Abstract:
  We analyze recent results concerning the hypothesis of a privileged direction
in the space-time that is made by considering a background of the Lorentz
symmetry violation determined by a fixed spacelike vector field and the
analysis of quantum effects of this background on the interaction of a
nonrelativistic electron with a nonuniform electric field produced by a uniform
electric charge distribution. We show that the conclusions derived by the
authors are an artifact of the truncation of the Frobenius series by means of
the tree-term recurrence relation for the expansion coefficients. Thus, the
existence of allowed angular frequencies stemming from this procedure is
meaningless and unphysical.


Abstract:
  \textit{Let $E$ be an infinite set on which a property $(\bf P)$ is defined.
Suppose that $E=\cup_{i\in I} E_i$ is a partition, where each $E_i$ is
infinite. Suppose also that, in each $E_i$, the number of elements satisfying
$(\bf P)$ is finite. Then, clearly the density of the elements satisfying $(\bf
P)$ is 0 in every $E_i$. Is it possible that the density of the subset of $E$
containing all the elements satisfying $(\bf P)$ will be at least equal to $
1/2$?}
  We were first confronted with this situation while reading the paper of Arno
et al. [1]. In fact, it is in the paper [1] where it is shown that the density
of certain algebraic numbers in $\overline{\mathbb{Q}}$, which we will call
Arno et al. numbers in section 5, is equal to $1/\zeta(3)$. We have partitioned
$\overline{\mathbb{Q}}$ in a way that suggests these Arno et al. numbers are
rare. This phenomenom struck us as contradictory, which lead us to consider the
situation in greater detail. We will show in the sequel, through two examples,
that the answer to the above question may be positive. At first glance, this
problem resembles to the so called Simpson paradox in probability and
statistics. In this paper, when we say the density, we mean the natural
density.


Abstract:
  Bibliometrics is useful to analyze the research impact for measuring the
research quality. Different bibliographic databases like Scopus, Web of
Science, Google Scholar etc. are accessed for evaluating the trend of
publications and citations from time to time. Some of these databases are free
and some are subscription based. Its always debatable that which bibliographic
database is better and in what terms. To provide an optimal solution to
availability of multiple bibliographic databases, we have implemented a single
authentic database named as ``conflate'' which can be used for fetching
publication and citation trend of an author. To further strengthen the
generated database and to provide the transparent system to the stakeholders, a
consensus mechanism ``proof of reference (PoR)'' is proposed. Due to three
consent based checks implemented in PoR, we feel that it could be considered as
a authentic and honest citation data source for the calculation of unified
informetrics for an author.


Abstract:
  Special generic maps are smooth maps at each singular point of which we can
represent as $(x_1, \cdots, x_m) \mapsto
(x_1,\cdots,x_{n-1},\sum_{k=n}^{m}{x_k}^2)$ for suitable coordinates. Morse
functions with exactly two singular points on homotopy spheres and canonical
projections of unit spheres are special generic. They are known to restrict the
topologies and the differentiable structures of the manifolds in various
situations. On the other hands, various manifolds admit such maps.
  This article first presents a special generic map on a $7$-dimensional
manifold and the image. This results also seems to present a new example of
$7$-dimensional closed and simply-connected manifolds having non-vanishing
triple Massey products and seems to be a new work related to similar works by
Dranishnikov and Rudyak. We also review results on vanishing of products of
cohomology classes, previously obtained by the author. The images of special
generic maps are smoothly immersed manifolds whose dimensions are equal to the
dimensions of the targets. The author studied the topologies of these images
previously and studies on homology groups, cohomology rings and structures of
them for special generic maps having simple structures are presented as new
results.


Abstract:
  Many methods have been developed to understand complex predictive models and
high expectations are placed on post-hoc model explainability. It turns out
that such explanations are not robust nor trustworthy, and they can be fooled.
This paper presents techniques for attacking Partial Dependence (plots,
profiles, PDP), which are among the most popular methods of explaining any
predictive model trained on tabular data. We showcase that PD can be
manipulated in an adversarial manner, which is alarming, especially in
financial or medical applications where auditability became a must-have trait
supporting black-box models. The fooling is performed via poisoning the data to
bend and shift explanations in the desired direction using genetic and gradient
algorithms. To the best of our knowledge, this is the first work performing
attacks on variable dependence explanations. The novel approach of using a
genetic algorithm for doing so is highly transferable as it generalizes both
ways: in a model-agnostic and an explanation-agnostic manner.


Abstract:
  Motivated by the phenomenology in the condensed-matter flat-band Dirac
systems, we here construct a holographic model that imprints the symmetry
breaking pattern of a rather simple Dirac fermion model at zero chemical
potential.In the bulk we explicitly include the backreaction to the
corresponding Lifshitz geometry and compute the dynamical critical exponent.
Most importantly, we find that such a geometry is unstable towards a nematic
phase, exhibiting an anomalous Hall effect and featuring a Drude-like shift of
its spectral weight. Our findings should motivate further studies of the
quantum phases emerging from such holographic models.


Abstract:
  An `open' or $(\mu,P,T)$-ensemble describes equilibrium systems whose control
parameters are chemical potential $\mu$, pressure $P$ and temperature $T$. Such
an unconstrained ensemble is seldom used for applications to standard
thermodynamic systems due to the fact that the corresponding free energy
identically vanishes as a result of the Euler relation. However, an open
ensemble is perfectly regular for the case of black holes, as the entropy is a
quasi-homogeneous function of extensive thermodynamic variables with scaling
dictated by the Smarr formula. Following a brief discussion on thermodynamics
in the open ensemble, we compute the general form of logarithmic corrections to
the entropy of a typical system, due to fluctuations in energy, thermodynamic
volume and a generic charge $N$. This is then used to obtain the exact analytic
form of the logarithmically corrected black hole entropy for charged and
rotating black holes in anti-de Sitter spacetimes.


Abstract:
  Referring image segmentation aims to segment the objects referred by a
natural language expression. Previous methods usually focus on designing an
implicit and recurrent feature interaction mechanism to fuse the
visual-linguistic features to directly generate the final segmentation mask
without explicitly modeling the localization information of the referent
instances. To tackle these problems, we view this task from another perspective
by decoupling it into a "Locate-Then-Segment" (LTS) scheme. Given a language
expression, people generally first perform attention to the corresponding
target image regions, then generate a fine segmentation mask about the object
based on its context. The LTS first extracts and fuses both visual and textual
features to get a cross-modal representation, then applies a cross-model
interaction on the visual-textual features to locate the referred object with
position prior, and finally generates the segmentation result with a
light-weight segmentation network. Our LTS is simple but surprisingly
effective. On three popular benchmark datasets, the LTS outperforms all the
previous state-of-the-art methods by a large margin (e.g., +3.2% on RefCOCO+
and +3.4% on RefCOCOg). In addition, our model is more interpretable with
explicitly locating the object, which is also proved by visualization
experiments. We believe this framework is promising to serve as a strong
baseline for referring image segmentation.


Abstract:
  As the usage of deep learning becomes increasingly popular in mobile and
embedded solutions, it is necessary to convert the framework-specific network
representations into executable code for these embedded platforms. This paper
consists of two parts: The first section is made up of a survey and benchmark
of the available open source deep learning compiler toolchains, which focus on
the capabilities and performance of the individual solutions in regard to
targeting embedded devices and microcontrollers that are combined with a
dedicated accelerator in a heterogeneous fashion. The second part explores the
implementation and evaluation of a compilation flow for such a heterogeneous
device and reuses one of the existing toolchains to demonstrate the necessary
steps for hardware developers that plan to build a software flow for their own
hardware.


Abstract:
  In a multi-dimensional diffusion framework, the price of a financial
derivative can be expressed as an iterated conditional expectation, where the
inner conditional expectation conditions on the future of an auxiliary process
that enters into the dynamics for the spot. Inspired by results from non-linear
filtering theory, we show that this inner conditional expectation solves a
backward SPDE (a so-called `conditional Feynman-Kac formula'), thereby
establishing a connection between SPDE and derivative pricing theory. The
benefits of this representation are potentially significant and of both
theoretical and practical interest. In particular, this representation leads to
an alternative class of so-called mixed Monte-Carlo / PDE numerical methods.


Abstract:
  We investigate Hardy-Rellich inequalities for perturbed Laplacians. In
particular, we show that a non-trivial angular perturbation of the free
operator typically improves the inequality, and may also provide an estimate
which does not hold in the free case. The main examples are related to the
introduction of a magnetic field: this is a manifestation of the diamagnetic
phenomenon, which has been observed by Laptev and Weidl in \cite{LW1999} for
the Hardy inequality, later by Evans and Lewis in \cite{EL2005} for the Rellich
inequality; however, to the best of our knowledge, the so called Hardy-Rellich
inequality has not yet been investigated in this regards. After showing the
optimal inequality, we prove that the best constant is not attained by any
function in the domain of the estimate.


Abstract:
  The quantum indeterminacy caused by non-commutativity of observables at
different times sets a lower bound on the voltage noise power spectrum in any
conducting material. This bound is calculated explicitly in the case of
semiconductors with a conical energy-momentum dispersion of charge carriers. It
possesses all characteristic properties of 1/f noise. Its momentum
decomposition is found to be singular at zero particle momentum, a measurable
consequence being a sharp peak in the noise magnitude at small charge carrier
density. In application to monolayer graphene, this peak becomes M-shaped on
account of a continuous transition from the electron to hole conductivity. A
comparison with experimental data is made which demonstrates that the
calculated power spectrum is close in magnitude and congruent to the observed.


Abstract:
  We introduce a protocol for dynamical dispersion engineering in an atomic
chain consisting of an ordered array of multi-level atoms with subwavelength
lattice constant. This chain supports dark states that are protected from
dissipation in the form of photon emission and can be understood as propagating
spin waves traveling along the array. By using an external control field with a
spatially-varying elliptical polarization we correlate internal and external
degrees of freedom of the array in a controllable way. The coherent control
over the atomic states translates into control over the group velocity of the
spin waves. A traveling excitation can be stored and released without
dissipation by adiabatically changing the control field amplitude. This
protocol is an alternative to the more conventional electromagnetically-induced
transparency, and exemplifies the rich physics born of the interplay between
coherent control and correlated decay.


