Abstract:
  Ammonia (NH3) is commonly used as group V precursor in gallium nitride (GaN)
metalorganic chemical vapor deposition (MOCVD). The high background carbon (C)
impurity in MOCVD GaN is related to the low pyrolysis efficiency of NH3, which
represents one of the fundamental challenges hindering the development of high
purity thick GaN for vertical high power device applications. This work uses a
laser-assisted MOCVD (LA-MOCVD) growth technique to address the high-C issue in
MOCVD GaN. Carbon dioxide (CO2) laser with wavelength of 9.219 um was utilized
to facilitate NH3 decomposition via resonant vibrational excitation. The
LA-MOCVD GaN growth rate (as high as 10 um/hr) shows a strong linear
relationship with the trimethylgallium (TMGa) flow rate, indicating high
effective V/III ratios and hence efficient NH3 decomposition. Pits-free surface
morphology of LA-MOCVD GaN was demonstrated for films with growth rate as high
as 8.5 um/hr. The background [C] in LA-MOCVD GaN films decreases monotonically
as the laser power increases. A low [C] at 5.5E15 cm-3 was achieved in LA-MOCVD
GaN film grown with the growth rate of 4 um/hr. Charge transport
characterization of LA-MOCVD GaN films reveals high crystalline quality with
room temperature mobility >1000 cm2/Vs. LA-MOCVD growth technique provides an
enabling route to achieve high quality GaN epitaxy with low-C impurity and fast
growth rate simultaneously. This technique can also be extended for epitaxy of
other nitride-based semiconductors.


Abstract:
  In recent years, compressed sensing (CS) based image coding has become a hot
topic in image processing field. However, since the bit depth required for
encoding each CS sample is too large, the compression performance of this
paradigm is unattractive. To address this issue, a novel CS-based image coding
system by using gray transformation is proposed. In the proposed system, we use
a gray transformation to preprocess the original image firstly and then use CS
to sample the transformed image. Since gray transformation makes the
probability distribution of CS samples centralized, the bit depth required for
encoding each CS sample is reduced significantly. Consequently, the proposed
system can considerably improve the compression performance of CS-based image
coding. Simulation results show that the proposed system outperforms the
traditional one without using gray transformation in terms of compression
performance.


Abstract:
  Human teams exhibit both implicit and explicit intention sharing. To further
development of human-robot collaboration, intention recognition is crucial on
both sides. Present approaches rely on a vast sensor suite on and around the
robot to achieve intention recognition. This relegates intuitive human-robot
collaboration purely to such bulky systems, which are inadequate for
large-scale, real-world scenarios due to their complexity and cost. In this
paper we propose an intention recognition system that is based purely on a
portable head-mounted display. In addition robot intention visualisation is
also supported. We present experiments to show the quality of our human goal
estimation component and some basic interactions with an industrial robot. HAIR
should raise the quality of interaction between robots and humans, instead of
such interactions raising the hair on the necks of the human coworkers.


Abstract:
  There has been a recent surge in adversarial attacks on deep learning based
automatic speech recognition (ASR) systems. These attacks pose new challenges
to deep learning security and have raised significant concerns in deploying ASR
systems in safety-critical applications. In this work, we introduce WaveGuard:
a framework for detecting adversarial inputs that are crafted to attack ASR
systems. Our framework incorporates audio transformation functions and analyses
the ASR transcriptions of the original and transformed audio to detect
adversarial inputs. We demonstrate that our defense framework is able to
reliably detect adversarial examples constructed by four recent audio
adversarial attacks, with a variety of audio transformation functions. With
careful regard for best practices in defense evaluations, we analyze our
proposed defense and its strength to withstand adaptive and robust attacks in
the audio domain. We empirically demonstrate that audio transformations that
recover audio from perceptually informed representations can lead to a strong
defense that is robust against an adaptive adversary even in a complete
white-box setting. Furthermore, WaveGuard can be used out-of-the box and
integrated directly with any ASR model to efficiently detect audio adversarial
examples, without the need for model retraining.


Abstract:
  In this study we investigate the nuclear quantum effects (NQEs) on the
acidity constant (pKA) of liquid water isotopologues at the ambient condition
by path integral molecular dynamics (PIMD) simulations. We compared simulations
using a fully explicit solvent model with a classical polarizable force field,
density functional tight binding, and ab initio density functional theory,
which correspond to empirical, semiempirical, and ab initio PIMD simulations,
respectively. The centroid variable with respect to the proton coordination
number of a water molecule was restrained to compute the gradient of the free
energy, which measures the reversible work of the proton abstraction for the
quantum mechanical system. The free energy curve obtained by thermodynamic
integration was used to compute the pKA value based on probabilistic
determination. This technique not only reproduces the pKA value of liquid D2O
experimentally measured (14.86) but also allows for a theoretical prediction of
the pKA values of liquid T2O, aqueous HDO and HTO which are unknown due to its
scarcity. It is also shown that the NQEs on the free energy curve can result in
a downshift of 4.5 +/- 0.9 pKA units in the case of liquid water, which
indicates that the NQEs plays an indispensable role in the absolute
determination of pKA. The results of this study can help to inform further
extensions into the calculation of the acidity constants of isotope substituted
species with high accuracy.


Abstract:
  The FitzHugh-Rinzel system is able to describe some biophysical phenomena,
such as bursting oscillations, and the study of its solutions can help to
better understand several behaviours of the complex dynamics of biological
systems. We express the solutions by means of an integral equation involving
the fundamental solution $ H(x,t) $ related to a non linear
integro-differential equation. Properties of $ H(x,t) $ allow us to obtain a
priori estimates for solutions determined in the whole space, showing both the
influence of the initial data and the source term.


Abstract:
  We study the position distribution $P(\vec{R},N)$ of a run-and-tumble
particle (RTP) in arbitrary dimension $d$, after $N$ runs. We assume that the
constant speed $v>0$ of the particle during each running phase is independently
drawn from a probability distribution $W(v)$ and that the direction of the
particle is chosen isotropically after each tumbling. The position distribution
is clearly isotropic, $P(\vec{R},N)\to P(R,N)$ where $R=|\vec{R}|$. We show
that, under certain conditions on $d$ and $W(v)$ and for large $N$, a
condensation transition occurs at some critical value of $R=R_c\sim O(N)$
located in the large deviation regime of $P(R,N)$. For $R<R_c$ (subcritical
fluid phase), all runs are roughly of the same size in a typical trajectory. In
contrast, an RTP trajectory with $R>R_c$ is typically dominated by a
`condensate', i.e., a large single run that subsumes a finite fraction of the
total displacement (supercritical condensed phase). Focusing on the family of
speed distributions $W(v)=\alpha(1-v/v_0)^{\alpha-1}/v_0$, parametrized by
$\alpha>0$, we show that, for large $N$, $P(R,N)\sim
\exp\left[-N\psi_{d,\alpha}(R/N)\right]$ and we compute exactly the rate
function $\psi_{d,\alpha}(z)$ for any $d$ and $\alpha$. We show that the
transition manifests itself as a singularity of this rate function at $R=R_c$
and that its order depends continuously on $d$ and $\alpha$. We also compute
the distribution of the condensate size for $R>R_c$. Finally, we study the
model when the total duration $T$ of the RTP, instead of the total number of
runs, is fixed. Our analytical predictions are confirmed by numerical
simulations, performed using a constrained Markov chain Monte Carlo technique,
with precision $\sim 10^{-100}$.


Abstract:
  The fractional Brownian motion (fBm) extends the standard Brownian motion by
introducing some dependence between non-overlapping increments. Consequently,
if one considers for example that log-prices follow an fBm, one can exploit the
non-Markovian nature of the fBm to forecast future states of the process and
make statistical arbitrages. We provide new insights into forecasting an fBm,
by proposing theoretical formulas for accuracy metrics relevant to a systematic
trader, from the hit ratio to the expected gain and risk of a simple strategy.
In addition, we answer some key questions about optimizing trading strategies
in the fBm framework: Which lagged increments of the fBm, observed in discrete
time, are to be considered? If the predicted increment is close to zero, up to
which threshold is it more profitable not to invest? We also propose empirical
applications on high-frequency FX rates, as well as on realized volatility
series, exploring the rough volatility concept in a forecasting perspective.


Abstract:
  This paper provides a unified stochastic operator framework to analyze the
convergence of iterative optimization algorithms for both static problems and
online optimization and learning. In particular, the framework is well suited
for algorithms that are implemented in an inexact or stochastic fashion because
of (i) stochastic errors emerging in algorithmic steps, and because (ii) the
algorithm may feature random coordinate updates. To this end, the paper focuses
on separable operators of the form $T x = (T_1 x, \ldots, T_n x)$, defined over
the direct sum of possibly infinite-dimensional Hilbert spaces, and
investigates the convergence of the associated stochastic Banach-Picard
iteration. Results in terms of convergence in mean and in high-probability are
presented when the errors affecting the operator follow a sub-Weibull
distribution and when updates $T_i x$ are performed based on a Bernoulli random
variable. In particular, the results are derived for the cases where $T$ is
contractive and averaged in terms of convergence to the unique fixed point and
cumulative fixed-point residual, respectively. The results do not assume
vanishing errors or vanishing parameters of the operator, as typical in the
literature (this case is subsumed by the proposed framework), and links with
exiting results in terms of almost sure convergence are provided. In the online
optimization context, the operator changes at each iteration to reflect changes
in the underlying optimization problem. This leads to an online Banach-Picard
iteration, and similar results are derived where the bounds for the convergence
in mean and high-probability further depend on the evolution of fixed points
(i.e., optimal solutions of the time-varying optimization problem).


Abstract:
  We construct a Leray model for a discrete polymatroid with arbitrary building
set and we prove a generalized Goresky-MacPherson formula. The first row of the
model is the Chow ring of the polymatroid; we prove Poincar\'e duality, Hard
Lefschetz, and Hodge-Riemann theorems for the Chow ring. Furthermore we provide
a relative Lefschetz decomposition with respect to the deletion of an element.


