Abstract:
  In passive fluid-fluid phase separation, a single interfacial tension sets
both the capillary fluctuations of the interface and the rate of Ostwald
ripening. We show that these phenomena are governed by two different tensions
in active systems, and compute the capillary tension $\sigma_{cw}$ which sets
the relaxation rate of interfacial fluctuations in accordance with capillary
wave theory. We discover that strong enough activity can cause negative
$\sigma_{cw}$. In this regime, depending on the global composition, the system
self-organizes, either into a microphase-separated state in which coalescence
is highly inhibited, or into an `active foam' state. Our results are obtained
for Active Model B+, a minimal continuum model which, although generic, admits
significant analytical progress.


Abstract:
  This paper develops an unified framework to study finite-sample convergence
guarantees of a large class of value-based asynchronous reinforcement learning
(RL) algorithms. We do this by first reformulating the RL algorithms as
\textit{Markovian Stochastic Approximation} (SA) algorithms to solve
fixed-point equations. We then develop a Lyapunov analysis and derive
mean-square error bounds on the convergence of the Markovian SA. Based on this
result, we establish finite-sample mean-square convergence bounds for
asynchronous RL algorithms such as $Q$-learning, $n$-step TD, TD$(\lambda)$,
and off-policy TD algorithms including V-trace. As a by-product, by analyzing
the convergence bounds of $n$-step TD and TD$(\lambda)$, we provide theoretical
insights into the bias-variance trade-off, i.e., efficiency of bootstrapping in
RL. This was first posed as an open problem in (Sutton, 1999).


Abstract:
  This note has two related but independent parts. Firstly, we prove a
generalisation of a recent result of Gay on the smooth mapping class group of
$S^4$. Secondly, we prove that the Dehn twist along the boundary sphere of a
simply-connected closed smooth 4-manifold $X$ with $\partial X\cong S^3$ is
trivial after taking connected sums with enough copies of $S^2\times S^2$.


Abstract:
  Pre-stellar cores represent the initial conditions of star formation.
Although these initial conditions in nearby low-mass star-forming regions have
been investigated in detail, such initial conditions remain vastly unexplored
for massive star-forming regions. We report the detection of a cluster of
low-mass starless and pre-stellar core candidates in a massive star
protocluster forming cloud, NGC6334S. With the ALMA observations at a
$\sim$0.02 pc spatial resolution, we identified 17 low-mass starless core
candidates that do not show any evidence of protostellar activity. These
candidates present small velocity dispersions, high fractional abundances of
NH$_{2}$D, high NH$_{3}$ deuterium fractionations, and are completely dark in
the infrared wavelengths from 3.6 up to 70~$\mu$m. Turbulence is significantly
dissipated and the gas kinematics are dominated by thermal motions toward these
candidates. Nine out of the 17 cores are gravitationally bound, and therefore
are identified as pre-stellar core candidates. The embedded cores of NGC6334S
show a wide diversity in masses and evolutionary stages.


Abstract:
  Manually determining concepts present in a group of questions is a
challenging and time-consuming process. However, the process is an essential
step while modeling a virtual learning environment since a mapping between
concepts and questions using mastery level assessment and recommendation
engines are required. We investigated unsupervised semantic models (known as
topic modeling techniques) to assist computer science teachers in this task and
propose a method to transform Computer Science 1 teacher-provided code
solutions into representative text documents, including the code structure
information. By applying non-negative matrix factorization and latent Dirichlet
allocation techniques, we extract the underlying relationship between questions
and validate the results using an external dataset. We consider the
interpretability of the learned concepts using 14 university professors' data,
and the results confirm six semantically coherent clusters using the current
dataset. Moreover, the six topics comprise the main concepts present in the
test dataset, achieving 0.75 in the normalized pointwise mutual information
metric. The metric correlates with human ratings, making the proposed method
useful and providing semantics for large amounts of unannotated code.


Abstract:
  Political organizations worldwide keep innovating their use of social media
technologies. In the 2019 Indian general election, organizers used a network of
WhatsApp groups to manipulate Twitter trends through coordinated mass postings.
We joined 600 WhatsApp groups that support the Bharatiya Janata Party, the
right-wing party that won the general election, to investigate these campaigns.
We found evidence of 75 hashtag manipulation campaigns in the form of
mobilization messages with lists of pre-written tweets. Building on this
evidence, we estimate the campaigns' size, describe their organization and
determine whether they succeeded in creating controlled social media
narratives. Our findings show that the campaigns produced hundreds of
nationwide Twitter trends throughout the election. Centrally controlled but
voluntary in participation, this hybrid configuration of technologies and
organizational strategies shows how profoundly online tools transform campaign
politics. Trend alerts complicate the debates over the legitimate use of
digital tools for political participation and may have provided a blueprint for
participatory media manipulation by a party with popular support.


Abstract:
  Plasmon-induced transparency (PIT) in advanced materials has attracted
extensive attention for both theoretical and applied physics. Here, we
considered a scheme that can produce PIT and studied the characteristics of
ultraslow low-power magnetic solitons. The PIT metamaterial is constructed as
an array of unit cells that consist of two coupled varactor-loaded split-ring
resonators. Simulations verified that ultraslow magnetic solitons can be
generated in this type of metamaterial. To solve nonlinear equations, various
types of numerical methods can be applied by virtue of exact solutions, which
are always difficult to acquire. However, the initial conditions and
propagation distance impact the ultimate results. In this article, an
artificial neural network (ANN) was used as a supervised learning model to
predict the evolution and final mathematical expressions through training based
on samples with disparate initial conditions. Specifically, the influences of
the number of hidden layers were discussed. Additionally, the learning results
obtained by employing several training algorithms were analyzed and compared.
Our research opens a route for employing machine learning algorithms to save
time in both physical and engineering applications of Schr\"odinger-type
systems.


Abstract:
  We provide a variety of cases in which two factorizations have Hurwitz orbits
of the same size. We begin with prototypical results about factorizations of
length two, and show that cycling elements or flipping and inverting elements
in any factorization preserves Hurwitz orbit size. We prove that "double
reverse" factorizations in groups with special presentations have Hurwitz
orbits of equal size, and end with applications to complex reflection groups.


Abstract:
  We demonstrate that the pair-correlation function of Bose-Einstein condensed
atoms, which are confined in a quasi-one-dimensional toroidal/annular
potential, realizes a quantum time crystal in the ground state of the system.
We study this observable, assuming weak repulsive interatomic interactions and
a finite number of atoms.


Abstract:
  Radiation reaction (RR) terms at the third post-Minkowskian (3PM) order have
recently been found to be instrumental in restoring smooth continuity between
the non-relativistic, relativistic, and ultra-relativistic (including the
massless) regimes. Here we propose a new and intriguing connection between RR
and soft (bremsstrahlung) theorems which short-circuits the more involved
conventional loop computations. Although first noticed in the context of the
maximally supersymmetric theory, unitarity and analyticity arguments support
the general validity of this 3PM-order connection that we apply, in particular,
to Einstein's gravity and to its Jordan-Brans-Dicke extension. In the former
case we find full agreement with a recent result by Damour obtained through a
very different reasoning.


Abstract:
  We present an open-source library of natively differentiable physics and
robotics environments, accompanied by gradient-based control methods and a
benchmark-ing suite. The introduced environments allow auto-differentiation
through the simulation dynamics, and thereby permit fast training of
controllers. The library features several popular environments, including
classical control settings from OpenAI Gym. We also provide a novel
differentiable environment, based on deep neural networks, that simulates
medical ventilation. We give several use-cases of new scientific results
obtained using the library. This includes a medical ventilator simulator and
controller, an adaptive control method for time-varying linear dynamical
systems, and new gradient-based methods for control of linear dynamical systems
with adversarial perturbations.


Abstract:
  In this paper we introduce and study the theories of pseudo links and
singular links in the Solid Torus, ST. Pseudo links are links with some missing
crossing information that naturally generalize the notion of knot diagrams, and
that have potential use in molecular biology, while singular links are links
that contain a finite number of self-intersections. We consider pseudo links
and singular links in ST and we set up the appropriate topological theory in
order to construct invariants for these types of links in ST. In particular, we
formulate and prove the analogue of the Alexander theorem for pseudo links and
for singular links in ST. We then introduce the mixed pseudo braid monoid and
the mixed singular braid monoid, with the use of which, we formulate and prove
the analogue of the Markov theorem for pseudo links and for singular links in
ST. \smallbreak Moreover, we introduce the pseudo Hecke algebra of type A,
$P\mathcal{H}_n$, the cyclotomic and generalized pseudo Hecke algebras of type
B, $P\mathcal{H}_{1, n}$, and discuss how the pseudo braid monoid (cor. the
mixed pseudo braid monoid) can be represented by $P\mathcal{H}_{n}$ (cor. by
$P\mathcal{H}_{1, n}$). This is the first step toward the construction of
HOMFLYPT-type invariants for pseudo links in $S^3$ and in ST. We also introduce
the cyclotomic and generalized singular Hecke algebras of type B,
$S\mathcal{H}_{1, n}$, and we present two sets that we conjecture that they
form linear bases for $S\mathcal{H}_{1, n}$. Finally, we generalize the bracket
polynomial for pseudo links in ST.


Abstract:
  In this paper, we consider the sparse least squares regression problem with
probabilistic simplex constraint. Due to the probabilistic simplex constraint,
one could not apply the L1 regularization to the considered regression model.
To find a sparse solution, we reformulate the least squares regression problem
as a nonconvex and nonsmooth L1 regularized minimization problem over the unit
sphere. Then we propose a geometric proximal gradient method for solving the
regularized problem, where the explicit expression of the global solution to
every involved subproblem is obtained. The global convergence of the proposed
method is established under some mild assumptions. Some numerical results are
reported to illustrate the effectiveness of the proposed algorithm.


