Abstract:
  The FCC-ee physics program will deliver two complementary top-notch precision
determinations of the W boson mass, and width. The first and main measurement
relies on the rapid rise of the W-pair production cross section near its
kinematic threshold. This method is extremely simple and clean, involving only
the selection and counting of events, in all different decay channels. An
optimal threshold-scan strategy with a total integrated luminosity of $12\,{\rm
ab}^{-1}$ shared on energy points between 157 and 163 GeV will provide a
statistical uncertainty on the W mass of 0.5 MeV and on the W width of 1.2 MeV.
For these measurements, the goal of keeping the impact of systematic
uncertainties below the statistical precision will be demanding, but feasible.
The second method exploits the W-pair final state reconstruction and kinematic
fit, making use of events with either four jets or two jets, one lepton and
missing energy. The projected statistical precision of the second method is
similar to the first method's, with uncertainties of $\sim 0.5$ ($1$) MeV for
the W mass (width), employing W-pair data collected at the production threshold
and at 240-365 GeV. For the kinematic reconstruction method, the final impact
of systematic uncertainties is currently less clear, in particular
uncertainties connected to the modelling of the W hadronic decays. The use and
interplay of Z$\gamma$ and ZZ events, reconstructed and fitted with the same
techniques as the WW events, will be important for the extraction of W mass
measurements with data at the higher 240 and 365 GeV energies.


Abstract:
  When studying the evolution of complex systems one refers to model
representations comprising various descriptive parameters. There is hardly
research where system evolution is described on the base of information flows
in the system. The paper focuses on the link between the dynamics of
information and system evolution. Information, exchanged between different
system's parts, before being processed is first provided with meaning by the
system. Meanings are generated from the perspective of hindsight, i.e. against
the arrow of time. The same information can be differently interpreted by
different system's parts (i,e,provided with different meanings) so that the
number of options for possible system development is proliferated. Some options
eventually turn into observable system states. So that system evolutionary
dynamics can be considered as due to information processing within the system.
This process is considered here in a model representation. The model under
study is Triple Helix (TH) model, which was earlier used to describe
interactions between university, industry and government to foster innovations.
In TH model the system is comprised of three interacting parts where each part
process information ina different way. The model is not limited to the sphere
of innovation and can be used in a broader perspective. Here TH is
conceptualized in the framework of three compertment model used to describe
infectious disease. The paper demonstrates how the dynamics of information and
meaning can be incorporated in the description of Covid-19 infectious
propagation. The results show correspondence of model predictions with
observable infection dynamics.


Abstract:
  In the Maximum Minimal Vertex Cover (MMVC) problem, we are given a graph $G$
and a positive integer $k$, and the objective is to decide whether $G$ contains
a minimal vertex cover of size at least $k$. Motivated by the kernelization of
MMVC with parameter $k$, our main contribution is to introduce a simple general
framework to obtain kernelization lower bounds for a certain type of kernels
for optimization problems, which we call lop-kernels. Informally, this type of
kernels is required to preserve large optimal solutions in the reduced
instance, and captures the vast majority of existing kernels in the literature.
  As a consequence of this framework, we show that the trivial quadratic kernel
for MMVC is essentially optimal, answering a question of Boria et al. [Discret.
Appl. Math. 2015], and that the known cubic kernel for Maximum Minimal Feedback
Vertex Set is also essentially optimal. We present further applications for
Tree Deletion Set and for Maximum Independent Set on $K_t$-free graphs.
  Back to the MMVC problem, given the (plausible) non-existence of subquadratic
kernels for MMVC on general graphs, we provide subquadratic kernels on $H$-free
graphs for several graphs $H$, such as the bull, the paw, or the complete
graphs, by making use of the Erd\"os-Hajnal property. Finally, we prove that
MMVC does not admit polynomial kernels parameterized by the size of a minimum
vertex cover of the input graph, even on bipartite graphs, unless ${\sf NP}
\subseteq {\sf coNP} / {\sf poly}$.


