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Title: Assessing the reliability polynomial based on percolation theory,
Abstract: In this paper, we study the robustness of network topologies. We use the
concept of percolation as measuring tool to assess the reliability polynomial
of those systems which can be modeled as a general inhomogeneous random graph
as well as scale-free random graph. | [
0,
0,
1,
1,
0,
0
] |
Title: Interior Eigensolver for Sparse Hermitian Definite Matrices Based on Zolotarev's Functions,
Abstract: This paper proposes an efficient method for computing selected generalized
eigenpairs of a sparse Hermitian definite matrix pencil (A, B). Based on
Zolotarev's best rational function approximations of the signum function and
conformal mapping techniques, we construct the best rational function
approximation of a rectangular function supported on an arbitrary interval.
This new best rational function approximation is applied to construct spectrum
filters of (A, B). Combining fast direct solvers and the shift-invariant GMRES,
a hybrid fast algorithm is proposed to apply spectral filters efficiently.
Compared to the state-of-the-art algorithm FEAST, the proposed rational
function approximation is proved to be optimal among a larger function class,
and the numerical implementation of the proposed method is also faster. The
efficiency and stability of the proposed method are demonstrated by numerical
examples from computational chemistry. | [
1,
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0,
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] |
Title: A Polya-Vinogradov-type inequality on $\mathbb{Z}[i]$,
Abstract: We establish a Polya-Vinogradov-type bound for finite periodic multipicative
characters on the Gaussian integers. | [
0,
0,
1,
0,
0,
0
] |
Title: Face Super-Resolution Through Wasserstein GANs,
Abstract: Generative adversarial networks (GANs) have received a tremendous amount of
attention in the past few years, and have inspired applications addressing a
wide range of problems. Despite its great potential, GANs are difficult to
train. Recently, a series of papers (Arjovsky & Bottou, 2017a; Arjovsky et al.
2017b; and Gulrajani et al. 2017) proposed using Wasserstein distance as the
training objective and promised easy, stable GAN training across architectures
with minimal hyperparameter tuning. In this paper, we compare the performance
of Wasserstein distance with other training objectives on a variety of GAN
architectures in the context of single image super-resolution. Our results
agree that Wasserstein GAN with gradient penalty (WGAN-GP) provides stable and
converging GAN training and that Wasserstein distance is an effective metric to
gauge training progress. | [
1,
0,
0,
1,
0,
0
] |
Title: Network-based protein structural classification,
Abstract: Experimental determination of protein function is resource-consuming. As an
alternative, computational prediction of protein function has received
attention. In this context, protein structural classification (PSC) can help,
by allowing for determining structural classes of currently unclassified
proteins based on their features, and then relying on the fact that proteins
with similar structures have similar functions. Existing PSC approaches rely on
sequence-based or direct ("raw") 3-dimensional (3D) structure-based protein
features. In contrast, we first model 3D structures as protein structure
networks (PSNs). Then, we use ("processed") network-based features for PSC. We
propose the use of graphlets, state-of-the-art features in many domains of
network science, in the task of PSC. Moreover, because graphlets can deal only
with unweighted PSNs, and because accounting for edge weights when constructing
PSNs could improve PSC accuracy, we also propose a deep learning framework that
automatically learns network features from the weighted PSNs. When evaluated on
a large set of ~9,400 CATH and ~12,800 SCOP protein domains (spanning 36 PSN
sets), our proposed approaches are superior to existing PSC approaches in terms
of accuracy, with comparable running time. | [
0,
0,
0,
1,
1,
0
] |
Title: The function field Sathé-Selberg formula in arithmetic progressions and `short intervals',
Abstract: We use a function field analogue of a method of Selberg to derive an
asymptotic formula for the number of (square-free) monic polynomials in
$\mathbb{F}_q[X]$ of degree $n$ with precisely $k$ irreducible factors, in the
limit as $n$ tends to infinity. We then adapt this method to count such
polynomials in arithmetic progressions and short intervals, and by making use
of Weil's `Riemann hypothesis' for curves over $\mathbb{F}_q$, obtain better
ranges for these formulae than are currently known for their analogues in the
number field setting. Finally, we briefly discuss the regime in which $q$ tends
to infinity. | [
0,
0,
1,
0,
0,
0
] |
Title: On the Adjacency Spectra of Hypertrees,
Abstract: We extend the results of Zhang et al. to show that $\lambda$ is an eigenvalue
of a $k$-uniform hypertree $(k \geq 3)$ if and only if it is a root of a
particular matching polynomial for a connected induced subtree. We then use
this to provide a spectral characterization for power hypertrees. Notably, the
situation is quite different from that of ordinary trees, i.e., $2$-uniform
trees. We conclude by presenting an example (an $11$ vertex, $3$-uniform
non-power hypertree) illustrating these phenomena. | [
0,
0,
1,
0,
0,
0
] |
Title: Coherent structures and spectral energy transfer in turbulent plasma: a space-filter approach,
Abstract: Plasma turbulence at scales of the order of the ion inertial length is
mediated by several mechanisms, including linear wave damping, magnetic
reconnection, formation and dissipation of thin current sheets, stochastic
heating. It is now understood that the presence of localized coherent
structures enhances the dissipation channels and the kinetic features of the
plasma. However, no formal way of quantifying the relationship between
scale-to-scale energy transfer and the presence of spatial structures has so
far been presented. In this letter we quantify such relationship analyzing the
results of a two-dimensional high-resolution Hall-MHD simulation. In
particular, we employ the technique of space-filtering to derive a spectral
energy flux term which defines, in any point of the computational domain, the
signed flux of spectral energy across a given wavenumber. The characterization
of coherent structures is performed by means of a traditional two-dimensional
wavelet transformation. By studying the correlation between the spectral energy
flux and the wavelet amplitude, we demonstrate the strong relationship between
scale-to-scale transfer and coherent structures. Furthermore, by conditioning
one quantity with respect to the other, we are able for the first time to
quantify the inhomogeneity of the turbulence cascade induced by topological
structures in the magnetic field. Taking into account the low filling-factor of
coherent structures (i.e. they cover a small portion of space), it emerges that
80% of the spectral energy transfer (both in the direct and inverse cascade
directions) is localized in about 50% of space, and 50% of the energy transfer
is localized in only 25% of space. | [
0,
1,
0,
0,
0,
0
] |
Title: Bayesian Sparsification of Recurrent Neural Networks,
Abstract: Recurrent neural networks show state-of-the-art results in many text analysis
tasks but often require a lot of memory to store their weights. Recently
proposed Sparse Variational Dropout eliminates the majority of the weights in a
feed-forward neural network without significant loss of quality. We apply this
technique to sparsify recurrent neural networks. To account for recurrent
specifics we also rely on Binary Variational Dropout for RNN. We report 99.5%
sparsity level on sentiment analysis task without a quality drop and up to 87%
sparsity level on language modeling task with slight loss of accuracy. | [
1,
0,
0,
1,
0,
0
] |
Title: Curious Minds Wonder Alike: Studying Multimodal Behavioral Dynamics to Design Social Scaffolding of Curiosity,
Abstract: Curiosity is the strong desire to learn or know more about something or
someone. Since learning is often a social endeavor, social dynamics in
collaborative learning may inevitably influence curiosity. There is a scarcity
of research, however, focusing on how curiosity can be evoked in group learning
contexts. Inspired by a recently proposed theoretical framework that
articulates an integrated socio-cognitive infrastructure of curiosity, in this
work, we use data-driven approaches to identify fine-grained social scaffolding
of curiosity in child-child interaction, and propose how they can be used to
elicit and maintain curiosity in technology-enhanced learning environments. For
example, we discovered sequential patterns of multimodal behaviors across group
members and we describe those that maximize an individual's utility, or
likelihood, of demonstrating curiosity during open-ended problem-solving in
group work. We also discovered, and describe here, behaviors that directly or
in a mediated manner cause curiosity related conversational behaviors in the
interaction, with twice as many interpersonal causal influences compared to
intrapersonal ones. We explain how these findings form a solid foundation for
developing curiosity-increasing learning technologies or even assisting a human
coach to induce curiosity among learners. | [
1,
0,
0,
0,
0,
0
] |
Title: Continuous Functional Calculus for Quaternionic Bounded Normal Operators,
Abstract: In this article we give an approach to define continuous functional calculus
for bounded quaternionic normal operators defined on a right quaternionic
Hilbert space. | [
0,
0,
1,
0,
0,
0
] |
Title: Learning Graph Weighted Models on Pictures,
Abstract: Graph Weighted Models (GWMs) have recently been proposed as a natural
generalization of weighted automata over strings and trees to arbitrary
families of labeled graphs (and hypergraphs). A GWM generically associates a
labeled graph with a tensor network and computes a value by successive
contractions directed by its edges. In this paper, we consider the problem of
learning GWMs defined over the graph family of pictures (or 2-dimensional
words). As a proof of concept, we consider regression and classification tasks
over the simple Bars & Stripes and Shifting Bits picture languages and provide
an experimental study investigating whether these languages can be learned in
the form of a GWM from positive and negative examples using gradient-based
methods. Our results suggest that this is indeed possible and that
investigating the use of gradient-based methods to learn picture series and
functions computed by GWMs over other families of graphs could be a fruitful
direction. | [
0,
0,
0,
1,
0,
0
] |
Title: Solutions for biharmonic equations with steep potential wells,
Abstract: In this paper, we are concerned with the existence of least energy solutions
for the following biharmonic equations: $$\Delta^2 u+(\lambda
V(x)-\delta)u=|u|^{p-2}u \quad in\quad \mathbb{R}^N$$ where $N\geq 5,
2<p\leq\frac{2N}{N-4}, \lambda>0$ is a parameter, $V(x)$ is a nonnegative
potential function with nonempty zero sets $\mbox{int} V^{-1}(0)$,
$0<\delta<\mu_0$ and $\mu_0$ is the principle eigenvalue of $\Delta^2$ in the
zero sets $\mbox{int} V^{-1}(0)$ of $V(x)$. Here $\mbox{int} V^{-1}(0)$ denotes
the interior part of the set $V^{-1}(0):=\{x\in \mathbb{R}^N: V(x)=0\}$. We
prove that the above equation admits a least energy solution which is trapped
near the zero sets $\mbox{int} V^{-1}(0)$ for $\lambda>0$ large. | [
0,
0,
1,
0,
0,
0
] |
Title: General three and four person two color Hat Game,
Abstract: N distinguishable players are randomly fitted with a white or black hat,
where the probabilities of getting a white or black hat may be different for
each player, but known to all the players. All players guess simultaneously the
color of their own hat observing only the hat colors of the other N-1 players.
It is also allowed for each player to pass: no color is guessed. The team wins
if at least one player guesses his hat color correctly and none of the players
has an incorrect guess. No communication of any sort is allowed, except for an
initial strategy session before the game begins. Our goal is to maximize the
probability of winning the game and to describe winning strategies, using the
concept of an adequate set. We find explicit solutions in case of N =3 and N
=4. | [
1,
0,
0,
0,
0,
0
] |
Title: The loss surface of deep and wide neural networks,
Abstract: While the optimization problem behind deep neural networks is highly
non-convex, it is frequently observed in practice that training deep networks
seems possible without getting stuck in suboptimal points. It has been argued
that this is the case as all local minima are close to being globally optimal.