Abstract:
  Human trajectory prediction is critical for autonomous platforms like
self-driving cars or social robots. We present a latent belief energy-based
model (LB-EBM) for diverse human trajectory forecast. LB-EBM is a probabilistic
model with cost function defined in the latent space to account for the
movement history and social context. The low-dimensionality of the latent space
and the high expressivity of the EBM make it easy for the model to capture the
multimodality of pedestrian trajectory distributions. LB-EBM is learned from
expert demonstrations (i.e., human trajectories) projected into the latent
space. Sampling from or optimizing the learned LB-EBM yields a belief vector
which is used to make a path plan, which then in turn helps to predict a
long-range trajectory. The effectiveness of LB-EBM and the two-step approach
are supported by strong empirical results. Our model is able to make accurate,
multi-modal, and social compliant trajectory predictions and improves over
prior state-of-the-arts performance on the Stanford Drone trajectory prediction
benchmark by 10.9% and on the ETH-UCY benchmark by 27.6%.


Abstract:
  The spatial decorrelation of dense molecular gas and young stars observed on
$\lesssim 1$ kiloparsec scales in nearby galaxies indicates rapid dispersal of
star-forming regions by stellar feedback. We explore the sensitivity of this
decorrelation to different processes controlling the structure of the
interstellar medium, the abundance of molecular gas, star formation, and
feedback in a suite of simulations of an isolated dwarf galaxy with structural
properties similar to NGC300 that self-consistently model radiative transfer
and molecular chemistry. Our fiducial simulation reproduces the magnitude of
decorrelation and its scale dependence measured in NGC300, and we show that
this agreement is due to different aspects of feedback, including H$_2$
dissociation, gas heating by the locally variable UV field, early mechanical
feedback, and supernovae. In particular, early radiative and mechanical
feedback affects the correlation on $\lesssim 100$ pc scales, while supernovae
play a significant role on $\gtrsim 100$ pc scales. The correlation is also
sensitive to the choice of the local star formation efficiency per freefall
time, $\epsilon_{\rm ff}$, which provides a strong observational constraint on
$\epsilon_{\rm ff}$ when the global star formation rate is independent of its
value. Finally, we explicitly show that the degree of correlation between the
peaks of molecular gas and star formation density is directly related to the
distribution of the lifetimes of star-forming regions.


Abstract:
  In 1975 Bollob\'as, Erd\H os, and Szemer\'edi asked the following question:
given positive integers $n, t, r$ with $2\le t\le r-1$, what is the largest
minimum degree $\delta(G)$ among all $r$-partite graphs $G$ with parts of size
$n$ and which do not contain a copy of $K_{t+1}$? The $r=t+1$ case has
attracted a lot of attention and was fully resolved by Haxell and Szab\'{o},
and Szab\'{o} and Tardos in 2006. In this paper we investigate the $r>t+1$ case
of the problem, which has remained dormant for over forty years. We resolve the
problem exactly in the case when $r \equiv -1 \pmod{t}$, and up to an additive
constant for many other cases, including when $r \geq (3t-1)(t-1)$. Our
approach utilizes a connection to the related problem of determining the
maximum of the minimum degrees among the family of balanced $r$-partite
$rn$-vertex graphs of chromatic number at most $t$.


Abstract:
  We show that, for a generic measure preserving transformation $T$, the closed
group generated by $T$ is not isomorphic to the topological group $L^0(\lambda,
{\mathbb T})$ of all Lebesgue measurable functions from $[0,1]$ to $\mathbb T$
(taken with pointwise multiplication and the topology of convergence in
measure). This result answers a question of Glasner and Weiss. The main step in
the proof consists of showing that Koopman representations of ergodic boolean
actions of $L^0(\lambda, {\mathbb T})$ possess a non-trivial property not
shared by all unitary representations of $L^0(\lambda, {\mathbb T})$. In
proving that theorem, we use an ergodic theorem for ergodic boolean actions of
$L^0(\lambda, {\mathbb T})$ also established in the present paper. The main
tool underlying our arguments is a theorem on the form of unitary
representations of $L^0(\lambda, {\mathbb T})$ from our earlier work.


Abstract:
  Observationally, the interstellar gas-phase abundance of deuterium (D) is
considerably depleted and the missing D atoms are often postulated to have been
locked up into carbonaceous solids and polycyclic aromatic hydrocarbon (PAH)
molecules. An accurate knowledge of the fractional amount of D (relative to H)
tied up in carbon dust and PAHs has important cosmological implications since D
originated exclusively from the Big Bang and the present-day D abundance, after
accounting for the astration it has experienced during the Galactic evolution,
provides essential clues to the primordial nucleosynthesis and the cosmological
parameters. To quantitatively explore the extent to which PAHs could possibly
accommodate the observed D depletion, we have previously quantum-chemically
computed the infrared vibrational spectra of mono-deuterated PAHs and derived
the mean intrinsic band strengths of the 3.3 $\mu$m C--H stretch (A$_{3.3}$)
and the 4.4 $\mu$m C--D stretch (A$_{4.4}$). Here we extend our previous work
to multi-deuterated PAH species of different deuterations, sizes and
structures. We find that both the intrinsic band strengths A$_{3.3}$ and
A$_{4.4}$ and their ratios A$_{4.4}$/A$_{3.3}$ not only show little variations
among PAHs of different deuterations, sizes and structures, they are also
closely similar to that of mono-deuterated PAHs. Therefore, a PAH deuteration
level (i.e., the fraction of peripheral atoms attached to C atoms in the form
of D) of ~2.4% previously estimated from the observed 4.4 $\mu$m to 3.3 $\mu$m
band ratio based on the A$_{4.4}$/A$_{3.3}$ ratio of mono-deuterated PAHs is
robust.


Abstract:
  Attractive interaction between spinless fermions in a two-dimensional lattice
drives the formation of a topological superfluid. But the topological phase is
dynamically unstable towards phase separation when the system has a high
density of states and large interaction strength. This limits the critical
temperature to an experimentally challenging regime where, for example, even
ultracold atoms and molecules in optical lattices would struggle to realize the
topological superfluid. We propose that the introduction of a weaker
longer-range repulsion, in addition to the short-range attraction between
lattice fermions, will suppress the phase separation instability. Taking the
honeycomb lattice as an example, we show that our proposal significantly
enlarges the stable portion of the topological superfluid phase and increases
the critical temperature by an order of magnitude. Our work opens a route to
enhance the stability of topological superfluids by engineering inter-particle
interactions.


Abstract:
  I study costly information acquisition in a two-sided matching problem, such
as matching applicants to schools. An applicant's utility is a sum of common
and idiosyncratic components. The idiosyncratic component is unknown to the
applicant but can be learned at a cost. When applicants are assigned using an
ordinal strategy-proof mechanism, too few acquire information, generating a
significant welfare loss. Affirmative action and other realistic policies may
lead to a Pareto improvement. As incentives to acquire information differ
across mechanisms, ignoring such incentives may lead to incorrect welfare
assessments, for example, in comparing a popular Immediate Assignment and an
ordinal strategy-proof mechanism.


Abstract:
  The Assmus-Mattson theorem gives a way to identify block designs arising from
codes. This result was broadened to matroids and weighted designs. In this work
we present a further two-fold generalisation: first from matroids to
polymatroids and also from sets to vector spaces. To achieve this, we introduce
the characteristic polynomial of a $q$-polymatroid and outline several of its
properties.


Abstract:
  Emotions are reactions that can be expressed through a variety of social
signals. For example, anger can be expressed through a scowl, narrowed eyes, a
long stare, or many other expressions. This complexity is problematic when
attempting to recognize a human's expression in a human-robot interaction:
categorical emotion models used in HRI typically use only a few prototypical
classes, and do not cover the wide array of expressions in the wild. We propose
a data-driven method towards increasing the number of known emotion classes
present in human-robot interactions, to 28 classes or more. The method includes
the use of automatic segmentation of video streams into short (<10s) videos,
and annotation using the large set of widely-understood emojis as categories.
In this work, we showcase our initial results using a large in-the-wild HRI
dataset (UE-HRI), with 61 clips randomly sampled from the dataset, labeled with
28 different emojis. In particular, our results showed that the "skeptical"
emoji was a common expression in our dataset, which is not often considered in
typical emotion taxonomies. This is the first step in developing a rich
taxonomy of emotional expressions that can be used in the future as labels for
training machine learning models, towards more accurate perception of humans by
robots.


Abstract:
  Deep neural networks are known to be data-driven and label noise can have a
marked impact on model performance. Recent studies have shown great robustness
to classic image recognition even under a high noisy rate. In medical
applications, learning from datasets with label noise is more challenging since
medical imaging datasets tend to have asymmetric (class-dependent) noise and
suffer from high observer variability.
  In this paper, we systematically discuss and define the two common types of
label noise in medical images - disagreement label noise from inconsistency
expert opinions and single-target label noise from wrong diagnosis record. We
then propose an uncertainty estimation-based framework to handle these two
label noise amid the medical image classification task. We design a
dual-uncertainty estimation approach to measure the disagreement label noise
and single-target label noise via Direct Uncertainty Prediction and
Monte-Carlo-Dropout.
  A boosting-based curriculum training procedure is later introduced for robust
learning. We demonstrate the effectiveness of our method by conducting
extensive experiments on three different diseases: skin lesions, prostate
cancer, and retinal diseases. We also release a large re-engineered database
that consists of annotations from more than ten ophthalmologists with an
unbiased golden standard dataset for evaluation and benchmarking.


Abstract:
  We investigate ultrafast phonon dynamics in the Bi$_{1-x}$Sb$_{x}$ alloy
system for various compositions $x$ using a reflective femtosecond pump-probe
technique. The coherent optical phonons corresponding to the A$_{1g}$ local
vibrational modes of Bi-Bi, Bi-Sb, and Sb-Sb are generated and observed in the
time domain with a few picoseconds dephasing time. The frequencies of the
coherent optical phonons were found to change as the Sb composition $x$ was
varied, and more importantly, the relaxation time of those phonon modes was
dramatically reduced for $x$ values in the range 0.5--0.8. We argue that the
phonon relaxation dynamics are not simply governed by alloy scattering, but are
significantly modified by anharmonic phonon-phonon scattering with implied
minor contributions from electron-phonon scattering in a Weyl-semimetal phase.