Abstract:
  Recently, methods based on Convolutional Neural Networks (CNN) achieved
impressive success in semantic segmentation tasks. However, challenges such as
the class imbalance and the uncertainty in the pixel-labeling process are not
completely addressed. As such, we present a new approach that calculates a
weight for each pixel considering its class and uncertainty during the labeling
process. The pixel-wise weights are used during training to increase or
decrease the importance of the pixels. Experimental results show that the
proposed approach leads to significant improvements in three challenging
segmentation tasks in comparison to baseline methods. It was also proved to be
more invariant to noise. The approach presented here may be used within a wide
range of semantic segmentation methods to improve their robustness.


Abstract:
  Classification between different activities in an indoor environment using
wireless signals is an emerging technology for various applications, including
intrusion detection, patient care, and smart home. Researchers have shown
different methods to classify activities and their potential benefits by
utilizing WiFi signals. In this paper, we analyze classification of moving
objects by employing machine learning on real data from a massive
multi-input-multi-output (MIMO) system in an indoor environment. We conduct
measurements for different activities in both line-of-sight and non
line-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We
propose algorithms to exploit amplitude and phase-based features classification
task. For the considered setup, we benchmark the classification performance and
show that we can achieve up to 98% accuracy using real massive MIMO data, even
with a small number of experiments. Furthermore, we demonstrate the gain in
performance results with a massive MIMO system as compared with that of a
limited number of antennas such as in WiFi devices.


Abstract:
  This paper deals with the quasilinear fully parabolic attraction-repulsion
chemotaxis system \begin{align*} u_t=\nabla \cdot (D(u)\nabla u)
  -\nabla \cdot (G(u)\chi(v)\nabla v)
  +\nabla\cdot(H(u)\xi(w)\nabla w), \quad v_t=d_1\Delta v+\alpha u-\beta v,
\quad w_t=d_2\Delta w+\gamma u-\delta w, \quad x \in \Omega,\ t>0, \end{align*}
under homogeneous Neumann boundary conditions and initial conditions, where
$\Omega \subset \mathbb{R}^n$ $(n \ge 1)$ is a bounded domain with smooth
boundary, $d_1, d_2, \alpha, \beta, \gamma, \delta>0$ are constants. Also, the
diffusivity $D$, the density-dependent sensitivities $G, H$ fulfill
$D(s)=a_0(s+1)^{m-1}$ with $a_0>0$ and $m \in \mathbb{R}$; $0 \le G(s) \le
b_0(s+1)^{q-1}$ with $b_0>0$ and $q<\min\{2,\ m+1\}$; $0 \le H(s) \le
c_0(s+1)^{r-1}$ with $c_0>0$ and $r<\min\{2,\ m+1\}$, and the signal-dependent
sensitivities $\chi, \xi$ satisfy $0<\chi(s)\le \frac{\chi_0}{s^{k_1}}$ with
$\chi_0>0$ and $k_1>1$; $0<\xi(s)\le \frac{\xi_0}{s^{k_2}}$ with $\xi_0>0$ and
$k_2>1$. Global existence and boundedness in the case that $w=0$ were proved by
Ding (J. Math. Anal. Appl.; 2018;461;1260-1270) and Jia-Yang (J. Math. Anal.
Appl.; 2019;475;139-153). However, there is no work on the above fully
parabolic attraction-repulsion chemotaxis system with nonlinear diffusion and
signal-dependent sensitivity. This paper develops global existence and
boundedness of classical solutions to the above system by introducing a new
test function.


Abstract:
  Simple polyhedra are $2$-dimensional polyhedra and important objects in
low-dimensional geometry and in the applications of fold maps, defined as
smooth maps regarded as higher dimensional variants of Morse functions. They
are also important since they are locally so-called Reeb spaces of (so-called
stable) fold maps into the plane and represent the manifolds compactly. The
Reeb space of a fold map is defined as the space of all connected components of
preimages of the map and is a polyhedron whose dimension is same as that of the
manifold of the target. Is a given simple polyhedron homeomorphic to the Reeb
space of a suitable stable fold map? What can we say about their global
topologies? Previously the author has challenged this for a specific case and
presented fundamental construction and topological properties of the polyhedra
as related new results. The present paper extends some of these works and
results and present new related results.


Abstract:
  The peculiar motion of the observer, if not accurately accounted for, is
bound to induce a well-defined clustering signal in the distribution of
galaxies. This signal is related to the Kaiser rocket effect. Here we examine
the amplitude and form of this effect, both analytically and numerically, and
discuss possible implications for the analysis and interpretation of
forthcoming cosmological surveys. For an idealistic cosmic variance dominated
full-sky survey with a Gaussian selection function peaked at $z\sim 1.5$ it is
a $> 5\sigma$ effect and it can in principle bias very significantly the
inference of cosmological parameters, especially for primordial
non-Gaussianity. For forthcoming surveys, with realistic masks and selection
functions, the Kaiser rocket is not a significant concern for cosmological
parameter inference. However, it is a systematic effect whose origin, nature
and imprint on galaxy maps are well known and thus should be subtracted or
mitigated. We present several approaches to do so.


Abstract:
  The space of $n \times m$ complex matrices can be regarded as an algebraic
variety on which the group ${\bf GL}_n \times {\bf GL}_m$ acts. There is a rich
interaction between geometry and representation theory in this example. In an
important paper, de Concini, Eisenbud, and Procesi classified the equivariant
ideals in the coordinate ring. More recently, we proved a noetherian result for
families of equivariant modules as $n$ and $m$ vary. In this paper, we
establish analogs of these results for the space of $(n|n) \times (m|m)$
isomeric matrices with respect to the action of ${\bf Q}_n \times {\bf Q}_m$,
where ${\bf Q}_n$ is the automorphism group of the isomeric structure (commonly
known as the "queer supergroup"). Our work is motivated by connections to the
Brauer category and the theory of twisted commutative algebras.


Abstract:
  Models focusing on interactions between land-use and transportation mostly
assume an exogenous provision of transportation infrastructures. We investigate
here co-evolutionary processes between land-use and transportation, at the
scale of Mega-City Regions, by introducing a toy model of corresponding
processes. In particular, our model is specifically tailored to include
governance processes ruling the growth of transportation infrastructure. We
show through stylised numerical simulations the potentialities of our model to
reproduce a variety of dynamics when co-evolution is taken into account. We
then apply the model to a case study, by calibrating it for the Pearl River
Delta Mega-city Region (China, 1990-2010). To go beyond this first modelling
step, we elaborate on the challenges to overcome to go further towards more
complex models integrating co-evolution between transportation networks and
territories in urban systems.


Abstract:
  This paper is concerned with space-time homogenization problems for damped
wave equations with spatially periodic oscillating elliptic coefficients and
temporally (arithmetic) quasi-periodic oscillating viscosity coefficients. Main
results consist of a homogenization theorem, qualitative properties of
homogenized matrices which appear in homogenized equations and a corrector
result for gradients of solutions. In particular, homogenized equations and
cell problems will turn out to deeply depend on the quasi-periodicity as well
as the log ratio of spatial and temporal periods of the coefficients. Even
types of equations will change depending on the log ratio and
quasi-periodicity. Proofs of the main results are based on a (very weak)
space-time two-scale convergence theory.


Abstract:
  The trigonal/bcc orientation relationships (ORs) and their likelihood of
occurrence are extensively studied using dispersed Y4Zr3O12nano-precipitates in
bcc Fe matrix by means of transmission electron microscopy, image simulations,
and a crystallographic model. Two orientation relationships related by a
rotation:[1-20]||[111] with (21-2)||(-110) and [1-20]||[111] with
(00-3)||(-110), are established and periodic arrays of misfit dislocation
doublets are identified at the strained interface in (110) for both ORs.
Further eighteen energetically feasible ORs in Y4Zr3O12/bcc system are deduced
by combining stereographic projections, which include the two predominant ORs
in this study and other ORs in literature. The orientation relationship which
generates the interface with a minimum number of dislocation doublets is the
most frequent.


Abstract:
  We study the problem of user association, namely finding the optimal
assignment of user equipment to base stations to achieve a targeted network
performance. In this paper, we focus on the knowledge transferability of
association policies. Indeed, traditional non-trivial user association schemes
are often scenario-specific or deployment-specific and require a policy
re-design or re-learning when the number or the position of the users change.
In contrast, transferability allows to apply a single user association policy,
devised for a specific scenario, to other distinct user deployments, without
needing a substantial re-learning or re-design phase and considerably reducing
its computational and management complexity. To achieve transferability, we
first cast user association as a multi-agent reinforcement learning problem.
Then, based on a neural attention mechanism that we specifically conceived for
this context, we propose a novel distributed policy network architecture, which
is transferable among users with zero-shot generalization capability i.e.,
without requiring additional training.Numerical results show the effectiveness
of our solution in terms of overall network communication rate, outperforming
centralized benchmarks even when the number of users doubles with respect to
the initial training point.


Abstract:
  Neuroscientists often describe neural activity as a representation of
something, or claim to have found evidence for a neural representation. But
what do these statements mean? The reasons to call some neural activity a
representation and the assumptions that come with this term are not generally
made clear from its common uses in neuroscience. Representation is a central
concept in philosophy of mind, with a rich history going back to the ancient
period. In order to clarify its usage in neuroscience, here we advance a link
between the connotations of this term across these disciplines. We draw on a
broad range of discourse in philosophy to distinguish three key aspects of
representation: correspondence, functional role, and teleology. We argue that
each of these aspects are implied by the explanatory role the term plays in
neuroscience. However, evidence related to all three aspects is rarely
presented or discussed in the course of individual studies that aim to identify
representations. Overlooking the significance of all three aspects hinders
communication in neuroscience, as it obscures the limitations of experimental
paradigms and conceals gaps in our understanding of the phenomena of primary
interest. Working from this three-part view, we discuss how to move toward
clearer communication about representations in the brain.