Abstract:
  This paper proposes parametric and non-parametric hypothesis testing
algorithms for detecting anisotropy -- rotational variance of the covariance
function in random fields. Both algorithms are based on resampling mechanisms,
which enable avoiding relying on asymptotic assumptions, as is common in
previous algorithms. The algorithms' performance is illustrated numerically in
simulation experiments and on real datasets representing a variety of potential
challenges.


Abstract:
  In this paper, we consider a multi-agent resilient consensus problem, where
some of the nodes may behave maliciously. The approach is to equip all nodes
with a scheme to detect neighboring nodes when they behave in an abnormal
fashion. To this end, the nodes exchange not only their own states but also
information regarding their neighbor nodes. Such two-hop communication has long
been studied in fault-tolerant algorithms in computer science. We propose two
distributed schemes for detection of malicious nodes and resilient consensus
with different requirements on resources for communication and the structures
of the networks. In particular, the detection schemes become effective under
certain connectivity properties in the network so that the non-malicious nodes
can share enough information about their neighbors. It is shown that the
requirements are however less stringent than those for conventional algorithms.
A numerical example is presented to demonstrate the performance of the proposed
methods in wireless sensor networks.


Abstract:
  Recently, the European Commission supported by many European countries has
announced large investments towards the commercialization of quantum technology
(QT) to address and mitigate some of the biggest challenges facing today's
digital era - e.g. secure communication and computing power. For more than two
decades the QT community has been working on the development of QTs, which
promise landmark breakthroughs leading to commercialization in various areas.
The ambitious goals of the QT community and expectations of EU authorities
cannot be met solely by individual initiatives of single countries, and
therefore, require a combined European effort of large and unprecedented
dimensions comparable only to the Galileo or Copernicus programs. Strong
international competition calls for a coordinated European effort towards the
development of QT in and for space, including research and development of
technology in the areas of communication and sensing. Here, we aim at
summarizing the state of the art in the development of quantum technologies
which have an impact in the field of space applications. Our goal is to outline
a complete framework for the design, development, implementation, and
exploitation of quantum technology in space.


Abstract:
  Traditionally, practitioners use formal methods pre-dominately for one half
of the quality-assurance process: verification (do we build the software
right?). The other half -- validation (do we build the right software?) -- has
been given comparatively little attention. While verification is the core of
refinement-based formal methods, where each new refinement step must preserve
all properties of its abstract model, validation is usually postponed until the
latest stages of the development, when models can be automatically executed.
Thus mistakes in requirements or in their interpretation are caught too late:
usually at the end of the development process. In this paper, we present a
novel approach to check compliance between requirements and their formal
refinement-based specification during the earlier stages of development. Our
proposed approach -- "validation obligations" -- is based on the simple idea
that both verification and validation are an integral part of all refinement
steps of a system.


Abstract:
  We study the problem of recovering the common $k$-sized support of a set of
$n$ samples of dimension $d$, using $m$ noisy linear measurements per sample.
Most prior work has focused on the case when $m$ exceeds $k$, in which case $n$
of the order $(k/m)\log(d/k)$ is both necessary and sufficient. Thus, in this
regime, only the total number of measurements across the samples matter, and
there is not much benefit in getting more than $k$ measurements per sample. In
the measurement-constrained regime where we have access to fewer than $k$
measurements per sample, we show an upper bound of $O((k^{2}/m^{2})\log d)$ on
the sample complexity for successful support recovery when $m\ge 2\log d$.
Along with the lower bound from our previous work, this shows a phase
transition for the sample complexity of this problem around $k/m=1$. In fact,
our proposed algorithm is sample-optimal in both the regimes. It follows that,
in the $m\ll k$ regime, multiple measurements from the same sample are more
valuable than measurements from different samples.