Abstract:
  A high-accuracy time discretization is discussed to numerically solve the
nonlinear fractional diffusion equation forced by a space-time white noise. The
main purpose of this paper is to improve the temporal convergence rate by
modifying the semi-implicit Euler scheme. The solution of the equation is only
H\"older continuous in time, which is disadvantageous to improve the temporal
convergence rate. Firstly, the system is transformed into an equivalent form
having better regularity than the original one in time. But the regularity of
nonlinear term remains unchanged. Then, combining Lagrange mean value theorem
and independent increments of Brownian motion leads to a higher accuracy
discretization of nonlinear term which ensures the implementation of the
proposed time discretization scheme without loss of convergence rate. Our
scheme can improve the convergence rate from
${\min\{\frac{\gamma}{2\alpha},\frac{1}{2}\}}$ to
${\min\{\frac{\gamma}{\alpha},1\}}$ in the sense of mean-squared $L^2$-norm.
The theoretical error estimates are confirmed by extensive numerical
experiments.


Abstract:
  Human recognition of the actions of other humans is very efficient and is
based on patterns of movements. Our theoretical starting point is that the
dynamics of the joint movements is important to action categorization. On the
basis of this theory, we present a novel action recognition system that employs
a hierarchy of Self-Organizing Maps together with a custom supervised neural
network that learns to categorize actions. The system preprocesses the input
from a Kinect like 3D camera to exploit the information not only about joint
positions, but also their first and second order dynamics. We evaluate our
system in two experiments with publicly available data sets, and compare its
performance to the performance with less sophisticated preprocessing of the
input. The results show that including the dynamics of the actions improves the
performance. We also apply an attention mechanism that focuses on the parts of
the body that are the most involved in performing the actions.


Abstract:
  The Simons Observatory (SO) Large Aperture Telescope Receiver (LATR) will be
coupled to the Large Aperture Telescope located at an elevation of 5,200 m on
Cerro Toco in Chile. The resulting instrument will produce arcminute-resolution
millimeter-wave maps of half the sky with unprecedented precision. The LATR is
the largest cryogenic millimeter-wave camera built to date with a diameter of
2.4 m and a length of 2.6 m. It cools 1200 kg of material to 4 K and 200 kg to
100 mk, the operating temperature of the bolometric detectors with bands
centered around 27, 39, 93, 145, 225, and 280 GHz. Ultimately, the LATR will
accommodate 13 40 cm diameter optics tubes, each with three detector wafers and
a total of 62,000 detectors. The LATR design must simultaneously maintain the
optical alignment of the system, control stray light, provide cryogenic
isolation, limit thermal gradients, and minimize the time to cool the system
from room temperature to 100 mK. The interplay between these competing factors
poses unique challenges. We discuss the trade studies involved with the design,
the final optimization, the construction, and ultimate performance of the
system.


Abstract:
  In two recent papers by Pore et al. and Khuyagbaatar et al. discovery of the
new isotope Md-244 was reported. The decay data, however, are conflicting.
While Pore et al. report two isomeric states decaying by alpha emission with
E(1)=8.66(2) MeV, T_1/2=0.4+0.4/-0.1s and E(2)=8.31(2) MeV, T_1/2 approx 6 s,
Khuyagbaatar et al. report only a single transition with a broad energy
distribution of E=(8.73-8.86) MeV and T_1/2=0.30/-0.09 s. The data published by
Pore et al. very similar to those published for Md-245 (E=8.64(2), 8.68(2) MeV
T_1/2=0.35+0.23/-0.16}$ s ). Therefore, we compare the data presented for
Md-244 by Pore et al. with those reported for Md-245 by Ninov et al. and also
by Khuyagbaatar et al.. We conclude that the data presented by Pore et al.
shall be attributed to Md-245 with small contributions (one event each) from
Fm-245 and probably Md-246.


Abstract:
  We study the Lagrangian structure of relativistic Vlasov systems, such as the
relativistic Vlasov-Poisson and the relativistic quasi-eletrostatic limit of
Vlasov-Maxwell equations. We show that renormalized solutions of these systems
are Lagrangian and that these notions of solution, in fact, coincide. As a
consequence, finite-energy solutions are shown to be transported by a global
flow. Moreover, we extend the notion of generalized solution for "effective"
densities and we prove its existence. Finally, under a higher integrability
assumption of the initial condition, we show that solutions have every energy
bounded, even in the gravitational case. These results extend to our setting
those obtained by Ambrosio, Colombo, and Figalli \cite{vlasovpoisson} for the
Vlasov-Poisson system; here, we analyse relativistic systems and we consider
the contribution of the magnetic force into the evolution equation.


Abstract:
  Proton imaging is a powerful technique for imaging electromagnetic fields
within an experimental volume, in which spatial variations in proton fluence
are a result of deflections to proton trajectories due to interaction with the
fields. When deflections are large, proton trajectories can overlap, and this
nonlinearity creates regions of greatly increased proton fluence on the image,
known as caustics. The formation of caustics has been a persistent barrier to
reconstructing the underlying fields from proton images. We have developed a
new method for reconstructing the path-integrated magnetic fields which begins
to address the problem posed by caustics. Our method uses multiple proton
images of the same object, each image at a different energy, to fill in the
information gaps and provide some uniqueness when reconstructing caustic
features. We use a differential evolution algorithm to iteratively estimate the
underlying deflection function which accurately reproduces the observed proton
fluence at multiple proton energies simultaneously. We test this reconstruction
method using synthetic proton images generated for three different,
cylindrically symmetric field geometries at various field amplitudes and levels
of proton statistics, and present reconstruction results from a set of
experimental images. The method we propose requires no assumption of deflection
linearity and can reliably solve for fields underlying linear, nonlinear, and
caustic proton image features for the selected geometries, and is shown to be
fairly robust to noise in the input proton intensity.


Abstract:
  We study a model for the collective behavior of self-propelled particles
subject to pairwise copying interactions and noise. Particles move at a
constant speed $v$ on a two--dimensional space and, in a single step of the
dynamics, each particle adopts the direction of motion of a randomly chosen
neighboring particle, with the addition of a perturbation of amplitude $\eta$
(noise). We investigate how the global level of particles' alignment (order) is
affected by their motion and the noise amplitude $\eta$. In the static case
scenario $v=0$ where particles are fixed at the sites of a square lattice and
interact with their first neighbors, we find that for any noise $\eta_c>0$ the
system reaches a steady state of complete disorder in the thermodynamic limit,
while for $\eta=0$ full order is eventually achieved for a system with any
number of particles $N$. Therefore, the model displays a transition at zero
noise when particles are static, and thus there are no ordered steady states
for a finite noise ($\eta>0$). We show that the finite-size transition noise
vanishes with $N$ as $\eta_c^{1D} \sim N^{-1}$ and $\eta_c^{2D} \sim \left(N
\ln N \right)^{-1/2}$ in one and two--dimensional lattices, respectively, which
is linked to known results on the behavior of a type of noisy voter model for
catalytic reactions. When particles are allowed to move in the space at a
finite speed $v>0$, an ordered phase emerges, characterized by a fraction of
particles moving in a similar direction. The system exhibits an order-disorder
phase transition at a noise amplitude $\eta_c>0$ that is proportional to $v$,
and that scales approximately as $\eta_c \sim v \, (-\ln v)^{-1/2}$ for $v \ll
1$. These results show that the motion of particles is able to sustain a state
of global order in a system with voter-like interactions.


Abstract:
  Certain fractional quantum Hall wavefunctions -- particularly including the
Laughlin, Moore-Read, and Read-Rezayi wavefunctions -- have special structure
that makes them amenable to analysis using an exeptionally wide range of
techniques including conformal field theory (CFT), thin cylinder or torus
limit, study of symmetric polynomials and Jack polynomials, and so-called
``special" parent Hamiltonians. This review discusses these techniques as well
as explaining to what degree some other quantum Hall wavefunctions share this
special structure. Along the way we will explore the physics of quantum Hall
edges, entanglement spectra, quasiparticles, nonabelian braiding statistics,
and Hall viscosity, among other topics. As compared to a number of other recent
reviews, most of this review is written so as to {\it not} rely on results from
conformal field theory -- although a short discussion of a few key relations to
CFT are included near the end.


Abstract:
  The success of deep denoisers on real-world color photographs usually relies
on the modeling of sensor noise and in-camera signal processing (ISP) pipeline.
Performance drop will inevitably happen when the sensor and ISP pipeline of
test images are different from those for training the deep denoisers (i.e.,
noise discrepancy). In this paper, we present an unpaired learning scheme to
adapt a color image denoiser for handling test images with noise discrepancy.
We consider a practical training setting, i.e., a pre-trained denoiser, a set
of test noisy images, and an unpaired set of clean images. To begin with, the
pre-trained denoiser is used to generate the pseudo clean images for the test
images. Pseudo-ISP is then suggested to jointly learn the pseudo ISP pipeline
and signal-dependent rawRGB noise model using the pairs of test and pseudo
clean images. We further apply the learned pseudo ISP and rawRGB noise model to
clean color images to synthesize realistic noisy images for denoiser adaption.
Pseudo-ISP is effective in synthesizing realistic noisy sRGB images, and
improved denoising performance can be achieved by alternating between
Pseudo-ISP training and denoiser adaption. Experiments show that our Pseudo-ISP
not only can boost simple Gaussian blurring-based denoiser to achieve
competitive performance against CBDNet, but also is effective in improving
state-of-the-art deep denoisers, e.g., CBDNet and RIDNet.


Abstract:
  We propose using Carrier Sensing (CS) for distributed interference management
in millimeter-wave (mmWave) cellular networks where spectrum is shared by
multiple operators that do not coordinate among themselves. In addition, even
the base station sites can be shared by the operators. We describe important
challenges in using traditional CS in this setting and propose enhanced CS
protocols to address these challenges. Using stochastic geometry, we develop a
general framework for downlink coverage probability analysis of our shared
mmWave network in the presence of CS and derive the downlink coverage
probability expressions for several CS protocols. To the best of our knowledge,
our work is the first to investigate and analyze (using stochastic geometry) CS
for mmWave networks with spectrum and BS sites shared among non-coordinating
operators. We evaluate the downlink coverage probability of our shared mmWave
network using simulations as well as numerical examples based on our analysis.
Our evaluations show that our proposed enhancements lead to an improvement in
downlink coverage probability, compared to the downlink coverage probability
with no CS, for higher values of signal-to-interference and noise ratio (SINR).
Interestingly, our evaluations also reveal that for lower values of SINR, not
using any CS is the best strategy in terms of the downlink coverage
probability.


