LPS), is equivalent to simultaneously optimizing two
commonly used objective metrics: segmental signal-to-noise ratio (SSNR) and
logspectral distortion (LSD). Therefore, the learning process is called
multi-metrics learning (MML). Experimental results confirm the effectiveness of
the proposed CNN with RI spectrograms and MML in terms of improved standardized
evaluation metrics on a speech enhancement task. | [
1,
0,
0,
1,
0,
0
] |
Title: Optimal Threshold Design for Quanta Image Sensor,
Abstract: Quanta Image Sensor (QIS) is a binary imaging device envisioned to be the
next generation image sensor after CCD and CMOS. Equipped with a massive number
of single photon detectors, the sensor has a threshold $q$ above which the
number of arriving photons will trigger a binary response "1", or "0"
otherwise. Existing methods in the device literature typically assume that
$q=1$ uniformly. We argue that a spatially varying threshold can significantly
improve the signal-to-noise ratio of the reconstructed image. In this paper, we
present an optimal threshold design framework. We make two contributions.
First, we derive a set of oracle results to theoretically inform the maximally
achievable performance. We show that the oracle threshold should match exactly
with the underlying pixel intensity. Second, we show that around the oracle
threshold there exists a set of thresholds that give asymptotically unbiased
reconstructions. The asymptotic unbiasedness has a phase transition behavior
which allows us to develop a practical threshold update scheme using a
bisection method. Experimentally, the new threshold design method achieves
better rate of convergence than existing methods. | [
1,
0,
0,
0,
0,
0
] |
Title: Statistics of $K$-groups modulo $p$ for the ring of integers of a varying quadratic number field,
Abstract: For each odd prime $p$, we conjecture the distribution of the $p$-torsion
subgroup of $K_{2n}(\mathcal{O}_F)$ as $F$ ranges over real quadratic fields,
or over imaginary quadratic fields. We then prove that the average size of the
$3$-torsion subgroup of $K_{2n}(\mathcal{O}_F)$ is as predicted by this
conjecture. | [
0,
0,
1,
0,
0,
0
] |
Title: A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets,
Abstract: Following the rapidly growing digital image usage, automatic image
categorization has become preeminent research area. It has broaden and adopted
many algorithms from time to time, whereby multi-feature (generally,
hand-engineered features) based image characterization comes handy to improve
accuracy. Recently, in machine learning, pre-trained deep convolutional neural
networks (DCNNs or ConvNets) have been that the features extracted through such
DCNN can improve classification accuracy. Thence, in this paper, we further
investigate a feature embedding strategy to exploit cues from multiple DCNNs.
We derive a generalized feature space by embedding three different DCNN
bottleneck features with weights respect to their Softmax cross-entropy loss.
Test outcomes on six different object classification data-sets and an action
classification data-set show that regardless of variation in image statistics
and tasks the proposed multi-DCNN bottleneck feature fusion is well suited to
image classification tasks and an effective complement of DCNN. The comparisons
to existing fusion-based image classification approaches prove that the
proposed method surmounts the state-of-the-art methods and produces competitive
results with fully trained DCNNs as well. | [
1,
0,
0,
0,
0,
0
] |
Title: Non-existence of a Wente's $L^\infty$ estimate for the Neumann problem,
Abstract: We provide a counterexample of Wente's inequality in the context of Neumann
boundary conditions. We will also show that Wente's estimates fails for general
boundary conditions of Robin type. | [
0,
0,
1,
0,
0,
0
] |
Title: Regular characters of classical groups over complete discrete valuation rings,
Abstract: Let $\mathfrak{o}$ be a complete discrete valuation ring with finide residue
field $\mathsf{k}$ of odd characteristic, and let $\mathbf{G}$ be a symplectic
or special orthogonal group scheme over $\mathfrak{o}$. For any
$\ell\in\mathbb{N}$ let $G^\ell$ denote the $\ell$-th principal congruence
subgroup of $\mathbf{G}(\mathfrak{o})$. An irreducible character of the group
$\mathbf{G}(\mathfrak{o})$ is said to be regular if it is trivial on a subgroup
$G^{\ell+1}$ for some $\ell$, and if its restriction to
$G^\ell/G^{\ell+1}\simeq \mathrm{Lie}(\mathbf{G})(\mathsf{k})$ consists of
characters of minimal $\mathbf{G}(\mathsf{k}^{\rm alg})$ stabilizer dimension.