Abstract:
  Confinement of atoms inside various cavities has been studied for nearly
eight decades. However, the Compton profile for such systems has not yet been
investigated. Here we construct the Compton profile (CP) for a H atom radially
confined inside a \emph{hard} spherical enclosure, as well as in \emph{free
condition}. Some exact analytical relations for the CP's of circular or
nodeless states of free atom is presented. By means of a scaling idea, this has
been further extended to the study of an H-like atom trapped inside an
impenetrable cavity. The accuracy of these constructed CP has been confirmed by
computing various momentum moments. Apart from that, several information
theoretical measures, like Shannon entropy ($S$) and Onicescu energy ($E$) have
been exploited to characterize these profiles. Exact closed form expressions
are derived for $S$ and $E$ using the ground state CP in free H-like atoms. A
detailed study reveals that, increase in confinement inhibits the rate of
dissipation of kinetic energy. At a fixed $\ell$, this rate diminishes with
rise in $n$. However, at a certain $n$, this rate accelerates with progress in
$\ell$. A similar analysis on the respective free counterpart displays an
exactly opposite trend as that in confined system. However, in both free and
confined environments, CP generally gets broadened with rise in $Z$.
Representative calculations are done numerically for low-lying states of the
confined systems, taking two forms of position-space wave functions: (a) exact
(b) highly accurate eigenfunctions through a generalized pseudospectral method.
In essence, CPs are reported for confined H atom (and isoelectronic series) and
investigated adopting an information-theoretic framework.


Abstract:
  Capturing high-dimensional (HD) data is a long-term challenge in signal
processing and related fields. Snapshot compressive imaging (SCI) uses a
two-dimensional (2D) detector to capture HD ($\ge3$D) data in a {\em snapshot}
measurement. Via novel optical designs, the 2D detector samples the HD data in
a {\em compressive} manner; following this, algorithms are employed to
reconstruct the desired HD data-cube. SCI has been used in hyperspectral
imaging, video, holography, tomography, focal depth imaging, polarization
imaging, microscopy, \etc.~Though the hardware has been investigated for more
than a decade, the theoretical guarantees have only recently been derived.
Inspired by deep learning, various deep neural networks have also been
developed to reconstruct the HD data-cube in spectral SCI and video SCI. This
article reviews recent advances in SCI hardware, theory and algorithms,
including both optimization-based and deep-learning-based algorithms. Diverse
applications and the outlook of SCI are also discussed.


Abstract:
  Traditional breast cancer image classification methods require manual
extraction of features from medical images, which not only require professional
medical knowledge, but also have problems such as time-consuming and
labor-intensive and difficulty in extracting high-quality features. Therefore,
the paper proposes a computer-based feature fusion Convolutional neural network
breast cancer image classification and detection method. The paper pre-trains
two convolutional neural networks with different structures, and then uses the
convolutional neural network to automatically extract the characteristics of
features, fuse the features extracted from the two structures, and finally use
the classifier classifies the fused features. The experimental results show
that the accuracy of this method in the classification of breast cancer image
data sets is 89%, and the classification accuracy of breast cancer images is
significantly improved compared with traditional methods.


Abstract:
  Thin films are gaining ground in photonics and optoelectronics, promising
improvements in their efficiency and functionality as well as decreased
material usage as compared to bulk technologies. However, proliferation of thin
films would benefit not only from continuous improvements in their fabrication,
but also from a unified and accurate theoretical framework of the interplay of
photons and charge carriers. In particular, such a framework would need to
account quantitatively and self-consistently for photon recycling and
interference effects. To this end, here we combine the drift-diffusion
formalism of charge carrier dynamics and the fluctuational electrodynamics of
photon transport self-consistently using the recently introduced
interference-extended radiative transfer equations. The resulting equation
system can be solved numerically using standard simulation tools, and as an
example, here we apply it to study well-known GaAs thin-film solar cells. In
addition to obtaining the expected device characteristics, we analyze the
underlying complex photon transport and recombination-generation processes,
demonstrating the physical insight provided for unevenly excited structures
through the direct and self-consistent description of photons and charge
carriers. The methodology proposed in this work is general and can be used to
obtain accurate physical insight into a wide range of planar optoelectronic
devices, of which the thin-film single-junction solar cells studied here are
only one example.


Abstract:
  The notions of synchronizing and partitioning sets are recently introduced
variants of locally consistent parsings with great potential in
problem-solving. In this paper we propose a deterministic algorithm that
constructs for a given readonly string of length $n$ over the alphabet
$\{0,1,\ldots,n^{\mathcal{O}(1)}\}$ a variant of $\tau$-partitioning set with
size $\mathcal{O}(b)$ and $\tau = \frac{n}{b}$ using $\mathcal{O}(b)$ space and
$\mathcal{O}(\frac{1}{\epsilon}n)$ time provided $b \ge n^\epsilon$, for
$\epsilon > 0$. As a corollary, for $b \ge n^\epsilon$ and constant $\epsilon >
0$, we obtain linear construction algorithms with $\mathcal{O}(b)$ space on top
of the string for two major small-space indexes: a sparse suffix tree, which is
a compacted trie built on $b$ chosen suffixes of the string, and a longest
common extension (LCE) index, which occupies $\mathcal{O}(b)$ space and allows
us to compute the longest common prefix for any pair of substrings in
$\mathcal{O}(n/b)$ time. For both, the $\mathcal{O}(b)$ construction storage is
asymptotically optimal since the tree itself takes $\mathcal{O}(b)$ space and
any LCE index with $\mathcal{O}(n/b)$ query time must occupy at least
$\mathcal{O}(b)$ space by a known trade-off (at least for $b \ge \Omega(n /
\log n)$). In case of arbitrary $b \ge \Omega(\log^2 n)$, we present
construction algorithms for the partitioning set, sparse suffix tree, and LCE
index with $\mathcal{O}(n\log_b n)$ running time and $\mathcal{O}(b)$ space,
thus also improving the state of the art.


Abstract:
  We obtain entropy formulas for SRB measures with finite entropy given by
inducing schemes. In the first part of the work, we obtain Pesin entropy
formula for the class of noninvertible systems whose SRB measures are given by
Gibbs-Markov induced maps. In the second part, we obtain Pesin entropy formula
for invertible maps whose SRB measures given by Young sets, taking into account
a classical compression technique along the stable direction that allows a
reduction of the return map associated with a Young set to a Gibbs-Markov map.
In both cases, we give applications of our main results to several classes of
dynamical systems with singular sets, where the classical results by Ruelle and
Pesin cannot be applied. We also present examples of systems with SRB measures
given by inducing schemes for which Ruelle inequality does not hold.


Abstract:
  Technical Debt is a metaphor used to describe the situation in which
long-term software artifact quality is traded for short-term goals in software
projects. In recent years, the concept of self-admitted technical debt (SATD)
was proposed, which focuses on debt that is intentionally introduced and
described by developers. Although prior work has made important observations
about admitted technical debt in source code, little is known about SATD in
build systems. In this paper, we set out to better understand the
characteristics of SATD in build systems. To do so, through a qualitative
analysis of 500 SATD comments in the Maven build system of 291 projects, we
characterize SATD by location and rationale (reason and purpose). Our results
show that limitations in tools and libraries, and complexities of dependency
management are the most frequent causes, accounting for 50% and 24% of the
comments. We also find that developers often document SATD as issues to be
fixed later. As a first step towards the automatic detection of SATD rationale,
we train classifiers to detect the two most frequently occurring reasons and
the four most frequently occurring purposes of SATD in the content of comments
in Maven build systems. The classifier performance is promising, achieving an
F1-score of 0.71-0.79. Finally, within 16 identified 'ready-to-be-addressed'
SATD instances, the three SATD submitted by pull requests and the five SATD
submitted by issue reports were resolved after developers were made aware. Our
work presents the first step towards understanding technical debt in build
systems and opens up avenues for future work, such as tool support to track and
manage SATD backlogs.


Abstract:
  Currently, practitioners follow a top-down approach in automotive development
projects. However, recent studies have shown that this top-down approach is not
suitable for the implementation and testing of modern automotive systems.
Specifically, practitioners increasingly fail to specify requirements and tests
for systems with complex component interactions (e.g., e-mobility systems). In
this paper, we address this research gap and propose an integrated and
iterative scenario-based technique for the specification of requirements and
test scenarios. Our idea is to combine both a top-down and a bottom-up
integration strategy. For the top-down approach, we use a behavior-driven
development (BDD) technique to drive the modeling of high-level system
interactions from the user's perspective. For the bottom-up approach, we
discovered that natural language processing (NLP) techniques are suited to make
textual specifications of existing components accessible to our technique. To
integrate both directions, we support the joint execution and automated
analysis of system-level interactions and component-level behavior. We
demonstrate the feasibility of our approach by conducting a case study at
Kostal (Tier1 supplier). The case study corroborates, among other things, that
our approach supports practitioners in improving requirements and test
specifications for integrated system behavior.


Abstract:
  The generalized Langevin equation is a model for the motion of coarse-grained
particles where dissipative forces are represented by a memory term. The
numerical realization of such a model requires the implementation of a
stochastic delay-differential equation and the estimation of a corresponding
memory kernel. Here we develop a new approach for computing a data-driven
Markov model for the motion of the particles, given equidistant samples of
their velocity autocorrelation function. Our method bypasses the determination
of the underlying memory kernel by representing it via up to about twenty
auxiliary variables. The algorithm is based on a sophisticated variant of the
Prony method for exponential interpolation and employs the Positive Real Lemma
from model reduction theory to extract the associated Markov model. We
demonstrate the potential of this approach for the test case of anomalous
diffusion, where data are given analytically, and then apply our method to
velocity autocorrelation data of molecular dynamics simulations of a colloid in
a Lennard-Jones fluid. In both cases, the VACF and the memory kernel can be
reproduced very accurately. Moreover, we show that the algorithm can also
handle input data with large statistical noise. We anticipate that it will be a
very useful tool in future studies that involve dynamic coarse-graining of
complex soft matter systems.