Abstract:
  We investigate the ultrafast electron dynamics triggered by terahertz and
optical pulses in thin platinum and gold films by probing their transient
optical reflectivity. The response of the platinum film to an intense terahertz
pulse is similar to the optically-induced dynamics of both films and can be
described by a two-temperature model. Surprisingly, gold can exhibit a much
smaller terahertz pulse-induced reflectivity change and with opposite sign. For
platinum, we estimate a 20% larger electron-phonon coupling for the
terahertz-driven dynamics compared to the optically-induced one, which we
ascribe to an additional nonthermal electron-phonon coupling contribution. We
explain the remarkable response of gold to terahertz radiation with the field
emission of electrons due the Fowler-Nordheim tunneling process, in samples
with thickness below the structural percolation threshold where near-field
enhancement is possible. Our results provide a fundamental insight into the
ultrafast processes relevant to modern electro- and magneto-optical
applications.


Abstract:
  We introduce a long-period generic spatial modulation into a typical model of
the Thouless pump, namely, the Rice--Mele (RM) model, to examine the lattice
analog of the fermion charge in quantum field theory. We derive a Diophantine
equation relating the fermion charge and the pumped charge, which leads to the
one-dimensional (1D) analog of the Streda formula in the quantum Hall effect
(QHE). This formula implies that an adiabatic change of the periodicity of the
spatial modulation yields a spatial charge pump such that the rightmost charge
is pumped to the right by the Chern number compared with the leftmost charge.
This causes a change in the length of the fermion chain by an integer, thus
providing the opportunity for direct measurement of the Streda formula in 1D
systems.


Abstract:
  In this work, we estimate the background components in muography using the
MuTe: a hybrid muon telescope composed of two subdetectors -a scintillator
hodoscope and a Water Cherenkov Detector (WCD). The hodoscope records the
trajectories of particles crossing the telescope, while the WCD measures their
energy loss. The MuTe hodoscope reconstructs 3841 different directions with an
angular resolution of 32 mrad for an inter-panel distance of 2.5 m. The spatial
resolution can reach $\sim$25.6 m assuming an 800 m distance to the target. The
WCD measures the deposited energy from 50 MeV to 1.5 GeV with a resolution of
0.72 MeV.
  MuTe discriminates muography background sources such as: upward coming muons,
scattered muons, the soft component of Extensive Air Showers (EAS), and
particles arriving simultaneously. They are filtered by using measurements of
deposited energy (WCD) and Time-of-Flight. The WCD differentiates single muons,
electrons/positrons, and multiparticle events. On the other hand, the ToF
measurements allow us to estimate the muon momentum establishing an energy
threshold to decrease the background contribution of scattered muons. Upward
coming muons are rejected by means of the particle arrival direction determined
by the ToF sign.
  We concluded that near 36% of the recorded events belong to the
electromagnetic component (electrons and positrons), roughly 30.4% is caused by
multiple particle events that arrive with time differences < 100 ns and the
last 34% are caused by muons. The muonic soft component (< 1 GeV/c) represents
46% of the single-muon events. The upward going particles add up the 22% of the
total flux crossing the MuTe.


Abstract:
  The Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine
the neutrino mass ordering and measure neutrino oscillation parameters. A
precise muon reconstruction is crucial to reduce one of the major backgrounds
induced by cosmic muons. This article proposes a novel muon reconstruction
method based on convolutional neural network (CNN) models. In this method, the
track information reconstructed by the top tracker is used for network
training. The training dataset is augmented by applying a rotation to muon
tracks to compensate for the limited angular coverage of the top tracker. The
muon reconstruction with the CNN model can produce unbiased tracks with
performance that spatial resolution is better than 10 cm and angular resolution
is better than 0.6 degrees. By using a GPU accelerated implementation a speedup
factor of 100 compared to existing CPU techniques has been demonstrated.


Abstract:
  Digital Twin, as an emerging technology related to Cyber-Physical Systems
(CPS) and Internet of Things (IoT), has attracted increasing attentions during
the past decade. Conceptually, a Digital Twin is a digital replica of a
physical entity in the real world, and this technology is leveraged in this
study to design a cooperative driving system at non-signalized intersections,
allowing connected vehicles to cooperate with each other to cross intersections
without any full stops. Within the proposed Digital Twin framework, we
developed an enhanced first-in-first-out (FIFO) slot reservation algorithm to
schedule the sequence of crossing vehicles, a consensus motion control
algorithm to calculate vehicles' referenced longitudinal motion, and a
model-based motion estimation algorithm to tackle communication delay and
packet loss. Additionally, an augmented reality (AR) human-machine-interface
(HMI) is designed to provide the guidance to drivers to cooperate with other
connected vehicles. Agent-based modeling and simulation of the proposed system
is conducted in Unity game engine based on a real-world map in San Francisco,
and the human-in-the-loop (HITL) simulation results prove the benefits of the
proposed algorithms with 20% reduction in travel time and 23.7% reduction in
energy consumption, respectively, when compared with traditional signalized
intersections.


Abstract:
  For every natural number $n$ we introduce a new weak choice principle
$\mathrm{nRC_{fin}}$: Given any infinite set $x$, there is an infinite subset
$y\subseteq x$ and a selection function $f$ that chooses an $n$-element subset
from every finite $z\subseteq y$ containing at least $n$ elements. By
constructing new permutation models built on a set of atoms obtained as
Fra\"iss\'e limits, we will study the relation of $\mathrm{nRC_{fin}}$ to the
weak choice principles $\mathrm{RC_m}$ (that has already been studied by
Montenegro, Halbeisen and Tachtsis): Given any infinite set $x$, there is an
infinite subset $y\subseteq x$ with a choice function $f$ on the family of all
$m$-element subsets of $y$. Moreover, we prove a stronger analogue of
Montenegros results when we study the relation between $\mathrm{nRC_{fin}}$ and
$\mathrm{kC_{fin}^-}$ which is defined by: Given any infinite family
$\mathcal{F}$ of finite sets of cardinality greater than $k$, there is an
infinite subfamily $\mathcal{A}\subseteq \mathcal{F}$ with a selection function
$f$ that chooses a $k$-element subset from each $A\in\mathcal{A}$.


Abstract:
  Nuclear physics experiments are always in need of more and more advanced
detection systems. During the last years relevant technological developments
have come out with many improvements in terms of performance and compactness of
detector materials, transducers, electronics, computing and data transmission.
In light of these achievements some applications previously prohibitive, mainly
because of size and cost, have become feasible. A few applications of nuclear
detection techniques are discussed, starting from the neighboring field of
particle beam diagnostics, moving to the medical diagnostics and ending up into
the radioactive waste handling. New radiation sensors are shown and explained,
as exploited in the DMNR project for the radioactive waste online monitoring
which merged into the MICADO Euratom project.


Abstract:
  Humans' experience of the world is profoundly multimodal from the beginning,
so why do existing state-of-the-art language models only use text as a modality
to learn and represent semantic meaning? In this paper we review the literature
on the role of embodiment and emotion in the interactive setting of spoken
dialogue as necessary prerequisites for language learning for human children,
including how words in child vocabularies are largely concrete, then shift to
become more abstract as the children get older. We sketch a model of semantics
that leverages current transformer-based models and a word-level grounded
model, then explain the robot-dialogue system that will make use of our
semantic model, the setting for the system to learn language, and existing
benchmarks for evaluation.


Abstract:
  Sampling from probability distributions of quantum circuits is a
fundamentally and practically important task which can be used to demonstrate
quantum supremacy using noisy intermediate-scale quantum devices. In the
present work, we examine classical simulability of sampling from the output
photon-number distribution of linear-optical circuits composed of random beam
splitters with equally distributed squeezed vacuum states and single-photon
states input. We provide efficient classical algorithms to simulate
linear-optical random circuits and show that the algorithms' error is
exponentially small up to a depth less than quadratic in the distance between
sources using a classical random walk behavior of random linear-optical
circuits. Notably, the average-case depth allowing an efficient classical
simulation is larger than the worst-case depth limit, which is linear in the
distance. Besides, our results together with the hardness of boson sampling
give a lower-bound on the depth for constituting global Haar-random unitary
circuits.


Abstract:
  This paper presents BSTC (Baidu Speech Translation Corpus), a large-scale
Chinese-English speech translation dataset. This dataset is constructed based
on a collection of licensed videos of talks or lectures, including about 68
hours of Mandarin data, their manual transcripts and translations into English,
as well as automated transcripts by an automatic speech recognition (ASR)
model. We have further asked three experienced interpreters to simultaneously
interpret the testing talks in a mock conference setting. This corpus is
expected to promote the research of automatic simultaneous translation as well
as the development of practical systems. We have organized simultaneous
translation tasks and used this corpus to evaluate automatic simultaneous
translation systems.


Abstract:
  The intelligibility of speech severely degrades in the presence of
environmental noise and reverberation. In this paper, we propose a novel deep
learning based system for modifying the speech signal to increase its
intelligibility under the equal-power constraint, i.e., signal power before and
after modification must be the same. To achieve this, we use generative
adversarial networks (GANs) to obtain time-frequency dependent amplification
factors, which are then applied to the input raw speech to reallocate the
speech energy. Instead of optimizing only a single, simple metric, we train a
deep neural network (DNN) model to simultaneously optimize multiple advanced
speech metrics, including both intelligibility- and quality-related ones, which
results in notable improvements in performance and robustness. Our system can
not only work in non-realtime mode for offline audio playback but also support
practical real-time speech applications. Experimental results using both
objective measurements and subjective listening tests indicate that the
proposed system significantly outperforms state-ofthe-art baseline systems
under various noisy and reverberant listening conditions.


Abstract:
  In the d-dimensional online bin packing problem, d-dimensional cubes of
positive sizes no larger than 1 are presented one by one to be assigned to
positions in d-dimensional unit cube bins. In this work, we provide improved
upper bounds on the asymptotic competitive ratio for square and cube bin
packing problems, where our bounds do not exceed 2.0885 and 2.5735 for square
and cube packing, respectively. To achieve these results, we adapt and improve
a previously designed harmonic-type algorithm, and apply a different method for
defining weight functions. We detect deficiencies in the state-of-the-art
results by providing counter-examples to the current best algorithms and the
analysis, where the claimed bounds were 2.1187 for square packing and 2.6161
for cube packing.