We show that this is (almost) true, in fact almost all local minima are
globally optimal, for a fully connected network with squared loss and analytic
activation function given that the number of hidden units of one layer of the
network is larger than the number of training points and the network structure
from this layer on is pyramidal. | [
1,
0,
0,
1,
0,
0
] |
Title: Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis,
Abstract: Global Style Tokens (GSTs) are a recently-proposed method to learn latent
disentangled representations of high-dimensional data. GSTs can be used within
Tacotron, a state-of-the-art end-to-end text-to-speech synthesis system, to
uncover expressive factors of variation in speaking style. In this work, we
introduce the Text-Predicted Global Style Token (TP-GST) architecture, which
treats GST combination weights or style embeddings as "virtual" speaking style
labels within Tacotron. TP-GST learns to predict stylistic renderings from text
alone, requiring neither explicit labels during training nor auxiliary inputs
for inference. We show that, when trained on a dataset of expressive speech,
our system generates audio with more pitch and energy variation than two
state-of-the-art baseline models. We further demonstrate that TP-GSTs can
synthesize speech with background noise removed, and corroborate these analyses
with positive results on human-rated listener preference audiobook tasks.
Finally, we demonstrate that multi-speaker TP-GST models successfully factorize
speaker identity and speaking style. We provide a website with audio samples
for each of our findings. | [
1,
0,
0,
1,
0,
0
] |
Title: Independence of Sources in Social Networks,
Abstract: Online social networks are more and more studied. The links between users of
a social network are important and have to be well qualified in order to detect
communities and find influencers for example. In this paper, we present an
approach based on the theory of belief functions to estimate the degrees of
cognitive independence between users in a social network. We experiment the
proposed method on a large amount of data gathered from the Twitter social
network. | [
1,
0,
0,
0,
0,
0
] |
Title: Classical and quantum systems: transport due to rare events,
Abstract: We review possible mechanisms for energy transfer based on 'rare' or
'non-perturbative' effects, in physical systems that present a many-body
localized phenomenology. The main focus is on classical systems, with or
without quenched disorder. For non-quantum systems, the breakdown of
localization is usually not regarded as an issue, and we thus aim at
identifying the fastest channels for transport. Next, we contemplate the
possibility of applying the same mechanisms in quantum systems, including
disorder free systems (e.g. Bose-Hubbard chain), disordered many-body localized
systems with mobility edges at energies below the edge, and strongly disordered
lattice systems in $d>1$. For quantum mechanical systems, the relevance of
these considerations for transport is currently a matter of debate. | [
0,
1,
0,
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] |
Title: Relaxed Wasserstein with Applications to GANs,
Abstract: We propose a novel class of statistical divergences called \textit{Relaxed
Wasserstein} (RW) divergence. RW divergence generalizes Wasserstein divergence
and is parametrized by a class of strictly convex and differentiable functions.
We establish for RW divergence several probabilistic properties, which are
critical for the success of Wasserstein divergence. In particular, we show that
RW divergence is dominated by Total Variation (TV) and Wasserstein-$L^2$
divergence, and that RW divergence has continuity, differentiability and
duality representation. Finally, we provide a nonasymptotic moment estimate and
a concentration inequality for RW divergence.
Our experiments on the image generation task demonstrate that RW divergence
is a suitable choice for GANs. Indeed, the performance of RWGANs with
Kullback-Leibler (KL) divergence is very competitive with other
state-of-the-art GANs approaches. Furthermore, RWGANs possess better
convergence properties than the existing WGANs with competitive inception
scores. To the best of our knowledge, our new conceptual framework is the first
to not only provide the flexibility in designing effective GANs scheme, but
also the possibility in studying different losses under a unified mathematical
framework. | [
1,
0,
0,
1,
0,
0
] |
Title: Extremal functions for the Moser--Trudinger inequality of Adimurthi--Druet type in $W^{1,N}(\mathbb R^N)$,
Abstract: We study the existence and nonexistence of maximizers for variational problem
concerning to the Moser--Trudinger inequality of Adimurthi--Druet type in
$W^{1,N}(\mathbb R^N)$ \[ MT(N,\beta, \alpha) =\sup_{u\in W^{1,N}(\mathbb R^N),
\|\nabla u\|_N^N + \|u\|_N^N\leq 1} \int_{\mathbb R^N} \Phi_N(\beta(1+\alpha
\|u\|_N^N)^{\frac1{N-1}} |u|^{\frac N{N-1}}) dx, \] where $\Phi_N(t) =e^{t}
-\sum_{k=0}^{N-2} \frac{t^k}{k!}$, $0\leq \alpha < 1$ both in the subcritical
case $\beta < \beta_N$ and critical case $\beta =\beta_N$ with $\beta_N = N
\omega_{N-1}^{\frac1{N-1}}$ and $\omega_{N-1}$ denotes the surface area of the
unit sphere in $\mathbb R^N$. We will show that $MT(N,\beta,\alpha)$ is
attained in the subcritical case if $N\geq 3$ or $N=2$ and $\beta \in
(\frac{2(1+2\alpha)}{(1+\alpha)^2 B_2},\beta_2)$ with $B_2$ is the best
constant in a Gagliardo--Nirenberg inequality in $W^{1,2}(\mathbb R^2)$. We
also show that $MT(2,\beta,\alpha)$ is not attained for $\beta$ small which is
different from the context of bounded domains. In the critical case, we prove
that $MT(N,\beta_N,\alpha)$ is attained for $\alpha\geq 0$ small enough. To
prove our results, we first establish a lower bound for $MT(N,\beta,\alpha)$
which excludes the concentrating or vanishing behaviors of their maximizer
sequences. This implies the attainability of $MT(N,\beta,\alpha)$ in the
subcritical case. The proof in the critical case is based on the blow-up
analysis method. Finally, by using the Moser sequence together the scaling
argument, we show that $MT(N,\beta_N,1) =\infty$. Our results settle the
questions left open in \cite{doO2015,doO2016}. | [
0,
0,
1,
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0
] |
Title: The Genus-One Global Mirror Theorem for the Quintic Threefold,
Abstract: We prove the genus-one restriction of the all-genus
Landau-Ginzburg/Calabi-Yau conjecture of Chiodo and Ruan, stated in terms of
the geometric quantization of an explicit symplectomorphism determined by
genus-zero invariants. This provides the first evidence supporting the
higher-genus Landau-Ginzburg/Calabi-Yau correspondence for the quintic
threefold, and exhibits the first instance of the "genus zero controls higher
genus" principle, in the sense of Givental's quantization formalism, for
non-semisimple cohomological field theories. | [
0,
0,
1,
0,
0,
0
] |
Title: Towards Open Data for the Citation Content Analysis,
Abstract: The paper presents first results of the CitEcCyr project funded by RANEPA.
The project aims to create a source of open citation data for research papers
written in Russian. Compared to existing sources of citation data, CitEcCyr is
working to provide the following added values: a) a transparent and distributed
architecture of a technology that generates the citation data; b) an openness
of all built/used software and created citation data; c) an extended set of
citation data sufficient for the citation content analysis; d) services for
public control over a quality of the citation data and a citing activity of
researchers. | [
1,
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0,
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0
] |
Title: Time-dynamic inference for non-Markov transition probabilities under independent right-censoring,
Abstract: In this article, weak convergence of the general non-Markov state transition
probability estimator by Titman (2015) is established which, up to now, has not
been verified yet for other general non-Markov estimators. A similar theorem is
shown for the bootstrap, yielding resampling-based inference methods for
statistical functionals. Formulas of the involved covariance functions are
presented in detail. Particular applications include the conditional expected
length of stay in a specific state, given occupation of another state in the
past, as well as the construction of time-simultaneous confidence bands for the
transition probabilities. The expected lengths of stay in the two-sample liver
cirrhosis data-set by Andersen et al. (1993) are compared and confidence
intervals for their difference are constructed. With borderline significance
and in comparison to the placebo group, the treatment group has an elevated
expected length of stay in the healthy state given an earlier disease state
occupation. In contrast, the Aalen-Johansen estimator-based confidence
interval, which relies on a Markov assumption, leads to a drastically different
conclusion. Also, graphical illustrations of confidence bands for the
transition probabilities demonstrate the biasedness of the Aalen-Johansen
estimator in this data example. The reliability of these results is assessed in
a simulation study. | [
0,
0,
1,
1,
0,
0
] |
Title: Multitask Learning with CTC and Segmental CRF for Speech Recognition,
Abstract: Segmental conditional random fields (SCRFs) and connectionist temporal
classification (CTC) are two sequence labeling methods used for end-to-end
training of speech recognition models. Both models define a transcription
probability by marginalizing decisions about latent segmentation alternatives
to derive a sequence probability: the former uses a globally normalized joint
model of segment labels and durations, and the latter classifies each frame as
either an output symbol or a "continuation" of the previous label. In this
paper, we train a recognition model by optimizing an interpolation between the
SCRF and CTC losses, where the same recurrent neural network (RNN) encoder is
used for feature extraction for both outputs. We find that this multitask
objective improves recognition accuracy when decoding with either the SCRF or
CTC models. Additionally, we show that CTC can also be used to pretrain the RNN
encoder, which improves the convergence rate when learning the joint model. | [
1,
0,
0,
0,
0,
0
] |
Title: Two-Stream RNN/CNN for Action Recognition in 3D Videos,
Abstract: The recognition of actions from video sequences has many applications in
health monitoring, assisted living, surveillance, and smart homes. Despite
advances in sensing, in particular related to 3D video, the methodologies to
process the data are still subject to research. We demonstrate superior results
by a system which combines recurrent neural networks with convolutional neural
networks in a voting approach. The gated-recurrent-unit-based neural networks
are particularly well-suited to distinguish actions based on long-term
information from optical tracking data; the 3D-CNNs focus more on detailed,
recent information from video data. The resulting features are merged in an SVM
which then classifies the movement. In this architecture, our method improves
recognition rates of state-of-the-art methods by 14% on standard data sets. | [
1,
0,
0,
0,
0,
0
] |
Title: DNA translocation through alpha-haemolysin nano-pores with potential application to macromolecular data storage,
Abstract: Digital information can be encoded in the building-block sequence of
macromolecules, such as RNA and single-stranded DNA. Methods of "writing" and
"reading" macromolecular strands are currently available, but they are slow and
expensive. In an ideal molecular data storage system, routine operations such
as write, read, erase, store, and transfer must be done reliably and at high
speed within an integrated chip. As a first step toward demonstrating the
feasibility of this concept, we report preliminary results of DNA readout
experiments conducted in miniaturized chambers that are scalable to even
smaller dimensions. We show that translocation of a single-stranded DNA
molecule (consisting of 50 adenosine bases followed by 100 cytosine bases)
through an ion-channel yields a characteristic signal that is attributable to
the 2-segment structure of the molecule. We also examine the dependence of the
translocation rate and speed on the adjustable parameters of the experiment. | [
1,
1,
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0,
0,
0
] |
Title: Contextuality from missing and versioned data,
Abstract: Traditionally categorical data analysis (e.g. generalized linear models)
works with simple, flat datasets akin to a single table in a database with no
notion of missing data or conflicting versions. In contrast, modern data
analysis must deal with distributed databases with many partial local tables
that need not always agree. The computational agents tabulating these tables
are spatially separated, with binding speed-of-light constraints and data
arriving too rapidly for these distributed views ever to be fully informed and
globally consistent. Contextuality is a mathematical property which describes a
kind of inconsistency arising in quantum mechanics (e.g. in Bell's theorem). In
this paper we show how contextuality can arise in common data collection
scenarios, including missing data and versioning (as in low-latency distributed
databases employing snapshot isolation). In the companion paper, we develop
statistical models adapted to this regime. | [
1,
0,
0,
1,
0,
0
] |
Title: Reveal the Mantle and K-40 Components of Geoneutrinos with Liquid Scintillator Cherenkov Neutrino Detectors,
Abstract: In this article we present an idea of using liquid scintillator Cherenkov
neutrino detectors to detect the mantle and K-40 components of geoneutrinos.