Abstract:
  The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics
captures the most relevant developments in AI Ethics since October 2020. It
aims to help anyone, from machine learning experts to human rights activists
and policymakers, quickly digest and understand the field's ever-changing
developments. Through research and article summaries, as well as expert
commentary, this report distills the research and reporting surrounding various
domains related to the ethics of AI, including: algorithmic injustice,
discrimination, ethical AI, labor impacts, misinformation, privacy, risk and
security, social media, and more.
  In addition, The State of AI Ethics includes exclusive content written by
world-class AI Ethics experts from universities, research institutes,
consulting firms, and governments. Unique to this report is "The Abuse and
Misogynoir Playbook," written by Dr. Katlyn Tuner (Research Scientist, Space
Enabled Research Group, MIT), Dr. Danielle Wood (Assistant Professor, Program
in Media Arts and Sciences; Assistant Professor, Aeronautics and Astronautics;
Lead, Space Enabled Research Group, MIT) and Dr. Catherine D'Ignazio (Assistant
Professor, Urban Science and Planning; Director, Data + Feminism Lab, MIT). The
piece (and accompanying infographic), is a deep-dive into the historical and
systematic silencing, erasure, and revision of Black women's contributions to
knowledge and scholarship in the United Stations, and globally. Exposing and
countering this Playbook has become increasingly important following the firing
of AI Ethics expert Dr. Timnit Gebru (and several of her supporters) at Google.
  This report should be used not only as a point of reference and insight on
the latest thinking in the field of AI Ethics, but should also be used as a
tool for introspection as we aim to foster a more nuanced conversation
regarding the impacts of AI on the world.


Abstract:
  We consider the random $\beta$-transformation $K_{\beta}$, defined on
$\{0,1\}^{\mathbb N}\times[0, \frac{\lfloor\beta\rfloor}{\beta-1}]$, that
generates all possible expansions of the form
$x=\sum_{i=0}^{\infty}\frac{a_i}{\beta^i}$, where $a_i\in
\{0,1,\cdots,\lfloor\beta\rfloor\}$. This transformation was first introduced
by Dajani and Kraaikamp, and later studied by Dajani and de Vries, where two
natural invariant ergodic measures were found. The first is the unique measure
of maximal entropy, and the second is a measure of the form $m_p\times
\mu_{\beta}$, with $m_p$ the Bernoulli $(p,1-p)$ product measure and
$\mu_{\beta}$ is a measure equivalent to Lebesgue measure. In this paper, we
give an uncountable family of $K_{\beta}$-invariant exact $g$-measures for a
certain collection of algebraic $\beta$'s.


Abstract:
  This paper describes the solution of Shanda Innovations team to Task 1 of
KDD-Cup 2012. A novel approach called Multifaceted Factorization Models is
proposed to incorporate a great variety of features in social networks. Social
relationships and actions between users are integrated as implicit feedbacks to
improve the recommendation accuracy. Keywords, tags, profiles, time and some
other features are also utilized for modeling user interests. In addition, user
behaviors are modeled from the durations of recommendation records. A
context-aware ensemble framework is then applied to combine multiple predictors
and produce final recommendation results. The proposed approach obtained
0.43959 (public score) / 0.41874 (private score) on the testing dataset, which
achieved the 2nd place in the KDD-Cup competition.


Abstract:
  Since human-labeled samples are free for the target set, unsupervised person
re-identification (Re-ID) has attracted much attention in recent years, by
additionally exploiting the source set. However, due to the differences on
camera styles, illumination and backgrounds, there exists a large gap between
source domain and target domain, introducing a great challenge on cross-domain
matching. To tackle this problem, in this paper we propose a novel method named
Dual-stream Reciprocal Disentanglement Learning (DRDL), which is quite
efficient in learning domain-invariant features. In DRDL, two encoders are
first constructed for id-related and id-unrelated feature extractions, which
are respectively measured by their associated classifiers. Furthermore,
followed by an adversarial learning strategy, both streams reciprocally and
positively effect each other, so that the id-related features and id-unrelated
features are completely disentangled from a given image, allowing the encoder
to be powerful enough to obtain the discriminative but domain-invariant
features. In contrast to existing approaches, our proposed method is free from
image generation, which not only reduces the computational complexity
remarkably, but also removes redundant information from id-related features.
Extensive experiments substantiate the superiority of our proposed method
compared with the state-of-the-arts. The source code has been released in
https://github.com/lhf12278/DRDL.