Abstract:
  We study two methods for differentially private analysis of bounded data and
extend these to nonnegative queries. We first recall that for the Laplace
mechanism, boundary inflated truncation (BIT) applied to nonnegative queries
and truncation both lead to strictly positive bias. We then consider a
generalization of BIT using translated ramp functions. We explicitly
characterise the optimal function in this class for worst case bias. We show
that applying any square-integrable post-processing function to a Laplace
mechanism leads to a strictly positive maximal absolute bias. A corresponding
result is also shown for a generalisation of truncation, which we refer to as
restriction. We also briefly consider an alternative approach based on
multiplicative mechanisms for positive data and show that, without additional
restrictions, these mechanisms can lead to infinite bias.


Abstract:
  Complex networks contain complete subgraphs such as nodes, edges, triangles,
etc., referred to as simplices and cliques of different orders. Notably,
cavities consisting of higher-order cliques play an important role in brain
functions. Since searching for maximum cliques is an NP-complete problem, we
use k-core decomposition to determine the computability of a given network. For
a computable network, we design a search method with an implementable algorithm
for finding cliques of different orders, obtaining also the Euler
characteristic number. Then, we compute the Betti numbers by using the ranks of
boundary matrices of adjacent cliques. Furthermore, we design an optimized
algorithm for finding cavities of different orders. Finally, we apply the
algorithm to the neuronal network of C. elegans with data from one typical
dataset, and find all of its cliques and some cavities of different orders,
providing a basis for further mathematical analysis and computation of its
structure and function.


Abstract:
  A Maximum Likelihood recursive state estimator is derived for non-linear and
non-Gaussian state-space models. The estimator combines a particle filter to
generate the conditional density and the Expectation Maximization algorithm to
compute the maximum likelihood state estimate iteratively. Algorithms for
maximum likelihood state filtering, prediction and smoothing are presented. The
convergence properties of these algorithms, which are inherited from the
Expectation Maximization algorithm, are proven and examined in two examples. It
is shown that, with randomized reinitialization, which is feasible because of
the algorithm simplicity, these methods are able to converge to the Maximum
Likelihood Estimate (MLE) of multimodal, truncated and skewed densities, as
well as those of disjoint support.


Abstract:
  The baryon form factor of charged pions arises since isospin symmetry is
broken with unequal up and down quark masses, $m_d>m_u$, as well as
electromagnetic effects. We obtain estimates for this basic property in two
phenomenological ways: from simple constituent quark models, as well as from
fitting the $e^+e^- \to \pi^+ \pi^-$ data. All our methods yield the result
that the baryon mean square radius, extracted from the slope of the form
factor, is positive for $\pi^+$, hence a picture where the outer region has a
net baryon, and the inner region a net antibaryon density, both compensating
each other such that the total baryon number is zero. For $\pi^-$ the effect is
equal and opposite. We estimate the corresponding mean squared baryon radius as
$\langle r^2 \rangle_B^{\pi^{+}} = (0.03-0.04~{\rm fm})^2$.


Abstract:
  High-dimensional data sets are often analyzed and explored via the
construction of a latent low-dimensional space which enables convenient
visualization and efficient predictive modeling or clustering. For complex data
structures, linear dimensionality reduction techniques like PCA may not be
sufficiently flexible to enable low-dimensional representation. Non-linear
dimension reduction techniques, like kernel PCA and autoencoders, suffer from
loss of interpretability since each latent variable is dependent of all input
dimensions. To address this limitation, we here present path lasso penalized
autoencoders. This structured regularization enhances interpretability by
penalizing each path through the encoder from an input to a latent variable,
thus restricting how many input variables are represented in each latent
dimension. Our algorithm uses a group lasso penalty and non-negative matrix
factorization to construct a sparse, non-linear latent representation. We
compare the path lasso regularized autoencoder to PCA, sparse PCA, autoencoders
and sparse autoencoders on real and simulated data sets. We show that the
algorithm exhibits much lower reconstruction errors than sparse PCA and
parameter-wise lasso regularized autoencoders for low-dimensional
representations. Moreover, path lasso representations provide a more accurate
reconstruction match, i.e. preserved relative distance between objects in the
original and reconstructed spaces.


Abstract:
  In this work, we consider a multi-user mobile edge computing system with
multiple computing access points (CAPs). Each mobile user has multiple
dependent tasks that must be processed in a round-by-round schedule. In every
round, a user may process their individual task locally, or choose to offload
their task to one of the $M$ CAPs or the remote cloud server, in order to
possibly reduce their processing cost. We aim to jointly optimize the
offloading decisions of the users and the resource allocation decisions for
each CAP over a global objective function, defined as a weighted sum of total
energy consumption and the round time. We first present a centralized heuristic
solution, termed MCAP, where the original problem is relaxed to a semi-definite
program (SDP) to probabilistically generate the offloading decision. Then,
recognizing that the users often exhibit selfish behavior to reduce their
individual cost, we propose a game-theoretical approach, termed MCAP-NE, which
allows us to compute a Nash Equilibrium (NE) through a finite improvement
method starting from the previous SDP solution. This approach leads to a
solution from which the users have no incentive to deviate, with substantially
reduced NE computation time. In simulation, we compare the system cost of the
NE solution with those of MCAP, MCAP-NE, a random mapping, and the optimal
solution, showing that our NE solution attains near optimal performance under a
wide set of parameter settings, as well as demonstrating the advantages of
using MCAP to produce the initial point for MCAP-NE.


Abstract:
  Although Shapley Values (SV) are widely used in explainable AI, they can be
poorly understood and estimated, which implies that their analysis may lead to
spurious inferences and explanations. As a starting point, we remind an
invariance principle for SV and derive the correct approach for computing the
SV of categorical variables that are particularly sensitive to the encoding
used. In the case of tree-based models, we introduce two estimators of Shapley
Values that exploit efficiently the tree structure and are more accurate than
state-of-the-art methods. For interpreting additive explanations, we recommend
to filter the non-influential variables and to compute the Shapley Values only
for groups of influential variables. For this purpose, we use the concept of
"Same Decision Probability" (SDP) that evaluates the robustness of a prediction
when some variables are missing. This prior selection procedure produces sparse
additive explanations easier to visualize and analyse. Simulations and
comparisons are performed with state-of-the-art algorithm, and show the
practical gain of our approach.


Abstract:
  Monte Carlo rendering algorithms are widely used to produce photorealistic
computer graphics images. However, these algorithms need to sample a
substantial amount of rays per pixel to enable proper global illumination and
thus require an immense amount of computation. In this paper, we present a
hybrid rendering method to speed up Monte Carlo rendering algorithms. Our
method first generates two versions of a rendering: one at a low resolution
with a high sample rate (LRHS) and the other at a high resolution with a low
sample rate (HRLS). We then develop a deep convolutional neural network to fuse
these two renderings into a high-quality image as if it were rendered at a high
resolution with a high sample rate. Specifically, we formulate this fusion task
as a super resolution problem that generates a high resolution rendering from a
low resolution input (LRHS), assisted with the HRLS rendering. The HRLS
rendering provides critical high frequency details which are difficult to
recover from the LRHS for any super resolution methods. Our experiments show
that our hybrid rendering algorithm is significantly faster than the
state-of-the-art Monte Carlo denoising methods while rendering high-quality
images when tested on both our own BCR dataset and the Gharbi dataset.
\url{https://github.com/hqqxyy/msspl}


Abstract:
  Popular social media networks provide the perfect environment to study the
opinions and attitudes expressed by users. While interactions in social media
such as Twitter occur in many natural languages, research on stance detection
(the position or attitude expressed with respect to a specific topic) within
the Natural Language Processing field has largely been done for English.
Although some efforts have recently been made to develop annotated data in
other languages, there is a telling lack of resources to facilitate
multilingual and crosslingual research on stance detection. This is partially
due to the fact that manually annotating a corpus of social media texts is a
difficult, slow and costly process. Furthermore, as stance is a highly domain-
and topic-specific phenomenon, the need for annotated data is specially
demanding. As a result, most of the manually labeled resources are hindered by
their relatively small size and skewed class distribution. This paper presents
a method to obtain multilingual datasets for stance detection in Twitter.
Instead of manually annotating on a per tweet basis, we leverage user-based
information to semi-automatically label large amounts of tweets. Empirical
monolingual and cross-lingual experimentation and qualitative analysis show
that our method helps to overcome the aforementioned difficulties to build
large, balanced and multilingual labeled corpora. We believe that our method
can be easily adapted to easily generate labeled social media data for other
Natural Language Processing tasks and domains.


Abstract:
  Using the Attouch-Th\'era duality, we study the cycles, gap vectors and fixed
point sets of compositions of proximal mappings. Sufficient conditions are
given for the existence of cycles and gap vectors. A primal-dual framework
provides an exact relationship between the cycles and gap vectors. We also
introduce the generalized cycle and gap vectors to tackle the case when the
classical ones do not exist. Examples are given to illustrate our results.


Abstract:
  Chaotic attractors, chaotic saddles and periodic orbits are examples of
chain-recurrent sets. Using arbitrary small controls, a trajectory starting
from any point in a chain-recurrent set can be steered to any other in that
set. The qualitative behavior of a dynamical system can be encapsulated in a
graph. Its nodes are chain-recurrent sets. There is an edge from node A to node
B if, using arbitrary small controls, a trajectory starting from any point of A
can be steered to any point of B. We discuss physical systems that have
infinitely many disjoint coexisting nodes. Such infinite collections can occur
for many carefully chosen parameter values. The logistic map is such a system,
as we showed in arXiv:2008.08338. To illustrate these very common phenomena, we
compare the Lorenz system and the logistic map and we show how extremely
similar their bifurcation diagrams are in some parameter ranges.


Abstract:
  We present a method to infer the 3D pose of mice, including the limbs and
feet, from monocular videos. Many human clinical conditions and their
corresponding animal models result in abnormal motion, and accurately measuring
3D motion at scale offers insights into health. The 3D poses improve
classification of health-related attributes over 2D representations. The
inferred poses are accurate enough to estimate stride length even when the feet
are mostly occluded. This method could be applied as part of a continuous
monitoring system to non-invasively measure animal health.