Liquid scintillator Cherenkov detectors feature both energy and direction
measurement for charge particles. Geoneutrinos can be detected with the elastic
scattering process of neutrino and electron. With the directionality, the
dominant intrinsic background originated from solar neutrinos in common liquid
scintillator detectors can be suppressed. The mantle geoneutrinos can be
distinguished because they come mainly underneath. The K-40 geoneutrinos can
also be identified, if the detection threshold for direction measurement can be
lower than, for example, 0.8 MeV. According to our calculation, a moderate,
kilo-ton scale, detector can observe tens of candidates, and is a practical
start for an experiment. | [
0,
1,
0,
0,
0,
0
] |
Title: Improving Stock Movement Prediction with Adversarial Training,
Abstract: This paper contributes a new machine learning solution for stock movement
prediction, which aims to predict whether the price of a stock will be up or
down in the near future. The key novelty is that we propose to employ
adversarial training to improve the generalization of a recurrent neural
network model. The rationality of adversarial training here is that the input
features to stock prediction are typically based on stock price, which is
essentially a stochastic variable and continuously changed with time by nature.
As such, normal training with stationary price-based features (e.g. the closing
price) can easily overfit the data, being insufficient to obtain reliable
models. To address this problem, we propose to add perturbations to simulate
the stochasticity of continuous price variable, and train the model to work
well under small yet intentional perturbations. Extensive experiments on two
real-world stock data show that our method outperforms the state-of-the-art
solution with 3.11% relative improvements on average w.r.t. accuracy, verifying
the usefulness of adversarial training for stock prediction task. Codes will be
made available upon acceptance. | [
0,
0,
0,
0,
0,
1
] |
Title: SVSGAN: Singing Voice Separation via Generative Adversarial Network,
Abstract: Separating two sources from an audio mixture is an important task with many
applications. It is a challenging problem since only one signal channel is
available for analysis. In this paper, we propose a novel framework for singing
voice separation using the generative adversarial network (GAN) with a
time-frequency masking function. The mixture spectra is considered to be a
distribution and is mapped to the clean spectra which is also considered a
distribtution. The approximation of distributions between mixture spectra and
clean spectra is performed during the adversarial training process. In contrast
with current deep learning approaches for source separation, the parameters of
the proposed framework are first initialized in a supervised setting and then
optimized by the training procedure of GAN in an unsupervised setting.
Experimental results on three datasets (MIR-1K, iKala and DSD100) show that
performance can be improved by the proposed framework consisting of
conventional networks. | [
1,
0,
0,
0,
0,
0
] |
Title: The SysML/KAOS Domain Modeling Approach,
Abstract: A means of building safe critical systems consists of formally modeling the
requirements formulated by stakeholders and ensuring their consistency with
respect to application domain properties. This paper proposes a metamodel for
an ontology modeling formalism based on OWL and PLIB. This modeling formalism
is part of a method for modeling the domain of systems whose requirements are
captured through SysML/KAOS. The formal semantics of SysML/KAOS goals are
represented using Event-B specifications. Goals provide the set of events,
while domain models will provide the structure of the system state of the
Event-B specification. Our proposal is illustrated through a case study dealing
with a Cycab localization component specification. The case study deals with
the specification of a localization software component that uses GPS,Wi-Fi and
sensor technologies for the realtime localization of the Cycab vehicle, an
autonomous ground transportation system designed to be robust and completely
independent. | [
1,
0,
0,
0,
0,
0
] |
Title: Spinless hourglass nodal-line semimetals,
Abstract: Nodal-line semimetals, one of the topological semimetals, have degeneracy
along nodal lines where the band gap is closed. In many cases, the nodal lines
appear accidentally, and in such cases it is impossible to determine whether
the nodal lines appear or not, only from the crystal symmetry and the electron
filling. In this paper, for spinless systems, we show that in specific space
groups at $4N+2$ fillings ($8N+4$ fillings including the spin degree of
freedom), presence of the nodal lines is required regardless of the details of
the systems. Here, the spinless systems refer to crystals where the spin-orbit
coupling is negligible and the spin degree of freedom can be omitted because of
the SU(2) spin degeneracy. In this case the shape of the band structure around
these nodal lines is like an hourglass, and we call this a spinless hourglass
nodal-line semimetal. We construct a model Hamiltonian as an example and we
show that it is always in the spinless hourglass nodal-line semimetal phase
even when the model parameters are changed without changing the symmetries of
the system. We also establish a list of all the centrosymmetric space groups,
under which spinless systems always have hourglass nodal lines, and illustrate
where the nodal lines are located. We propose that Al$_3$FeSi$_2$, whose
space-group symmetry is Pbcn (No. 60), is one of the nodal-line semimetals
arising from this mechanism. | [
0,
1,
0,
0,
0,
0
] |
Title: Insights on representational similarity in neural networks with canonical correlation,
Abstract: Comparing different neural network representations and determining how
representations evolve over time remain challenging open questions in our
understanding of the function of neural networks. Comparing representations in
neural networks is fundamentally difficult as the structure of representations
varies greatly, even across groups of networks trained on identical tasks, and
over the course of training. Here, we develop projection weighted CCA
(Canonical Correlation Analysis) as a tool for understanding neural networks,
building off of SVCCA, a recently proposed method (Raghu et al., 2017). We
first improve the core method, showing how to differentiate between signal and
noise, and then apply this technique to compare across a group of CNNs,
demonstrating that networks which generalize converge to more similar
representations than networks which memorize, that wider networks converge to
more similar solutions than narrow networks, and that trained networks with
identical topology but different learning rates converge to distinct clusters
with diverse representations. We also investigate the representational dynamics
of RNNs, across both training and sequential timesteps, finding that RNNs
converge in a bottom-up pattern over the course of training and that the hidden
state is highly variable over the course of a sequence, even when accounting
for linear transforms. Together, these results provide new insights into the
function of CNNs and RNNs, and demonstrate the utility of using CCA to
understand representations. | [
0,
0,
0,
1,
0,
0
] |
Title: Complex Hadamard matrices with noncommutative entries,
Abstract: We axiomatize and study the matrices of type $H\in M_N(A)$, having unitary
entries, $H_{ij}\in U(A)$, and whose rows and columns are subject to
orthogonality type conditions. Here $A$ can be any $C^*$-algebra, for instance
$A=\mathbb C$, where we obtain the usual complex Hadamard matrices, or
$A=C(X)$, where we obtain the continuous families of complex Hadamard matrices.
Our formalism allows the construction of a quantum permutation group $G\subset
S_N^+$, whose structure and computation is discussed here. | [
0,
0,
1,
0,
0,
0
] |
Title: Subregular Complexity and Deep Learning,
Abstract: This paper argues that the judicial use of formal language theory and
grammatical inference are invaluable tools in understanding how deep neural
networks can and cannot represent and learn long-term dependencies in temporal
sequences. Learning experiments were conducted with two types of Recurrent
Neural Networks (RNNs) on six formal languages drawn from the Strictly Local
(SL) and Strictly Piecewise (SP) classes. The networks were Simple RNNs
(s-RNNs) and Long Short-Term Memory RNNs (LSTMs) of varying sizes. The SL and
SP classes are among the simplest in a mathematically well-understood hierarchy
of subregular classes. They encode local and long-term dependencies,
respectively. The grammatical inference algorithm Regular Positive and Negative
Inference (RPNI) provided a baseline. According to earlier research, the LSTM
architecture should be capable of learning long-term dependencies and should
outperform s-RNNs. The results of these experiments challenge this narrative.
First, the LSTMs' performance was generally worse in the SP experiments than in
the SL ones. Second, the s-RNNs out-performed the LSTMs on the most complex SP
experiment and performed comparably to them on the others. | [
1,
0,
0,
0,
0,
0
] |
Title: Chaotic Dynamics of Inner Ear Hair Cells,
Abstract: Experimental records of active bundle motility are used to demonstrate the
presence of a low-dimensional chaotic attractor in hair cell dynamics.
Dimensionality tests from dynamic systems theory are applied to estimate the
number of independent variables sufficient for modeling the hair cell response.
Poincare maps are constructed to observe a quasiperiodic transition from chaos
to order with increasing amplitudes of mechanical forcing. The onset of this
transition is accompanied by a reduction of Kolmogorov entropy in the system
and an increase in mutual information between the stimulus and the hair bundle,
indicative of signal detection. A simple theoretical model is used to describe
the observed chaotic dynamics. The model exhibits an enhancement of sensitivity
to weak stimuli when the system is poised in the chaotic regime. We propose
that chaos may play a role in the hair cell's ability to detect low-amplitude
sounds. | [
0,
1,
0,
0,
0,
0
] |
Title: CHIME FRB: An application of FFT beamforming for a radio telescope,
Abstract: We have developed FFT beamforming techniques for the CHIME radio telescope,
to search for and localize the astrophysical signals from Fast Radio Bursts
(FRBs) over a large instantaneous field-of-view (FOV) while maintaining the
full angular resolution of CHIME. We implement a hybrid beamforming pipeline in
a GPU correlator, synthesizing 256 FFT-formed beams in the North-South
direction by four formed beams along East-West via exact phasing, tiling a sky
area of ~250 square degrees. A zero-padding approximation is employed to
improve chromatic beam alignment across the wide bandwidth of 400 to 800 MHz.
We up-channelize the data in order to achieve fine spectral resolution of
$\Delta\nu$=24 kHz and time cadence of 0.983 ms, desirable for detecting
transient and dispersed signals such as those from FRBs. | [
0,
1,
0,
0,
0,
0
] |
Title: Incident Light Frequency-based Image Defogging Algorithm,
Abstract: Considering the problem of color distortion caused by the defogging algorithm
based on dark channel prior, an improved algorithm was proposed to calculate
the transmittance of all channels respectively. First, incident light
frequency's effect on the transmittance of various color channels was analyzed
according to the Beer-Lambert's Law, from which a proportion among various
channel transmittances was derived; afterwards, images were preprocessed by
down-sampling to refine transmittance, and then the original size was restored
to enhance the operational efficiency of the algorithm; finally, the
transmittance of all color channels was acquired in accordance with the
proportion, and then the corresponding transmittance was used for image
restoration in each channel. The experimental results show that compared with
the existing algorithm, this improved image defogging algorithm could make
image colors more natural, solve the problem of slightly higher color
saturation caused by the existing algorithm, and shorten the operation time by
four to nine times. | [
1,
0,
0,
0,
0,
0
] |
Title: Tangent points of d-lower content regular sets and $β$ numbers,
Abstract: We present a generalisation of C. Bishop and P. Jones' result in [BJ1], where
they give a characterisation of the tangent points of a Jordan curve in terms
of $\beta$ numbers. Instead of the $L^\infty$ Jones' $\beta$ numbers, we use an
averaged version of them, firstly introduced by J. Azzam and R. Schul in [AS1].