Abstract:
  In this paper, we investigate the contributions of anomalous quartic gauge
couplings (aQGCs) to $Z\gamma jj$ production at the Large Hadron Collider (LHC)
in the context of Standard Model effective theory (SMEFT). When energy scale is
large, the validity of SMEFT becomes an important issue. To ensure the
validity, the unitarity bound is applied in a model independent approach, which
is found to have significant suppressive effects on the signals of $O_{M_i}$
operators. The kinematic and polarization features of the aQGC signals are also
studied. The polarization effect is useful to highlight the signals of
$O_{T_i}$ operators. The sensitivity estimates on dimension-8 operators with
unitarity bounds at $\sqrt{s}=14$ TeV are obtained.


Abstract:
  Intelligent reflecting surface (IRS) is a promising technology for achieving
spectrum and energy efficient wireless networks cost-effectively. Most existing
works on IRS have focused on exploiting IRS to enhance the performance of
wireless communication or wireless information transmission (WIT), while its
potential for boosting the efficiency of radio-frequency (RF) wireless energy
transmission (WET) still remains largely open. Although IRS-aided WET shares
similar characteristics with IRS-aided WIT, they differ fundamentally in terms
of design objective, receiver architecture, and practical constraints. In this
paper, we provide a tutorial overview on how to efficiently design IRS-aided
WET systems as well as IRS-aided systems with both WIT and WET, namely
IRS-aided simultaneous wireless information and power transfer (SWIPT) and
IRS-aided wireless powered communication network (WPCN), mainly from a
communication and signal processing perspective. In particular, we present
state-of-the-art solutions to tackle the unique challenges in operating these
systems, such as IRS passive reflection optimization, channel estimation and
deployment. In addition, we also propose new solution approaches and point out
important directions for future research and investigation.


Abstract:
  By coloring a signed graph by signed colors, one obtains the signed chromatic
polynomial of the signed graph. For each signed graph we construct graded
cohomology groups whose graded Euler characteristic yields the signed chromatic
polynomial of the signed graph. We show that the cohomology groups satisfy a
long exact sequence which corresponds to signed deletion-contraction rule. This
work is motivated by Helme-Guizon and Rong's construction of the
categorification for the chromatic polynomial of unsigned graphs.


Abstract:
  Quantum circuit theory has become a powerful and indispensable tool to
predict the dynamics of superconducting circuits. Surprisingly however, the
question of how to properly account for a time-dependent driving via external
magnetic fields has hardly been addressed so far. Here, we derive a general
recipe to construct a low-energy Hamiltonian, taking as input only the circuit
geometry and the solution of the external magnetic fields. A gauge fixing
procedure for the scalar and vector potentials is given which assures that
time-varying magnetic fluxes make contributions only to the potential function
in the Schr\"odinger equation. Our proposed procedure is valid for continuum
geometries and thus significantly generalizes previous efforts, which were
based on discrete circuits. We study some implications of our results for the
concrete example of a parallel-plate SQUID circuit. We show that if we insist
on representing the response of this SQUID with individual, discrete
capacitances associated with each individual Josephson junction, this is only
possible if we permit the individual capacitance values to be negative,
time-dependent or even momentarily singular. Finally, we provide some
experimentally testable predictions, such as a strong enhancement of the qubit
relaxation rates arising from the effective negative capacitances, and the
emergence of a Berry phase due to time dependence of these capacitances.


Abstract:
  Action quality assessment (AQA) aims at automatically judging human action
based on a video of the said action and assigning a performance score to it.
The majority of works in the existing literature on AQA transform RGB videos to
higher-level representations using C3D networks. These higher-level
representations are used to perform action quality assessment. Due to the
relatively shallow nature of C3D, the quality of extracted features is lower
than what could be extracted using a deeper convolutional neural network. In
this paper, we experiment with deeper convolutional neural networks with
residual connections for learning representations for action quality
assessment. We assess the effects of the depth and the input clip size of the
convolutional neural network on the quality of action score predictions. We
also look at the effect of using (2+1)D convolutions instead of 3D convolutions
for feature extraction. We find that the current clip level feature
representation aggregation technique of averaging is insufficient to capture
the relative importance of features. To overcome this, we propose a
learning-based weighted-averaging technique that can perform better. We achieve
a new state-of-the-art Spearman's rank correlation of 0.9315 (an increase of
0.45%) on the MTL-AQA dataset using a 34 layer (2+1)D convolutional neural
network with the capability of processing 32 frame clips, using our proposed
aggregation technique.


Abstract:
  Most mobile health apps employ data visualization to help people view their
health and activity data, but these apps provide limited support for visual
data exploration. Furthermore, despite its huge potential benefits, mobile
visualization research in the personal data context is sparse. This work aims
to empower people to easily navigate and compare their personal health data on
smartphones by enabling flexible time manipulation with speech. We designed and
developed Data@Hand, a mobile app that leverages the synergy of two
complementary modalities: speech and touch. Through an exploratory study with
13 long-term Fitbit users, we examined how multimodal interaction helps
participants explore their own health data. Participants successfully adopted
multimodal interaction (i.e., speech and touch) for convenient and fluid data
exploration. Based on the quantitative and qualitative findings, we discuss
design implications and opportunities with multimodal interaction for better
supporting visual data exploration on mobile devices.


Abstract:
  Reinforcement learning is finding its way to real-world problem application,
transferring from simulated environments to physical setups. In this work, we
implement vision-based alignment of an optical Mach-Zehnder interferometer with
a confocal telescope in one arm, which controls the diameter and divergence of
the corresponding beam. We use a continuous action space; exponential scaling
enables us to handle actions within a range of over two orders of magnitude.
Our agent trains only in a simulated environment with domain randomizations. In
an experimental evaluation, the agent significantly outperforms an existing
solution and a human expert.


Abstract:
  This paper introduces the first open-source FPGA-based infrastructure,
MetaSys, with a prototype in a RISC-V core, to enable the rapid implementation
and evaluation of a wide range of cross-layer techniques in real hardware.
Hardware-software cooperative techniques are powerful approaches to improve the
performance, quality of service, and security of general-purpose processors.
They are however typically challenging to rapidly implement and evaluate in
real hardware as they require full-stack changes to the hardware, OS, system
software, and instruction-set architecture (ISA).
  MetaSys implements a rich hardware-software interface and lightweight
metadata support that can be used as a common basis to rapidly implement and
evaluate new cross-layer techniques. We demonstrate MetaSys's versatility and
ease-of-use by implementing and evaluating three cross-layer techniques for:
(i) prefetching for graph analytics; (ii) bounds checking in memory unsafe
languages, and (iii) return address protection in stack frames; each technique
only requiring ~100 lines of Chisel code over MetaSys.
  Using MetaSys, we perform the first detailed experimental study to quantify
the performance overheads of using a single metadata management system to
enable multiple cross-layer optimizations in CPUs. We identify the key sources
of bottlenecks and system inefficiency of a general metadata management system.
We design MetaSys to minimize these inefficiencies and provide increased
versatility compared to previously-proposed metadata systems. Using three use
cases and a detailed characterization, we demonstrate that a common metadata
management system can be used to efficiently support diverse cross-layer
techniques in CPUs.


Abstract:
  We propose a black-box reduction that turns a certain reinforcement learning
algorithm with optimal regret in a (near-)stationary environment into another
algorithm with optimal dynamic regret in a non-stationary environment,
importantly without any prior knowledge on the degree of non-stationarity. By
plugging different algorithms into our black-box, we provide a list of examples
showing that our approach not only recovers recent results for (contextual)
multi-armed bandits achieved by very specialized algorithms, but also
significantly improves the state of the art for (generalized) linear bandits,
episodic MDPs, and infinite-horizon MDPs in various ways. Specifically, in most
cases our algorithm achieves the optimal dynamic regret
$\widetilde{\mathcal{O}}(\min\{\sqrt{LT}, \Delta^{1/3}T^{2/3}\})$ where $T$ is
the number of rounds and $L$ and $\Delta$ are the number and amount of changes
of the world respectively, while previous works only obtain suboptimal bounds
and/or require the knowledge of $L$ and $\Delta$.


Abstract:
  Rather than using wooden sticks to simulate the breakage of trees in high
winds as in most research, we employed fresh samples with branches and leaves
to certify the crucial role played by the tree crown. By using the blowdown
wind tunnel with a maximum wind speed of 60 m/s, we purposely reduce the number
of leaves and show that the drag force will drop by as much as two thirds when
half pruned. Based on real observations, we model the leaf by an open and full
cone in the presence of light and strong wind, and calculate how their
corresponding cross-sectional area and drag force vary with wind speed.
Different power-law relations are predicted and confirmed by experiments for
these properties before and after the formation of a full cone. Compared to the
empirical value of 1/3 and 3/4, our simple model gave 2/5 and 2/3 for the
power-law exponent of cross-sectional area at low and high winds. Discrepancy
can be accounted for by including further details, such as the reorientation of
open cones and the movement of branches.


Abstract:
  Motivated by biological considerations, we study sparse neural maps from an
input layer to a target layer with sparse activity, and specifically the
problem of storing $K$ input-target associations $(x,y)$, or memories, when the
target vectors $y$ are sparse. We mathematically prove that $K$ undergoes a
phase transition and that in general, and somewhat paradoxically, sparsity in
the target layers increases the storage capacity of the map. The target vectors
can be chosen arbitrarily, including in random fashion, and the memories can be
both encoded and decoded by networks trained using local learning rules,
including the simple Hebb rule. These results are robust under a variety of
statistical assumptions on the data. The proofs rely on elegant properties of
random polytopes and sub-gaussian random vector variables. Open problems and
connections to capacity theories and polynomial threshold maps are discussed.


Abstract:
  We characterize the magnetic properties and domain structure of Pt/Ni/Co
asymmetric superlattices in comparison to the more established Pt/Co/Ni
structure. This reversal in stacking sequence leads to a marked drop in
interfacial magnetic anisotropy and the magnitude of the interfacial
Dzyaloshinskii-Moriya interaction (DMI) as inferred from the DW structure,
which we speculate could be related to a degradation of the Pt/Co interface
when Pt is deposited on top of the Co layer. Lorentz transmission electron
microscopy reveals exclusively N\'eel type domain walls and, with a
perpendicular field, N\'eel skyrmions in the Pt/Co/Ni films. Conversely, the
Pt/Ni/Co samples show only achiral Bloch domain walls, which leads to the
formation of achiral Bloch ($Q=1$) and type II bubbles ($Q=0$) at increased
perpendicular field. Combined with the reduced anisotropy leading to greater
bubble densities, the latter case makes for an excellent test bed to examine
the benefits of topological charge on stability. Simultaneous observation of
Bloch and type II bubbles shows a roughly 50 mT larger annihilation field for
the former. An in-plane component to the magnetic field is shown to both impact
the structure of the formed bubbles and separately suppress the topological
benefit.