Abstract:
  As a statistical tool to assist formative assessments in educational
settings, diagnostic classification models (DCMs) have been increasingly used
to provide diagnostic information regarding examinees' attributes. DCMs often
adopt dichotomous division such as mastery and non-mastery of attributes to
express mastery states of attributes. However, many practical settings involve
different levels of mastery states rather than a simple dichotomy in a single
attribute. Although this practical demand can be addressed by
polytomous-attribute DCMs, their computational cost in a Markov chain Monte
Carlo estimation impedes their large-scale applications due to the larger
number of polytomous-attribute mastery patterns than that of binary-attribute
ones. This study considers a scalable Bayesian estimation method for
polytomous-attribute DCMs and developed a variational Bayesian (VB) algorithm
for a polytomous-attribute saturated DCM -- a generalization of
polytomous-attribute DCMs -- by building on the existing literature in VB for
binary-attribute DCMs and polytomous-attribute DCMs. Furthermore, we proposed
the configuration of parallel computing for the proposed VB algorithm to
achieve better computational efficiency. Monte Carlo simulations revealed that
our method exhibited the high performance in parameter recovery under a wide
range of conditions. An empirical example is used to demonstrate the utility of
our method.


Abstract:
  Light projection displays play an increasingly important role in our modern
life. Core projection systems including liquid crystal displays and digital
micromirror devices can impose spatial light modulation and actively shape
light waves. Recently, the advent of metasurfaces has revolutionized design
concepts in nanophotonics, enabling a new family of optical elements with
exceptional degrees of freedom. Here, we demonstrate a light projection display
technology based on optical metasurfaces, called digital metasurface device
(DMSD). Each metasurface pixel in a DMSD is electrically reconfigurable with
well-controlled programmability and addressability. The DMSD can not only
continuously modulate the intensity of light with high contrast, but also shape
the wavefront of light generated by each metasurface pixel and dynamically
switch between arbitrary holographic patterns. Our approach will pave an avenue
towards the development of customized light projection devices. It will also
drive the field of dynamic optical metasurfaces with fresh momentum and
inspiring concepts.


Abstract:
  The advancement of convolutional neural networks (CNNs) on various vision
applications has attracted lots of attention. Yet the majority of CNNs are
unable to satisfy the strict requirement for real-world deployment. To overcome
this, the recent popular network pruning is an effective method to reduce the
redundancy of the models. However, the ranking of filters according to their
"importance" on different pruning criteria may be inconsistent. One filter
could be important according to a certain criterion, while it is unnecessary
according to another one, which indicates that each criterion is only a partial
view of the comprehensive "importance". From this motivation, we propose a
novel framework to integrate the existing filter pruning criteria by exploring
the criteria diversity. The proposed framework contains two stages: Criteria
Clustering and Filters Importance Calibration. First, we condense the pruning
criteria via layerwise clustering based on the rank of "importance" score.
Second, within each cluster, we propose a calibration factor to adjust their
significance for each selected blending candidates and search for the optimal
blending criterion via Evolutionary Algorithm. Quantitative results on the
CIFAR-100 and ImageNet benchmarks show that our framework outperforms the
state-of-the-art baselines, regrading to the compact model performance after
pruning.


Abstract:
  The estimate of the cumulative incidence of SARS-CoV-2 of 76% in Manaus by
October 2020 by Buss et al. relies on the assumption of an
exponentially-declining probability of sero-reversion over time. We present an
alternative, empirically-supported approach that is based on the observed
dynamics of antibody titers in sero-positive cases. Through this approach we
revise the cumulative incidence estimate to 66% (63.3% - 68.5%) by October
2020. This estimate has implications for the proximity to herd immunity and
future estimates of fitness advantages of virus variants, such as the P.1
variant of concern. This methodology is also relevant for any other sero-survey
analysis that has to adjust for sero-reversion.


Abstract:
  We formally prove the existence of a quantization procedure that makes the
path integral of a general diffeomorphism-invariant theory of gravity, with
fixed total spacetime volume, equivalent to that of its unimodular version.
This is achieved by means of a partial gauge fixing of diffeomorphisms together
with a careful definition of the unimodular measure. The statement holds also
in the presence of matter. As an explicit example, we consider scalar-tensor
theories and compute the corresponding logarithmic divergences in both
settings. In spite of significant differences in the coupling of the scalar
field to gravity, the results are equivalent for all couplings, including
non-minimal ones.


Abstract:
  Two of the most important technological advancements currently underway are
the advent of quantum technologies, and the transitioning of global financial
systems towards cryptographic assets, notably blockchain-based cryptocurrencies
and smart contracts. There is, however, an important interplay between the two,
given that, in due course, quantum technology will have the ability to directly
compromise the cryptographic foundations of blockchain. We explore this complex
interplay by building financial models for quantum failure in various
scenarios, including pricing quantum risk premiums. We call this quantum
crypto-economics.


Abstract:
  We prove Archimedes' principle for a macroscopic ball in ideal gas consisting
of point particles with non-zero mass. The main result is an asymptotic
theorem, as the number of point particles goes to infinity and their total mass
remains constant. We also show that, asymptotically, the gas has an exponential
density as a function of height. We find the asymptotic inverse temperature of
the gas. We derive an accurate estimate of the volume of the phase space using
the local central limit theorem.


Abstract:
  What is the creative process through which an artist goes from an original
image to a painting? Can we examine this process using techniques from computer
vision and pattern recognition? Here we set the first preliminary steps to
algorithmically deconstruct some of the transformations that an artist applies
to an original image in order to establish centres of interest, which are focal
areas of a painting that carry meaning. We introduce a comparative methodology
that first cuts out the minimal segment from the original image on which the
painting is based, then aligns the painting with this source, investigates
micro-differences to identify centres of interest and attempts to understand
their role. In this paper we focus exclusively on micro-differences with
respect to edges. We believe that research into where and how artists create
centres of interest in paintings is valuable for curators, art historians,
viewers, and art educators, and might even help artists to understand and
refine their own artistic method.


Abstract:
  Review articles summarize state-of-the-art work and provide a means to
organize the growing number of scholarly publications. However, the current
review method and publication mechanisms hinder the impact review articles can
potentially have. Among other limitations, reviews only provide a snapshot of
the current literature and are generally not readable by machines. In this
work, we identify the weaknesses of the current review method. Afterwards, we
present the SmartReview approach addressing those weaknesses. The approach
pushes towards semantic community-maintained review articles. At the core of
our approach, knowledge graphs are employed to make articles more
machine-actionable and maintainable.


Abstract:
  We consider the dynamics of fluctuations in the quantum asymmetric simple
exclusion process (Q-ASEP) with periodic boundary conditions. The Q-ASEP
describes a chain of spinless fermions with random hoppings that are induced by
a Markovian environment. We show that fluctuations of the fermionic degrees of
freedom obey evolution equations of Lindblad type, and derive the corresponding
Lindbladians. We identify the underlying algebraic structure by mapping them to
non-Hermitian spin chains and demonstrate that the operator space fragments
into exponentially many (in system size) sectors that are invariant under time
evolution. At the level of quadratic fluctuations we consider the Lindbladian
on the sectors that determine the late time dynamics for the particular case of
the quantum symmetric simple exclusion process (Q-SSEP). We show that the
corresponding blocks in some cases correspond to known Yang-Baxter integrable
models and investigate the level-spacing statistics in others. We carry out a
detailed analysis of the steady states and slow modes that govern the late time
behaviour and show that the dynamics of fluctuations of observables is
described in terms of closed sets of coupled linear differential-difference
equations. The behaviour of the solutions to these equations is essentially
diffusive but with relevant deviations, that at sufficiently late times and
large distances can be described in terms of a continuum scaling limit which we
construct. We numerically check the validity of this scaling limit over a
significant range of time and space scales. These results are then applied to
the study of operator spreading at large scales, focusing on out-of-time
ordered correlators and operator entanglement.


Abstract:
  Health economic models simulate the costs and effects of health technologies
for use in health technology assessment (HTA) to inform efficient use of scarce
resources. Models have historically been developed using spreadsheet software
(e.g., Microsoft Excel) and while use of R is growing, general purpose modeling
software is still limited. hesim is an R package that helps fill this gap by
facilitating parameterization, simulation, and analysis of economic models in
an integrated manner. Supported model types include cohort discrete time state
transition models (cDTSTMs), individual continuous time state transition models
(iCTSTMs), and partitioned survival models (PSMs), encompassing Markov
(time-homogeneous and time-inhomogeneous) and semi-Markov processes. A modular
design based on R6 and S3 classes allows users to combine separate submodels
for disease progression, costs, and utility in a flexible way. Probabilistic
sensitivity analysis (PSA) is used to propagate uncertainty in model parameters
to model outputs. Simulation code is written in C++ so complex simulations such
as those combining PSA and individual simulation can be run much more quickly
than previously possible. Decision analysis within a cost-effectiveness
framework is performed using simulated costs and quality-adjusted life years
(QALYs) from a PSA.


Abstract:
  We prove global existence of solutions of a loglog energy-supercritical
Klein-Gordon equation for n=3,4,5. Assuming that blow-up occurs at a time of
maximal existence, we perform an analysis close to this time in order to find a
finite bound of a Strichartz-type norm, which eventually leads to a contraction
with the blow-up assumption.


Abstract:
  We propose a modern method to estimate population size based on
capture-recapture designs of K samples. The observed data is formulated as a
sample of n i.i.d. K-dimensional vectors of binary indicators, where the k-th
component of each vector indicates the subject being caught by the k-th sample,
such that only subjects with nonzero capture vectors are observed. The target
quantity is the unconditional probability of the vector being nonzero across
both observed and unobserved subjects. We cover models assuming a single
constraint (identification assumption) on the K-dimensional distribution such
that the target quantity is identified and the statistical model is
unrestricted. We present solutions for linear and non-linear constraints
commonly assumed to identify capture-recapture models, including no K-way
interaction in linear and log-linear models, independence or conditional
independence. We demonstrate that the choice of constraint has a dramatic
impact on the value of the estimand, showing that it is crucial that the
constraint is known to hold by design. For the commonly assumed constraint of
no K-way interaction in a log-linear model, the statistical target parameter is
only defined when each of the $2^K - 1$ observable capture patterns is present,
and therefore suffers from the curse of dimensionality. We propose a targeted
MLE based on undersmoothed lasso model to smooth across the cells while
targeting the fit towards the single valued target parameter of interest. For
each identification assumption, we provide simulated inference and confidence
intervals to assess the performance on the estimator under correct and
incorrect identifying assumptions. We apply the proposed method, alongside
existing estimators, to estimate prevalence of a parasitic infection using
multi-source surveillance data from a region in southwestern China, under the
four identification assumptions.