A fundamental tool in the proof will be the Reifenberg parameterisation Theorem
of G. David and T. Toro (see [DT1]). | [
0,
0,
1,
0,
0,
0
] |
Title: Quasi-Oracle Estimation of Heterogeneous Treatment Effects,
Abstract: Flexible estimation of heterogeneous treatment effects lies at the heart of
many statistical challenges, such as personalized medicine and optimal resource
allocation. In this paper, we develop a general class of two-step algorithms
for heterogeneous treatment effect estimation in observational studies. We
first estimate marginal effects and treatment propensities in order to form an
objective function that isolates the causal component of the signal. Then, we
optimize this data-adaptive objective function. Our approach has several
advantages over existing methods. From a practical perspective, our method is
flexible and easy to use: In both steps, we can use any loss-minimization
method, e.g., penalized regression, deep neutral networks, or boosting;
moreover, these methods can be fine-tuned by cross validation. Meanwhile, in
the case of penalized kernel regression, we show that our method has a
quasi-oracle property: Even if the pilot estimates for marginal effects and
treatment propensities are not particularly accurate, we achieve the same error
bounds as an oracle who has a priori knowledge of these two nuisance
components. We implement variants of our approach based on both penalized
regression and boosting in a variety of simulation setups, and find promising
performance relative to existing baselines. | [
0,
0,
1,
1,
0,
0
] |
Title: Level bounds for exceptional quantum subgroups in rank two,
Abstract: There is a long-standing belief that the modular tensor categories
$\mathcal{C}(\mathfrak{g},k)$, for $k\in\mathbb{Z}_{\geq1}$ and
finite-dimensional simple complex Lie algebras $\mathfrak{g}$, contain
exceptional connected étale algebras at only finitely many levels $k$. This
premise has known implications for the study of relations in the Witt group of
nondegenerate braided fusion categories, modular invariants of conformal field
theories, and the classification of subfactors in the theory of von Neumann
algebras. Here we confirm this conjecture when $\mathfrak{g}$ has rank 2,
contributing proofs and explicit bounds when $\mathfrak{g}$ is of type $B_2$ or
$G_2$, adding to the previously known positive results for types $A_1$ and
$A_2$. | [
0,
0,
1,
0,
0,
0
] |
Title: Convolution Forgetting Curve Model for Repeated Learning,
Abstract: Most of mathematic forgetting curve models fit well with the forgetting data
under the learning condition of one time rather than repeated. In the paper, a
convolution model of forgetting curve is proposed to simulate the memory
process during learning. In this model, the memory ability (i.e. the central
procedure in the working memory model) and learning material (i.e. the input in
the working memory model) is regarded as the system function and the input
function, respectively. The status of forgetting (i.e. the output in the
working memory model) is regarded as output function or the convolution result
of the memory ability and learning material. The model is applied to simulate
the forgetting curves in different situations. The results show that the model
is able to simulate the forgetting curves not only in one time learning
condition but also in multi-times condition. The model is further verified in
the experiments of Mandarin tone learning for Japanese learners. And the
predicted curve fits well on the test points. | [
1,
0,
0,
0,
1,
0
] |
Title: Efficient computation of multidimensional theta functions,
Abstract: An important step in the efficient computation of multi-dimensional theta
functions is the construction of appropriate symplectic transformations for a
given Riemann matrix assuring a rapid convergence of the theta series. An
algorithm is presented to approximately map the Riemann matrix to the Siegel
fundamental domain. The shortest vector of the lattice generated by the Riemann
matrix is identified exactly, and the algorithm ensures that its length is
larger than $\sqrt{3}/2$. The approach is based on a previous algorithm by
Deconinck et al. using the LLL algorithm for lattice reductions. Here, the LLL
algorithm is replaced by exact Minkowski reductions for small genus and an
exact identification of the shortest lattice vector for larger values of the
genus. | [
0,
1,
1,
0,
0,
0
] |
Title: The careless use of language in quantum information,
Abstract: An imperative aspect of modern science is that scientific institutions act
for the benefit of a common scientific enterprise, rather than for the personal
gain of individuals within them. This implies that science should not
perpetuate existing or historical unequal social orders. Some scientific
terminology, though, gives a very different impression. I will give two
examples of terminology invented recently for the field of quantum information
which use language associated with subordination, slavery, and racial
segregation: 'ancilla qubit' and 'quantum supremacy'. | [
0,
1,
0,
0,
0,
0
] |
Title: Generalizing the first-difference correlated random walk for marine animal movement data,
Abstract: Animal telemetry data are often analysed with discrete time movement models
assuming rotation in the movement. These models are defined with equidistant
distant time steps. However, telemetry data from marine animals are observed
irregularly. To account for irregular data, a time-irregularised
first-difference correlated random walk model with drift is introduced. The
model generalizes the commonly used first-difference correlated random walk
with regular time steps by allowing irregular time steps, including a drift
term, and by allowing different autocorrelation in the two coordinates. The
model is applied to data from a ringed seal collected through the Argos
satellite system, and is compared to related movement models through
simulations. Accounting for irregular data in the movement model results in
accurate parameter estimates and reconstruction of movement paths. Measured by
distance, the introduced model can provide more accurate movement paths than
the regular time counterpart. Extracting accurate movement paths from uncertain
telemetry data is important for evaluating space use patterns for marine
animals, which in turn is crucial for management. Further, handling irregular
data directly in the movement model allows efficient simultaneous analysis of
several animals. | [
0,
0,
0,
0,
1,
0
] |
Title: Low spin wave damping in the insulating chiral magnet Cu$_{2}$OSeO$_{3}$,
Abstract: Chiral magnets with topologically nontrivial spin order such as Skyrmions
have generated enormous interest in both fundamental and applied sciences. We
report broadband microwave spectroscopy performed on the insulating chiral
ferrimagnet Cu$_{2}$OSeO$_{3}$. For the damping of magnetization dynamics we
find a remarkably small Gilbert damping parameter of about $1\times10^{-4}$ at
5 K. This value is only a factor of 4 larger than the one reported for the best
insulating ferrimagnet yttrium iron garnet. We detect a series of sharp
resonances and attribute them to confined spin waves in the mm-sized samples.
Considering the small damping, insulating chiral magnets turn out to be
promising candidates when exploring non-collinear spin structures for high
frequency applications. | [
0,
1,
0,
0,
0,
0
] |
Title: Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation,
Abstract: For human pose estimation in monocular images, joint occlusions and
overlapping upon human bodies often result in deviated pose predictions. Under
these circumstances, biologically implausible pose predictions may be produced.
In contrast, human vision is able to predict poses by exploiting geometric
constraints of joint inter-connectivity. To address the problem by
incorporating priors about the structure of human bodies, we propose a novel
structure-aware convolutional network to implicitly take such priors into
account during training of the deep network. Explicit learning of such
constraints is typically challenging. Instead, we design discriminators to
distinguish the real poses from the fake ones (such as biologically implausible
ones). If the pose generator (G) generates results that the discriminator fails
to distinguish from real ones, the network successfully learns the priors. | [
1,
0,
0,
0,
0,
0
] |
Title: Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression,
Abstract: Genome-wide association studies (GWAS) have achieved great success in the
genetic study of Alzheimer's disease (AD). Collaborative imaging genetics
studies across different research institutions show the effectiveness of
detecting genetic risk factors. However, the high dimensionality of GWAS data
poses significant challenges in detecting risk SNPs for AD. Selecting relevant
features is crucial in predicting the response variable. In this study, we
propose a novel Distributed Feature Selection Framework (DFSF) to conduct the
large-scale imaging genetics studies across multiple institutions. To speed up
the learning process, we propose a family of distributed group Lasso screening
rules to identify irrelevant features and remove them from the optimization.
Then we select the relevant group features by performing the group Lasso
feature selection process in a sequence of parameters. Finally, we employ the
stability selection to rank the top risk SNPs that might help detect the early
stage of AD. To the best of our knowledge, this is the first distributed
feature selection model integrated with group Lasso feature selection as well
as detecting the risk genetic factors across multiple research institutions
system. Empirical studies are conducted on 809 subjects with 5.9 million SNPs
which are distributed across several individual institutions, demonstrating the
efficiency and effectiveness of the proposed method. | [
1,
0,
0,
1,
0,
0
] |
Title: Predicting shim gaps in aircraft assembly with machine learning and sparse sensing,
Abstract: A modern aircraft may require on the order of thousands of custom shims to
fill gaps between structural components in the airframe that arise due to
manufacturing tolerances adding up across large structures. These shims are
necessary to eliminate gaps, maintain structural performance, and minimize
pull-down forces required to bring the aircraft into engineering nominal
configuration for peak aerodynamic efficiency. Gap filling is a time-consuming
process, involving either expensive by-hand inspection or computations on vast
quantities of measurement data from increasingly sophisticated metrology
equipment. Either case amounts to significant delays in production, with much
of the time spent in the critical path of aircraft assembly. This work presents
an alternative strategy for predictive shimming, based on machine learning and
sparse sensing to first learn gap distributions from historical data, and then
design optimized sparse sensing strategies to streamline data collection and
processing. This new approach is based on the assumption that patterns exist in
shim distributions across aircraft, which may be mined and used to reduce the
burden of data collection and processing in future aircraft. Specifically,
robust principal component analysis is used to extract low-dimensional patterns
in the gap measurements while rejecting outliers. Next, optimized sparse
sensors are obtained that are most informative about the dimensions of a new
aircraft in these low-dimensional principal components. We demonstrate the
success of the proposed approach, called PIXel Identification Despite
Uncertainty in Sensor Technology (PIXI-DUST), on historical production data
from 54 representative Boeing commercial aircraft. Our algorithm successfully
predicts $99\%$ of shim gaps within the desired measurement tolerance using
$3\%$ of the laser scan points typically required; all results are
cross-validated. | [
0,
0,
0,
1,
0,
0
] |
Title: Multivariate central limit theorems for Rademacher functionals with applications,
Abstract: Quantitative multivariate central limit theorems for general functionals of
possibly non-symmetric and non-homogeneous infinite Rademacher sequences are
proved by combining discrete Malliavin calculus with the smart path method for
normal approximation. In particular, a discrete multivariate second-order
Poincaré inequality is developed. As a first application, the normal
approximation of vectors of subgraph counting statistics in the
Erdős-Rényi random graph is considered. In this context, we further
specialize to the normal approximation of vectors of vertex degrees. In a
second application we prove a quantitative multivariate central limit theorem
for vectors of intrinsic volumes induced by random cubical complexes. | [
0,
0,
1,
0,
0,
0
] |
Title: Modification of Social Dominance in Social Networks by Selective Adjustment of Interpersonal Weights,
Abstract: According to the DeGroot-Friedkin model of a social network, an individual's
social power evolves as the network discusses individual opinions over a
sequence of issues. Under mild assumptions on the connectivity of the network,
the social power of every individual converges to a constant strictly positive