Abstract:
  We show that a unified framework based on an $SU(2)_H$ horizontal symmetry
which generates a naturally large neutrino transition magnetic moment and
explains the XENON1T electron recoil excess also predicts a positive shift in
the muon anomalous magnetic moment. This shift is of the right magnitude to be
consistent with the Brookhaven measurement as well as the recent Fermilab
measurement of the muon $g-2$. A relatively light neutral scalar from a Higgs
doublet with mass near 100 GeV contributes to muon $g-2$, while its charged
partner induces the neutrino magnetic moment. We analyze the collider tests of
this framework and find that the HL-LHC can probe the entire parameter space of
these models.


Abstract:
  We propose a novel pairwise distance measure between image keypoint sets, for
the purpose of large-scale medical image indexing. Our measure generalizes the
Jaccard index to account for soft set equivalence (SSE) between keypoint
elements, via an adaptive kernel framework modeling uncertainty in keypoint
appearance and geometry. A new kernel is proposed to quantify the variability
of keypoint geometry in location and scale. Our distance measure may be
estimated between $O(N^2)$ image pairs in $O(N~\log~N)$ operations via keypoint
indexing. Experiments report the first results for the task of predicting
family relationships from medical images, using 1010 T1-weighted MRI brain
volumes of 434 families including monozygotic and dizygotic twins, siblings and
half-siblings sharing 100%-25% of their polymorphic genes. Soft set equivalence
and the keypoint geometry kernel improve upon standard hard set equivalence
(HSE) and appearance kernels alone in predicting family relationships.
Monozygotic twin identification is near 100%, and three subjects with uncertain
genotyping are automatically paired with their self-reported families, the
first reported practical application of image-based family identification. Our
distance measure can also be used to predict group categories, sex is predicted
with an AUC=0.97. Software is provided for efficient fine-grained curation of
large, generic image datasets.


Abstract:
  Deep Neural Network (DNN)-based physical layer techniques are attracting
considerable interest due to their potential to enhance communication systems.
However, most studies in the physical layer have tended to focus on the
application of DNN models to wireless communication problems but not to
theoretically understand how does a DNN work in a communication system. In this
letter, we aim to quantitatively analyse why DNNs can achieve comparable
performance in the physical layer comparing with traditional techniques and
their cost in terms of computational complexity. We further investigate and
also experimentally validate how information is flown in a DNN-based
communication system under the information theoretic concepts.


Abstract:
  Current face forgery detection methods achieve high accuracy under the
within-database scenario where training and testing forgeries are synthesized
by the same algorithm. However, few of them gain satisfying performance under
the cross-database scenario where training and testing forgeries are
synthesized by different algorithms. In this paper, we find that current
CNN-based detectors tend to overfit to method-specific color textures and thus
fail to generalize. Observing that image noises remove color textures and
expose discrepancies between authentic and tampered regions, we propose to
utilize the high-frequency noises for face forgery detection. We carefully
devise three functional modules to take full advantage of the high-frequency
features. The first is the multi-scale high-frequency feature extraction module
that extracts high-frequency noises at multiple scales and composes a novel
modality. The second is the residual-guided spatial attention module that
guides the low-level RGB feature extractor to concentrate more on forgery
traces from a new perspective. The last is the cross-modality attention module
that leverages the correlation between the two complementary modalities to
promote feature learning for each other. Comprehensive evaluations on several
benchmark databases corroborate the superior generalization performance of our
proposed method.


Abstract:
  We theoretically investigate advanced multi-functional integrated photonic
filters formed by three waveguide coupled Sagnac loop reflectors (3WC-SLRs). By
tailoring the coherent mode interference, the spectral response of the 3WC-SLR
resonators is engineered to achieve diverse filtering functions with high
performance. These include optical analogues of Fano resonances that yield
ultrahigh spectral extinction ratios (ERs) and slope rates, resonance mode
splitting with high ERs and low free spectral ranges, and classical
Butterworth, Bessel, Chebyshev, and elliptic filters. A detailed analysis of
the impact of the structural parameters and fabrication tolerances is provided
to facilitate device design and optimization. The requirements for practical
applications are also considered. These results theoretically verify the
effectiveness of using 3WC-SLR resonators as multi-functional integrated
photonic filters for flexible spectral engineering in diverse applications.


Abstract:
  Motivated by experiments and formal asymptotic expansions in the physics
literature, Maor and Shachar (J. Elasticity 134 (2019), 149-173) studied the
behaviour of a model elastic energy of maps between manifolds with incompatible
metrics. For thin objects they analysed the scaling of the minimal elastic
energy as a function of the thickness. In particular they showed that for maps
from a ball of radius h in an oriented Riemannian manifold to Euclidean space,
the infimum of a model elastic energy per unit volume scales like the fourth
power of h and after rescaling one gets convergence to a quadratic expression
in the curvature tensor R(p), where p denotes the centre of the ball. In this
paper we show the same result for general compact oriented Riemannian targets
with R(p) replaced by a suitable difference of the curvature tensors in the
target and the domain, thus answering Open Question 1 in the paper by Maor and
Shachar. The result extends noncompact targets provided they satisfy a uniform
regularity condition. A key idea in the proof is to use Lipschitz
approximations to define a suitable notion of convergence.


Abstract:
  In this paper, we consider the global well-posedness of the incompressible
Hall-MHD equations in $\mathbb{R}^3$. We prove that the solution of this system
is globally regular if the initial data is axisymmetric and the swirl
components of the velocity and magnetic vorticity are trivial. It should be
pointed out that the initial data without any smallness and in low regularity
spaces. This improves a previous result established in \cite{Fan2013}.


Abstract:
  The capability of the traditional semi-supervised learning (SSL) methods is
far from real-world application since they do not consider (1) class imbalance
and (2) class distribution mismatch between labeled and unlabeled data. This
paper addresses such a relatively under-explored problem, imbalanced
semi-supervised learning, where heavily biased pseudo-labels can harm the model
performance. Interestingly, we find that the semantic pseudo-labels from a
similarity-based classifier in feature space and the traditional pseudo-labels
from the linear classifier show the complementary property. To this end, we
propose a general pseudo-labeling framework to address the bias motivated by
this observation. The key idea is to class-adaptively blend the semantic
pseudo-label to the linear one, depending on the current pseudo-label
distribution. Thereby, the increased semantic pseudo-label component suppresses
the false positives in the majority classes and vice versa. We term the novel
pseudo-labeling framework for imbalanced SSL as Distribution-Aware
Semantics-Oriented (DASO) Pseudo-label. Extensive evaluation on CIFAR10/100-LT
and STL10-LT shows that DASO consistently outperforms both recently proposed
re-balancing methods for label and pseudo-label. Moreover, we demonstrate that
typical SSL algorithms can effectively benefit from unlabeled data with DASO,
especially when (1) class imbalance and (2) class distribution mismatch exist
and even on recent real-world Semi-Aves benchmark.


Abstract:
  The vast majority of binaries containing a compact object and a regular star
spend most of their time in a quiescent state where no strong interactions
occur between components. Detection of these binaries is extremely challenging
and only few candidates have been detected through optical spectroscopy.
Self-lensing represents a new means of detecting compact objects in binaries,
where gravitational lensing of the light from the visible component by the
compact object produces periodic optical flares. Here we show that current and
planned large-area optical surveys can detect a significant number ($\sim
100$-$10,000$s) of these self-lensing binaries and provide insights into the
properties of the compact lenses. We show that many of the predicted population
of observable self-lensing binaries will be observed with multiple self-lensing
flares; this both improves the chances of detection and also immediately
distinguishes them from chance-alignment micro-lensing events. Through
self-lensing we can investigate long - but previously hidden - stages of binary
evolution and consequently provide new constraints on evolutionary models which
impact on the number and nature of double compact object mergers.


Abstract:
  A $[k,n,1]$-graph is a $k$-partite graph with parts of order $n$ such that
the bipartite graph induced by any pair of parts is a matching. An independent
transversal in such a graph is an independent set that intersects each part in
a single vertex. A factor of independent transversals is a set of $n$
pairwise-disjoint independent transversals. Let $f(k)$ be the smallest integer
$n_0$ such that every $[k,n,1]$-graph has a factor of independent transversals
assuming $n \ge n_0$. Several known conjectures imply that for $k \ge 2$,
$f(k)=k$ if $k$ is even and $f(k)=k+1$ if $k$ is odd. While a simple greedy
algorithm based on iterating Hall's Theorem shows that $f(k) \le 2k-2$, no
better bound is known and in fact, there are instances showing that the bound
$2k-2$ is tight for the greedy algorithm. Here we significantly improve upon
the greedy algorithm bound and prove that $f(k) \le 1.78k$ for all $k$
sufficiently large, answering a question of MacKeigan.


Abstract:
  The coronavirus disease 2019 (COVID-19) is a severe global pandemic that has
claimed millions of lives and continues to overwhelm public health systems in
many countries. The spread of COVID-19 pandemic has negatively impacted the
human mobility patterns such as daily transportation-related behavior of the
public. There is a requirement to understand the disease spread patterns and
its routes among neighboring individuals for the timely implementation of
corrective measures at the required placement. To increase the effectiveness of
contact tracing, countries across the globe are leveraging advancements in
mobile technology and Internet of Things (IoT) to aid traditional manual
contact tracing to track individuals who have come in close contact with
identified COVID-19 patients. Even as the first administration of vaccines
begins in 2021, the COVID-19 management strategy will continue to be
multi-pronged for the foreseeable future with digital contact tracing being a
vital component of the response along with the use of preventive measures such
as social distancing and the use of face masks. After some months of deployment
of digital contact tracing technology, deeper insights into the merits of
various approaches and the usability, privacy, and ethical trade-offs involved
are emerging. In this paper, we provide a comprehensive analysis of digital
contact tracing solutions in terms of their methodologies and technologies in
the light of the new data emerging about international experiences of
deployments of digital contact tracing technology. We also provide a discussion
on open challenges such as scalability, privacy, adaptability and highlight
promising directions for future work.