Abstract:
  As surgical robots become more common, automating away some of the burden of
complex direct human operation becomes ever more feasible. Model-free
reinforcement learning (RL) is a promising direction toward generalizable
automated surgical performance, but progress has been slowed by the lack of
efficient and realistic learning environments. In this paper, we describe
adding reinforcement learning support to the da Vinci Skill Simulator, a
training simulation used around the world to allow surgeons to learn and
rehearse technical skills. We successfully teach an RL-based agent to perform
sub-tasks in the simulator environment, using either image or state data. As
far as we know, this is the first time an RL-based agent is taught from visual
data in a surgical robotics environment. Additionally, we tackle the sample
inefficiency of RL using a simple-to-implement system which we term
hybrid-batch learning (HBL), effectively adding a second, long-term replay
buffer to the Q-learning process. Additionally, this allows us to bootstrap
learning from images from the data collected using the easier task of learning
from state. We show that HBL decreases our learning times significantly.


Abstract:
  We consider the Scale-Free Adversarial Multi Armed Bandits(MAB) problem. At
the beginning of the game, the player only knows the number of arms $n$. It
does not know the scale and magnitude of the losses chosen by the adversary or
the number of rounds $T$. In each round, it sees bandit feedback about the loss
vectors $l_1,\dots, l_T \in \mathbb{R}^n$. The goal is to bound its regret as a
function of $n$ and norms of $l_1,\dots, l_T$. We design a bandit Follow The
Regularized Leader (FTRL) algorithm, that uses an adaptive learning rate and
give two different regret bounds, based on the exploration parameter used. With
non-adaptive exploration, our algorithm has a regret of
$\tilde{\mathcal{O}}(\sqrt{nL_2} + L_\infty\sqrt{nT})$ and with adaptive
exploration, it has a regret of $\tilde{\mathcal{O}}(\sqrt{nL_2} +
L_\infty\sqrt{nL_1})$. Here $L_\infty = \sup_t \| l_t\|_\infty$, $L_2 =
\sum_{t=1}^T \|l_t\|_2^2$, $L_1 = \sum_{t=1}^T \|l_t\|_1$ and the
$\tilde{\mathcal{O}}$ notation suppress logarithmic factors. These are the
first MAB bounds that adapt to the $\|\cdot\|_2$, $\|\cdot\|_1$ norms of the
losses. The second bound is the first data-dependent scale-free MAB bound as
$T$ does not directly appear in the regret. We also develop a new technique for
obtaining a rich class of local-norm lower-bounds for Bregman Divergences. This
technique plays a crucial role in our analysis for controlling the regret when
using importance weighted estimators of unbounded losses. This technique could
be of independent interest.


Abstract:
  A class of noncanonical effective potentials is introduced allowing stable,
radially symmetric, solutions to first order Bogomol'nyi equations for a real
scalar field in a fixed spacetime background. This class of effective
potentials generalizes those found previously by Bazeia, Menezes, and Menezes
[Phys.Rev.Lett. 91 (2003) 241601] for radially symmetric defects in a flat
spacetime. Use is made of the "on-shell method" introduced by Atmaja and
Ramadhan [Phys.Rev.D 90 (2014) 10, 105009] of reducing the second order
equation of motion to a first order one, along with a constraint equation. This
method and class of potentials admits radially symmetric, stable solutions for
four dimensional static, radially symmetric spacetimes. Stability against
radial fluctuations is established with a modified version of Derrick's
theorem, along with demonstrating that the radial stress vanishes. Several
examples of scalar field configurations are given.


Abstract:
  Slot filling and intent detection have become a significant theme in the
field of natural language understanding. Even though slot filling is
intensively associated with intent detection, the characteristics of the
information required for both tasks are different while most of those
approaches may not fully aware of this problem. In addition, balancing the
accuracy of two tasks effectively is an inevitable problem for the joint
learning model. In this paper, a Continual Learning Interrelated Model (CLIM)
is proposed to consider semantic information with different characteristics and
balance the accuracy between intent detection and slot filling effectively. The
experimental results show that CLIM achieves state-of-the-art performace on
slot filling and intent detection on ATIS and Snips.


Abstract:
  After recent observational events like the LIGO-Virgo detections of
gravitational waves and the shadow image of M87* supermassive black hole by
Event Horizon Telescope (EHT), the theoretical study of black holes was
significantly improved. Quantities as quasinormal frequencies, shadows, and
light deflection become more important to analyze black hole models. In this
context, an interesting scenario to study is a black hole in the bumblebee
gravity. The bumblebee vector field imposes a spontaneous symmetry breaking
that allows the field to acquire a vacuum expectation value that generates
Lorentz-Violating (LV) into the black hole. In order to compute the quasinormal
modes (QNMs) via the WKB method, we obtain the Reege-Wheeler's equation with a
bell-shaped potential for this black hole. Both QNMs, the scalar and tensorial
modes, are computed for the black hole in the bumblebee scenario. Moreover, the
stability of the Schwarzschild-like solution and some bounds to LV parameters
are analyzed.


Abstract:
  Demand response for electrical power networks is a mature field that has
yielded numerous efficiency and resilience benefits like managing the peaks and
valleys of electricity usage, reduction in peak electricity usage, to name a
few. However, only recently has the study of the counterpart to demand response
for electric power in natural gas started to receive similar levels of
attention. Natural gas systems are increasingly operating at or near capacity,
which challenges these systems to meet all the needs for gas, especially during
severe winter weather. However, unlike demand response programs in electrical
networks, demand response in gas networks cannot shift peak usage. Here, we
develop analogues to demand response that can help improve the resilience of
natural gas systems by reducing peak consumption and thereby limiting potential
disruptions such events can cause. This paper develops a mathematical
formulation to support a residential-level demand response analogue for natural
gas based on current and anticipated smart thermostat technologies. The
mathematical formulation takes the form of an optimal control problem (OCP)
that leverages physics constraints to model temperature changes, balance
equitable service, and optimize the gas consumption for collections of houses.
On test problems, the formulation demonstrates significant benefits, including
the ability to cut peak demand by 15% while still ensuring equitable service to
customers.


Abstract:
  An expansion of row Markov matrices in terms of matrices related to
permutations with repetitions, is introduced.It generalises the Birkhoff-von
Neumann expansion of doubly stochastic matrices in terms of permutation
matrices (without repetitions).An interpretation of the formalism in terms of
sequences of integers that open random safes described by the Markov matrices,
is presented. Various quantities that describe probabilities and correlations
in this context, are discussed. The Gini index is used to quantify the sparsity
(certainty) of various probability vectors. The formalism is used in the
context of multipartite quantum systems with finite dimensional Hilbert space,
which can be viewed as quantum permutations with repetitions or as quantum
safes. The scalar product of row Markov matrices, the various Gini indices,
etc, are novel probabilistic quantities that describe the statistics of
multipartite quantum systems. Local and global Fourier transforms are used to
define locally dual and also globally dual statistical quantities. The latter
depend on off-diagonal elements that entangle (in general) the various
components of the system. Examples which demonstrate these ideas are also
presented.


Abstract:
  Generalized Linear Bandits (GLBs) are powerful extensions to the Linear
Bandit (LB) setting, broadening the benefits of reward parametrization beyond
linearity. In this paper we study GLBs in non-stationary environments,
characterized by a general metric of non-stationarity known as the
variation-budget or \emph{parameter-drift}, denoted $B_T$. While previous
attempts have been made to extend LB algorithms to this setting, they overlook
a salient feature of GLBs which flaws their results. In this work, we introduce
a new algorithm that addresses this difficulty. We prove that under a geometric
assumption on the action set, our approach enjoys a
$\tilde{\mathcal{O}}(B_T^{1/3}T^{2/3})$ regret bound. In the general case, we
show that it suffers at most a $\tilde{\mathcal{O}}(B_T^{1/5}T^{4/5})$ regret.
At the core of our contribution is a generalization of the projection step
introduced in Filippi et al. (2010), adapted to the non-stationary nature of
the problem. Our analysis sheds light on central mechanisms inherited from the
setting by explicitly splitting the treatment of the learning and tracking
aspects of the problem.


Abstract:
  An innovative technique, called conversion, is introduced to model
circumferential cracks in thin cylindrical shells. The semi-analytical finite
element method is applied to investigate the modal deformation of the cylinder.
An element including the crack is divided into three sub-elements with four
nodes in which the stiffness matrix is enriched. The crack characteristics are
included in the finite element method relations through conversion matrices and
a rotational spring corresponding to the crack. Conversion matrices obtained by
applying continuity conditions at the crack tip are used to transform
displacements of the middle nodes to those of the main nodes. Moreover, another
technique, called spring set, is represented based on a set of springs to model
the crack as a separated element. Components of the stiffness matrix related to
the separated element are incorporated while the geometric boundary conditions
at the crack tip are satisfied. The effects of the circumferential mode number,
the crack depth and the length of the cylinder on the critical buckling load
are investigated. Experimental tests, ABAQUS modeling and results from
literature are used to verify and validate the results and derived relations.
In addition, the crack effect on the natural frequency is examined using the
vibration analysis based on the conversion technique.


Abstract:
  Light scattering by tissue severely limits how deep beneath the surface one
can image, and the spatial resolution one can obtain from these images. Diffuse
optical tomography (DOT) is one of the most powerful techniques for imaging
deep within tissue -- well beyond the conventional $\sim$10-15 mean scattering
lengths tolerated by ballistic imaging techniques such as confocal and
two-photon microscopy. Unfortunately, existing DOT systems are limited,
achieving only centimeter-scale resolution. Furthermore, they suffer from slow
acquisition times and slow reconstruction speeds making real-time imaging
infeasible. We show that time-of-flight diffuse optical tomography (ToF-DOT)
and its confocal variant (CToF-DOT), by exploiting the photon travel time
information, allow us to achieve millimeter spatial resolution in the highly
scattered diffusion regime ($> 50 $ mean free paths). In addition, we
demonstrate two additional innovations: focusing on confocal measurements, and
multiplexing the illumination sources allow us to significantly reduce the
measurement acquisition time. Finally, we rely on a novel convolutional
approximation that allows us to develop a fast reconstruction algorithm,
achieving a 100$\times$ speedup in reconstruction time compared to traditional
DOT reconstruction techniques. Together, we believe that these technical
advances serve as the first step towards real-time, millimeter resolution, deep
tissue imaging using DOT.