Abstract:
  A boost in the development of flexible and wearable electronics facilitates
the design of new materials to be applied as transparent conducting films
(TCFs). Although single-walled carbon nanotube (SWCNT) films are the most
promising candidates for flexible TCFs, they still do not meet optoelectronic
requirements demanded their successful industrial integration. In this study,
we proposed and thoroughly investigated a new approach that comprises
simultaneous bilateral (outer and inner surfaces) SWCNT doping after their
opening by thermal treatment at 400 C under an ambient air atmosphere. Doping
by a chloroauric acid (HAuCl$_{4}$) ethanol solution allowed us to achieve the
record value of sheet resistance of 31 $\pm$ 4 $\Omega$/sq at a transmittance
of 90% in the middle of visible spectra (550 nm). The strong p-doping was
examined by open-circuit potential (OCP) measurements and confirmed by ab
initio calculations demonstrating a downshift of Fermi level around 1 eV for
the case of bilateral doping.


Abstract:
  In this paper, we consider the Cauchy problem for semi-linear wave equations
with structural damping term $\nu (-\Delta)^2 u_t$, where $\nu >0$ is a
constant. As being mentioned in [8,10], the linear principal part brings both
the diffusion phenomenon and the regularity loss of solutions. This implies
that, for the nonlinear problems, the choice of solution spaces plays an
important role to obtain global solutions with sharp decay properties in time.
Our main purpose of this paper is to prove the global (in time) existence of
solutions for the small data and their decay properties for the supercritical
nonlinearities.


Abstract:
  We investigate several aspects of BPS latitude Wilson loops in gauge theories
in three dimensions with $\mathcal{N}\ge 4$ supersymmetry. We derive a matrix
model for the bosonic latitude Wilson loop in ABJM using supersymmetric
localization, and show how to extend the computation to more general
Chern-Simons-matter theories. We then define latitude type Wilson and vortex
loop operators in theories without Chern-Simons terms, and explore a connection
to the recently derived superalgebra defining local Higgs and Coulomb branch
operators in these theories. Finally, we identify a BPS loop operator dual to
the bosonic latitude Wilson loop which is a novel bound state of Wilson and
vortex loops, defined using a worldvolume supersymmetric quantum mechanics.


Abstract:
  In this paper we derive closed-form formulas of feedback capacity and
nonfeedback achievable rates, for Additive Gaussian Noise (AGN) channels driven
by nonstationary autoregressive moving average (ARMA) noise (with unstable one
poles and zeros), based on time-invariant feedback codes and channel input
distributions. From the analysis and simulations follows the surprising
observations, (i) the use of time-invariant channel input distributions gives
rise to multiple regimes of capacity that depend on the parameters of the ARMA
noise, which may or may not use feedback, (ii) the more unstable the pole
(resp. zero) of the ARMA noise the higher (resp. lower) the feedback capacity,
(iii) certain conditions, known as detectability and stabilizability are
necessary and sufficient to ensure the feedback capacity formulas and
nonfeedback achievable rates {\it are independent of the initial state of the
ARMA noise}. Another surprizing observation is that Kim's \cite{kim2010}
characterization of feedback capacity which is developed for stable ARMA noise,
if applied to the unstable ARMA noise, gives a lower value of feedback capacity
compared to our feedback capacity formula.