Abstract:
  Dvir and Moran proved the following upper bound for the size of a family
$\mbox{$\cal F$}$ of subsets of $[n]$ with $\mbox{Vdim}(\mbox{$\cal F$} \Delta
\mbox{$\cal F$})\leq d$.
  Let $d\leq n$ be integers. Let $\mbox{$\cal F$}$ be a family of subsets of
$[n]$ with $\mbox{Vdim}(\mbox{$\cal F$} \Delta \mbox{$\cal F$})\leq d$. Then \[
\left|\mbox{$\cal F$}\right|\le 2\sum_{k=0}^{\lfloor d/2 \rfloor}\binom nk. \]
  Our main result is the following uniform version of Dvir and Moran's result.
  Let $d\leq n$ be integers. Let $\mbox{$\cal F$}$ be an uniform family of
subsets of $[n]$ with $\mbox{Vdim}(\mbox{$\cal F$} \Delta \mbox{$\cal F$})\leq
d$. Then \[ \left|\mbox{$\cal F$}\right|\le 2 {n \choose \lfloor d/2 \rfloor}.
\]
  Denote by $\mathbf{v_F}\in \{0,1\}^n$ the characteristic vector of a set $F
\subseteq [n]$.
  Our proof is based on the following uniform version of Croot-Lev-Pach Lemma:
  Let $0\leq d\leq n$ be integers. Let $\mbox{$\cal H$}$ be a $k$-uniform
family of subsets of $[n]$. Let $\mathbb F$ be a field. Suppose that there
exists a polynomial $P(x_1, \ldots ,x_n,y_1, \ldots ,y_n)\in \mathbb F[x_1,
\ldots ,x_n,y_1, \ldots ,y_n]$ with $\mbox{deg}(P)\leq d$ such that
$P(\mathbf{v_F},\mathbf{v_F})\ne 0$ for each $F\in \mbox{$\cal H$}$ and
$P(\mathbf{v_F},\mathbf{v_G})= 0$ for each $F\ne G\in \mbox{$\cal H$}$. Then \[
\left|\mbox{$\cal H$}\right|\le 2 {n \choose \lfloor d/2 \rfloor}. \]


Abstract:
  In this article, we study several aspects of the intersections of
algorithmically random closed sets. First, we answer a question of Cenzer and
Weber, showing that the operation of intersecting relatively random closed sets
(with respect to certain underlying measures induced by Bernoulli measures on
the space of codes of closed sets), which preserves randomness, can be
inverted: a random closed set of the appropriate type can be obtained as the
intersection of two relatively random closed sets. We then extend the
Cenzer/Weber analysis to the intersection of multiple random closed sets,
identifying the Bernoulli measures with respect to which the intersection of
relatively random closed sets can be non-empty. We lastly apply our analysis to
provide a characterization of the effective Hausdorff dimension of sequences in
terms of the degree of intersectability of random closed sets that contain
them.


Abstract:
  A small group of postdocs, graduate students, and undergraduates
inadvertently formed a longitudinal study contrasting expected productivity
levels with actual productivity levels. Over the last nine months, our group
self-reported 559 tasks, dates, and completion times -- expected and actual.
Here, I show which types of tasks we are the worst at completing in the
originally planned amount of time (spoiler: coding and writing tasks), whether
more senior researchers have more accurate expectations (spoiler: not much),
and whether our expectations improve with time (spoiler: only a little).


Abstract:
  We consider two conformal defects close to each other in a free theory, and
study what happens as the distance between them goes to zero. This limit is the
same as zooming out, and the two defects have fused to another defect. As we
zoom in we find a non-conformal effective action for the fused defect. Among
other things this means that we cannot in general decompose the two-point
correlator of two defects in terms of other conformal defects. We prove the
fusion using the path integral formalism by treating the defects as sources for
a scalar in the bulk.


Abstract:
  In 2017 Bar-Natan and the first author showed that solutions to the
Kashiwara--Vergne equations are in bijection with certain knot invariants:
homomorphic expansions of welded foams. Welded foams are a class of knotted
tubes in $\mathbb{R}^4$, which can be finitely presented algebraically as a
circuit algebra, or, equivalently, a wheeled prop. In this paper we describe
the Kashiwara-Vergne groups $\mathsf{KV}$ and $\mathsf{KRV}$ -- the symmetry
groups of Kashiwara-Vergne solutions -- as automorphisms of the completed
circuit algebras of welded foams, and their associated graded circuit algebra
of arrow diagrams, respectively. Finally, we provide a description of the
graded Grothendieck-Teichm\"uller group $\mathsf{GRT}_1$ as automorphisms of
arrow diagrams.


Abstract:
  Face authentication usually utilizes deep learning models to verify users
with high recognition accuracy. However, face authentication systems are
vulnerable to various attacks that cheat the models by manipulating the digital
counterparts of human faces. So far, lots of liveness detection schemes have
been developed to prevent such attacks. Unfortunately, the attacker can still
bypass these schemes by constructing wide-ranging sophisticated attacks. We
study the security of existing face authentication services (e.g., Microsoft,
Amazon, and Face++) and typical liveness detection approaches. Particularly, we
develop a new type of attack, i.e., the low-cost 3D projection attack that
projects manipulated face videos on a 3D face model, which can easily evade
these face authentication services and liveness detection approaches. To this
end, we propose FaceLip, a novel liveness detection scheme for face
authentication, which utilizes unforgeable lip motion patterns built upon
well-designed acoustic signals to enable a strong security guarantee. The
unique lip motion patterns for each user are unforgeable because FaceLip
verifies the patterns by capturing and analyzing the acoustic signals that are
dynamically generated according to random challenges, which ensures that our
signals for liveness detection cannot be manipulated. Specially, we develop
robust algorithms for FaceLip to eliminate the impact of noisy signals in the
environment and thus can accurately infer the lip motions at larger distances.
We prototype FaceLip on off-the-shelf smartphones and conduct extensive
experiments under different settings. Our evaluation with 44 participants
validates the effectiveness and robustness of FaceLip.


Abstract:
  Compact fiber-to-chip couplers play an important role in optical
interconnections, especially in data centers. However, the development of
couplers has been mostly limited to standard single mode fibers, with few
devices compatible with multicore and multimode fibers. Through the use of
state-of-the-art optimization algorithms, we designed a compact
dual-polarization coupler to interface chips and dense multicore fibers,
demonstrating, for the first time, coupling to both polarizations of all the
cores, with measured coupling efficiency of $-$4.3 dB and with a 3 dB bandwidth
of 48 nm. The dual-polarization coupler has footprint of 200 $\mu m^2$ per
core, which makes it the smallest fiber-to-chip coupler experimentally
demonstrated on a standard silicon-on-insulator platform.


Abstract:
  Co-evolving time series appears in a multitude of applications such as
environmental monitoring, financial analysis, and smart transportation. This
paper aims to address the following challenges, including (C1) how to
incorporate explicit relationship networks of the time series; (C2) how to
model the implicit relationship of the temporal dynamics. We propose a novel
model called Network of Tensor Time Series, which is comprised of two modules,
including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural
Network (TRNN). TGCN tackles the first challenge by generalizing Graph
Convolutional Network (GCN) for flat graphs to tensor graphs, which captures
the synergy between multiple graphs associated with the tensors. TRNN leverages
tensor decomposition to model the implicit relationships among co-evolving time
series. The experimental results on five real-world datasets demonstrate the
efficacy of the proposed method.


Abstract:
  We present a Greens function approach to calculate the Dzyaloshinskii-Moriya
interactions (DMI) from first principles electronic structure calculations,
that is computationally more efficient and accurate than the most-commonly
employed supercell and generalized Bloch-based approaches. The method is
applied to the (111) Co/Pt bilayer where the Co- and/or Pt-thickness dependence
of the DMI coefficients are calculated. Overall, the calculated DMI are in
relatively good agreement with the corresponding values reported
experimentally. Furthermore, we investigate the effect of strain in the DMI
tensor elements and show that the isotropic N\'{e}el DMI can be significantly
modulated by the normal strains, $\epsilon_{xx},\epsilon_{yy}$ and is
relatively insensitive to the shear strain, $\epsilon_{xy}$. Moreover, we show
that anisotropic strains, $(\epsilon_{xx}-\epsilon_{yy})$ and
  $\epsilon_{xy}$, result in the emergence of anisotropic N\'{e}el- and
Bloch-type DMIs, respectively.


Abstract:
  We prove a number of limiting distributions for statistics for unimodal
sequences of positive integers by adapting a probabilistic framework for
integer partitions introduced by Fristedt. The difficulty in applying the
direct analogue of Fristedt's techniques to unimodal sequences lies in the fact
that the generating function for partitions is an infinite product, while that
of unimodal sequences is not. Essentially, we get around this by conditioning
on the size of the largest part and working uniformly on contributing summands.
Our framework may be used to derive many distributions, and our results include
joint distributions for largest parts and multiplicities of small parts. We
discuss ranks as well. We further obtain analogous results for strongly
unimodal sequences.


Abstract:
  Automatic polyp segmentation has proven to be immensely helpful for endoscopy
procedures, reducing the missing rate of adenoma detection for endoscopists
while increasing efficiency. However, classifying a polyp as being neoplasm or
not and segmenting it at the pixel level is still a challenging task for
doctors to perform in a limited time. In this work, we propose a fine-grained
formulation for the polyp segmentation problem. Our formulation aims to not
only segment polyp regions, but also identify those at high risk of malignancy
with high accuracy. In addition, we present a UNet-based neural network
architecture called NeoUNet, along with a hybrid loss function to solve this
problem. Experiments show highly competitive results for NeoUNet on our
benchmark dataset compared to existing polyp segmentation models.


Abstract:
  Multiplayer Online Battle Area (MOBA) games are a recent huge success both in
the video game industry and the international eSports scene. These games
encourage team coordination and cooperation, short and long-term planning,
within a real-time combined action and strategy gameplay.
  Artificial Intelligence and Computational Intelligence in Games research
competitions offer a wide variety of challenges regarding the study and
application of AI techniques to different game genres. These events are widely
accepted by the AI/CI community as a sort of AI benchmarking that strongly
influences many other research areas in the field.
  This paper presents and describes in detail the Dota 2 Bot competition and
the Dota 2 AI framework that supports it. This challenge aims to join both,
MOBAs and AI/CI game competitions, inviting participants to submit AI
controllers for the successful MOBA \textit{Defense of the Ancients 2} (Dota 2)
to play in 1v1 matches, which aims for fostering research on AI techniques for
real-time games. The Dota 2 AI framework makes use of the actual Dota 2 game
modding capabilities to enable to connect external AI controllers to actual
Dota 2 game matches using the original Free-to-Play game.se of the actual Dota
2 game modding capabilities to enable to connect external AI controllers to
actual Dota 2 game matches using the original Free-to-Play game.