Abstract:
  In this note we observe that for constrained convex minimization problems
$\min_{x \in P}f(x)$ over a polytope $P$, dual prices for the linear program
$\min_{z \in P} \nabla f(x) z$ obtained from linearization at approximately
optimal solutions $x$ have a similar interpretation of rate of change in
optimal value as for linear programming, providing a convex form of sensitivity
analysis. This is of particular interest for Frank--Wolfe algorithms (also
called conditional gradients), forming an important class of first-order
methods, where a basic building block is linear minimization of gradients of
$f$ over $P$, which in most implementations already compute the dual prices as
a by-product.


Abstract:
  For the model of a multi-level system in the rotating wave approximation we
obtain the corrections for a usual weak coupling limit dynamics by means of
perturbation theory with Bogolubov-van Hove scaling. It generalizes our
previous results on a spin-boson model in the rotating wave approximation.
Additionally, in this work we take into account some dependence of the system
Hamiltonian on the small parameter. We show that the dynamics is long-time
Markovian, i.e. after the bath correlation time all the non-Markovianity could
be captured by the renormalization of initial condition and correlation
functions.


Abstract:
  In the problem "Localization and trilateration with the minimum number of
landmarks", we faced the 3-Guard and classic Art Gallery Problems. The goal of
the art gallery problem is to find the minimum number of guards within a simple
polygon to observe and protect its entirety. It has many applications in
robotics, telecommunications, etc. There are some approaches to handle the art
gallery problem that is theoretically NP-hard. This paper offers an efficient
method based on the Particle Filter algorithm which solves the most fundamental
state of the problem in a nearly optimal manner. The experimental results on
the random polygons generated by Bottino et al. \cite{bottino2011nearly} show
that the new method is more accurate with fewer or equal guards. Furthermore,
we discuss resampling and particle numbers to minimize the run time.


Abstract:
  Three-dimensional (3D) magnetohydrodynamic simulations are carried out to
explore magnetic reconnections in the presence of 3D magnetic nulls and
quasi-separatrix layers (QSLs). The initial magnetic fields are created by
superposing uniform vertical magnetic fields of two different magnitudes on a
linear force-free field. The interior of the numerical box contains two 3D
nulls with separatrix domes separated by a quasi-separator (or hyperbolic flux
tube) with QSLs. In the first simulation, the uniform vertical field is so
large that the nulls are located at low heights and the domes are separate.
Initially unbalanced Lorentz forces drive rotational flows that form strong
electric currents and strong torsional fan reconnection at the 3D nulls and
weak QSL reconnection at the hyperbolic flux tube. Flipping or slipping of
field lines is observed in both cases. In the second simulation, with a weaker
vertical field and larger domes, the separatrix surfaces meet at the central
quasi-separator and their rotation drives stronger QSL reconnection than
before.


Abstract:
  Encryption provides a method to protect data outsourced to a DBMS provider,
e.g., in the cloud. However, performing database operations over encrypted data
requires specialized encryption schemes that carefully balance security and
performance. In this paper, we present a new encryption scheme that can
efficiently perform equi-joins over encrypted data with better security than
the state-of-the-art. In particular, our encryption scheme reduces the leakage
to equality of rows that match a selection criterion and only reveals the
transitive closure of the sum of the leakages of each query in a series of
queries. Our encryption scheme is provable secure. We implemented our
encryption scheme and evaluated it over a dataset from the TPC-H benchmark.


Abstract:
  To interact seamlessly with robots, users must infer the causes of a robot's
behavior and be confident about that inference. Hence, trust is a necessary
condition for human-robot collaboration (HRC). Despite its crucial role, it is
largely unknown how trust emerges, develops, and supports human interactions
with nonhuman artefacts. Here, we review the literature on trust, human-robot
interaction, human-robot collaboration, and human interaction at large. Early
models of trust suggest that trust entails a trade-off between benevolence and
competence, while studies of human-to-human interaction emphasize the role of
shared behavior and mutual knowledge in the gradual building of trust. We then
introduce a model of trust as an agent's best explanation for reliable sensory
exchange with an extended motor plant or partner. This model is based on the
cognitive neuroscience of active inference and suggests that, in the context of
HRC, trust can be cast in terms of virtual control over an artificial agent. In
this setting, interactive feedback becomes a necessary component of the
trustor's perception-action cycle. The resulting model has important
implications for understanding human-robot interaction and collaboration, as it
allows the traditional determinants of human trust to be defined in terms of
active inference, information exchange and empowerment. Furthermore, this model
suggests that boredom and surprise may be used as markers for under and
over-reliance on the system. Finally, we examine the role of shared behavior in
the genesis of trust, especially in the context of dyadic collaboration,
suggesting important consequences for the acceptability and design of
human-robot collaborative systems.


Abstract:
  The unsupervised search for overdense regions in high-dimensional feature
spaces, where locally high population densities may be associated with
anomalous contaminations to an otherwise more uniform population, is of
relevance to applications ranging from fundamental research to industrial use
cases. Motivated by the specific needs of searches for new phenomena in
particle collisions, we propose a novel approach that targets signals of
interest populating compact regions of the feature space. The method consists
in a systematic scan of subspaces of a standardized copula of the feature
space, where the minimum p-value of a hypothesis test of local uniformity is
sought by gradient descent. We characterize the performance of the proposed
algorithm and show its effectiveness in several experimental situations.


Abstract:
  As for the unique continuation property (UCP) of solutions in
$(0,T)\times\Omega$ with a domain $\Omega\subset{\mathbb R}^n,\,n\in{\mathbb
N}$ for a multi-terms time fractional diffusion equation, we have already shown
it by assuming that the solutions are zero for $t\le0$ (see \cite{LN2019}).
Here the strongly elliptic operator for this diffusion equation can depend on
time and the orders of its time fractional derivatives are in $(0,2)$. This
paper is a continuation of the previous study. The aim of this paper is to drop
the assumption that the solutions are zero for $t\le0$. We have achieved this
aim by first using the usual Holmgren transformation together with the argument
in \cite{LN2019} to derive the UCP in $(T_1,T_2)\times B_1$ for some
$0<T_1<T_2<T$ and a ball $B_1\subset\Omega$. Then if $u$ is the solution of the
equation with $u=0$ in $(T_1,T_2)\times B_1$, we show $u=0$ also in
$((0,T_1]\cup[T_2,T))\times B_r$ for some $r<1$ by using the argument in
\cite{LN2019} which uses two Holmgren type transformations different from the
usual one. This together with spatial coordinates transformation, we can obtain
the usual UCP which we call it the classical UCP given in the title of this
paper for our time fractional diffusion equation.


Abstract:
  Magnetic topological materials have attracted much attention due to the
correlation between topology and magnetism. Recent studies suggest that
EuCd$_2$As$_2$ is an antiferromagnetic topological material. Here by carrying
out thorough magnetic, electrical and thermodynamic property measurements, we
discover a long time relaxation of the magnetic susceptibility in
EuCd$_2$As$_2$. The (001) in-plane magnetic susceptibility at 5 K is found to
continuously increase up to $\sim$10% over the time of $\sim$14 hours. The
magnetic relaxation is anisotropic and strongly depends on the temperature and
the applied magnetic field. These results will stimulate further theoretical
and experimental studies to understand the origin of the relaxation process and
its effect on the electronic structure and physical properties of the magnetic
topological materials.


Abstract:
  We present highly optimized data structures for the dynamic predecessor
problem, where the task is to maintain a set $S$ of $w$-bit numbers under
insertions, deletions, and predecessor queries (return the largest element in
$S$ no larger than a given key). The problem of finding predecessors can be
viewed as a generalized form of the membership problem, or as a simple version
of the nearest neighbour problem. It lies at the core of various real-world
problems such as internet routing.
  In this work, we engineer (1) a simple implementation of the idea of universe
reduction, similar to van-Emde-Boas trees (2) variants of y-fast tries
[Willard, IPL'83], and (3) B-trees with different strategies for organizing the
keys contained in the nodes, including an implementation of dynamic fusion
nodes [P\v{a}tra\c{s}cu and Thorup, FOCS'14]. We implement our data structures
for $w=32,40,64$, which covers most typical scenarios.
  Our data structures finish workloads faster than previous approaches while
being significantly more space-efficient, e.g., they clearly outperform
standard implementations of the STL by finishing up to four times as fast using
less than a third of the memory. Our tests also provide more general insights
on data structure design, such as how small sets should be stored and handled
and if and when new CPU instructions such as advanced vector extensions pay
off.


Abstract:
  This article proves a Pythagoras-type formula for the sides and diagonals of
a polygon inscribed in a semicircle having one of the sides of the polygon as
diameter.


Abstract:
  In the Einstein-bumblebee gravity, the Lorentz symmetry is spontaneously
broken by a vector field. In this paper, we attempt to test the Lorentz
symmetry via the observation of the shadow cast by the Kerr-like black hole
with or without plasma. A novel phenomenon of the Lorentz-violating parameter
on the shadow is observed. The result shows that when the observer gradually
moves from the poles to the equatorial plane, the shadow radius $R_{\rm s}$
firstly decreases and then increases with the Lorentz-violating parameter. Such
nonmonotonic behavior provides us an important understanding on the black hole
shadow in the Einstein-bumblebee gravity. Besides, three more distortion
observables are calculated, and found to increase with the Lorentz-violating
parameter. Moreover, when a homogeneous plasma is present, the motion of the
photon is analyzed. We further observe that the refractive index shrinks the
size, while enhances the deformation of the shadow. Finally, adopting the
observed data of the diameter of M87$^*$, we find the refractive index is more
favored in (0.914, 1).