Abstract:
  Let $X_{1,n}\le\cdots\le X_{n,n}$ be the order statistics of $n$ independent
random variables with a common distribution function $F$ having right heavy
tail with tail index $\gamma$. Given known constants $d_{i,n}$, $1\le i\le n$,
consider the weighted power sums
$\sum^{k_n}_{i=1}d_{n+1-i,n}\log^pX_{n+1-i,n}$, where $p>0$ and the $k_n$ are
positive integers such that $k_n\to\infty$ and $k_n/n\to0$ as $n\to\infty$.
Under some constraints on the weights $d_{i,n}$, we prove asymptotic normality
for the power sums over the whole heavy-tail model. We apply the obtained
result to construct a new class of estimators for the parameter $\gamma$.


Abstract:
  The magnetic damping constant is a critical parameter for magnetization
dynamics and the efficiency of memory devices and magnon transport. Therefore,
its manipulation by electric fields is crucial in spintronics. Here, we
theoretically demonstrate the voltage-control of magnetic damping in ferro- and
ferrimagnetic-insulator (FI)/topological-insulator (TI) bilayers. Assuming a
capacitor-like setup, we formulate an effective dissipation torque induced by
spin-charge pumping at the FI/TI interface as a function of an applied voltage.
By using realistic material parameters, we find that the effective damping for
a FI with 10nm thickness can be tuned by one order of magnitude under the
voltage with 0.25V. Also, we provide perspectives on the voltage-induced
modulation of the magnon spin transport on proximity-coupled FIs.


Abstract:
  The gradient flow exact renormalization group (GFERG) is an exact
renormalization group motivated by the Yang--Mills gradient flow and its
salient feature is a manifest gauge invariance. We generalize this GFERG,
originally formulated for the pure Yang--Mills theory, to vector-like gauge
theories containing fermion fields, keeping the manifest gauge invariance. For
the chiral symmetry we have two options: one possible formulation preserves the
conventional form of the chiral symmetry and the other simpler formulation
realizes the chiral symmetry in a modified form \`a la Ginsparg--Wilson. We
work out a gauge-invariant local Wilson action in quantum electrodynamics to
the lowest nontrivial order of perturbation theory. This Wilson action
reproduces the correct axial anomaly in~$D=2$.


Abstract:
  A detailed theoretical analysis of the 1s photoionization of neon is
presented. It is found that the most significant many-electron correlation in
computing photoionization of inner shells is the rearrangement of the outer
shells caused by the inner vacancy. Further noticeable effects are: (i) the
polarization of the ion core by the outgoing photoelectron and (ii) the
coherent effect of double excitation/ionization. The core polarization
increases the photoionization cross section by about 10% at the 1s threshold,
and the coherent excitation results in further increases by about 5%.
Incoherent excitation of the satellite channel leads to an additional 10%
increase in the photoabsorption cross section in the double-ionization
threshold region.


Abstract:
  We study the self-tuning subclass of kinetic gravity braiding and obtain
robust predictions on self-tuning and dynamics in the tadpole-free shift
symmetric sector of the theory. In particular, we show inevitability of cosmic
acceleration, prove the dynamical stability of this late-time asymptotic state,
and derive ghost and gradient stability constraints on the self-tuning vacuum.
We discuss the results concretely in the context of generalized cubic covariant
Galileon theory and an exponential kinetic gravity braiding.


Abstract:
  On April 7, 2001 the Muon g-2 Experiment at Fermilab presented its first
results which lead to a 4.2$\sigma$ discrepancy between the world average of
the muon anomalous magnetic dipole moment and the standard model prediction of
this quantity. I take issue with the statistical meaning of this finding.


Abstract:
  In this paper we suggest NICE: a new algorithm to generate counterfactual
explanations for heterogeneous tabular data. The design of our algorithm
specifically takes into account algorithmic requirements that often emerge in
real-life deployments: the ability to provide an explanation for all
predictions, being efficient in run-time, and being able to handle any
classification model (also non-differentiable ones). More specifically, our
approach exploits information from a nearest instance tospeed up the search
process. We propose four versions of NICE, where three of them optimize the
explanations for one of the following properties: sparsity, proximity or
plausibility. An extensive empirical comparison on 10 datasets shows that our
algorithm performs better on all properties than the current state-of-the-art.
These analyses show a trade-off between on the one hand plausiblity and on the
other hand proximity or sparsity, with our different optimization methods
offering the choice to select the preferred trade-off. An open-source
implementation of NICE can be found at https://github.com/ADMAntwerp/NICE.