In the present paper we consider the regular characters of such classical
groups over $\mathfrak{o}$, and construct and enumerate all regular characters
of $\mathbf{G}(\mathfrak{o})$, when the characteristic of $\mathsf{k}$ is
greater than two. As a result, we compute the regular part of their
representation zeta function. | [
0,
0,
1,
0,
0,
0
] |
Title: Quantitative analysis of nonadiabatic effects in dense H$_3$S and PH$_3$ superconductors,
Abstract: The comparison study of high pressure superconducting state of recently
synthesized H$_3$S and PH$_3$ compounds are conducted within the framework of
the strong-coupling theory. By generalization of the standard Eliashberg
equations to include the lowest-order vertex correction, we have investigated
the influence of the nonadiabatic effects on the Coulomb pseudopotential,
electron effective mass, energy gap function and on the $2\Delta(0)/T_C$ ratio.
We found that, for a fixed value of critical temperature ($178$ K for H$_3$S
and $81$ K for PH$_3$), the nonadiabatic corrections reduce the Coulomb
pseudopotential for H$_3$S from $0.204$ to $0.185$ and for PH$_3$ from $0.088$
to $0.083$, however, the electron effective mass and ratio $2\Delta(0)/T_C$
remain unaffected. Independently of the assumed method of analysis, the
thermodynamic parameters of superconducting H$_3$S and PH$_3$ strongly deviate
from the prediction of BCS theory due to the strong-coupling and retardation
effects. | [
0,
1,
0,
0,
0,
0
] |
Title: Janus: An Uncertain Cache Architecture to Cope with Side Channel Attacks,
Abstract: Side channel attacks are a major class of attacks to crypto-systems.
Attackers collect and analyze timing behavior, I/O data, or power consumption
in these systems to undermine their effectiveness in protecting sensitive
information. In this work, we propose a new cache architecture, called Janus,
to enable crypto-systems to introduce randomization and uncertainty in their
runtime timing behavior and power utilization profile. In the proposed cache
architecture, each data block is equipped with an on-off flag to enable/disable
the data block. The Janus architecture has two special instructions in its
instruction set to support the on-off flag. Beside the analytical evaluation of
the proposed cache architecture, we deploy it in an ARM-7 processor core to
study its feasibility and practicality. Results show a significant variation in
the timing behavior across all the benchmarks. The new secure processor
architecture has minimal hardware overhead and significant improvement in
protecting against power analysis and timing behavior attacks. | [
1,
0,
0,
0,
0,
0
] |
Title: Predicting Adversarial Examples with High Confidence,
Abstract: It has been suggested that adversarial examples cause deep learning models to
make incorrect predictions with high confidence. In this work, we take the
opposite stance: an overly confident model is more likely to be vulnerable to
adversarial examples. This work is one of the most proactive approaches taken
to date, as we link robustness with non-calibrated model confidence on noisy
images, providing a data-augmentation-free path forward. The adversarial
examples phenomenon is most easily explained by the trend of increasing
non-regularized model capacity, while the diversity and number of samples in
common datasets has remained flat. Test accuracy has incorrectly been
associated with true generalization performance, ignoring that training and
test splits are often extremely similar in terms of the overall representation
space. The transferability property of adversarial examples was previously used
as evidence against overfitting arguments, a perceived random effect, but
overfitting is not always random. | [
0,
0,
0,
1,
0,
0
] |
Title: Local-ring network automata and the impact of hyperbolic geometry in complex network link-prediction,
Abstract: Topological link-prediction can exploit the entire network topology (global
methods) or only the neighbourhood (local methods) of the link to predict.
Global methods are believed the best. Is this common belief well-founded?