Abstract:
  Simulating the dynamics of many-body quantum systems is believed to be one of
the first fields that quantum computers can show a quantum advantage over
classical computers. Noisy intermediate-scale quantum (NISQ) algorithms aim at
effectively using the currently available quantum hardware. For quantum
simulation, various types of NISQ algorithms have been proposed with individual
advantages as well as challenges. In this work, we propose a new algorithm,
truncated Taylor quantum simulator (TTQS), that shares the advantages of
existing algorithms and alleviates some of the shortcomings. Our algorithm does
not have any classical-quantum feedback loop and bypasses the barren plateau
problem by construction. The classical part in our hybrid quantum-classical
algorithm corresponds to a quadratically constrained quadratic program (QCQP)
with a single quadratic equality constraint, which admits a semidefinite
relaxation. The QCQP based classical optimization was recently introduced as
the classical step in quantum assisted eigensolver (QAE), a NISQ algorithm for
the Hamiltonian ground state problem. Thus, our work provides a conceptual
unification between the NISQ algorithms for the Hamiltonian ground state
problem and the Hamiltonian simulation. We recover differential equation-based
NISQ algorithms for Hamiltonian simulation such as quantum assisted simulator
(QAS) and variational quantum simulator (VQS) as particular cases of our
algorithm. We test our algorithm on some toy examples on current cloud quantum
computers. We also provide a systematic approach to improve the accuracy of our
algorithm.


Abstract:
  Wireless blockchain network is proposed to enable a decentralized and safe
wireless networks for various blockchain applications. To achieve blockchain
consensus in wireless network, one of the important steps is to broadcast new
block using wireless channel. Under wireless network protocols, the block
transmitting will be affected significantly. In this work, we focus on the
consensus process in blockchain-based wireless local area network (B-WLAN) by
investigating the impact of the media access control (MAC) protocol, CSMA/CA.
With the randomness of the backoff counter in CSMA/CA, it is possible for
latter blocks to catch up or outpace the earlier one, which complicates
blockchain forking problem. In view of this, we propose mining strategies to
pause mining for reducing the forking probability, and a discard strategy to
remove the forking blocks that already exist in CSMA/CA backoff procedure.
Based on the proposed strategies, we design Block Access Control (BAC)
approaches to effectively schedule block mining and transmitting for improving
the performance of B-WLAN. Then, Markov chain models are presented to conduct
performance analysis in B-WLAN. The results show that BAC approaches can help
the network to achieve a high transaction throughput while improving block
utilization and saving computational power. Meanwhile, the trade-off between
transaction throughput and block utilization is demonstrated, which can act as
a guidance for practical deployment of blockchain.


Abstract:
  Intelligent reflecting surface (IRS) is a new and revolutionary technology
capable of reconfiguring the wireless propagation environment by controlling
its massive low-cost passive reflecting elements. Different from prior works
that focus on optimizing IRS reflection coefficients or single-IRS placement,
we aim to maximize the minimum throughput of a single-cell multiuser system
aided by multiple IRSs, by joint multi-IRS placement and power control at the
access point (AP), which is a mixed-integer non-convex problem with drastically
increased complexity with the number of IRSs/users. To tackle this challenge, a
ring-based IRS placement scheme is proposed along with a power control policy
that equalizes the users' non-outage probability. An efficient searching
algorithm is further proposed to obtain a close-to-optimal solution for
arbitrary number of IRSs/rings. Numerical results validate our analysis and
show that our proposed scheme significantly outperforms the benchmark schemes
without IRS and/or with other power control policies. Moreover, it is shown
that the IRSs are preferably deployed near AP for coverage range extension,
while with more IRSs, they tend to spread out over the cell to cover more and
get closer to target users.


Abstract:
  Various remote sensing observations have been used so far to probe the
turbulent properties of the solar wind. Using the recently reported density
modulation indices that are derived using angular broadening observations of
Crab Nebula during 1952 - 2013, we measured the solar wind proton heating using
the kinetic $\rm Alfv\acute{e}n$ wave dispersion equation. The estimated
heating rates vary from $\approx 1.58 \times 10^{-14}$ to $1.01 \times 10^{-8}
~\rm erg~ cm^{-3}~ s^{-1}$ in the heliocentric distance range 5 - 45 $\rm
R_{\odot}$. Further, we found that heating rates vary with the solar cycle in
correlation with density modulation indices. The models derived using in-situ
measurements (for example, electron/proton density, temperature, and magnetic
field) that the recently launched Parker Solar Probe observes (planned closest
perihelia $\rm 9.86~ R_{\odot}$ from the center of the Sun) are useful in the
estimation of the turbulent heating rate precisely. Further, we compared our
heating rate estimates with the one derived using previously reported remote
sensing and in-situ observations.


Abstract:
  The Metis coronagraph is one of the remote sensing instruments hosted on
board the ESA/NASA Solar Orbiter mission. Metis is devoted to carry out the
first simultaneous imaging of the solar corona in both visible light (VL) and
ultraviolet (UV). High-energy particles penetrate spacecraft materials and may
limit the performance of on-board instruments. A study of galactic cosmic-ray
(GCR) tracks observed in the first VL images gathered by Metis during the
commissioning phase for a total of 60 seconds of exposure time is presented
here. A similar analysis is planned for the UV channel. A prediction of the GCR
flux up to hundreds of GeV is made here for the first part of the Solar Orbiter
mission to study the Metis coronagraph performance. GCR model predictions are
compared to observations gathered on board Solar Orbiter by the EPD/HET
experiment in the range 10 MeV-100 MeV in the summer 2020 and with previous
measurements. Estimated cosmic-ray fluxes above 70 MeV n$^{-1}$ have been also
parameterized and used for Monte Carlo simulations aiming at reproducing the
cosmic-ray track observations in the Metis coronagraph VL images. The same
parameterizations can also be used to study the performance of other detectors.
By comparing observations of cosmic-ray tracks in the Metis VL images with
FLUKA Monte Carlo simulations of cosmic-ray interactions in the VL detector, it
is found that cosmic rays fire a fraction of the order of 10$^{-4}$ of the
whole image pixel sample. Therefore, cosmic rays do not affect sensibly the
quality of Metis VL images. It is also found that the overall efficiency for
cosmic-ray identification in the Metis VL images is approximately equal to the
contribution of Z$>$2 particles. As a result, the Metis coronagraph may play
the role of a proton monitor for long-term GCR variations during the overall
mission duration.


Abstract:
  Recent re-determination of stellar atmospheric parameters for a sample of
stars observed during the {\it Kepler} mission allowed to enlarge the number of
{\it Kepler} B-type stars. We present the detailed frequency analysis for all
these objects. All stars exhibit pulsational variability with maximum
amplitudes at frequencies corresponding to high-order g modes. Peaks that could
be identified with low-order p/g modes are also extracted for a few stars. We
identified some patters in the oscillation spectra that can be associated with
the period spacings that can result from the asymptotic nature of the detected
pulsational modes. We also tentatively confront the observed oscillation
characteristics with predictions from linear nonadiabatic computations of
stellar pulsations. For high-order g modes the traditional approximation was
employed to include the effects of rotation on the frequency values and mode
instability.


Abstract:
  Humor is an important social phenomenon, serving complex social and
psychological functions. However, despite being studied for millennia humor is
computationally not well understood, often considered an AI-complete problem.
In this work, we introduce a novel setting in humor mining: automatically
detecting funny and unusual scientific papers. We are inspired by the Ig Nobel
prize, a satirical prize awarded annually to celebrate funny scientific
achievements (example past winner: "Are cows more likely to lie down the longer
they stand?"). This challenging task has unique characteristics that make it
particularly suitable for automatic learning. We construct a dataset containing
thousands of funny papers and use it to learn classifiers, combining findings
from psychology and linguistics with recent advances in NLP. We use our models
to identify potentially funny papers in a large dataset of over 630,000
articles. The results demonstrate the potential of our methods, and more
broadly the utility of integrating state-of-the-art NLP methods with insights
from more traditional disciplines.


Abstract:
  Privacy preference signals are digital representations of how users want
their personal data to be processed. Such signals must be adopted by both the
sender (users) and intended recipients (data processors). Adoption represents a
coordination problem that remains unsolved despite efforts dating back to the
1990s. Browsers implemented standards like the Platform for Privacy Preferences
(P3P) and Do Not Track (DNT), but vendors profiting from personal data faced
few incentives to receive and respect the expressed wishes of data subjects. In
the wake of recent privacy laws, a coalition of AdTech firms published the
Transparency and Consent Framework (TCF), which defines an opt-in consent
signal. This paper integrates post-GDPR developments into the wider history of
privacy preference signals. Our main contribution is a high-frequency
longitudinal study describing how TCF signal gained dominance as of February
2021. We explore which factors correlate with adoption at the website level.
Both the number of third parties on a website and the presence of Google Ads
are associated with higher adoption of TCF. Further, we show that vendors acted
as early adopters of TCF 2.0 and provide two case-studies describing how
Consent Management Providers shifted existing customers to TCF 2.0. We sketch
ways forward for a pro-privacy signal.


Abstract:
  Concerns for the privacy of communication is widely discussed in research and
overall society. For the public financial infrastructure of blockchains, this
discussion encompasses the privacy of transaction data and its broadcasting
throughout the network. To tackle this problem, we transform a discrete-time
protocol for contact networks over infinite trees into a computer network
protocol for peer-to-peer networks. Peer-to-peer networks are modeled as
organically growing graphs. We show that the distribution of shortest paths in
such a network can be modeled using a normal distribution
$\mathcal{N}(\mu,\sigma^2).$ We determine statistical estimators for
$\mu,\sigma$ via multivariate models. The model behaves logarithmic over the
number of nodes n and proportional to an inverse exponential over the number of
added edges k. These results facilitate the computation of optimal forwarding
probabilities during the dissemination phase for optimal privacy in a limited
information environment.