Abstract:
  We present recent updates in our development of a library for Universal
Algebra in the UniMath proof assistant. The code here discussed concerns
multi-sorted signatures and their algebras, along with the basics for equation
systems. Moreover, we give neat constructions of the corresponding univalent
categories by using the formalism of displayed categories, and show that the
term algebra over a signature is the initial object of the category.
  Besides the formalization, we reflect on the methodological principles --
based on the idea of evaluability of our elementary construction by the
built-in normalization procedure of the system -- leading our coding style, and
show that this path is practicable indeed by sketching simple examples.


Abstract:
  Recent controversies related to topics such as fake news, privacy, and
algorithmic bias have prompted increased public scrutiny of digital
technologies and soul-searching among many of the people associated with their
development. In response, the tech industry, academia, civil society, and
governments have rapidly increased their attention to "ethics" in the design
and use of digital technologies ("tech ethics"). Yet almost as quickly as
ethics discourse has proliferated across the world of digital technologies, the
limitations of these approaches have also become apparent: tech ethics is vague
and toothless, is subsumed into corporate logics and incentives, and has a
myopic focus on individual engineers and technology design rather than on the
structures and cultures of technology production. As a result of these
limitations, many have grown skeptical of tech ethics and its proponents,
charging them with "ethics-washing": promoting ethics research and discourse to
defuse criticism and government regulation without committing to ethical
behavior. By looking at how ethics has been taken up in both science and
business in superficial and depoliticizing ways, I recast tech ethics as a
terrain of contestation where the central fault line is not whether it is
desirable to be ethical, but what "ethics" entails and who gets to define it.
This framing highlights the significant limits of current approaches to tech
ethics and the importance of studying the formulation and real-world effects of
tech ethics. In order to identify and develop more rigorous strategies for
reforming digital technologies and the social relations that they mediate, I
describe a sociotechnical approach to tech ethics, one that reflexively applies
many of tech ethics' own lessons regarding digital technologies to tech ethics
itself.


Abstract:
  Detecting small targets at range is difficult because there is not enough
spatial information present in an image sub-region containing the target to use
correlation-based methods to differentiate it from dynamic confusers present in
the scene. Moreover, this lack of spatial information also disqualifies the use
of most state-of-the-art deep learning image-based classifiers. Here, we use
characteristics of target tracks extracted from video sequences as data from
which to derive distinguishing topological features that help robustly
differentiate targets of interest from confusers. In particular, we calculate
persistent homology from time-delayed embeddings of dynamic statistics
calculated from motion tracks extracted from a wide field-of-view video stream.
In short, we use topological methods to extract features related to target
motion dynamics that are useful for classification and disambiguation and show
that small targets can be detected at range with high probability.


Abstract:
  Images are an indispensable part of the news content we consume. Highly
emotional images from sources of misinformation can greatly influence our
judgements. We present two studies on the effects of emotional facial images on
users' perception of bias in news content and the credibility of sources. In
study 1, we investigate the impact of happy and angry facial images on users'
decisions. In study 2, we focus on sources' systematic emotional treatment of
specific politicians. Our results show that depending on the political
orientation of the source, the cumulative effect of angry facial emotions
impacts users' perceived content bias and source credibility. When sources
systematically portray specific politicians as angry, users are more likely to
find those sources as less credible and their content as more biased. These
results highlight how implicit visual propositions manifested by emotions in
facial expressions might have a substantial effect on our trust of news content
and sources.


Abstract:
  In network design problems, such as compact routing, the goal is to route
packets between nodes using the (approximated) shortest paths. A desirable
property of these routes is a small number of hops, which makes them more
reliable, and reduces the transmission costs. Following the overwhelming
success of stochastic tree embeddings for algorithmic design, Haeupler,
Hershkowitz, and Zuzic (STOC'21) studied hop-constrained Ramsey-type metric
embeddings into trees. Specifically, embedding $f:G(V,E)\rightarrow T$ has
Ramsey hop-distortion $(t,M,\beta,h)$ (here $t,\beta,h\ge1$ and $M\subseteq V$)
if $\forall u,v\in M$, $d_G^{(\beta\cdot h)}(u,v)\le d_T(u,v)\le t\cdot
d_G^{(h)}(u,v)$. $t$ is called the distortion, $\beta$ is called the
hop-stretch, and $d_G^{(h)}(u,v)$ denotes the minimum weight of a $u-v$ path
with at most $h$ hops. Haeupler {\em et al.} constructed embedding where $M$
contains $1-\epsilon$ fraction of the vertices and $\beta=t=O(\frac{\log^2
n}{\epsilon})$. They used their embedding to obtain multiple bicriteria
approximation algorithms for hop-constrained network design problems.
  In this paper, we first improve the Ramsey-type embedding to obtain
parameters $t=\beta=\frac{\tilde{O}(\log n)}{\epsilon}$, and generalize it to
arbitrary distortion parameter $t$ (in the cost of reducing the size of $M$).
This embedding immediately implies polynomial improvements for all the
approximation algorithms from Haeupler {\em et al.}. Further, we construct
hop-constrained clan embeddings (where each vertex has multiple copies), and
use them to construct bicriteria approximation algorithms for the group Steiner
tree problem, matching the state of the art of the non constrained version.
Finally, we use our embedding results to construct hop constrained distance
oracles, distance labeling, and most prominently, the first hop constrained
compact routing scheme with provable guarantees.


Abstract:
  Recent observations of young embedded clumpy clusters and statistical
identifications of binary star clusters have provided new insights into the
formation process and subsequent dynamical evolution of star clusters. The
early dynamical evolution of clumpy stellar structures provides the conditions
for the origin of binary star clusters. Here, we carry out $N$-body simulations
in order to investigate the formation of binary star clusters in the Milky Way
and in the Large Magellanic Cloud (LMC). We find that binary star clusters can
form from stellar aggregates with a variety of initial conditions. For a given
initial virial ratio, a higher degree of initial substructure results in a
higher fraction of binary star clusters. The number of binary star clusters
decreases over time due to merging or dissolution of the binary system.
Typically, $\sim 45\%$ of the aggregates evolve into binary/multiple clusters
within $t=20$~Myr in the Milky Way environment, while merely $\sim30\%$
survives beyond $t=50$~Myr, with separations $\lesssim 50$~pc. On the other
hand, in the LMC, $\sim 90\%$ of the binary/multiple clusters survive beyond
$t=20$~Myr and the fraction decreases to $\sim 80\%$ at $t=50$~Myr, with
separations $\lesssim 35~$pc. Multiple clusters are also rapidly formed for
highly-substructured and expanding clusters. The additional components tend to
detach and the remaining binary star cluster merges. The merging process can
produce fast rotating star clusters with mostly flat rotation curves that speed
up in the outskirts.


Abstract:
  The measurement of the energy spectrum of cosmic ray helium nuclei from 70
GeV to 80 TeV using 4.5 years of data recorded by the DArk Matter Particle
Explorer (DAMPE) is reported in this work. A hardening of the spectrum is
observed at an energy of about 1.3 TeV, similar to previous observations. In
addition, a spectral softening at about 34 TeV is revealed for the first time
with large statistics and well controlled systematic uncertainties, with an
overall significance of $4.3\sigma$. The DAMPE spectral measurements of both
cosmic protons and helium nuclei suggest a particle charge dependent softening
energy, although with current uncertainties a dependence on the number of
nucleons cannot be ruled out.


Abstract:
  Drug membrane interaction is a very significant bioprocess to consider in
drug discovery. Here, we propose a novel deep learning framework coined DMInet
to study drug-membrane interactions that leverages large-scale Martini
coarse-grained molecular simulations of permeation of drug-like molecules
across six different lipid membranes. The network of DMInet receives three
inputs, viz, the drug-like molecule, membrane type and spatial distance across
membrane thickness, and predicts the potential of mean force with structural
resolution across the lipid membrane and membrane selectivity. Inheriting from
coarse-grained Martini representation of organic molecules and combined with
deep learning, DMInet has the potential for more accelerated high throughput
screening in drug discovery across a much larger chemical space than that can
be explored by physics-based simulations alone. Moreover, DMInet is highly
flexible in its nature and holds the possibilities for other properties
prediction without significant change of the architecture. Last but not least,
the architecture of DMInet is general and can be applied to other membrane
problems involving permeation and selection.


Abstract:
  Superconductivity is significantly enhanced in monolayer FeSe grown on
SrTiO3, but not for multilayer films, in which large strength of nematicity
develops. However, the link between the high-transition temperature
superconductivity in monolayer and the correlation related nematicity in
multilayer FeSe films is not well understood. Here, we use low-temperature
scanning tunneling microscopy to study few-layer FeSe thin films grown by
molecular beam epitaxy. We observe an incommensurate long-range smectic phase,
which solely appears in bilayer FeSe films. The smectic order still locally
exists and gradually fades away with increasing film thickness, while it
suddenly vanishes in monolayer FeSe, indicative of an abrupt smectic phase
transition. Surface alkali-metal doping can suppress the smectic phase and
induce high-Tc superconductivity in bilayer FeSe. Our observations provide
evidence that the monolayer FeSe is in close proximity to the smectic phase,
and its superconductivity is likely enhanced by this electronic instability as
well.


Abstract:
  A large number of particle species allows to formulate quantum gravity in a
special double-scaling limit, the species limit. In this regime, quantum
gravitational amplitudes simplify substantially. An infinite set of
perturbative corrections, that usually blur the picture, vanishes, whereas the
collective and non-perturbative effects can be cleanly extracted. Such are the
effects that control physics of black holes and of de Sitter and their
entanglement curves. In string theory example, we show that the entropy of open
strings matches the Gibbons-Hawking entropy of a would-be de Sitter state at
the point of saturation of the species bound. This shows, from yet another
angle, why quantum gravity/string theory cannot tolerate a de Sitter vacuum.
Finally, we discuss various observational implications.