Stochastic-Block-Model (SBM) is a global method believed as one of the best
link-predictors, therefore it is considered a reference for comparison. But,
our results suggest that SBM, whose computational time is high, cannot in
general overcome the Cannistraci-Hebb (CH) network automaton model that is a
simple local-learning-rule of topological self-organization proved as the
current best local-based and parameter-free deterministic rule for
link-prediction. To elucidate the reasons of this unexpected result, we
formally introduce the notion of local-ring network automata models and their
relation with the nature of common-neighbours' definition in complex network
theory. After extensive tests, we recommend Structural-Perturbation-Method
(SPM) as the new best global method baseline. However, even SPM overall does
not outperform CH and in several evaluation frameworks we astonishingly found
the opposite. In particular, CH was the best predictor for synthetic networks
generated by the Popularity-Similarity-Optimization (PSO) model, and its
performance in PSO networks with community structure was even better than using
the original internode-hyperbolic-distance as link-predictor. Interestingly,
when tested on non-hyperbolic synthetic networks the performance of CH
significantly dropped down indicating that this rule of network
self-organization could be strongly associated to the rise of hyperbolic
geometry in complex networks. The superiority of global methods seems a
"misleading belief" caused by a latent geometry bias of the few small networks
used as benchmark in previous studies. We propose to found a latent geometry
theory of link-prediction in complex networks. | [
1,
0,
0,
0,
0,
0
] |
Title: Emission of Circularly Polarized Terahertz Wave from Inhomogeneous Intrinsic Josephson Junctions,
Abstract: We have theoretically demonstrated the emission of circularly-polarized
terahertz (THz) waves from intrinsic Josephson junctions (IJJs) which is
locally heated by an external heat source such as the laser irradiation. We
focus on a mesa-structured IJJ whose geometry is slightly deviate from a square
and find that the local heating make it possible to emit circularly-polarized
THz waves. In this mesa, the inhomogeneity of critical current density induced
by the local heating excites the electromagnetic cavity modes TM (1,0) and TM
(0,1), whose polarizations are orthogonal to each other. The mixture of these
modes results in the generation of circularly-polarized THz waves. We also show
that the circular polarization dramatically changes with the applied voltage.
The emitter based on IJJs can emit circularly-polarized and continuum THz waves
by the local heating, and will be useful for various technological application. | [
0,
1,
0,
0,
0,
0
] |
Title: Opportunistic Downlink Interference Alignment for Multi-Cell MIMO Networks,
Abstract: In this paper, we propose an opportunistic downlink interference alignment
(ODIA) for interference-limited cellular downlink, which intelligently combines
user scheduling and downlink IA techniques. The proposed ODIA not only
efficiently reduces the effect of inter-cell interference from other-cell base
stations (BSs) but also eliminates intra-cell interference among spatial
streams in the same cell. We show that the minimum number of users required to
achieve a target degrees-of-freedom (DoF) can be fundamentally reduced, i.e.,
the fundamental user scaling law can be improved by using the ODIA, compared
with the existing downlink IA schemes. In addition, we adopt a limited feedback
strategy in the ODIA framework, and then analyze the number of feedback bits
required for the system with limited feedback to achieve the same user scaling
law of the ODIA as the system with perfect CSI. We also modify the original
ODIA in order to further improve sum-rate, which achieves the optimal multiuser
diversity gain, i.e., $\log\log N$, per spatial stream even in the presence of
downlink inter-cell interference, where $N$ denotes the number of users in a
cell. Simulation results show that the ODIA significantly outperforms existing
interference management techniques in terms of sum-rate in realistic cellular
environments. Note that the ODIA operates in a non-collaborative and decoupled
manner, i.e., it requires no information exchange among BSs and no iterative
beamformer optimization between BSs and users, thus leading to an easier
implementation. | [
1,
0,
1,
0,
0,
0
] |
Title: The Repeated Divisor Function and Possible Correlation with Highly Composite Numbers,
Abstract: Let n be a non-null positive integer and $d(n)$ is the number of positive
divisors of n, called the divisor function. Of course, $d(n) \leq n$. $d(n) =
1$ if and only if $n = 1$. For $n > 2$ we have $d(n) \geq 2$ and in this paper