Abstract:
  Responsible development of technology involves applications being inclusive
of the diverse set of users they hope to support. An important part of this is
understanding the many ways to refer to a person and being able to fluently
change between the different forms as needed. We perform a case study on the
singular they, a common way to promote gender inclusion in English. We define a
re-writing task, create an evaluation benchmark, and show how a model can be
trained to produce gender-neutral English with <1% word error rate with no
human-labeled data. We discuss the practical applications and ethical
considerations of the task, providing direction for future work into inclusive
natural language systems.


Abstract:
  Physics of self-propelled objects at the nanoscale is a rapidly developing
research field where recent experiments focused on motion of individual
catalytic enzymes. Contrary to the experimental advancements, theoretical
understanding of possible self-propulsion mechanisms at these scales is
limited. A particularly puzzling question concerns origins of reportedly high
diffusivities of the individual enzymes. Here we start with the fundamental
principle of microscopic reversibility (MR) of chemical reactions powering the
self-propulsion and demonstrate that MR can lead to increase of the particle
mobility and of short- and long-time diffusion coefficients as compared to
dynamics where MR is neglected. Moreover, the diffusion coefficients can be
enhanced by a constant external force. We propose a way to use these effects in
experimental investigations of active propulsion mechanisms at the nanoscale.
Our results can shed new light on interpretation of the measured diffusivities
and help to test and to evaluate relevance of MR for the active motion of
individual nanoswimmers.


Abstract:
  The current event rate estimates of long gamma-ray bursts based on distinct
methods or samples especially at lower redshift are largely debated, which
motivates us to re-study the dependence of luminosity function and event rates
for different burst samples on the criteria of sample selection and threshold
effect in this letter. To ensure the sample completeness as possible, we have
chosen two samples including 88 and 118 long bright bursts with known redshift
and peak flux over 2.6 ph cm$^{-2}$ s$^{-1}$. It is found that the evolution of
luminosity with redshift can be expressed by $L\propto(1+z)^k$ with a diverse
$k$ relied more on the sample selection. Interestingly, the cumulative
distributions of either non-evolving luminosities or redshifts are found to be
also determined by the sample selection rather the instrumental sensitivity.
Nevertheless, the non-evolving luminosities of our samples are similarly
distributed with a comparable break luminosity of $L_0\sim10^{51}$ erg
s$^{-1}$. Importantly, we verify with a K-S test that three cases of event
rates for the two burst samples evolve with redshift similarly except a small
discrepancy due to sampling differences at low-redshift of $z<1$, in which all
event rates show an excess of gaussian profile instead of monotonous decline.
Most importantly, it is found that the low-redshift burst event rates violate
the star formation rates, while both of them are good in agreement with each
other in the higher-redshift regions as many authors discovered previously.
Consequently, we predict that two types of long gamma-ray bursts should be
expected on the basis of whether they match the star formation or not.


Abstract:
  Voltage control of magnetism and spintronics have been highly desirable, but
rarely realized. In this work, we show voltage-controlled spin-orbit torque
(SOT) switching in W/CoFeB/MgO films with perpendicular magnetic anisotropy
(PMA) with voltage administered through SrTiO3 with a high dielectric constant.
We show that a DC voltage can significantly lower PMA by 45%, reduce switching
current by 23%, and increase the damping-like torque as revealed by the first
and second-harmonic measurements. These are characteristics that are
prerequisites for voltage-controlled and voltage-select SOT switching
spintronic devices.