value as the number of issues discussed increases. If the network has a special
topology, termed "star topology", then all social power accumulates with the
individual at the centre of the star. This paper studies the strategic
introduction of new individuals and/or interpersonal relationships into a
social network with star topology to reduce the social power of the centre
individual. In fact, several strategies are proposed. For each strategy, we
derive necessary and sufficient conditions on the strength of the new
interpersonal relationships, based on local information, which ensures that the
centre individual no longer has the greatest social power within the social
network. Interpretations of these conditions show that the strategies are
remarkably intuitive and that certain strategies are favourable compared to
others, all of which is sociologically expected. | [
1,
0,
0,
0,
0,
0
] |
Title: Ground-state properties of unitary bosons: from clusters to matter,
Abstract: The properties of cold Bose gases at unitarity have been extensively
investigated in the last few years both theoretically and experimentally. In
this paper we use a family of interactions tuned to two-body unitarity and very
weak three-body binding to demonstrate the universal properties of both
clusters and matter. We determine the universal properties of finite clusters
up to 60 particles and, for the first time, explicitly demonstrate the
saturation of energy and density with particle number and compare with bulk
properties. At saturation in the bulk we determine the energy, density, two-
and three-body contacts and the condensate fraction. We find that uniform
matter is more bound than three-body clusters by nearly two orders of
magnitude, the two-body contact is very large in absolute terms, and yet the
condensate fraction is also very large, greater than 90%. Equilibrium
properties of these systems may be experimentally accessible through rapid
quenching of weakly-interacting boson superfluids. | [
0,
1,
0,
0,
0,
0
] |
Title: Lower bounds on the Noether number,
Abstract: The best known method to give a lower bound for the Noether number of a given
finite group is to use the fact that it is greater than or equal to the Noether
number of any of the subgroups or factor groups. The results of the present
paper show in particular that these inequalities are strict for proper
subgroups or factor groups. This is established by studying the algebra of
coinvariants of a representation induced from a representation of a subgroup. | [
0,
0,
1,
0,
0,
0
] |
Title: Beliefs in Markov Trees - From Local Computations to Local Valuation,
Abstract: This paper is devoted to expressiveness of hypergraphs for which uncertainty
propagation by local computations via Shenoy/Shafer method applies. It is
demonstrated that for this propagation method for a given joint belief
distribution no valuation of hyperedges of a hypergraph may provide with
simpler hypergraph structure than valuation of hyperedges by conditional
distributions. This has vital implication that methods recovering belief
networks from data have no better alternative for finding the simplest
hypergraph structure for belief propagation. A method for recovery
tree-structured belief networks has been developed and specialized for
Dempster-Shafer belief functions | [
1,
0,
0,
0,
0,
0
] |
Title: Dihedral Molecular Configurations Interacting by Lennard-Jones and Coulomb Forces,
Abstract: In this paper, we investigate periodic vibrations of a group of particles
with a dihedral configuration in the plane governed by the Lennard-Jones and
Coulomb forces. Using the gradient equivariant degree, we provide a full
topological classification of the periodic solutions with both temporal and
spatial symmetries. In the process, we provide with general formulae for the
spectrum of the linearized system which allows us to obtain the critical
frequencies of the particle motions which indicate the set of all critical
periods of small amplitude periodic solutions emerging from a given stationary
symmetric orbit of solutions. | [
0,
0,
1,
0,
0,
0
] |
Title: Fair Kernel Learning,
Abstract: New social and economic activities massively exploit big data and machine
learning algorithms to do inference on people's lives. Applications include
automatic curricula evaluation, wage determination, and risk assessment for
credits and loans. Recently, many governments and institutions have raised
concerns about the lack of fairness, equity and ethics in machine learning to
treat these problems. It has been shown that not including sensitive features
that bias fairness, such as gender or race, is not enough to mitigate the
discrimination when other related features are included. Instead, including
fairness in the objective function has been shown to be more efficient.
We present novel fair regression and dimensionality reduction methods built
on a previously proposed fair classification framework. Both methods rely on
using the Hilbert Schmidt independence criterion as the fairness term. Unlike
previous approaches, this allows us to simplify the problem and to use multiple
sensitive variables simultaneously. Replacing the linear formulation by kernel
functions allows the methods to deal with nonlinear problems. For both linear
and nonlinear formulations the solution reduces to solving simple matrix
inversions or generalized eigenvalue problems. This simplifies the evaluation
of the solutions for different trade-off values between the predictive error
and fairness terms. We illustrate the usefulness of the proposed methods in toy
examples, and evaluate their performance on real world datasets to predict
income using gender and/or race discrimination as sensitive variables, and
contraceptive method prediction under demographic and socio-economic sensitive
descriptors. | [
0,
0,
0,
1,
0,
0
] |
Title: Underwater Surveying via Bearing only Cooperative Localization,
Abstract: Bearing only cooperative localization has been used successfully on aerial
and ground vehicles. In this paper we present an extension of the approach to
the underwater domain. The focus is on adapting the technique to handle the
challenging visibility conditions underwater. Furthermore, data from inertial,
magnetic, and depth sensors are utilized to improve the robustness of the
estimation. In addition to robotic applications, the presented technique can be
used for cave mapping and for marine archeology surveying, both by human
divers. Experimental results from different environments, including a fresh
water, low visibility, lake in South Carolina; a cavern in Florida; and coral
reefs in Barbados during the day and during the night, validate the robustness
and the accuracy of the proposed approach. | [
1,
0,
0,
0,
0,
0
] |
Title: Effects of parametric uncertainties in cascaded open quantum harmonic oscillators and robust generation of Gaussian invariant states,
Abstract: This paper is concerned with the generation of Gaussian invariant states in
cascades of open quantum harmonic oscillators governed by linear quantum
stochastic differential equations. We carry out infinitesimal perturbation
analysis of the covariance matrix for the invariant Gaussian state of such a
system and the related purity functional subject to inaccuracies in the energy
and coupling matrices of the subsystems. This leads to the problem of balancing
the state-space realizations of the component oscillators through symplectic
similarity transformations in order to minimize the mean square sensitivity of
the purity functional to small random perturbations of the parameters. This
results in a quadratic optimization problem with an effective solution in the
case of cascaded one-mode oscillators, which is demonstrated by a numerical
example. We also discuss a connection of the sensitivity index with classical
statistical distances and outline infinitesimal perturbation analysis for
translation invariant cascades of identical oscillators. The findings of the
paper are applicable to robust state generation in quantum stochastic networks. | [
1,
0,
1,
0,
0,
0
] |
Title: Abelian Tensor Models on the Lattice,
Abstract: We consider a chain of Abelian Klebanov-Tarnopolsky fermionic tensor models
coupled through quartic nearest-neighbor interactions. We characterize the
gauge-singlet spectrum for small chains ($L=2,3,4,5$) and observe that the
spectral statistics exhibits strong evidences in favor of quasi-many body
localization. | [
0,
1,
0,
0,
0,
0
] |
Title: Distance-based Confidence Score for Neural Network Classifiers,
Abstract: The reliable measurement of confidence in classifiers' predictions is very
important for many applications and is, therefore, an important part of
classifier design. Yet, although deep learning has received tremendous
attention in recent years, not much progress has been made in quantifying the
prediction confidence of neural network classifiers. Bayesian models offer a
mathematically grounded framework to reason about model uncertainty, but
usually come with prohibitive computational costs. In this paper we propose a
simple, scalable method to achieve a reliable confidence score, based on the
data embedding derived from the penultimate layer of the network. We
investigate two ways to achieve desirable embeddings, by using either a
distance-based loss or Adversarial Training. We then test the benefits of our
method when used for classification error prediction, weighting an ensemble of
classifiers, and novelty detection. In all tasks we show significant
improvement over traditional, commonly used confidence scores. | [
1,
0,
0,
1,
0,
0
] |
Title: Making Sense of Bell's Theorem and Quantum Nonlocality,
Abstract: Bell's theorem has fascinated physicists and philosophers since his 1964
paper, which was written in response to the 1935 paper of Einstein, Podolsky,
and Rosen. Bell's theorem and its many extensions have led to the claim that
quantum mechanics and by inference nature herself are nonlocal in the sense
that a measurement on a system by an observer at one location has an immediate
effect on a distant "entangled" system (one with which the original system has
previously interacted). Einstein was repulsed by such "spooky action at a
distance" and was led to question whether quantum mechanics could provide a
complete description of physical reality. In this paper I argue that quantum
mechanics does not require spooky action at a distance of any kind and yet it
is entirely reasonable to question the assumption that quantum mechanics can
provide a complete description of physical reality. The magic of entangled
quantum states has little to do with entanglement and everything to do with
superposition, a property of all quantum systems and a foundational tenet of
quantum mechanics. | [
0,
1,
0,
0,
0,
0
] |
Title: Rationalizability and Epistemic Priority Orderings,
Abstract: At the beginning of a dynamic game, players may have exogenous theories about
how the opponents are going to play. Suppose that these theories are commonly
known. Then, players will refine their first-order beliefs, and challenge their
own theories, through strategic reasoning. I develop and characterize
epistemically a new solution concept, Selective Rationalizability, which
accomplishes this task under the following assumption: when the observed
behavior is not compatible with the beliefs in players' rationality and
theories of all orders, players keep the orders of belief in rationality that
are per se compatible with the observed behavior, and drop the incompatible
beliefs in the theories. Thus, Selective Rationalizability captures Common
Strong Belief in Rationality (Battigalli and Siniscalchi, 2002) and refines
Extensive-Form Rationalizability (Pearce, 1984; BS, 2002), whereas
Strong-$\Delta$-Rationalizability (Battigalli, 2003; Battigalli and
Siniscalchi, 2003) captures the opposite epistemic priority choice. Selective
Rationalizability can be extended to encompass richer epistemic priority
orderings among different theories of opponents' behavior. This allows to
establish a surprising connection with strategic stability (Kohlberg and
Mertens, 1986). | [
1,
0,
0,
0,
0,
0
] |
Title: Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning,
Abstract: Prediction is an appealing objective for self-supervised learning of
behavioral skills, particularly for autonomous robots. However, effectively
utilizing predictive models for control, especially with raw image inputs,
poses a number of major challenges. How should the predictions be used? What
happens when they are inaccurate? In this paper, we tackle these questions by
proposing a method for learning robotic skills from raw image observations,
using only autonomously collected experience. We show that even an imperfect
model can complete complex tasks if it can continuously retry, but this
requires the model to not lose track of the objective (e.g., the object of
interest). To enable a robot to continuously retry a task, we devise a
self-supervised algorithm for learning image registration, which can keep track
of objects of interest for the duration of the trial. We demonstrate that this
idea can be combined with a video-prediction based controller to enable complex