we try to find the smallest $k$ such that $d(d(...d(n)...)) = 2$ where the
divisor function is applied $k$ times. At the end of the paper we make a
conjecture based on some observations. | [
0,
0,
1,
0,
0,
0
] |
Title: A Language Hierarchy and Kitchens-Type Theorem for Self-Similar Groups,
Abstract: We generalize the notion of self-similar groups of infinite tree
automorphisms to allow for groups which are defined on a tree but do not act
faithfully on it. The elements of such a group correspond to labeled trees
which may be recognized by a tree automaton (e.g. Rabin, Büchi, etc.), or
considered as elements of a tree shift (e.g. of finite type, sofic) as in
symbolic dynamics. We give examples to show that the various classes of
self-similar groups defined in this way do not coincide. As the main result,
extending the classical result of Kitchens on one-dimensional group shifts, we
provide a sufficient condition for a self-similar group whose elements form a
sofic tree shift to be a tree shift of finite type. As an application, we show
that the closure of certain self-similar groups of tree automorphisms are not
Rabin-recognizable. \end{abstract} | [
0,
0,
1,
0,
0,
0
] |
Title: Distance Covariance in Metric Spaces: Non-Parametric Independence Testing in Metric Spaces (Master's thesis),
Abstract: The aim of this thesis is to find a solution to the non-parametric
independence problem in separable metric spaces. Suppose we are given finite
collection of samples from an i.i.d. sequence of paired random elements, where
each marginal has values in some separable metric space. The non-parametric
independence problem raises the question on how one can use these samples to
reasonably draw inference on whether the marginal random elements are
independent or not. We will try to answer this question by utilizing the
so-called distance covariance functional in metric spaces developed by Russell
Lyons. We show that, if the marginal spaces are so-called metric spaces of
strong negative type (e.g. seperable Hilbert spaces), then the distance
covariance functional becomes a direct indicator of independence. That is, one
can directly determine whether the marginals are independent or not based
solely on the value of this functional. As the functional formally takes the
simultaneous distribution as argument, its value is not known in the posed
non-parametric independence problem. Hence, we construct estimators of the
distance covariance functional, and show that they exhibit asymptotic
properties which can be used to construct asymptotically consistent statistical
tests of independence. Finally, as the rejection thresholds of these
statistical tests are non-traceable we argue that they can be reasonably
bootstrapped. | [
0,
0,
1,
1,
0,
0
] |
Title: Sum-Product-Quotient Networks,
Abstract: We present a novel tractable generative model that extends Sum-Product
Networks (SPNs) and significantly boosts their power. We call it
Sum-Product-Quotient Networks (SPQNs), whose core concept is to incorporate
conditional distributions into the model by direct computation using quotient
nodes, e.g. $P(A|B) = \frac{P(A,B)}{P(B)}$. We provide sufficient conditions
for the tractability of SPQNs that generalize and relax the decomposable and
complete tractability conditions of SPNs. These relaxed conditions give rise to
an exponential boost to the expressive efficiency of our model, i.e. we prove
that there are distributions which SPQNs can compute efficiently but require
SPNs to be of exponential size. Thus, we narrow the gap in expressivity between
tractable graphical models and other Neural Network-based generative models. | [
1,
0,
0,
1,
0,
0
] |
Title: Algebraic Description of Shape Invariance Revisited,
Abstract: We revisit the algebraic description of shape invariance method in
one-dimensional quantum mechanics. In this note we focus on four particular
examples: the Kepler problem in flat space, the Kepler problem in spherical
space, the Kepler problem in hyperbolic space, and the Rosen-Morse potential
problem. Following the prescription given by Gangopadhyaya et al., we first
introduce certain nonlinear algebraic systems. We then show that, if the model
parameters are appropriately quantized, the bound-state problems can be solved
solely by means of representation theory. | [
0,
1,
0,
0,
0,
0
] |
Title: Joint Smoothing, Tracking, and Forecasting Based on Continuous-Time Target Trajectory Fitting,
Abstract: We present a continuous time state estimation framework that unifies
traditionally individual tasks of smoothing, tracking, and forecasting (STF),
for a class of targets subject to smooth motion processes, e.g., the target
moves with nearly constant acceleration or affected by insignificant noises.