Abstract:
  We present a link between the theory of deep water waves and that of bubble
surface perturbations. Theory correspondence is shown analytically for small
wavelengths in the linear regime and investigated numerically in the nonlinear
regime. To do so, we develop the second-order spatial perturbation equations
for the Rayleigh-Plesset equation and solve them numerically. Our code is
publicly available. Studying capillary waves on stable bubbles, we recreate the
Kolmogorov-Zakharov spectrum predicted by weak turbulence theory, putting wave
turbulence theory to use for bubbles. In this investigation, it seems that
curvature does not affect turbulent properties. Calculated bubble surface
qualitatively responds to low gravity experiments. The link demonstrated opens
new possibilities for studying several bubble phenomena, including
sonoluminescence and cavitation, using the extensive tools developed in the
wave turbulence framework.


Abstract:
  We consider one-dimensional infinite chains of harmonic oscillators with
random exchanges of momenta and long-range interaction potentials which have
polynomial decay rate $|x|^{-\theta}, x \to \infty, \theta > 1$ where $x \in
\mathbb{Z}$ is the interaction range. The dynamics conserve total momentum,
total length and total energy. We prove that the systems evolve macroscopically
on superballistic space-time scale $(y \varepsilon^{-1}, t \varepsilon^{-
\frac{\theta - 1}{2}})$ when $1 < \theta < 3$, $(y \varepsilon^{-1}, t
\varepsilon^{-1} \sqrt{- \log(\varepsilon^{-1})}^{-1})$ when $\theta = 3$, and
ballistic space-time scale $(y \varepsilon^{-1}, t \varepsilon^{-1})$ when
$\theta > 3$. Combining our results and the results in [10], we show the
existence of two different space-time scales on which the systems evolve. In
addition, we prove scaling limits of recentered normal modes of superballistic
wave equations, which are analogues of Riemann invariants and capture
fluctuations around characteristics. The space-time scale is superdiffusive
when $2 < \theta \le 4$ and diffusive when $\theta > 4$.


Abstract:
  This dissertation presents a semiclassical analysis of conical topology
change in $1+1$ spacetime dimensions wherein, to lowest order, the ambient
spacetime is classical and fixed while the scalar field coupled to it is
quantized. The vacuum expectation value of the scalar field stress-energy
tensor is calculated via two different approaches. The first of these involves
the explicit determination of the so called Sorkin-Johnston state on the cone
and an original regularization scheme, while the latter employs the conformal
vacuum and the more conventional point-splitting renormalization. It is found
that conical topology change seems not to suffer from the same pathologies that
trousers-type topology change does. This provides tentative agreement with
conjectures due to Sorkin and Borde, which attempt to classify topology
changing spacetimes with respect to their Morse critical points and in
particular, that the cone and yarmulke in $1+1$ dimensions lack critical points
of unit Morse index.


Abstract:
  Reservoir computing is a best-in-class machine learning algorithm for
processing information generated by dynamical systems using observed
time-series data. Importantly, it requires very small training data sets, uses
linear optimization, and thus requires minimal computing resources. However,
the algorithm uses randomly sampled matrices to define the underlying recurrent
neural network and has a multitude of metaparameters that must be optimized.
Recent results demonstrate the equivalence of reservoir computing to nonlinear
vector autoregression, which requires no random matrices, fewer metaparameters,
and provides interpretable results. Here, we demonstrate that nonlinear vector
autoregression excels at reservoir computing benchmark tasks and requires even
shorter training data sets and training time, heralding the next generation of
reservoir computing.


Abstract:
  Fireballs created in relativistic heavy-ion collisions at different beam
energies have been argued to follow different trajectories in the QCD phase
diagram in which the QCD critical point serves as a landmark. Using a
(1+1)-dimensional model setting with transverse homogeneity, we study the
complexities introduced by the fact that the evolution history of each fireball
cannot be characterized by a single trajectory but rather covers an entire
swath of the phase diagram, with the finally emitted hadron spectra integrating
over contributions from many different trajectories. Studying the phase diagram
trajectories of fluid cells at different space-time rapidities, we explore how
baryon diffusion shuffles them around, and how they are affected by critical
dynamics near the QCD critical point. We find a striking insensitivity of
baryon diffusion to critical effects. Its origins are analyzed and possible
implications discussed.