behaviors to be learned from scratch using only raw visual inputs, including
grasping, repositioning objects, and non-prehensile manipulation. Our
real-world experiments demonstrate that a model trained with 160 robot hours of
autonomously collected, unlabeled data is able to successfully perform complex
manipulation tasks with a wide range of objects not seen during training. | [
1,
0,
0,
0,
0,
0
] |
Title: S-Isomap++: Multi Manifold Learning from Streaming Data,
Abstract: Manifold learning based methods have been widely used for non-linear
dimensionality reduction (NLDR). However, in many practical settings, the need
to process streaming data is a challenge for such methods, owing to the high
computational complexity involved. Moreover, most methods operate under the
assumption that the input data is sampled from a single manifold, embedded in a
high dimensional space. We propose a method for streaming NLDR when the
observed data is either sampled from multiple manifolds or irregularly sampled
from a single manifold. We show that existing NLDR methods, such as Isomap,
fail in such situations, primarily because they rely on smoothness and
continuity of the underlying manifold, which is violated in the scenarios
explored in this paper. However, the proposed algorithm is able to learn
effectively in presence of multiple, and potentially intersecting, manifolds,
while allowing for the input data to arrive as a massive stream. | [
1,
0,
0,
1,
0,
0
] |
Title: Extreme radio-wave scattering associated with hot stars,
Abstract: We use data on extreme radio scintillation to demonstrate that this
phenomenon is associated with hot stars in the solar neighbourhood. The ionized
gas responsible for the scattering is found at distances up to 1.75pc from the
host star, and on average must comprise 1.E5 distinct structures per star. We
detect azimuthal velocities of the plasma, relative to the host star, up to 9.7
km/s, consistent with warm gas expanding at the sound speed. The circumstellar
plasma structures that we infer are similar in several respects to the cometary
knots seen in the Helix, and in other planetary nebulae. There the ionized gas
appears as a skin around tiny molecular clumps. Our analysis suggests that
molecular clumps are ubiquitous circumstellar features, unrelated to the
evolutionary state of the star. The total mass in such clumps is comparable to
the stellar mass. | [
0,
1,
0,
0,
0,
0
] |
Title: Graded components of local cohomology modules,
Abstract: Let $A$ be a regular ring containing a field of characteristic zero and let
$R = A[X_1,\ldots, X_m]$. Consider $R$ as standard graded with $deg \ A = 0$
and $deg \ X_i = 1$ for all $i$. In this paper we present a comprehensive study
of graded components of local cohomology modules $H^i_I(R)$ where $I$ is an
\emph{arbitrary} homogeneous ideal in $R$. Our study seems to be the first in
this regard. | [
0,
0,
1,
0,
0,
0
] |
Title: Drug Selection via Joint Push and Learning to Rank,
Abstract: Selecting the right drugs for the right patients is a primary goal of
precision medicine. In this manuscript, we consider the problem of cancer drug
selection in a learning-to-rank framework. We have formulated the cancer drug
selection problem as to accurately predicting 1). the ranking positions of
sensitive drugs and 2). the ranking orders among sensitive drugs in cancer cell
lines based on their responses to cancer drugs. We have developed a new
learning-to-rank method, denoted as pLETORg , that predicts drug ranking
structures in each cell line via using drug latent vectors and cell line latent
vectors. The pLETORg method learns such latent vectors through explicitly
enforcing that, in the drug ranking list of each cell line, the sensitive drugs
are pushed above insensitive drugs, and meanwhile the ranking orders among
sensitive drugs are correct. Genomics information on cell lines is leveraged in
learning the latent vectors. Our experimental results on a benchmark cell
line-drug response dataset demonstrate that the new pLETORg significantly
outperforms the state-of-the-art method in prioritizing new sensitive drugs. | [
0,
0,
0,
1,
0,
0
] |
Title: Linear Discriminant Generative Adversarial Networks,
Abstract: We develop a novel method for training of GANs for unsupervised and class
conditional generation of images, called Linear Discriminant GAN (LD-GAN). The
discriminator of an LD-GAN is trained to maximize the linear separability
between distributions of hidden representations of generated and targeted
samples, while the generator is updated based on the decision hyper-planes
computed by performing LDA over the hidden representations. LD-GAN provides a
concrete metric of separation capacity for the discriminator, and we
experimentally show that it is possible to stabilize the training of LD-GAN
simply by calibrating the update frequencies between generators and
discriminators in the unsupervised case, without employment of normalization
methods and constraints on weights. In the class conditional generation tasks,
the proposed method shows improved training stability together with better
generalization performance compared to WGAN that employs an auxiliary
classifier. | [
1,
0,
0,
1,
0,
0
] |
Title: Sequential noise-induced escapes for oscillatory network dynamics,
Abstract: It is well known that the addition of noise in a multistable system can
induce random transitions between stable states. The rate of transition can be
characterised in terms of the noise-free system's dynamics and the added noise:
for potential systems in the presence of asymptotically low noise the
well-known Kramers' escape time gives an expression for the mean escape time.
This paper examines some general properties and examples of transitions between
local steady and oscillatory attractors within networks: the transition rates
at each node may be affected by the dynamics at other nodes. We use first
passage time theory to explain some properties of scalings noted in the
literature for an idealised model of initiation of epileptic seizures in small
systems of coupled bistable systems with both steady and oscillatory
attractors. We focus on the case of sequential escapes where a steady attractor
is only marginally stable but all nodes start in this state. As the nodes
escape to the oscillatory regime, we assume that the transitions back are very
infrequent in comparison. We quantify and characterise the resulting sequences
of noise-induced escapes. For weak enough coupling we show that a master
equation approach gives a good quantitative understanding of sequential
escapes, but for strong coupling this description breaks down. | [
0,
1,
0,
0,
0,
0
] |
Title: Confluence of Conditional Term Rewrite Systems via Transformations,
Abstract: Conditional term rewriting is an intuitive yet complex extension of term
rewriting. In order to benefit from the simpler framework of unconditional
rewriting, transformations have been defined to eliminate the conditions of
conditional term rewrite systems.
Recent results provide confluence criteria for conditional term rewrite
systems via transformations, yet they are restricted to CTRSs with certain
syntactic properties like weak left-linearity. These syntactic properties imply
that the transformations are sound for the given CTRS.
This paper shows how to use transformations to prove confluence of
operationally terminating, right-stable deterministic conditional term rewrite
systems without the necessity of soundness restrictions. For this purpose, it
is shown that certain rewrite strategies, in particular almost U-eagerness and
innermost rewriting, always imply soundness. | [
1,
0,
0,
0,
0,
0
] |
Title: Interplay of dilution and magnetic field in the nearest-neighbor spin-ice model on the pyrochlore lattice,
Abstract: We study the magnetic field effects on the diluted spin-ice materials using
the replica-exchange Monte Carlo simulation. We observe five plateaus in the
magnetization curve of the diluted nearest-neighbor spin-ice model on the
pyrochlore lattice when a magnetic field is applied in the [111] direction.
This is in contrast to the case of the pure model with two plateaus. The origin
of five plateaus is investigated from the spin configuration of two
corner-sharing tetrahedra in the case of the diluted model. | [
0,
1,
0,
0,
0,
0
] |
Title: RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process,
Abstract: An RNN-based forecasting approach is used to early detect anomalies in
industrial multivariate time series data from a simulated Tennessee Eastman
Process (TEP) with many cyber-attacks. This work continues a previously
proposed LSTM-based approach to the fault detection in simpler data. It is
considered necessary to adapt the RNN network to deal with data containing
stochastic, stationary, transitive and a rich variety of anomalous behaviours.
There is particular focus on early detection with special NAB-metric. A
comparison with the DPCA approach is provided. The generated data set is made
publicly available. | [
1,
0,
0,
0,
0,
0
] |
Title: Detecting singular weak-dissipation limit for flutter onset in reversible systems,
Abstract: A `flutter machine' is introduced for the investigation of a singular
interface between the classical and reversible Hopf bifurcations that is
theoretically predicted to be generic in nonconservative reversible systems
with vanishing dissipation. In particular, such a singular interface exists for
the Pflüger viscoelastic column moving in a resistive medium, which is proven
by means of the perturbation theory of multiple eigenvalues with the Jordan
block. The laboratory setup, consisting of a cantilevered viscoelastic rod
loaded by a positional force with non-zero curl produced by dry friction,
demonstrates high sensitivity of the classical Hopf bifurcation onset {to the
ratio between} the weak air drag and Kelvin-Voigt damping in the Pflüger
column. Thus, the Whitney umbrella singularity is experimentally confirmed,
responsible for discontinuities accompanying dissipation-induced instabilities
in a broad range of physical contexts. | [
0,
1,
0,
0,
0,
0
] |
Title: A Time Hierarchy Theorem for the LOCAL Model,
Abstract: The celebrated Time Hierarchy Theorem for Turing machines states, informally,
that more problems can be solved given more time. The extent to which a time
hierarchy-type theorem holds in the distributed LOCAL model has been open for
many years. It is consistent with previous results that all natural problems in
the LOCAL model can be classified according to a small constant number of
complexities, such as $O(1),O(\log^* n), O(\log n), 2^{O(\sqrt{\log n})}$, etc.
In this paper we establish the first time hierarchy theorem for the LOCAL
model and prove that several gaps exist in the LOCAL time hierarchy.
1. We define an infinite set of simple coloring problems called Hierarchical
$2\frac{1}{2}$-Coloring}. A correctly colored graph can be confirmed by simply
checking the neighborhood of each vertex, so this problem fits into the class
of locally checkable labeling (LCL) problems. However, the complexity of the
$k$-level Hierarchical $2\frac{1}{2}$-Coloring problem is $\Theta(n^{1/k})$,
for $k\in\mathbb{Z}^+$. The upper and lower bounds hold for both general graphs
and trees, and for both randomized and deterministic algorithms.
2. Consider any LCL problem on bounded degree trees. We prove an
automatic-speedup theorem that states that any randomized $n^{o(1)}$-time
algorithm solving the LCL can be transformed into a deterministic $O(\log
n)$-time algorithm. Together with a previous result, this establishes that on
trees, there are no natural deterministic complexities in the ranges
$\omega(\log^* n)$---$o(\log n)$ or $\omega(\log n)$---$n^{o(1)}$.
3. We expose a gap in the randomized time hierarchy on general graphs. Any
randomized algorithm that solves an LCL problem in sublogarithmic time can be
sped up to run in $O(T_{LLL})$ time, which is the complexity of the distributed
Lovasz local lemma problem, currently known to be $\Omega(\log\log n)$ and
$O(\log n)$. | [
1,
0,
0,
0,
0,
0
] |
Title: Doping-induced spin-orbit splitting in Bi-doped ZnO nanowires,
Abstract: Our predictions, based on density-functional calculations, reveal that
surface doping of ZnO nanowires with Bi leads to a linear-in-$k$ splitting of
the conduction-band states, through spin-orbit interaction, due to the lowering
of the symmetry in the presence of the dopant. This finding implies that spin
polarization of the conduction electrons in Bi-doped ZnO nanowires could be
controlled with applied electric (as opposed to magnetic) fields, making them
candidate materials for spin-orbitronic applications. Our findings also show
that the degree of spin splitting could be tuned by adjusting the dopant
concentration. Defect calculations and ab initio molecular dynamics simulations
indicate that stable doping configurations exhibiting the foregoing
linear-in-$k$ splitting could be realized under reasonable thermodynamic
conditions. | [
0,
1,
0,
0,
0,
0
] |
Title: Embodied Question Answering,
Abstract: We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person (egocentric) vision, and then answer the question ("orange").