Abstract:
  Along with the rapid expansion of information technology and digitalization
of health data, there is an increasing concern on maintaining data privacy
while garnering the benefits in medical field. Two critical challenges are
identified: Firstly, medical data is naturally distributed across multiple
local sites, making it difficult to collectively train machine learning models
without data leakage. Secondly, in medical applications, data are often
collected from different sources and views, resulting in heterogeneity and
complexity that requires reconciliation. This paper aims to provide a generic
Federated Multi-View Learning (FedMV) framework for multi-view data leakage
prevention, which is based on different types of local data availability and
enables to accommodate two types of problems: Vertical Federated Multi-View
Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV). We
experimented with real-world keyboard data collected from BiAffect study. The
results demonstrated that the proposed FedMV approach can make full use of
multi-view data in a privacy-preserving way, and both V-FedMV and H-FedMV
methods perform better than their single-view and pairwise counterparts.
Besides, the proposed model can be easily adapted to deal with multi-view
sequential data in a federated environment, which has been modeled and
experimentally studied. To the best of our knowledge, this framework is the
first to consider both vertical and horizontal diversification in the
multi-view setting, as well as their sequential federated learning.


Abstract:
  For a list $\cal{L}$ of finite groups and for a profinite group $G$, we
consider the intersection $T(G)$ of all open normal subgroups $N$ of $G$ with
$G/N$ in $\cal{L}$. We give a cohomological characterization of the
epimorphisms $\pi\colon S\to G$ of profinite groups (satisfying some additional
requirements) such that $\pi[T(S)]=T(G)$. For $p$ prime, this is used to
describe cohomologically the profinite groups $G$ whose $n$th term $G_{(n,p)}$
(resp., $G^{(n,p)}$) in the $p$-Zassenhaus filtration (resp., lower $p$-central
filtration) is an intersection of this form. When $G=G_F$ is the absolute
Galois group of a field $F$ containing a root of unity of order $p$, we recover
as special cases results by Minac, Spira and the author, describing $G_{(3,p)}$
and $G^{(3,p)}$ as $T(G)$ for appropriate lists $\cal{L}$.


Abstract:
  This paper proposes the SPARK dataset as a new unique space object
multi-modal image dataset. Image-based object recognition is an important
component of Space Situational Awareness, especially for applications such as
on-orbit servicing, active debris removal, and satellite formation. However,
the lack of sufficient annotated space data has limited research efforts in
developing data-driven spacecraft recognition approaches. The SPARK dataset has
been generated under a realistic space simulation environment, with a large
diversity in sensing conditions for different orbital scenarios. It provides
about 150k images per modality, RGB and depth, and 11 classes for spacecrafts
and debris. This dataset offers an opportunity to benchmark and further develop
object recognition, classification and detection algorithms, as well as
multi-modal RGB-Depth approaches under space sensing conditions. Preliminary
experimental evaluation validates the relevance of the data, and highlights
interesting challenging scenarios specific to the space environment.


Abstract:
  We propose a computationally efficient method to construct nonparametric,
heteroskedastic prediction bands for uncertainty quantification, with or
without any user-specified predictive model. The data-adaptive prediction band
is universally applicable with minimal distributional assumptions, with strong
non-asymptotic coverage properties, and easy to implement using standard convex
programs. Our approach can be viewed as a novel variance interpolation with
confidence and further leverages techniques from semi-definite programming and
sum-of-squares optimization. Theoretical and numerical performances for the
proposed approach for uncertainty quantification are analyzed.


Abstract:
  Failures in optical network backbone can cause significant interruption in
internet data traffic. Hence, it is very important to reduce such network
outages. Prediction of such failures would be a step forward to avoid such
disruption of internet services for users as well as operators. Several
research proposals are available in the literature which are applications of
data science and machine learning techniques. Most of the techniques rely on
significant amount of real time data collection. Network devices are assumed to
be equipped to collect data and these are then analysed by different algorithms
to predict failures. Every network element which is already deployed in the
field may not have these data gathering or analysis techniques designed into
them initially. However, such mechanisms become necessary later when they are
already deployed in the field. This paper proposes a Bayesian network based
failure prediction of network nodes, e.g., routers etc., using very basic
information from the log files of the devices and applying power law based data
augmentation to complement for scarce real time information. Numerical results
show that network node failure prediction can be performed with high accuracy
using the proposed mechanism.


Abstract:
  The algorithmic independence of conditionals, which postulates that the
causal mechanism is algorithmically independent of the cause, has recently
inspired many highly successful approaches to distinguish cause from effect
given only observational data. Most popular among these is the idea to
approximate algorithmic independence via two-part Minimum Description Length
(MDL). Although intuitively sensible, the link between the original postulate
and practical two-part MDL encodings is left vague. In this work, we close this
gap by deriving a two-part formulation of this postulate, in terms of
Kolmogorov complexity, which directly links to practical MDL encodings. To
close the cycle, we prove that this formulation leads on expectation to the
same inference result as the original postulate.


Abstract:
  We investigate Referring Image Segmentation (RIS), which outputs a
segmentation map corresponding to the given natural language description. To
solve RIS efficiently, we need to understand each word's relationship with
other words, each region in the image to other regions, and cross-modal
alignment between linguistic and visual domains. We argue that one of the
limiting factors in the recent methods is that they do not handle these
interactions simultaneously. To this end, we propose a novel architecture
called JRNet, which uses a Joint Reasoning Module(JRM) to concurrently capture
the inter-modal and intra-modal interactions. The output of JRM is passed
through a novel Cross-Modal Multi-Level Fusion (CMMLF) module which further
refines the segmentation masks by exchanging contextual information across
visual hierarchy through linguistic features acting as a bridge. We present
thorough ablation studies and validate our approach's performance on four
benchmark datasets, showing considerable performance gains over the existing
state-of-the-art methods.


Abstract:
  A relation between matrix-valued complete Bernstein functions and
matrix-valued Stieltjes functions is applied to prove that the solutions of
matricial convolution equations with extended LICM kernels belong to special
classes of functions. In particular the cases of the solutions of the
viscoelastic duality relation and the solutions of the matricial Sonine
equation are discussed, with applications in anisotropic linear viscoelasticity
and a generalization of fractional calculus.
  In the first case it is in particular shown that duality of completely
monotone relaxation functions and Bernstein creep functions in general requires
inclusion in the relaxation function of a Newtonian viscosity term in addition
to the memory effects represented by the completely monotone kernel.
  We define anisotropic generalized fractional derivatives (GFD) by replacing
the kernel $t^{-\alpha}/\Gamma(1-\alpha)$ of the Caputo derivatives with
completely monotone matrix-valued kernels which are weakly singular at 0.


Abstract:
  Formal Methods for the Informal Engineer (FMIE) was a workshop held at the
Broad Institute of MIT and Harvard in 2021 to explore the potential role of
verified software in the biomedical software ecosystem. The motivation for
organizing FMIE was the recognition that the life sciences and medicine are
undergoing a transition from being passive consumers of software and AI/ML
technologies to fundamental drivers of new platforms, including those which
will need to be mission and safety-critical. Drawing on conversations leading
up to and during the workshop, we make five concrete recommendations to help
software leaders organically incorporate tools, techniques, and perspectives
from formal methods into their project planning and development trajectories.


Abstract:
  In this article, we construct samples of SLE-like curves out of samples of
CLE and Poisson point process of Brownian excursions. We show that the law of
these curves depends continuously on the intensity measure of the Brownian
excursions. Using such construction of curves, we extend the notion of level
lines of GFF to the case when the boundary condition is measure-valued.


Abstract:
  We report on experimental investigation of thermal contact resistance of the
noncuring graphene thermal interface materials with the surfaces characterized
by different degree of roughness. It is found that the thermal contact
resistance depends on the graphene loading non-monotonically, achieving its
minimum at the loading fraction of ~15 wt.%. Increasing the surface roughness
by ~1 micrometer results in approximately the factor of x2 increase in the
thermal contact resistance for this graphene loading. The obtained dependences
of the thermal conductivity, thermal contact resistance, and the total thermal
resistance of the thermal interface material layer on the graphene loading and
surface roughness indicate the need for optimization of the loading fraction
for specific materials and roughness of the connecting surfaces. Our results
are important for developing graphene technologies for thermal management of
high-power-density electronics.


Abstract:
  The exact solutions of both the cubic Duffing equation and cubic-quintic
Duffing equation are presented by using only leaf functions. In previous
studies, exact solutions of the cubic Duffing equation have been proposed using
functions that integrate leaf functions in the phase of trigonometric
functions. Because they are not simple, the procedures for transforming the
exact solutions are complicated and not convenient. The first derivative of the
leaf function can be derived as the root. This derivative can be factored.
These factors or multiplications of factors are exact solutions to the Duffing
equation. Some of these exact solutions are of the same type as the cubic
Duffing equation reported in previously. Some of these exact solutions satisfy
the exact solutions of the cubic--quintic Duffing equations with high
nonlinearity. In this study, the relationship between the parameters of these
exact solutions and the coefficients of the terms of the Duffing equation is
clarified. We numerically analyze these exact solutions, plot the waveform, and
discuss the periodicity and amplitude of the waveform.


Abstract:
  Recently predicted and observed Pauli crystals are structures formed by
trapped ultracold non-interacting particles obeying Fermi statistics. The
relative positions of the particles are determined by the trapping potential
and the Pauli exclusion principle. Measuring all particles at once, their
positions tend to be close to vertices of non-trivial polyhedrons. Similar
shapes are found for systems of particles bound to the surface of a sphere.


Abstract:
  Least squares estimators, when trained on a few target domain samples, may
predict poorly. Supervised domain adaptation aims to improve the predictive
accuracy by exploiting additional labeled training samples from a source
distribution that is close to the target distribution. Given available data, we
investigate novel strategies to synthesize a family of least squares estimator
experts that are robust with regard to moment conditions. When these moment
conditions are specified using Kullback-Leibler or Wasserstein-type
divergences, we can find the robust estimators efficiently using convex
optimization. We use the Bernstein online aggregation algorithm on the proposed
family of robust experts to generate predictions for the sequential stream of
target test samples. Numerical experiments on real data show that the robust
strategies may outperform non-robust interpolations of the empirical least
squares estimators.