Abstract:
  Nanophotonic chiral sensing has recently attracted a lot of attention. The
idea is to exploit the strong light-matter interaction in nanophotonic
resonators to determine the concentration of chiral molecules at ultra-low
thresholds, which is highly attractive for numerous applications in life
science and chemistry. However, a thorough understanding of the underlying
interactions is still missing. The theoretical description relies on either
simple approximations or on purely numerical approaches. We close this gap and
present a general theory of chiral light-matter interactions in arbitrary
resonators. Our theory describes the chiral interaction as a perturbation of
the resonator modes, also known as resonant states or quasi-normal modes. We
observe two dominant contributions: A chirality-induced resonance shift and
changes in the modes excitation and emission efficiencies. Our theory brings
new and deep insights for tailoring and enhancing chiral light-matter
interactions. Furthermore, it allows to predict spectra much more efficiently
in comparison to conventional approaches. This is particularly true as chiral
interactions are inherently weak and therefore perturbation theory fits
extremely well for this problem.


Abstract:
  Let $V$ be a valuation ring and $K$ be its field of fraction. We show that
the canonical map $\Br(V) \to \Br(K)$ is injective.


Abstract:
  Stars can either be formed in or captured by the accretion disks in Active
Galactic Nuclei (AGN). These AGN stars are irradiated and subject to extreme
levels of accretion, which can turn even low-mass stars into very massive ones
($M > 100 {\rm M}_\odot$) whose evolution may result in the formation of
massive compact objects ($M > 10 {\rm M}_\odot$). Here we explore the spins of
these AGN stars and the remnants they leave behind. We find that AGN stars
rapidly spin up via accretion, eventually reaching near-critical rotation
rates. They further maintain near-critical rotation even as they shed their
envelopes, become compact, and undergo late stages of burning. This makes them
good candidates to produce high-spin massive black holes, such as the ones seen
by LIGO-Virgo in GW190521g, as well as long Gamma Ray Bursts (GRBs) and the
associated chemical pollution of the AGN disk.


Abstract:
  The Stockpile blending problem is an important component of mine production
scheduling, where stockpiles are used to store and blend raw material. The goal
of blending material from stockpiles is to create parcels of concentrate which
contain optimal metal grades based on the material available. The volume of
material that each stockpile provides to a given parcel is dependent on a set
of mine schedule conditions and customer demands. Therefore, the problem can be
formulated as a continuous optimization problem. In the real-world application,
there are several constraints required to guarantee parcels that meet the
demand of downstream customers. It is a challenge in solving the stockpile
blending problems since its scale can be very large. We introduce two repaired
operators for the problems to convert the infeasible solutions into the
solutions without violating the two tight constraints. Besides, we introduce a
multi-component fitness function for solving the large-scale stockpile blending
problem which can maximize the volume of metal over the plan and maintain the
balance between stockpiles according to the usage of metal. Furthermore, we
investigate the well-known approach in this paper, which is used to solve
optimization problems over continuous space, namely the differential evolution
(DE) algorithm. The experimental results show that the DE algorithm combined
with two proposed duration repair methods is significantly better in terms of
the values of results than the results on real-world instances for both
one-month problems and large-scale problems.


Abstract:
  Current Artificial Intelligence (AI) methods, most based on deep learning,
have facilitated progress in several fields, including computer vision and
natural language understanding. The progress of these AI methods is measured
using benchmarks designed to solve challenging tasks, such as visual question
answering. A question remains of how much understanding is leveraged by these
methods and how appropriate are the current benchmarks to measure understanding
capabilities. To answer these questions, we have analysed existing benchmarks
and their understanding capabilities, defined by a set of understanding
capabilities, and current research streams. We show how progress has been made
in benchmark development to measure understanding capabilities of AI methods
and we review as well how current methods develop understanding capabilities.


Abstract:
  In this paper we propose an extension of the notion of deviation-based
aggregation function tailored to aggregate multidimensional data. Our objective
is both to improve the results obtained by other methods that try to select the
best aggregation function for a particular set of data, such as penalty
functions, and to reduce the temporal complexity required by such approaches.
We discuss how this notion can be defined and present three illustrative
examples of the applicability of our new proposal in areas where temporal
constraints can be strict, such as image processing, deep learning and decision
making, obtaining favourable results in the process.


Abstract:
  In current Medium Voltage DC (MVDC) Shipboard Power Systems (SPSs), multiple
sources exist to supply power to a common dc bus. Conventionally, the power
management of such systems is performed by controlling Power Generation Modules
(PGMs) which include fuel operated generators and underlying converters.
Moreover, energy management is performed by the emerging single or hybrid
Energy Storage Systems (ESSs). This paper presents a model and load predictive
control framework for power and energy management of SPSs. Here, MPC with load
prediction is used for three main objectives: (1) to request power and energy
from generators and energy storage elements according to their individual State
of Power (SOP) and ramp-rate limitations, (2) to consider and integrate the
generator cost and degradation, and (3) to reach a specific parking (final)
State of Charge (SOC) for the ESSs at the end of the prediction horizon. The
solution of the optimization problem is demonstrated using MATLAB and the
functionality of the control framework is validated in real-time simulation
environment.


Abstract:
  Nowadays, an important topic that is considered a lot is how to integrate
Machine Learning(ML) to cloud resources management. In this study, our goal is
to explore the most important cloud resources management issues that have been
combined with ML and which present many promising results. To accomplish this,
we used chronological charts based on some keywords that we considered
important and tried to answer the question: is ML suitable for resources
management problems in the cloud? Furthermore, a short discussion takes place
on the data that are available and the open challenges on it. A big collection
of researches is used to make sensible comparisons between the ML techniques
that are used in the different kinds of cloud resources management fields and
we propose the most suitable ML model for each field. 1


Abstract:
  Let $0 \leq s \leq 1$ and $0 \leq t \leq 2$. An $(s,t)$-Furstenberg set is a
set $K \subset \mathbb{R}^{2}$ with the following property: there exists a line
set $\mathcal{L}$ of Hausdorff dimension $\dim_{\mathrm{H}} \mathcal{L} \geq t$
such that $\dim_{\mathrm{H}} (K \cap \ell) \geq s$ for all $\ell \in
\mathcal{L}$. We prove that for $s\in (0,1)$, and $t \in (s,2]$, the Hausdorff
dimension of $(s,t)$-Furstenberg sets in $\mathbb{R}^{2}$ is no smaller than
$2s + \epsilon$, where $\epsilon > 0$ depends only on $s$ and $t$. For $s>1/2$
and $t = 1$, this is an $\epsilon$-improvement over a result of Wolff from
1999.
  The same method also yields an $\epsilon$-improvement to Kaufman's projection
theorem from 1968. We show that if $s \in (0,1)$, $t \in (s,2]$ and $K \subset
\mathbb{R}^{2}$ is an analytic set with $\dim_{\mathrm{H}} K = t$, then
$$\dim_{\mathrm{H}} \{e \in S^{1} : \dim_{\mathrm{H}} \pi_{e}(K) \leq s\} \leq
s - \epsilon,$$ where $\epsilon > 0$ only depends on $s$ and $t$. Here
$\pi_{e}$ is the orthogonal projection to $\mathrm{span}(e)$.


Abstract:
  We study equilibrium properties of binary lattice-gases comprising $A$ and
$B$ particles, which undergo continuous exchanges with their respective
reservoirs, maintained at chemical potentials $\mu_A = \mu_B = \mu$. The
particles interact via on-site hard-core exclusion and also between the
nearest-neighbours: there are a soft repulsion for $AB$ pairs and interactions
of arbitrary strength $J$, positive or negative, for $AA$ and $BB$ pairs. For
tree-like Bethe and Husimi lattices, we determine the full phase diagram of
such a ternary mixture of particles and voids. We show that for $J$ being above
a lattice-dependent threshold value, the critical behaviour is similar: the
system undergoes a transition at $\mu = \mu_c$ from a phase with equal mean
densities of species into a phase with a spontaneously broken symmetry, in
which the mean densities are no longer equal. Depending on the value of $J$,
this transition can be either continuous or of the first order. For
sufficiently big negative $J$, the behaviour on the two lattices becomes
markedly different: while for the Bethe lattice there exists a continuous
transition into a phase with an alternating order followed by a continuous
re-entrant transition into a disordered phase, an alternating order phase is
absent on the Husimi lattice due to strong frustration effects.


Abstract:
  Deep learning empirically achieves high performance in many applications, but
its training dynamics has not been fully understood theoretically. In this
paper, we explore theoretical analysis on training two-layer ReLU neural
networks in a teacher-student regression model, in which a student network
learns an unknown teacher network through its outputs. We show that with a
specific regularization and sufficient over-parameterization, the student
network can identify the parameters of the teacher network with high
probability via gradient descent with a norm dependent stepsize even though the
objective function is highly non-convex. The key theoretical tool is the
measure representation of the neural networks and a novel application of a dual
certificate argument for sparse estimation on a measure space. We analyze the
global minima and global convergence property in the measure space.


Abstract:
  We investigate the problem dependent regime in the stochastic Thresholding
Bandit problem (TBP) under several shape constraints. In the TBP, the objective
of the learner is to output, at the end of a sequential game, the set of arms
whose means are above a given threshold. The vanilla, unstructured, case is
already well studied in the literature. Taking $K$ as the number of arms, we
consider the case where (i) the sequence of arm's means $(\mu_k)_{k=1}^K$ is
monotonically increasing (MTBP) and (ii) the case where $(\mu_k)_{k=1}^K$ is
concave (CTBP). We consider both cases in the problem dependent regime and
study the probability of error - i.e. the probability to mis-classify at least
one arm. In the fixed budget setting, we provide upper and lower bounds for the
probability of error in both the concave and monotone settings, as well as
associated algorithms. In both settings the bounds match in the problem
dependent regime up to universal constants in the exponential.