This challenging task requires a range of AI skills -- active perception,
language understanding, goal-driven navigation, commonsense reasoning, and
grounding of language into actions. In this work, we develop the environments,
end-to-end-trained reinforcement learning agents, and evaluation protocols for
EmbodiedQA. | [
1,
0,
0,
0,
0,
0
] |
Title: Data-Efficient Design Exploration through Surrogate-Assisted Illumination,
Abstract: Design optimization techniques are often used at the beginning of the design
process to explore the space of possible designs. In these domains illumination
algorithms, such as MAP-Elites, are promising alternatives to classic
optimization algorithms because they produce diverse, high-quality solutions in
a single run, instead of only a single near-optimal solution. Unfortunately,
these algorithms currently require a large number of function evaluations,
limiting their applicability. In this article we introduce a new illumination
algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate
modeling techniques to create a map of the design space according to
user-defined features while minimizing the number of fitness evaluations. On a
2-dimensional airfoil optimization problem SAIL produces hundreds of diverse
but high-performing designs with several orders of magnitude fewer evaluations
than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of
producing maps of high-performing designs in realistic 3-dimensional
aerodynamic tasks with an accurate flow simulation. Data-efficient design
exploration with SAIL can help designers understand what is possible, beyond
what is optimal, by considering more than pure objective-based optimization. | [
0,
0,
0,
1,
0,
0
] |
Title: Brain Computer Interface for Gesture Control of a Social Robot: an Offline Study,
Abstract: Brain computer interface (BCI) provides promising applications in
neuroprosthesis and neurorehabilitation by controlling computers and robotic
devices based on the patient's intentions. Here, we have developed a novel BCI
platform that controls a personalized social robot using noninvasively acquired
brain signals. Scalp electroencephalogram (EEG) signals are collected from a
user in real-time during tasks of imaginary movements. The imagined body
kinematics are decoded using a regression model to calculate the user-intended
velocity. Then, the decoded kinematic information is mapped to control the
gestures of a social robot. The platform here may be utilized as a
human-robot-interaction framework by combining with neurofeedback mechanisms to
enhance the cognitive capability of persons with dementia. | [
1,
0,
0,
0,
0,
0
] |
Title: Faster Algorithms for Mean-Payoff Parity Games,
Abstract: Graph games provide the foundation for modeling and synthesis of reactive
processes. Such games are played over graphs where the vertices are controlled
by two adversarial players. We consider graph games where the objective of the
first player is the conjunction of a qualitative objective (specified as a
parity condition) and a quantitative objective (specified as a mean-payoff
condition). There are two variants of the problem, namely, the threshold
problem where the quantitative goal is to ensure that the mean-payoff value is
above a threshold, and the value problem where the quantitative goal is to
ensure the optimal mean-payoff value; in both cases ensuring the qualitative
parity objective. The previous best-known algorithms for game graphs with $n$
vertices, $m$ edges, parity objectives with $d$ priorities, and maximal
absolute reward value $W$ for mean-payoff objectives, are as follows:
$O(n^{d+1} \cdot m \cdot W)$ for the threshold problem, and $O(n^{d+2} \cdot m
\cdot W)$ for the value problem. Our main contributions are faster algorithms,
and the running times of our algorithms are as follows: $O(n^{d-1} \cdot m
\cdot W)$ for the threshold problem, and $O(n^{d} \cdot m \cdot W \cdot \log
(n\cdot W))$ for the value problem. For mean-payoff parity objectives with two
priorities, our algorithms match the best-known bounds of the algorithms for
mean-payoff games (without conjunction with parity objectives). Our results are
relevant in synthesis of reactive systems with both functional requirement
(given as a qualitative objective) and performance requirement (given as a
quantitative objective). | [
1,
0,
0,
0,
0,
0
] |
Title: Similarity Function Tracking using Pairwise Comparisons,
Abstract: Recent work in distance metric learning has focused on learning
transformations of data that best align with specified pairwise similarity and
dissimilarity constraints, often supplied by a human observer. The learned
transformations lead to improved retrieval, classification, and clustering
algorithms due to the better adapted distance or similarity measures. Here, we
address the problem of learning these transformations when the underlying
constraint generation process is nonstationary. This nonstationarity can be due
to changes in either the ground-truth clustering used to generate constraints
or changes in the feature subspaces in which the class structure is apparent.
We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD),
a general adaptive, online approach for learning and tracking optimal metrics
as they change over time that is highly robust to a variety of nonstationary
behaviors in the changing metric. We apply the OCELAD framework to an ensemble
of online learners. Specifically, we create a retro-initialized composite
objective mirror descent (COMID) ensemble (RICE) consisting of a set of
parallel COMID learners with different learning rates, and demonstrate
parameter-free RICE-OCELAD metric learning on both synthetic data and a highly
nonstationary Twitter dataset. We show significant performance improvements and
increased robustness to nonstationary effects relative to previously proposed
batch and online distance metric learning algorithms. | [
1,
0,
0,
1,
0,
0
] |
Title: Deep Boosted Regression for MR to CT Synthesis,
Abstract: Attenuation correction is an essential requirement of positron emission
tomography (PET) image reconstruction to allow for accurate quantification.
However, attenuation correction is particularly challenging for PET-MRI as
neither PET nor magnetic resonance imaging (MRI) can directly image tissue
attenuation properties. MRI-based computed tomography (CT) synthesis has been
proposed as an alternative to physics based and segmentation-based approaches
that assign a population-based tissue density value in order to generate an
attenuation map. We propose a novel deep fully convolutional neural network
that generates synthetic CTs in a recursive manner by gradually reducing the
residuals of the previous network, increasing the overall accuracy and
generalisability, while keeping the number of trainable parameters within
reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT
pairs and a four-fold random bootstrapped validation with a 80:20 split is
performed. Quantitative results show that the proposed framework outperforms a
state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE)
from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction
error from 14.3% to 7.2%. | [
0,
0,
0,
1,
0,
0
] |
Title: Discretisation of regularity structures,
Abstract: We introduce a general framework allowing to apply the theory of regularity
structures to discretisations of stochastic PDEs. The approach pursued in this
article is that we do not focus on any one specific discretisation procedure.
Instead, we assume that we are given a scale $\varepsilon > 0$ and a "black
box" describing the behaviour of our discretised objects at scales below
$\varepsilon $. | [
0,
0,
1,
0,
0,
0
] |
Title: Optimization of Wireless Power Transfer Systems Enhanced by Passive Elements and Metasurfaces,
Abstract: This paper presents a rigorous optimization technique for wireless power
transfer (WPT) systems enhanced by passive elements, ranging from simple
reflectors and intermedi- ate relays all the way to general electromagnetic
guiding and focusing structures, such as metasurfaces and metamaterials. At its
core is a convex semidefinite relaxation formulation of the otherwise nonconvex
optimization problem, of which tightness and optimality can be confirmed by a
simple test of its solutions. The resulting method is rigorous, versatile, and
general -- it does not rely on any assumptions. As shown in various examples,
it is able to efficiently and reliably optimize such WPT systems in order to
find their physical limitations on performance, optimal operating parameters
and inspect their working principles, even for a large number of active
transmitters and passive elements. | [
1,
0,
1,
0,
0,
0
] |
Title: Knotted solutions, from electromagnetism to fluid dynamics,
Abstract: Knotted solutions to electromagnetism and fluid dynamics are investigated,
based on relations we find between the two subjects. We can write fluid
dynamics in electromagnetism language, but only on an initial surface, or for
linear perturbations, and we use this map to find knotted fluid solutions, as
well as new electromagnetic solutions. We find that knotted solutions of
Maxwell electromagnetism are also solutions of more general nonlinear theories,
like Born-Infeld, and including ones which contain quantum corrections from
couplings with other modes, like Euler-Heisenberg and string theory DBI. Null
configurations in electromagnetism can be described as a null pressureless
fluid, and from this map we can find null fluid knotted solutions. A type of
nonrelativistic reduction of the relativistic fluid equations is described,
which allows us to find also solutions of the (nonrelativistic) Euler's
equations. | [
0,
1,
0,
0,
0,
0
] |
Title: Healthcare Robotics,
Abstract: Robots have the potential to be a game changer in healthcare: improving
health and well-being, filling care gaps, supporting care givers, and aiding
health care workers. However, before robots are able to be widely deployed, it
is crucial that both the research and industrial communities work together to
establish a strong evidence-base for healthcare robotics, and surmount likely
adoption barriers. This article presents a broad contextualization of robots in
healthcare by identifying key stakeholders, care settings, and tasks; reviewing
recent advances in healthcare robotics; and outlining major challenges and
opportunities to their adoption. | [
1,
0,
0,
0,
0,
0
] |
Title: The same strain of Piscine orthoreovirus (PRV-1) is involved with the development of different, but related, diseases in Atlantic and Pacific Salmon in British Columbia,
Abstract: Piscine orthoreovirus Strain PRV-1 is the causative agent of heart and
skeletal muscle inflammation (HSMI) in Atlantic salmon (Salmo salar). Given its
high prevalence in net pen salmon, debate has arisen on whether PRV poses a
risk to migratory salmon, especially in British Columbia (BC) where
commercially important wild Pacific salmon are in decline. Various strains of
PRV have been associated with diseases in Pacific salmon, including
erythrocytic inclusion body syndrome (EIBS), HSMI-like disease, and
jaundice/anemia in Japan, Norway, Chile and Canada. We examine the
developmental pathway of HSMI and jaundice/anemia associated with PRV-1 in
farmed Atlantic and Chinook (Oncorhynchus tshawytscha) salmon in BC,
respectively. In situ hybridization localized PRV-1 within developing lesions
in both diseases. The two diseases showed dissimilar pathological pathways,
with inflammatory lesions in heart and skeletal muscle in Atlantic salmon, and
degenerative-necrotic lesions in kidney and liver in Chinook salmon, plausibly
explained by differences in PRV load tolerance in red blood cells. Viral genome
sequencing revealed no consistent differences in PRV-1 variants intimately
involved in the development of both diseases, suggesting that migratory Chinook
salmon may be at more than a minimal risk of disease from exposure to the high
levels of PRV occurring on salmon farms. | [
0,
0,
0,
0,
1,
0
] |
Title: On Learning the $cμ$ Rule in Single and Parallel Server Networks,
Abstract: We consider learning-based variants of the $c \mu$ rule for scheduling in
single and parallel server settings of multi-class queueing systems.
In the single server setting, the $c \mu$ rule is known to minimize the
expected holding-cost (weighted queue-lengths summed over classes and a fixed
time horizon). We focus on the problem where the service rates $\mu$ are
unknown with the holding-cost regret (regret against the $c \mu$ rule with
known $\mu$) as our objective. We show that the greedy algorithm that uses
empirically learned service rates results in a constant holding-cost regret
(the regret is independent of the time horizon). This free exploration can be
explained in the single server setting by the fact that any work-conserving
policy obtains the same number of samples in a busy cycle.
In the parallel server setting, we show that the $c \mu$ rule may result in
unstable queues, even for arrival rates within the capacity region. We then
present sufficient conditions for geometric ergodicity under the $c \mu$ rule.
Using these results, we propose an almost greedy algorithm that explores only
when the number of samples falls below a threshold. We show that this algorithm
delivers constant holding-cost regret because a free exploration condition is
eventually satisfied. | [
1,
0,
0,
0,
0,
0
] |
Title: Crafting Adversarial Examples For Speech Paralinguistics Applications,
Abstract: Computational paralinguistic analysis is increasingly being used in a wide
range of cyber applications, including security-sensitive applications such as
speaker verification, deceptive speech detection, and medical diagnostics.