Abstract:
  For the fractional quantum Hall states on a finite disc, we study the
thermoelectric transport properties under the influence of an edge and its
reconstruction. In a recent study on a torus [Phys. Rev. B 101, 241101 (2020)],
Sheng and Fu found a universal non-Fermi liquid power-law scaling of the
thermoelectric conductivity $\alpha_{xy} \propto T^{\eta}$ for the gapless
composite Fermi-liquid state. The exponent $\eta \sim 0.5$ appears an
independence of the filling factors and the details of the interactions. In the
presence of an edge, we find the properties of the edge spectrum dominants the
low-temperature behaviors and breaks the universal scaling law of the
thermoelectric conductivity. In order to consider individually the effects of
the edge states, the entanglement spectrum in real space is employed and tuned
by varying the area of subsystem. In non-Abelian Moore-Read state, the Majorana
neutral edge mode is found to have more significant effect than that of the
charge mode in the low temperature.


Abstract:
  The spread of the novel coronavirus disease caused schools in Japan to close
to cope with the pandemic. In response to this, parents of students were
obliged to care for their children during the daytime when they were usually at
school. Does the increase in burden of childcare influence parents mental
health? Based on short panel data from mid-March to mid-April 2020, we explored
how school closures influenced the mental health of parents with school-aged
children. Using the fixed effects model, we found that school closures lead to
students mothers suffering from worse mental health than other females, while
the fathers mental health did not differ from other males. This tendency was
only observed for less educated mothers who had children attending primary
school, but not those attending junior high school. The contribution of this
paper is to show that school closures increased the inequality of mental health
between genders and the educational background of parents.


Abstract:
  In this paper, we propose an approach named psc2code to denoise the process
of extracting source code from programming screencasts. First, psc2code
leverages the Convolutional Neural Network based image classification to remove
non-code and noisy-code frames. Then, psc2code performs edge detection and
clustering-based image segmentation to detect sub-windows in a code frame, and
based on the detected sub-windows, it identifies and crops the screen region
that is most likely to be a code editor. Finally, psc2code calls the API of a
professional OCR tool to extract source code from the cropped code regions and
leverages the OCRed cross-frame information in the programming screencast and
the statistical language model of a large corpus of source code to correct
errors in the OCRed source code.
  We conduct an experiment on 1,142 programming screencasts from YouTube. We
find that our CNN-based image classification technique can effectively remove
the non-code and noisy-code frames, which achieves an F1-score of 0.95 on the
valid code frames. Based on the source code denoised by psc2code, we implement
two applications: 1) a programming screencast search engine; 2) an
interaction-enhanced programming screencast watching tool. Based on the source
code extracted from the 1,142 collected programming screencasts, our
experiments show that our programming screencast search engine achieves the
precision@5, 10, and 20 of 0.93, 0.81, and 0.63, respectively.


Abstract:
  Multipole representation is proposed for the anisotropic Coulomb interactions
in solids. Any local interactions can be expressed as the product of two
multipole operators, and the interaction parameters are systematically
classified based on the point group symmetry. The form of the multipole
interactions are restricted not only by the symmetry and Hermiticity but also
by the spatial structure of the interaction, which is closely related to the
presence or absence of the odd-rank multipoles. As an exemplary demonstration,
the screened Coulomb interaction for SrVO$_3$ is considered, where only a few
parameters are necessary for its description. By comparing it with the
unscreened version, the totally symmetric $A_1$ representation is found to be
strongly suppressed, but the $A_1$ component still gives a dominant
contribution for the anisotropic part of the interaction. The anisotropic
interactions are also applied to the localized two $f$-electron wave functions,
which give the same-order contribution as the one-body level splitting
estimated by the band structure calculation.


Abstract:
  Being low-level radiation exposure and less harmful to health, low-dose
computed tomography (LDCT) has been widely adopted in the early screening of
lung cancer and COVID-19. LDCT images inevitably suffer from the degradation
problem caused by complex noises. It was reported that, compared with
commercial iterative reconstruction methods, deep learning (DL)-based LDCT
denoising methods using convolutional neural network (CNN) achieved competitive
performance. Most existing DL-based methods focus on the local information
extracted by CNN, while ignoring both explicit non-local and context
information (which are leveraged by radiologists). To address this issue, we
propose a novel deep learning model named radiologist-inspired deep denoising
network (RIDnet) to imitate the workflow of a radiologist reading LDCT images.
Concretely, the proposed model explicitly integrates all the local, non-local
and context information rather than local information only. Our
radiologist-inspired model is potentially favoured by radiologists as a
familiar workflow. A double-blind reader study on a public clinical dataset
shows that, compared with state-of-the-art methods, our proposed model achieves
the most impressive performance in terms of the structural fidelity, the noise
suppression and the overall score. As a physicians-inspired model, RIDnet gives
a new research roadmap that takes into account the behavior of physicians when
designing decision support tools for assisting clinical diagnosis. Models and
code are available at https://github.com/tonyckc/RIDnet_demo.


Abstract:
  We introduce posets with interfaces (iposets) and generalise the serial
composition of posets to a new gluing composition of iposets. In partial order
semantics of concurrency, this amounts to designate events that continue their
execution across components. Alternatively, in terms of decomposing concurrent
systems, it allows cutting through some events, whereas serial composition may
cut through edges only.
  We show that iposets under gluing composition form a category, extending the
monoid of posets under serial composition, and a 2-category when enriched with
a subsumption order and a suitable parallel composition as a lax tensor. This
generalises the interchange monoids used in concurrent Kleene algebra.
  We also consider gp-iposets, which are generated from singletons by finitary
gluing and parallel compositions. We show that the class includes the
series-parallel posets as well as the interval orders, which are also well
studied in concurrency theory. Finally, we show that not all posets are
gp-iposets, exposing several posets that cannot occur as induced substructures
of gp-iposets.


Abstract:
  Identifying the origin of a sample image in biometric systems can be
beneficial for data authentication in case of attacks against the system and
for initiating sensor-specific processing pipelines in sensor-heterogeneous
environments. Motivated by shortcomings of the photo response non-uniformity
(PRNU) based method in the biometric context, we use a texture classification
approach to detect the origin of finger vein sample images. Based on eight
publicly available finger vein datasets and applying eight classical yet simple
texture descriptors and SVM classification, we demonstrate excellent sensor
model identification results for raw finger vein samples as well as for the
more challenging region of interest data. The observed results establish
texture descriptors as effective competitors to PRNU in finger vein sensor
model identification.


Abstract:
  Age-related macular degeneration (AMD) is one of the leading causes of
permanent vision loss in people aged over 60 years. Accurate segmentation of
biomarkers such as drusen that points to the early stages of AMD is crucial in
preventing further vision impairment. However, segmenting drusen is extremely
challenging due to their varied sizes and appearances, low contrast and noise
resemblance. Most existing literature, therefore, have focused on size
estimation of drusen using classification, leaving the challenge of accurate
segmentation less tackled. Additionally, obtaining the pixel-wise annotations
is extremely costly and such labels can often be noisy, suffering from
inter-observer and intra-observer variability. Quantification of uncertainty
associated with segmentation tasks offers principled measures to inspect the
segmentation output. Realizing its utility in identifying erroneous
segmentation and the potential applications in clinical decision making, here
we develop a U-Net based drusen segmentation model and quantify the
segmentation uncertainty. We investigate epistemic and aleatoric uncertainty
capturing model confidence and data uncertainty respectively. We present
segmentation results and show how uncertainty can help formulate robust
evaluation strategies. We visually inspect the pixel-wise uncertainty and
segmentation results on test images. We finally analyze the correlation between
segmentation uncertainty and accuracy. Our results demonstrate the utility of
leveraging uncertainties in developing and explaining segmentation models for
medical image analysis.


Abstract:
  The dipole strength of the nuclide $^{76}$Ge was studied in photon-scattering
experiments using bremsstrahlung produced with electron beams of energies of
7.8 and 12.3 MeV at the $\gamma$ELBE facility. We identified 210 levels up to
an excitation energy of 9.4 MeV and assigned spin $J$ = 1 to most of them. The
quasicontinuum of unresolved transitions was included in the analysis of the
spectra and the intensities of branching transitions were estimated on the
basis of simulations of statistical $\gamma$-ray cascades. The photoabsorption
cross section up to the neutron-separation energy was determined and is
compared with predictions of the statistical reaction model. The derived photon
strength function is compared with results of experiments using other
reactions.


Abstract:
  We study rotation programs within the standard implementation frame-work
under complete information. A rotation program is a myopic stableset whose
states are arranged circularly, and agents can effectively moveonly between two
consecutive states. We provide characterizing conditionsfor the implementation
of efficient rules in rotation programs. Moreover,we show that the conditions
fully characterize the class of implementablemulti-valued and efficient rules


Abstract:
  Visual steel surface defect detection is an essential step in steel sheet
manufacturing. Several machine learning-based automated visual inspection (AVI)
methods have been studied in recent years. However, most steel manufacturing
industries still use manual visual inspection due to training time and
inaccuracies involved with AVI methods. Automatic steel defect detection
methods could be useful in less expensive and faster quality control and
feedback. But preparing the annotated training data for segmentation and
classification could be a costly process. In this work, we propose to use the
Transfer Learning-based U-Net (TLU-Net) framework for steel surface defect
detection. We use a U-Net architecture as the base and explore two kinds of
encoders: ResNet and DenseNet. We compare these nets' performance using random
initialization and the pre-trained networks trained using the ImageNet data
set. The experiments are performed using Severstal data. The results
demonstrate that the transfer learning performs 5% (absolute) better than that
of the random initialization in defect classification. We found that the
transfer learning performs 26% (relative) better than that of the random
initialization in defect segmentation. We also found the gain of transfer
learning increases as the training data decreases, and the convergence rate
with transfer learning is better than that of the random initialization.