Fundamentally different from the conventional Markov transition formulation,
the state process is modeled by a continuous trajectory function of time (FoT)
and the STF problem is formulated as an online data fitting problem with the
goal of finding the trajectory FoT that best fits the observations in a sliding
time-window. Then, the state of the target, whether the past (namely,
smoothing), the current (filtering) or the near-future (forecasting), can be
inferred from the FoT. Our framework releases stringent statistical modeling of
the target motion in real time, and is applicable to a broad range of real
world targets of significance such as passenger aircraft and ships which move
on scheduled, (segmented) smooth paths but little statistical knowledge is
given about their real time movement and even about the sensors. In addition,
the proposed STF framework inherits the advantages of data fitting for
accommodating arbitrary sensor revisit time, target maneuvering and missed
detection. The proposed method is compared with state of the art estimators in
scenarios of either maneuvering or non-maneuvering target. | [
1,
0,
0,
1,
0,
0
] |
Title: Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions,
Abstract: We search for digital biomarkers from Parkinson's Disease by observing
approximate repetitive patterns matching hypothesized step and stride periodic
cycles. These observations were modeled as a cycle of hidden states with
randomness allowing deviation from a canonical pattern of transitions and
emissions, under the hypothesis that the averaged features of hidden states
would serve to informatively characterize classes of patients/controls. We
propose a Hidden Semi-Markov Model (HSMM), a latent-state model, emitting
3D-acceleration vectors. Transitions and emissions are inferred from data. We
fit separate models per unique device and training label. Hidden Markov Models
(HMM) force geometric distributions of the duration spent at each state before
transition to a new state. Instead, our HSMM allows us to specify the
distribution of state duration. This modified version is more effective because
we are interested more in each state's duration than the sequence of distinct
states, allowing inclusion of these durations the feature vector. | [
1,
0,
0,
1,
0,
0
] |
Title: Bifurcation to locked fronts in two component reaction-diffusion systems,
Abstract: We study invasion fronts and spreading speeds in two component
reaction-diffusion systems. Using a variation of Lin's method, we construct
traveling front solutions and show the existence of a bifurcation to locked
fronts where both components invade at the same speed. Expansions of the wave
speed as a function of the diffusion constant of one species are obtained. The
bifurcation can be sub or super-critical depending on whether the locked fronts
exist for parameter values above or below the bifurcation value. Interestingly,
in the sub-critical case numerical simulations reveal that the spreading speed
of the PDE system does not depend continuously on the coefficient of diffusion. | [
0,
1,
1,
0,
0,
0
] |
Title: Contrastive Hebbian Learning with Random Feedback Weights,
Abstract: Neural networks are commonly trained to make predictions through learning
algorithms. Contrastive Hebbian learning, which is a powerful rule inspired by
gradient backpropagation, is based on Hebb's rule and the contrastive
divergence algorithm. It operates in two phases, the forward (or free) phase,
where the data are fed to the network, and a backward (or clamped) phase, where
the target signals are clamped to the output layer of the network and the
feedback signals are transformed through the transpose synaptic weight
matrices. This implies symmetries at the synaptic level, for which there is no
evidence in the brain. In this work, we propose a new variant of the algorithm,
called random contrastive Hebbian learning, which does not rely on any synaptic
weights symmetries. Instead, it uses random matrices to transform the feedback
signals during the clamped phase, and the neural dynamics are described by
first order non-linear differential equations. The algorithm is experimentally
verified by solving a Boolean logic task, classification tasks (handwritten
digits and letters), and an autoencoding task. This article also shows how the
parameters affect learning, especially the random matrices. We use the
pseudospectra analysis to investigate further how random matrices impact the
learning process. Finally, we discuss the biological plausibility of the
proposed algorithm, and how it can give rise to better computational models for
learning. | [
0,
0,
0,
1,
1,
0
] |
Title: Above threshold scattering about a Feshbach resonance for ultracold atoms in an optical collider,
Abstract: Ultracold atomic gases have realised numerous paradigms of condensed matter
physics where control over interactions has crucially been afforded by tunable
Feshbach resonances. So far, the characterisation of these Feshbach resonances
has almost exclusively relied on experiments in the threshold regime near zero
energy. Here we use a laser-based collider to probe a narrow magnetic Feshbach
resonance of rubidium above threshold. By measuring the overall atomic loss
from colliding clouds as a function of magnetic field, we track the
energy-dependent resonance position. At higher energy, our collider scheme
broadens the loss feature, making the identification of the narrow resonance
challenging. However, we observe that the collisions give rise to shifts in the
centre-of-mass positions of outgoing clouds. The shifts cross zero at the