Abstract:
  We introduce SynSE, a novel syntactically guided generative approach for
Zero-Shot Learning (ZSL). Our end-to-end approach learns progressively refined
generative embedding spaces constrained within and across the involved
modalities (visual, language). The inter-modal constraints are defined between
action sequence embedding and embeddings of Parts of Speech (PoS) tagged words
in the corresponding action description. We deploy SynSE for the task of
skeleton-based action sequence recognition. Our design choices enable SynSE to
generalize compositionally, i.e., recognize sequences whose action descriptions
contain words not encountered during training. We also extend our approach to
the more challenging Generalized Zero-Shot Learning (GZSL) problem via a
confidence-based gating mechanism. We are the first to present zero-shot
skeleton action recognition results on the large-scale NTU-60 and NTU-120
skeleton action datasets with multiple splits. Our results demonstrate SynSE's
state of the art performance in both ZSL and GZSL settings compared to strong
baselines on the NTU-60 and NTU-120 datasets. The code and pretrained models
are available at https://github.com/skelemoa/synse-zsl


Abstract:
  Multimodal sentiment analysis benefits various applications such as
human-computer interaction and recommendation systems. It aims to infer the
users' bipolar ideas using visual, textual, and acoustic signals. Although
researchers affirm the association between cognitive cues and emotional
manifestations, most of the current multimodal approaches in sentiment analysis
disregard user-specific aspects. To tackle this issue, we devise a novel method
to perform multimodal sentiment prediction using cognitive cues, such as
personality. Our framework constructs an adaptive tree by hierarchically
dividing users and trains the LSTM-based submodels, utilizing an
attention-based fusion to transfer cognitive-oriented knowledge within the
tree. Subsequently, the framework consumes the conclusive agglomerative
knowledge from the adaptive tree to predict final sentiments. We also devise a
dynamic dropout method to facilitate data sharing between neighboring nodes,
reducing data sparsity. The empirical results on real-world datasets determine
that our proposed model for sentiment prediction can surpass trending rivals.
Moreover, compared to other ensemble approaches, the proposed transfer-based
algorithm can better utilize the latent cognitive cues and foster the
prediction outcomes. Based on the given extrinsic and intrinsic analysis
results, we note that compared to other theoretical-based techniques, the
proposed hierarchical clustering approach can better group the users within the
adaptive tree.


Abstract:
  Most evolutionary algorithms have parameters, which allow a great flexibility
in controlling their behavior and adapting them to new problems. To achieve the
best performance, it is often needed to control some of the parameters during
optimization, which gave rise to various parameter control methods. In recent
works, however, similar advantages have been shown, and even proven, for
sampling parameter values from certain, often heavy-tailed, fixed
distributions. This produced a family of algorithms currently known as "fast
evolution strategies" and "fast genetic algorithms".
  However, only little is known so far about the influence of these
distributions on the performance of evolutionary algorithms, and about the
relationships between (dynamic) parameter control and (static) parameter
sampling. We contribute to the body of knowledge by presenting, for the first
time, an algorithm that computes the optimal static distributions, which
describe the mutation operator used in the well-known simple $(1+\lambda)$
evolutionary algorithm on a classic benchmark problem OneMax. We show that, for
large enough population sizes, such optimal distributions may be surprisingly
complicated and counter-intuitive. We investigate certain properties of these
distributions, and also evaluate the performance regrets of the $(1+\lambda)$
evolutionary algorithm using commonly used mutation distributions.


Abstract:
  Unsupervised image-to-image translation methods have received a lot of
attention in the last few years. Multiple techniques emerged tackling the
initial challenge from different perspectives. Some focus on learning as much
as possible from several target style images for translations while other make
use of object detection in order to produce more realistic results on
content-rich scenes. In this work, we assess how a method that has initially
been developed for single object translation performs on more diverse and
content-rich images. Our work is based on the FUNIT[1] framework and we train
it with a more diverse dataset. This helps understanding how such method
behaves beyond their initial frame of application. We present a way to extend a
dataset based on object detection. Moreover, we propose a way to adapt the
FUNIT framework in order to leverage the power of object detection that one can
see in other methods.


Abstract:
  The analysis of the compression effects in generative adversarial networks
(GANs) after training, i.e. without any fine-tuning, remains an unstudied,
albeit important, topic with the increasing trend of their computation and
memory requirements. While existing works discuss the difficulty of compressing
GANs during training, requiring novel methods designed with the instability of
GANs training in mind, we show that existing compression methods (namely
clipping and quantization) may be directly applied to compress GANs
post-training, without any additional changes. High compression levels may
distort the generated set, likely leading to an increase of outliers that may
negatively affect the overall assessment of existing k-nearest neighbor (KNN)
based metrics. We propose two new precision and recall metrics based on
locality-sensitive hashing (LSH), which, on top of increasing the outlier
robustness, decrease the complexity of assessing an evaluation sample against
$n$ reference samples from $O(n)$ to $O(\log(n))$, if using LSH and KNN, and to
$O(1)$, if only applying LSH. We show that low-bit compression of several
pre-trained GANs on multiple datasets induces a trade-off between precision and
recall, retaining sample quality while sacrificing sample diversity.


Abstract:
  To tackle ever-increasing city traffic congestion problems, researchers have
proposed deep learning models to aid decision-makers in the traffic control
domain. Although the proposed models have been remarkably improved in recent
years, there are still questions that need to be answered before deploying
models. For example, it is difficult to figure out which models provide
state-of-the-art performance, as recently proposed models have often been
evaluated with different datasets and experiment environments. It is also
difficult to determine which models would work when traffic conditions change
abruptly (e.g., rush hour). In this work, we conduct two experiments to answer
the two questions. In the first experiment, we conduct an experiment with the
state-of-the-art models and the identical public datasets to compare model
performance under a consistent experiment environment. We then extract a set of
temporal regions in the datasets, whose speeds change abruptly and use these
regions to explore model performance with difficult intervals. The experiment
results indicate that Graph-WaveNet and GMAN show better performance in
general. We also find that prediction models tend to have varying performances
with data and intervals, which calls for in-depth analysis of models on
difficult intervals for real-world deployment.


Abstract:
  The notion of normal category was introduced by KSS Nambooripad in connection
with the study of the structure of regular semigroups using cross
connections\cite{nambooripad1994theory}. It is an abstraction of the category
of principal left ideals of a regular semigroup. A normal band is a semigroup
$B$ satisfying $a^2=a$ and $abca=acba$ for all $a,b,c \in B$. Since the normal
bands are regular semigroups, the category $\mathcal{L}(B)$ of principal left
ideals of a normal band $B$ is a normal category. One of the special properties
of this category is that the morphism sets admit a partial order compatible
with the composition of morphisms. In this article we derive several properties
of this partial order and obtain a new characterization of this partial order.


Abstract:
  Fairness and accountability are two essential pillars for trustworthy
Artificial Intelligence (AI) in healthcare. However, the existing AI model may
be biased in its decision marking. To tackle this issue, we propose an
adversarial multi-task training strategy to simultaneously mitigate and detect
bias in the deep learning-based medical image analysis system. Specifically, we
propose to add a discrimination module against bias and a critical module that
predicts unfairness within the base classification model. We further impose an
orthogonality regularization to force the two modules to be independent during
training. Hence, we can keep these deep learning tasks distinct from one
another, and avoid collapsing them into a singular point on the manifold.
Through this adversarial training method, the data from the underprivileged
group, which is vulnerable to bias because of attributes such as sex and skin
tone, are transferred into a domain that is neutral relative to these
attributes. Furthermore, the critical module can predict fairness scores for
the data with unknown sensitive attributes. We evaluate our framework on a
large-scale public-available skin lesion dataset under various fairness
evaluation metrics. The experiments demonstrate the effectiveness of our
proposed method for estimating and improving fairness in the deep
learning-based medical image analysis system.


Abstract:
  The standard nonlinear Schr\"{o}dinger equation (NLSE) is one of the elegant
equations to find the information about the modulational instability criteria
of dust-ion-acoustic (DIA) waves (DIAWs) and associated DIA rogue waves
(DIARWs) in a three-component dusty plasma medium having inertialess
super-thermal kappa distributed electrons, and inertial warm positive ions and
negative dust grains. It can be seen that under the consideration of inertial
warm ions along with inertial negatively charged dust grains, the plasma system
supports both fast and slow DIA modes. The charge state and number density of
the ion and dust grain are responsible to change the instability conditions of
the DIAWs and the configuration of DIARWs. These results are to be considered
the cornerstone for explaining the real puzzles in space and laboratory dusty
plasmas.


Abstract:
  Users are demanding increased data security. As a result, security is rapidly
becoming a first-order design constraint in next generation computing systems.
Researchers and practitioners are exploring various security technologies to
meet user demand such as trusted execution environments (e.g., Intel SGX, ARM
TrustZone), homomorphic encryption, and differential privacy. Each technique
provides some degree of security, but differs with respect to threat coverage,
performance overheads, as well as implementation and deployment challenges. In
this paper, we present a systemization of knowledge (SoK) on these design
considerations and trade-offs using several prominent security technologies.
Our study exposes the need for \textit{software-hardware-security} codesign to
realize efficient and effective solutions of securing user data. In particular,
we explore how design considerations across applications, hardware, and
security mechanisms must be combined to overcome fundamental limitations in
current technologies so that we can minimize performance overhead while
achieving sufficient threat model coverage. Finally, we propose a set of
guidelines to facilitate putting these secure computing technologies into
practice.


Abstract:
  Given entropy's central role in multiple areas of physics and science, one
important task is to develop a systematic and unifying approach to defining
entropy. Games of chance become a natural candidate for characterising the
uncertainty of a physical system, as a system's performance in gambling games
depends solely on the uncertainty of its output. In this work, we construct
families of games which induce pre-orders corresponding to majorization,
conditional majorization, and channel majorization. Finally, we provide
operational interpretations for all pre-orders, show the relevance of these
results to dynamical resource theories, and find the only asymptotically
continuous classical dynamic entropy.


Abstract:
  Significant deviations from the Standard Model are observed in semileptonic
charged and neutral-current B-decays, the muon magnetic moment, and the
extraction of the Cabibbo angle. We propose that these deviations point towards
a coherent pattern of New Physics effects induced by two scalar mediators, a
leptoquark $S_1$ and a charged singlet $\phi^+$. While $S_1$ can provide
solutions to charged-current $B$-decays and the muon magnetic moment, and
$\phi^+$ can accommodate the Cabibbo-angle anomaly independently, their
one-loop level synergy can also address neutral-current $B$-decays. This
framework provides the most minimal explanation to the above-mentioned
anomalies, while being consistent with all other phenomenological constraints.