Abstract:
  Anomalies or failures in large computer systems, such as the cloud, have an
impact on a large number of users that communicate, compute, and store
information. Therefore, timely and accurate anomaly detection is necessary for
reliability, security, safe operation, and mitigation of losses in these
increasingly important systems. Recently, the evolution of the software
industry opens up several problems that need to be tackled including (1)
addressing the software evolution due software upgrades, and (2) solving the
cold-start problem, where data from the system of interest is not available. In
this paper, we propose a framework for anomaly detection in log data, as a
major troubleshooting source of system information. To that end, we utilize
pre-trained general-purpose language models to preserve the semantics of log
messages and map them into log vector embeddings. The key idea is that these
representations for the logs are robust and less invariant to changes in the
logs, and therefore, result in a better generalization of the anomaly detection
models. We perform several experiments on a cloud dataset evaluating different
language models for obtaining numerical log representations such as BERT,
GPT-2, and XL. The robustness is evaluated by gradually altering log messages,
to simulate a change in semantics. Our results show that the proposed approach
achieves high performance and robustness, which opens up possibilities for
future research in this direction.


Abstract:
  The categorical modeling of Petri nets has received much attention recently.
The Dialectica construction has also had its fair share of attention. We
revisit the use of the Dialectica construction as a categorical model for Petri
nets generalizing the original application to suggest that Petri nets with
different kinds of transitions can be modeled in the same categorical
framework. Transitions representing truth-values, probabilities, rates or
multiplicities, evaluated in different algebraic structures called lineales are
useful and are modeled here in the same category. We investigate (categorical
instances of) this generalized model and its connections to more recent models
of categorical nets.


Abstract:
  Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined
beamforming codebooks for both initial access and data transmission. Being
pre-defined, however, these codebooks are commonly not optimized for specific
environments, user distributions, and/or possible hardware impairments. This
leads to large codebook sizes with high beam training overhead which increases
the initial access/tracking latency and makes it hard for these systems to
support highly mobile applications. To overcome these limitations, this paper
develops a deep reinforcement learning framework that learns how to iteratively
optimize the codebook beam patterns (shapes) relying only on the receive power
measurements and without requiring any explicit channel knowledge. The
developed model learns how to autonomously adapt the beam patterns to best
match the surrounding environment, user distribution, hardware impairments, and
array geometry. Further, this approach does not require any knowledge about the
channel, array geometry, RF hardware, or user positions. To reduce the learning
time, the proposed model designs a novel Wolpertinger-variant architecture that
is capable of efficiently searching for an optimal policy in a large discrete
action space, which is important for large antenna arrays with quantized phase
shifters. This complex-valued neural network architecture design respects the
practical RF hardware constraints such as the constant-modulus and quantized
phase shifter constraints. Simulation results based on the publicly available
DeepMIMO dataset confirm the ability of the developed framework to learn
near-optimal beam patterns for both line-of-sight (LOS) and non-LOS scenarios
and for arrays with hardware impairments without requiring any channel
knowledge.


Abstract:
  In this paper, we propose a new numerical scheme for the coupled Stokes-Darcy
model with Beavers-Joseph-Saffman interface condition. We use the weak Galerkin
method to discretize the Stokes equation and the mixed finite element method to
the Darcy equation. A discrete inf-sup condition is proved and optimal error
estimates are also derived. Numerical experiments validate the theoretical
analysis.


Abstract:
  Deep learning models have become increasingly useful in many different
industries. On the domain of image classification, convolutional neural
networks proved the ability to learn robust features for the closed set
problem, as shown in many different datasets, such as MNIST FASHIONMNIST,
CIFAR10, CIFAR100, and IMAGENET. These approaches use deep neural networks with
dense layers with softmax activation functions in order to learn features that
can separate classes in a latent space. However, this traditional approach is
not useful for identifying classes unseen on the training set, known as the
open set problem. A similar problem occurs in scenarios involving learning on
small data. To tackle both problems, few-shot learning has been proposed. In
particular, metric learning learns features that obey constraints of a metric
distance in the latent space in order to perform classification. However, while
this approach proves to be useful for the open set problem, current
implementation requires pair-wise training, where both positive and negative
examples of similar images are presented during the training phase, which
limits the applicability of these approaches in large data or large class
scenarios given the combinatorial nature of the possible inputs.In this paper,
we present a constraint-based approach applied to the representations in the
latent space under the normalized softmax loss, proposed by[18]. We
experimentally validate the proposed approach for the classification of unseen
classes on different datasets using both metric learning and the normalized
softmax loss, on disjoint and joint scenarios. Our results show that not only
our proposed strategy can be efficiently trained on larger set of classes, as
it does not require pairwise learning, but also present better classification
results than the metric learning strategies surpassing its accuracy by a
significant margin.


Abstract:
  We address the problem of sparsity-promoting optimal control of
cyber-physical systems (CPSs) in the presence of communication delays. The
delays are categorized into two types - namely, an inter-layer delay for
passing state and control information between the physical layer and the cyber
layer, and an intra-layer delay that operates between the computing agents,
referred to here as control nodes (CNs), within the cyber-layer. Our objective
is to minimize the closed-loop H2-norm of the physical system by co-designing
an optimal combination of these two delays and a sparse state-feedback
controller while respecting a given bandwidth cost constraint. We propose a
two-loop optimization algorithm for this. Based on the alternating directions
method of multipliers (ADMM), the inner loop handles the conflicting directions
between the decreasing H2-norm and the increasing sparsity level of the
controller. The outer loop comprises a semidefinite program (SDP)-based
relaxation of non-convex inequalities necessary for closed-loop stability.
Moreover, for CPSs where the state and control information assigned to the CNs
are not private, we derive an additional algorithm that further sparsifies the
communication topology by modifying the row and column structures of the
obtained controller, resulting in reassigning the communication map between the
cyber and physical layers, and determining which physical agent should send its
state information to which CN. Proofs for closed-loop stability and optimality
are provided for both algorithms, followed by numerical simulations.


Abstract:
  People spend an enormous amount of time and effort looking for lost objects.
To help remind people of the location of lost objects, various computational
systems that provide information on their locations have been developed.
However, prior systems for assisting people in finding objects require users to
register the target objects in advance. This requirement imposes a cumbersome
burden on the users, and the system cannot help remind them of unexpectedly
lost objects. We propose GO-Finder ("Generic Object Finder"), a
registration-free wearable camera based system for assisting people in finding
an arbitrary number of objects based on two key features: automatic discovery
of hand-held objects and image-based candidate selection. Given a video taken
from a wearable camera, Go-Finder automatically detects and groups hand-held
objects to form a visual timeline of the objects. Users can retrieve the last
appearance of the object by browsing the timeline through a smartphone app. We
conducted a user study to investigate how users benefit from using GO-Finder
and confirmed improved accuracy and reduced mental load regarding the object
search task by providing clear visual cues on object locations.


Abstract:
  We present a simple but effective approach for leveraging Wikipedia for
neural machine translation as well as cross-lingual tasks of image captioning
and dependency parsing without using any direct supervision from external
parallel data or supervised models in the target language. We show that first
sentences and titles of linked Wikipedia pages, as well as cross-lingual image
captions, are strong signals for a seed parallel data to extract bilingual
dictionaries and cross-lingual word embeddings for mining parallel text from
Wikipedia. Our final model achieves high BLEU scores that are close to or
sometimes higher than strong supervised baselines in low-resource languages;
e.g. supervised BLEU of 4.0 versus 12.1 from our model in English-to-Kazakh.
Moreover, we tailor our wikily supervised translation models to unsupervised
image captioning, and cross-lingual dependency parser transfer. In image
captioning, we train a multi-tasking machine translation and image captioning
pipeline for Arabic and English from which the Arabic training data is a
translated version of the English captioning data, using our wikily-supervised
translation models. Our captioning results on Arabic are slightly better than
that of its supervised model. In dependency parsing, we translate a large
amount of monolingual text, and use it as artificial training data in an
annotation projection framework. We show that our model outperforms recent work
on cross-lingual transfer of dependency parsers.


Abstract:
  With the pandemic of COVID-19, relevant fake news is spreading all over the
sky throughout the social media. Believing in them without discrimination can
cause great trouble to people's life. However, universal language models may
perform weakly in these fake news detection for lack of large-scale annotated
data and sufficient semantic understanding of domain-specific knowledge. While
the model trained on corresponding corpora is also mediocre for insufficient
learning. In this paper, we propose a novel transformer-based language model
fine-tuning approach for these fake news detection. First, the token vocabulary
of individual model is expanded for the actual semantics of professional
phrases. Second, we adapt the heated-up softmax loss to distinguish the
hard-mining samples, which are common for fake news because of the
disambiguation of short text. Then, we involve adversarial training to improve
the model's robustness. Last, the predicted features extracted by universal
language model RoBERTa and domain-specific model CT-BERT are fused by one
multiple layer perception to integrate fine-grained and high-level specific
representations. Quantitative experimental results evaluated on existing
COVID-19 fake news dataset show its superior performances compared to the
state-of-the-art methods among various evaluation metrics. Furthermore, the
best weighted average F1 score achieves 99.02%.


Abstract:
  In this paper, we study the wave propagation in a metallic parallel-plate
structure with glide-symmetric elliptical holes. To perform this study, we
derived a mode-matching technique based on the generalized Floquet theorem for
glide-symmetric structures. This mode-matching technique benefits from a lower
computational cost since it takes advantage of the glide symmetry in the
structure. It also provides physical insight on the specific properties of
Floquet modes propagating in these specific structures. With our analysis, we
demonstrate that glide-symmetric structures with periodic elliptical holes
exhibit an anisotropic refractive index over a wide range of frequencies. The
equivalent refractive index can be controlled by tuning the dimensions of the
holes. Finally, by combining the anisotropy related to the elliptical holes and
transformation optics, a Maxwell fish-eye lens with a 33.33% size compression
is designed. This lens operates in a wideband frequency range from 2.5 GHz to
10 GHz.