resonance and this allows us to accurately determine its location well above
threshold. Our inferred resonance positions are in excellent agreement with
theory. | [
0,
1,
0,
0,
0,
0
] |
Title: Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data,
Abstract: Identifying anomalous patterns in real-world data is essential for
understanding where, when, and how systems deviate from their expected
dynamics. Yet methods that separately consider the anomalousness of each
individual data point have low detection power for subtle, emerging
irregularities. Additionally, recent detection techniques based on subset
scanning make strong independence assumptions and suffer degraded performance
in correlated data. We introduce methods for identifying anomalous patterns in
non-iid data by combining Gaussian processes with novel log-likelihood ratio
statistic and subset scanning techniques. Our approaches are powerful,
interpretable, and can integrate information across multiple data streams. We
illustrate their performance on numeric simulations and three open source
spatiotemporal datasets of opioid overdose deaths, 311 calls, and storm
reports. | [
0,
0,
0,
1,
0,
0
] |
Title: UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies and Implementation,
Abstract: As air pollution is becoming the largest environmental health risk, the
monitoring of air quality has drawn much attention in both theoretical studies
and practical implementations. In this article, we present a real-time,
fine-grained and power-efficient air quality monitoring system based on aerial
and ground sensing. The architecture of this system consists of four layers:
the sensing layer to collect data, the transmission layer to enable
bidirectional communications, the processing layer to analyze and process the
data, and the presentation layer to provide graphic interface for users. Three
major techniques are investigated in our implementation, given by the data
processing, the deployment strategy and the power control. For data processing,
spacial fitting and short-term prediction are performed to eliminate the
influences of the incomplete measurement and the latency of data uploading. The
deployment strategies of ground sensing and aerial sensing are investigated to
improve the quality of the collected data. The power control is further
considered to balance between power consumption and data accuracy. Our
implementation has been deployed in Peking University and Xidian University
since February 2018, and has collected about 100 thousand effective data
samples by June 2018. | [
1,
0,
0,
0,
0,
0
] |
Title: Photoinduced vibronic coupling in two-level dissipative systems,
Abstract: Interaction of an electron system with a strong electromagnetic wave leads to
rearrangement both the electron and vibrational energy spectra of a dissipative
system. For instance, the optically coupled electron levels become split in the
conditions of the ac Stark effect that gives rise to appearance of the
nonadiabatic coupling between the electron and vibrational motions. The
nonadiabatic coupling exerts a substantial impact on the electron and phonon
dynamics and must be taken into account to determine the system wave functions.
In this paper, the vibronic coupling induced by the ac Stark effect is
considered. It is shown that the interaction between the electron states
dressed by an electromagnetic field and the forced vibrations of reservoir
oscillators under the action of rapid changing of the electron density with the
Rabi frequency is responsible for establishment of the photoinduced vibronic
coupling. However, if the resonance conditions for the optical phonon frequency
and the transition frequency of electrons in the dressed state basis are
satisfied, the vibronic coupling is due to the electron-phonon interaction.
Additionally, photoinduced vibronic coupling results in appearance of the
doubly dressed states which are formed by both the electron-photon and
electron-vibrational interactions. | [
0,
1,
0,
0,
0,
0
] |
Title: Procedural Content Generation via Machine Learning (PCGML),
Abstract: This survey explores Procedural Content Generation via Machine Learning
(PCGML), defined as the generation of game content using machine learning
models trained on existing content. As the importance of PCG for game
development increases, researchers explore new avenues for generating
high-quality content with or without human involvement; this paper addresses
the relatively new paradigm of using machine learning (in contrast with
search-based, solver-based, and constructive methods). We focus on what is most
often considered functional game content such as platformer levels, game maps,
interactive fiction stories, and cards in collectible card games, as opposed to
cosmetic content such as sprites and sound effects. In addition to using PCG
for autonomous generation, co-creativity, mixed-initiative design, and
compression, PCGML is suited for repair, critique, and content analysis because
of its focus on modeling existing content. We discuss various data sources and
representations that affect the resulting generated content. Multiple PCGML
methods are covered, including neural networks, long short-term memory (LSTM)
networks, autoencoders, and deep convolutional networks; Markov models,
$n$-grams, and multi-dimensional Markov chains; clustering; and matrix
factorization. Finally, we discuss open problems in the application of PCGML,
including learning from small datasets, lack of training data, multi-layered
learning, style-transfer, parameter tuning, and PCG as a game mechanic. | [
1,
0,
0,
0,
0,
0
] |
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