Abstract:
  In this work, we revisit the non-minimally coupled Higgs Inflation scenario
and investigate its observational viability in light of the current Cosmic
Microwave Background, Baryon Acoustic Oscillation and type Ia Supernovae data.
We explore the effects of the Coleman-Weinberg approximation to the Higgs
potential in the primordial universe, connecting the predictions for the
Lagrangian parameters at inflationary scales to the electroweak observables
through Renormalization Group methods at two-loop order. As the main result, we
find that observations on the electroweak scale are in disagreement with the
constraints obtained from the cosmological data sets used in the analysis.
Specifically, an $\approx 8\sigma$-discrepancy between the inflationary
parameters and the electroweak value of the top quark mass is found, which
suggests that a significant deviation from the scenario analysed is required by
the cosmological data.


Abstract:
  In this paper, we propose CI-VI an efficient and scalable solver for
semi-implicit variational inference (SIVI). Our method, first, maps SIVI's
evidence lower bound (ELBO) to a form involving a nonlinear functional nesting
of expected values and then develops a rigorous optimiser capable of correctly
handling bias inherent to nonlinear nested expectations using an
extrapolation-smoothing mechanism coupled with gradient sketching. Our
theoretical results demonstrate convergence to a stationary point of the ELBO
in general non-convex settings typically arising when using deep network models
and an order of $O(t^{-\frac{4}{5}})$ gradient-bias-vanishing rate. We believe
these results generalise beyond the specific nesting arising from SIVI to other
forms. Finally, in a set of experiments, we demonstrate the effectiveness of
our algorithm in approximating complex posteriors on various data-sets
including those from natural language processing.


Abstract:
  Adversarial learning strategy has demonstrated remarkable performance in
dealing with single-source Domain Adaptation (DA) problems, and it has recently
been applied to Multi-source DA (MDA) problems. Although most existing MDA
strategies rely on a multiple domain discriminator setting, its effect on the
latent space representations has been poorly understood. Here we adopt an
information-theoretic approach to identify and resolve the potential adverse
effect of the multiple domain discriminators on MDA: disintegration of
domain-discriminative information, limited computational scalability, and a
large variance in the gradient of the loss during training. We examine the
above issues by situating adversarial DA in the context of information
regularization. This also provides a theoretical justification for using a
single and unified domain discriminator. Based on this idea, we implement a
novel neural architecture called a Multi-source Information-regularized
Adaptation Networks (MIAN). Large-scale experiments demonstrate that MIAN,
despite its structural simplicity, reliably and significantly outperforms other
state-of-the-art methods.


Abstract:
  Since their appearance, Smart Cities have aimed at improving the daily life
of people, helping to make public services smarter and more efficient. Several
of these services are often intended to provide better security conditions for
citizens and drivers. In this vein, we present Heimdall, an AI-based video
surveillance system for traffic monitoring and anomalies detection. The
proposed system features three main tiers: a ground level, consisting of a set
of smart lampposts equipped with cameras and sensors, and an advanced AI unit
for detecting accidents and traffic anomalies in real time; a territorial
level, which integrates and combines the information collected from the
different lampposts, and cross-correlates it with external data sources, in
order to coordinate and handle warnings and alerts; a training level, in charge
of continuously improving the accuracy of the modules that have to sense the
environment. Finally, we propose and discuss an early experimental approach for
the detection of anomalies, based on a Faster R-CNN, and adopted in the
proposed infrastructure.


Abstract:
  We report on a process for the fiber-coupling of electrically driven
cavity-enhanced quantum dot light emitting devices. The developed technique
allows for the direct and permanent coupling of p-i-n-doped quantum dot
micropillar cavities to single-mode optical fibers. The coupling process, fully
carried out at room temperature, involves a spatial scanning technique, where
the fiber facet is positioned relative to a device with a diameter of 2 $\mu$m
using the fiber-coupled electroluminescence of the cavity emission as a
feedback parameter. Subsequent gluing and UV curing enables a rigid and
permanent coupling between micropillar and fiber core. Comparing our
experimental results with finite element method simulations indicates a
cavity-to-fiber mode-coupling efficiency of ~46%. Furthermore, we demonstrate
pulsed current injection at a repetition rate exceeding 200 MHz as well as
low-temperature operation down to 77 K of the fiber-coupled micropillar device.
The technique presented in this work is an important step in the quest for
efficient and practical quantum light sources for applications in quantum
information.


Abstract:
  We study the weakly stable hyperbolic boundary value problem with a large
zero order oscillatory coefficient. This problem is related to linearized
problems in the study of Mach stem and vortex sheets. We wish to establish a
uniform energy estimate with respect to {\epsilon}, which is needed in
mentioned applications and justification of geometric optics solutions, but the
zero order oscillatory term gives rise to great obstacles. In this paper we
obtain positive results in the small/medium frequency region by adapting the
approach of Williams to a more general situation than the one treated there. We
also show that it is possible to construct high order approximate solutions by
the method of geometric optics for those systems without any restriction on
frequencies.


Abstract:
  We introduce a framework for uncertainty estimation that both describes and
extends many existing methods. We consider typical hyperparameters involved in
classical training as random variables and marginalise them out to capture
various sources of uncertainty in the parameter space. We investigate which
forms and combinations of marginalisation are most useful from a practical
point of view on standard benchmarking data sets. Moreover, we discuss how some
marginalisations may produce reliable estimates of uncertainty without the need
for extensive hyperparameter tuning and/or large-scale ensembling.


Abstract:
  Embedding silicon nanoparticles in an intermetallic matrix is a promising
strategy to produce remarkable bulk anode materials for lithium-ion (Li-ion)
batteries with low potential, high electrochemical capacity and good cycling
stability. These composite materials can be synthetized at a large scale using
mechanical milling. However, for Si-Ni3Sn4 composites, milling also induces a
chemical reaction between the two components leading to the formation of free
Sn and NiSi2, which is detrimental to the performance of the electrode. To
prevent this reaction, a modification of the surface chemistry of the silicon
has been undertaken. Si nanoparticles coated with a surface layer of either
carbon or oxide were used instead of pure silicon. The influence of the coating
on the composition, (micro)structure and electrochemical properties of
Si-Ni3Sn4 composites is studied and compared with that of pure Si. Si coating
strongly reduces the reaction between Si and Ni3Sn4 during milling. Moreover,
contrary to pure silicon, Si-coated composites have a plate-like mor-phology in
which the surface-modified silicon particles are surrounded by a
nanostructured, Ni3Sn4-based matrix leading to smooth potential profiles during
electrochemical cycling. The chemical homogeneity of the matrix is more uniform
for carbon-coated than for oxygen-coated silicon. As a consequence, different
electrochemical behaviors are obtained depending on the surface chemistry, with
better lithiation properties for the carbon-covered silicon able to deliver
over 500 mAh/g for at least 400 cycles.


Abstract:
  Stylized models of the neurodynamics that underpin sensory motor control in
animals are proposed and studied. The voluntary motions of animals are
typically initiated by high level intentions created in the primary cortex
through a combination of perceptions of the current state of the environment
along with memories of past reactions to similar states. Muscle movements are
produced as a result of neural processes in which the parallel activity of
large multiplicities of neurons generate signals that collectively lead to
desired actions. Essential to coordinated muscle movement are intentionality,
prediction, regions of the cortex dealing with misperceptions of sensory cues,
and a significant level of resilience with respect to disruptions in the neural
pathways through which signals must propagate. While linear models of feedback
control systems have been well studied over decades, this paper proposes and
analyzes a class of models whose aims are to capture some of the essential
features of neural control of movement. Whereas most linear models of feedback
systems entail a state component whose dimension is higher than the number of
inputs or outputs, the work that follows will treat models in which the numbers
of input and output channels greatly exceed the state dimension. While we begin
by considering continuous time systems, the aim will be to treat systems whose
evolution involves classes of inputs that emulate neural spike trains. Within
the proposed class of models, the paper will study resilience to channel
dropouts, the ways in which noise and uncertainty can be mitigated by an
appropriate notion of consensus among noisy inputs, and finally, by a simple
model in which binary activations of a multiplicity of input channels produce a
dynamic response that closely approximates the dynamics of a prescribed linear
system whose inputs are continuous functions of time.


Abstract:
  In a recent paper by N. Santowsky et al. [Phys. Rev. D 102, 056014 (2020)],
covariant coupled equations were derived to describe a tetraquark in terms of a
mix of four-quark states $2q 2\bar{q}$ and two-quark states $q\bar{q}$. These
equations were expressed in terms of vertices describing the disintegration of
a tetraquark into identical two-meson states, into a diquark-antidiquark pair,
and into a quark-antiquark pair. We show that these equations are inconsistent
as they imply a $q\bar{q}$ Bethe-Salpeter kernel that is $q\bar{q}$-reducible.


Abstract:
  In this work, we investigate the regularized solutions and their finite
element solutions to the inverse source problems governed by partial
differential equations, and establish the stochastic convergence and optimal
finite element convergence rates of these solutions, under pointwise
measurement data with random noise.
  Unlike most existing regularization theories, the regularization error
estimates are derived without any source conditions, while the error estimates
of finite element solutions show their explicit dependence on the noise level,
regularization parameter, mesh size, and time step size, which can guide
practical choices among these key parameters in real applications. The error
estimates also suggest an iterative algorithm for determining an optimal
regularization parameter. Numerical experiments are presented to demonstrate
the effectiveness of the analytical results.


Abstract:
  Matching markets are often organized in a multi-stage and decentralized
manner. Moreover, participants in real-world matching markets often have
uncertain preferences. This article develops a framework for learning optimal
strategies in such settings, based on a nonparametric statistical approach and
variational analysis. We propose an efficient algorithm, built upon concepts of
"lower uncertainty bound" and "calibrated decentralized matching," for
maximizing the participants' expected payoff. We show that there exists a
welfare-versus-fairness trade-off that is characterized by the uncertainty
level of acceptance. Participants will strategically act in favor of a low
uncertainty level to reduce competition and increase expected payoff. We prove
that participants can be better off with multi-stage matching compared to
single-stage matching. We demonstrate aspects of the theoretical predictions
through simulations and an experiment using real data from college admissions.