Abstract:
  Context: As Industrial Cyber-Physical Systems (ICPS) become more connected
and widely-distributed, often operating in safety-critical environments, we
require innovative approaches to detect and diagnose the faults that occur in
them. Objective: We profile fault identification and diagnosis techniques
employed in the aerospace, automotive, and industrial control domains. By
examining both theoretical presentations as well as case studies from
production environments, we present a profile of the current approaches being
employed and identify gaps. Methodology: A scoping study was used to identify
and compare fault detection and diagnosis methodologies that are presented in
the current literature. Results: Fault identification and analysis studies from
127 papers published from 2004 to 2019 reveal a wide diversity of promising
techniques, both emerging and in-use. These range from traditional
Physics-based Models to Data-Driven Artificial Intelligence (AI) and
Knowledge-Based approaches. Predictive diagnostics or prognostics featured
prominently across all sectors, along with discussions of techniques including
Fault trees, Petri nets and Markov approaches. We also profile some of the
techniques that have reached the highest Technology Readiness Levels, showing
how those methods are being applied in real-world environments beyond the
laboratory. Conclusions: Our results suggest that the continuing wide use of
both Model-Based and Data-Driven AI techniques across all domains, especially
when they are used together in hybrid configuration, reflects the complexity of
the current ICPS application space. While creating sufficiently-complete models
is labor intensive, Model-free AI techniques were evidenced as a viable way of
addressing aspects of this challenge, demonstrating the increasing
sophistication of current machine learning systems.(Abridged)


Abstract:
  Recent cosmic-ray measurements are challenging our models of propagation in
the Galaxy. A good characterization of the secondary cosmic rays (B, Be, Li and
sub-iron species) is crucial to constrain these models and exploit the
precision of modern CR experiments. In this work, a Markov chain Monte Carlo
analysis has been implemented to fit the experimental flux ratios between B, Be
and Li and their flux ratios to the primary nuclei C and O. We have fitted the
data using two different parametrizations for the spallation cross sections.
The uncertainties in the evaluation of the spectra of these secondary cosmic
rays, due to spallation cross sections, have been considered by introducing
scale factors as nuisance parameters. We have also tested two different
formulations for the diffusion coefficient, which differ in the origin of the
high energy hardening of cosmic rays. Additionally, two different approaches
are used to scale the cross sections, one based on a combined analysis of all
the species ("combined" analysis) and the other reproducing the high energy
spectra of the secondary-to-secondary flux ratios of Be/B, Li/B, Li/Be
("scaled" analysis). This allows us to make a better comparison between the
propagation parameters inferred from the cross sections parametrizations tested
in this work. This novel analysis has been successfully implemented using the
numerical code DRAGON2 to reproduce the cosmic-ray nuclei data up to $Z=14$
from the AMS-02 experiment. It is found that the ratios of Li favor a harder
spectral index of the diffusion coefficient, but compatible with the other
ratios inside the observed $2\sigma$ uncertainties. In addition, it is shown
that, including these scale factors, the secondary-to-primary flux ratios can
be simultaneously reproduced.


Abstract:
  Long secondary periods (LSPs), observed in a third of pulsating red giant
stars, are the only unexplained type of large-amplitude stellar variability
known at this time. Here we show that this phenomenon is a manifestation of a
substellar or stellar companion orbiting the red giant star. Our investigation
is based on a sample of about 16,000 well-defined LSP variables detected in the
long-term OGLE photometric database of the Milky Way and Magellanic Clouds,
combined with the mid-infrared data extracted from the NEOWISE-R archive. From
this collection, we selected about 700 objects with stable, large-amplitude,
well-sampled infrared light curves and found that about half of them exhibit
secondary eclipses, thus presenting an important piece of evidence that the
physical mechanism responsible for LSPs is binarity. Namely, the LSP light
changes are due to the presence of a dusty cloud orbiting the red giant
together with the companion and obscuring the star once per orbit. The
secondary eclipses, visible only in the infrared wavelength, occur when the
cloud is hidden behind the giant. In this scenario, the low-mass companion is a
former planet that has accreted a significant amount of mass from the envelope
of its host star and grown into a brown dwarf.


Abstract:
  We analyze the Hawking evaporation process of Reissner-Nordstr\"om black
holes. It is shown that the characteristic radiation quanta emitted by the
charged black holes may turn near-extremal black-hole spacetimes into
horizonless naked singularities. The present analysis therefore reveals the
intriguing possibility that the semi-classical Hawking evaporation process of
black holes may violate the fundamental Penrose cosmic censorship conjecture.


Abstract:
  Wide dynamic range (WDR) images contain more scene details and contrast when
compared to common images. However, it requires tone mapping to process the
pixel values in order to display properly. The details of WDR images can
diminish during the tone mapping process. In this work, we address the problem
by combining a novel reformulated Laplacian pyramid and deep learning. The
reformulated Laplacian pyramid always decompose a WDR image into two frequency
bands where the low-frequency band is global feature-oriented, and the
high-frequency band is local feature-oriented. The reformulation preserves the
local features in its original resolution and condenses the global features
into a low-resolution image. The generated frequency bands are reconstructed
and fine-tuned to output the final tone mapped image that can display on the
screen with minimum detail and contrast loss. The experimental results
demonstrate that the proposed method outperforms state-of-the-art WDR image
tone mapping methods. The code is made publicly available at
https://github.com/linmc86/Deep-Reformulated-Laplacian-Tone-Mapping.


Abstract:
  This paper concerns the modeling and numerical simulation of the process of
speciation. In particular, given conditions for which one or more speciation
events within an ecosystem occur, our aim is to develop the necessary modeling
and simulation tools. Care is also taken to establish a solid mathematical
foundation on which our modeling framework is built. This is the subject of the
first half of the paper. The second half is devoted to developing a multi-scale
framework for eco-evolutionary modeling, where the relevant scales are that of
species and individual/population, respectively. Hence, a system of interacting
species can be described at the species level, while for branching species a
population level description is necessary. Our multi-scale framework thus
consists of coupling the species and population level models where speciation
events are detected in advance and then resolved at the population scale until
the branching is complete. Moreover, since the population level model is
formulated as a PDE, we first establish the well-posedness in the time-discrete
setting, and then derive the a posteriori error estimates which provides a
fully computable upper bound on an energy-type error, including also for the
case of general smooth distributions (which will be useful for the detection of
speciation events). Several numerical tests validate our framework in practice.


Abstract:
  Visual defect detection (VDD) for high-mix low-volume production of
non-convex metal objects, such as high-pressure cylindrical piping joint parts
(VDD-HPPPs), is challenging because subtle difference in domain (e.g., metal
objects, imaging device, viewpoints, lighting) significantly affects the
specular reflection characteristics of individual metal object types. In this
paper, we address this issue by introducing a tailor-made VDD framework that
can be automatically adapted to a new domain. Specifically, we formulate this
adaptation task as the problem of network architecture search (NAS) on a deep
object-detection network, in which the network architecture is searched via
reinforcement learning. We demonstrate the effectiveness of the proposed
framework using the VDD-HPPPs task as a factory case study. Experimental
results show that the proposed method achieved higher burr detection accuracy
compared with the baseline method for data with different training/test domains
for the non-convex HPPPs, which are particularly affected by domain shifts.


Abstract:
  We present a novel method for efficient acquisition of shape and spatially
varying reflectance of 3D objects using polarization cues. Unlike previous
works that have exploited polarization to estimate material or object
appearance under certain constraints (known shape or multiview acquisition), we
lift such restrictions by coupling polarization imaging with deep learning to
achieve high quality estimate of 3D object shape (surface normals and depth)
and SVBRDF using single-view polarization imaging under frontal flash
illumination. In addition to acquired polarization images, we provide our deep
network with strong novel cues related to shape and reflectance, in the form of
a normalized Stokes map and an estimate of diffuse color. We additionally
describe modifications to network architecture and training loss which provide
further qualitative improvements. We demonstrate our approach to achieve
superior results compared to recent works employing deep learning in
conjunction with flash illumination.


Abstract:
  Recent high-profile attacks on the Internet of Things (IoT) have brought to
the forefront the vulnerability of "smart" devices, and have resulted in
numerous IoT-focused security analyses. Many of the attacks had weak device
configuration as the root cause. One potential source of rich and definitive
information about the configuration of an IoT device is the device's firmware.
However, firmware analysis is complex and automated firmware analyses have thus
far been confined to devices with more traditional operating systems such as
Linux or VxWorks. Most IoT peripherals, due to lacking traditional operating
systems and implementing a wide variety of communication technologies, have
only been the subject of smaller-scale analyses. Peripheral firmware analysis
is further complicated by the fact that such firmware files are predominantly
available as stripped binaries, without the ELF headers and symbol tables that
would simplify reverse engineering.
  In this paper, we present argXtract, an open-source automated static analysis
tool, which extracts security-relevant configuration information from stripped
IoT peripheral firmware. Specifically, we focus on binaries that target the ARM
Cortex-M architecture, due to its growing popularity among IoT peripherals.
argXtract overcomes the challenges associated with stripped Cortex-M analysis
and is able to retrieve arguments to security-relevant supervisor and function
calls, enabling automated bulk analysis of firmware files. We demonstrate this
via three real-world case studies. The largest case study covers a dataset of
243 Bluetooth Low Energy binaries targeting Nordic Semiconductor chipsets,
while the other two focus on Nordic ANT and STMicroelectronics BlueNRG
binaries. The results reveal widespread lack of security and privacy controls
in IoT, such as minimal or no protection for data, fixed passkeys and trackable
device addresses.


Abstract:
  We prove the decay in the energy space for the solution to the defocusing
biharmonic Hartree-Fock equations with mass-supercritical and
energy-subcritical Choquard-type nonlinearity in space dimension $d\geq3$. We
treat both the free and the perturbed by a potential case. As a direct
consequence, we obtain large-data scattering in $H^2(\mathbb R^d)^N, \, N\geq
1$.


Abstract:
  The BBM equation is a Hamiltonian PDE which revealed to be a very interesting
test-model to study the transformation property of Gaussian measures along the
flow. In this paper we study the BBM equation with critical dispersion (which
is a Benjamin-Ono type model). We prove that the image of the Gaussian measures
supported on fractional Sobolev spaces of increasing regularity are absolutely
continuous, but we cannot identify the density, for which new ideas are needed.