While state-of-the-art machine learning techniques, such as deep neural
networks, can provide robust and accurate speech analysis, they are susceptible
to adversarial attacks. In this work, we propose an end-to-end scheme to
generate adversarial examples for computational paralinguistic applications by
perturbing directly the raw waveform of an audio recording rather than specific
acoustic features. Our experiments show that the proposed adversarial
perturbation can lead to a significant performance drop of state-of-the-art
deep neural networks, while only minimally impairing the audio quality. | [
1,
0,
0,
1,
0,
0
] |
Title: The Ebb and Flow of Controversial Debates on Social Media,
Abstract: We explore how the polarization around controversial topics evolves on
Twitter - over a long period of time (2011 to 2016), and also as a response to
major external events that lead to increased related activity. We find that
increased activity is typically associated with increased polarization;
however, we find no consistent long-term trend in polarization over time among
the topics we study. | [
1,
1,
0,
0,
0,
0
] |
Title: Generalized Concomitant Multi-Task Lasso for sparse multimodal regression,
Abstract: In high dimension, it is customary to consider Lasso-type estimators to
enforce sparsity. For standard Lasso theory to hold, the regularization
parameter should be proportional to the noise level, yet the latter is
generally unknown in practice. A possible remedy is to consider estimators,
such as the Concomitant/Scaled Lasso, which jointly optimize over the
regression coefficients as well as over the noise level, making the choice of
the regularization independent of the noise level. However, when data from
different sources are pooled to increase sample size, or when dealing with
multimodal datasets, noise levels typically differ and new dedicated estimators
are needed. In this work we provide new statistical and computational solutions
to deal with such heteroscedastic regression models, with an emphasis on
functional brain imaging with combined magneto- and electroencephalographic
(M/EEG) signals. Adopting the formulation of Concomitant Lasso-type estimators,
we propose a jointly convex formulation to estimate both the regression
coefficients and the (square root of the) noise covariance. When our framework
is instantiated to de-correlated noise, it leads to an efficient algorithm
whose computational cost is not higher than for the Lasso and Concomitant
Lasso, while addressing more complex noise structures. Numerical experiments
demonstrate that our estimator yields improved prediction and support
identification while correctly estimating the noise (square root) covariance.
Results on multimodal neuroimaging problems with M/EEG data are also reported. | [
0,
0,
1,
1,
0,
0
] |
Title: A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging,
Abstract: We propose a novel automatic method for accurate segmentation of the prostate
in T2-weighted magnetic resonance imaging (MRI). Our method is based on
convolutional neural networks (CNNs). Because of the large variability in the
shape, size, and appearance of the prostate and the scarcity of annotated
training data, we suggest training two separate CNNs. A global CNN will
determine a prostate bounding box, which is then resampled and sent to a local
CNN for accurate delineation of the prostate boundary. This way, the local CNN
can effectively learn to segment the fine details that distinguish the prostate
from the surrounding tissue using the small amount of available training data.
To fully exploit the training data, we synthesize additional data by deforming
the training images and segmentations using a learned shape model. We apply the
proposed method on the PROMISE12 challenge dataset and achieve state of the art
results. Our proposed method generates accurate, smooth, and artifact-free
segmentations. On the test images, we achieve an average Dice score of 90.6
with a small standard deviation of 2.2, which is superior to all previous
methods. Our two-step segmentation approach and data augmentation strategy may
be highly effective in segmentation of other organs from small amounts of
annotated medical images. | [
1,
0,
0,
1,
0,
0
] |
Title: A note on signature of Lefschetz fibrations with planar fiber,
Abstract: Using theorems of Eliashberg and McDuff, Etnyre [Et] proved that the
intersection form of a symplectic filling of a contact 3-manifold supported by
planar open book is negative definite.
In this paper, we prove a signature formula for allowable Lefschetz
fibrations over $D^2$ with planar fiber by computing Maslov index appearing in
Wall's non-additivity formula.
The signature formula leads to an alternative proof of Etnyre's theorem via
works of Niederkrüger and Wendl [NWe] and Wendl [We].
Conversely, Etnyre's theorem, together with the existence theorem of Stein
structures on Lefschetz fibrations over $D^2$ with bordered fiber by Loi and
Piergallini [LP], implies the formula. | [
0,
0,
1,
0,
0,
0
] |
Title: Engineering a flux-dependent mobility edge in disordered zigzag chains,
Abstract: There has been great interest in realizing quantum simulators of charged
particles in artificial gauge fields. Here, we perform the first quantum
simulation explorations of the combination of artificial gauge fields and
disorder. Using synthetic lattice techniques based on parametrically-coupled
atomic momentum states, we engineer zigzag chains with a tunable homogeneous
flux. The breaking of time-reversal symmetry by the applied flux leads to
analogs of spin-orbit coupling and spin-momentum locking, which we observe
directly through the chiral dynamics of atoms initialized to single lattice
sites. We additionally introduce precisely controlled disorder in the site
energy landscape, allowing us to explore the interplay of disorder and large
effective magnetic fields. The combination of correlated disorder and
controlled intra- and inter-row tunneling in this system naturally supports
energy-dependent localization, relating to a single-particle mobility edge. We
measure the localization properties of the extremal eigenstates of this system,
the ground state and the most-excited state, and demonstrate clear evidence for
a flux-dependent mobility edge. These measurements constitute the first direct
evidence for energy-dependent localization in a lower-dimensional system, as
well as the first explorations of the combined influence of artificial gauge
fields and engineered disorder. Moreover, we provide direct evidence for
interaction shifts of the localization transitions for both low- and
high-energy eigenstates in correlated disorder, relating to the presence of a
many-body mobility edge. The unique combination of strong interactions,
controlled disorder, and tunable artificial gauge fields present in this
synthetic lattice system should enable myriad explorations into intriguing
correlated transport phenomena. | [
0,
1,
0,
0,
0,
0
] |
Title: Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask,
Abstract: Singing voice separation based on deep learning relies on the usage of
time-frequency masking. In many cases the masking process is not a learnable
function or is not encapsulated into the deep learning optimization.
Consequently, most of the existing methods rely on a post processing step using
the generalized Wiener filtering. This work proposes a method that learns and
optimizes (during training) a source-dependent mask and does not need the
aforementioned post processing step. We introduce a recurrent inference
algorithm, a sparse transformation step to improve the mask generation process,
and a learned denoising filter. Obtained results show an increase of 0.49 dB
for the signal to distortion ratio and 0.30 dB for the signal to interference
ratio, compared to previous state-of-the-art approaches for monaural singing
voice separation. | [
1,
0,
0,
0,
0,
0
] |
Title: Confidence Bands for Coefficients in High Dimensional Linear Models with Error-in-variables,
Abstract: We study high-dimensional linear models with error-in-variables. Such models
are motivated by various applications in econometrics, finance and genetics.
These models are challenging because of the need to account for measurement
errors to avoid non-vanishing biases in addition to handle the high
dimensionality of the parameters. A recent growing literature has proposed
various estimators that achieve good rates of convergence. Our main
contribution complements this literature with the construction of simultaneous
confidence regions for the parameters of interest in such high-dimensional
linear models with error-in-variables.
These confidence regions are based on the construction of moment conditions
that have an additional orthogonal property with respect to nuisance
parameters. We provide a construction that requires us to estimate an
additional high-dimensional linear model with error-in-variables for each
component of interest. We use a multiplier bootstrap to compute critical values
for simultaneous confidence intervals for a subset $S$ of the components. We
show its validity despite of possible model selection mistakes, and allowing
for the cardinality of $S$ to be larger than the sample size.
We apply and discuss the implications of our results to two examples and
conduct Monte Carlo simulations to illustrate the performance of the proposed
procedure. | [
0,
0,
1,
1,
0,
0
] |
Title: Ultra-broadband On-chip Twisted Light Emitter,
Abstract: On-chip twisted light emitters are essential components for orbital angular
momentum (OAM) communication devices, which could address the growing demand
for high-capacity communication systems by providing an additional degree of
freedom for wavelength/frequency division multiplexing (WDM/FDM). Although
whispering gallery mode enabled OAM emitters have been shown to possess some
advantages, such as being compact and phase accurate, their inherent narrow
bandwidth prevents them from being compatible with WDM/FDM techniques. Here, we
demonstrate an ultra-broadband multiplexed OAM emitter that utilizes a novel
joint path-resonance phase control concept. The emitter has a micron sized
radius and nanometer sized features. Coaxial OAM beams are emitted across the
entire telecommunication band from 1450 to 1650 nm. We applied the emitter for
OAM communication with a data rate of 1.2 Tbit/s assisted by 30-channel optical
frequency combs (OFC). The emitter provides a new solution to further increase
of the capacity in the OFC communication scenario. | [
0,
1,
0,
0,
0,
0
] |
Title: On the Fourth Power Moment of Fourier Coefficients of Cusp Form,
Abstract: Let $a(n)$ be the Fourier coefficients of a holomorphic cusp form of weight
$\kappa=2n\geqslant12$ for the full modular group and
$A(x)=\sum\limits_{n\leqslant x}a(n)$. In this paper, we establish an
asymptotic formula of the fourth power moment of $A(x)$ and prove that
\begin{equation*}
\int_1^TA^4(x)\mathrm{d}x=\frac{3}{64\kappa\pi^4}s_{4;2}(\tilde{a})
T^{2\kappa}+O\big(T^{2\kappa-\delta_4+\varepsilon}\big) \end{equation*} with
$\delta_4=1/8$, which improves the previous result. | [
0,
0,
1,
0,
0,
0
] |
Title: Provable benefits of representation learning,
Abstract: There is general consensus that learning representations is useful for a
variety of reasons, e.g. efficient use of labeled data (semi-supervised
learning), transfer learning and understanding hidden structure of data.
Popular techniques for representation learning include clustering, manifold
learning, kernel-learning, autoencoders, Boltzmann machines, etc.
To study the relative merits of these techniques, it's essential to formalize
the definition and goals of representation learning, so that they are all
become instances of the same definition. This paper introduces such a formal
framework that also formalizes the utility of learning the representation. It
is related to previous Bayesian notions, but with some new twists. We show the
usefulness of our framework by exhibiting simple and natural settings -- linear
mixture models and loglinear models, where the power of representation learning
can be formally shown. In these examples, representation learning can be
performed provably and efficiently under plausible assumptions (despite being
NP-hard), and furthermore: (i) it greatly reduces the need for labeled data
(semi-supervised learning) and (ii) it allows solving classification tasks when
simpler approaches like nearest neighbors require too much data (iii) it is
more powerful than manifold learning methods. | [
1,
0,
0,
1,
0,
0
] |
Title: Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information,
Abstract: We introduce a new virtual environment for simulating a card game known as
"Big 2". This is a four-player game of imperfect information with a relatively
complicated action space (being allowed to play 1,2,3,4 or 5 card combinations
from an initial starting hand of 13 cards). As such it poses a challenge for
many current reinforcement learning methods. We then use the recently proposed
"Proximal Policy Optimization" algorithm to train a deep neural network to play
the game, purely learning via self-play, and find that it is able to reach a
level which outperforms amateur human players after only a relatively short
amount of training time and without needing to search a tree of future game
states. | [
0,
0,
0,
1,
0,
0
] |
Title: Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms,
Abstract: We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images
of 70,000 fashion products from 10 categories, with 7,000 images per category.
The training set has 60,000 images and the test set has 10,000 images.
Fashion-MNIST is intended to serve as a direct drop-in replacement for the
original MNIST dataset for benchmarking machine learning algorithms, as it
shares the same image size, data format and the structure of training and
testing splits. The dataset is freely available at
this https URL | [
1,
0,
0,
1,
0,
0
] |
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