Abstract:
  In many computer vision classification tasks, class priors at test time often
differ from priors on the training set. In the case of such prior shift,
classifiers must be adapted correspondingly to maintain close to optimal
performance. This paper analyzes methods for adaptation of probabilistic
classifiers to new priors and for estimating new priors on an unlabeled test
set. We propose a novel method to address a known issue of prior estimation
methods based on confusion matrices, where inconsistent estimates of decision
probabilities and confusion matrices lead to negative values in the estimated
priors. Experiments on fine-grained image classification datasets provide
insight into the best practice of prior shift estimation and classifier
adaptation, and show that the proposed method achieves state-of-the-art results
in prior adaptation. Applying the best practice to two tasks with naturally
imbalanced priors, learning from web-crawled images and plant species
classification, increased the recognition accuracy by 1.1% and 3.4%
respectively.


Abstract:
  We introduce a hybrid method to couple continuous Galerkin finite element
methods and high-order finite difference methods in a nonconforming multiblock
fashion. The aim is to optimize computational efficiency when complex
geometries are present. The proposed coupling technique requires minimal
changes in the existing schemes while maintaining strict stability, accuracy,
and energy conservation. Results are demonstrated on linear and nonlinear
scalar conservation laws in two spatial dimensions.


Abstract:
  We calculate the collinear anomalous dimensions in massless four-loop QCD and
$\mathcal{N} = 4$ supersymmetric Yang-Mills theory from the infrared poles of
vertex form factors. We give very precise numerical approximations and a
conjecture for the complete analytic results in both models we consider.


Abstract:
  Communication technologies like voice over IP operate under constrained
real-time conditions, with voice packets being subject to delays and losses
from the network. In such cases, the packet loss concealment (PLC) algorithm
reconstructs missing frames until a new real packet is received. Recently,
autoregressive deep neural networks have been shown to surpass the quality of
signal processing methods for PLC, specially for long-term predictions beyond
60 ms. In this work, we propose a non-autoregressive adversarial auto-encoder,
named PLAAE, to perform real-time PLC in the waveform domain. PLAAE has a
causal convolutional structure, and it learns in an auto-encoder fashion to
reconstruct signals with gaps, with the help of an adversarial loss. During
inference, it is able to predict smooth and coherent continuations of such gaps
in a single feed-forward step, as opposed to autoregressive models. Our
evaluation highlights the superiority of PLAAE over two classic PLCs and two
deep autoregressive models in terms of spectral and intonation reconstruction,
perceptual quality, and intelligibility.


Abstract:
  Informal lecture notes with examples on sheaf theory and the derived category
of sheaves; sheaves and Morse theory; perverse sheaves, and some applications
to representation theory. Added Oct 2021: cellular perverse sheaves. Proofs are
outlined with references for further details.


Abstract:
  Facial expression spotting is the preliminary step for micro- and
macro-expression analysis. The task of reliably spotting such expressions in
video sequences is currently unsolved. The current best systems depend upon
optical flow methods to extract regional motion features, before categorisation
of that motion into a specific class of facial movement. Optical flow is
susceptible to drift error, which introduces a serious problem for motions with
long-term dependencies, such as high frame-rate macro-expression. We propose a
purely deep learning solution which, rather than tracking frame differential
motion, compares via a convolutional model, each frame with two temporally
local reference frames. Reference frames are sampled according to calculated
micro- and macro-expression durations. We show that our solution achieves
state-of-the-art performance (F1-score of 0.105) in a dataset of high
frame-rate (200 fps) long video sequences (SAMM-LV) and is competitive in a low
frame-rate (30 fps) dataset (CAS(ME)2). In this paper, we document our deep
learning model and parameters, including how we use local contrast
normalisation, which we show is critical for optimal results. We surpass a
limitation in existing methods, and advance the state of deep learning in the
domain of facial expression spotting.


Abstract:
  This paper presents new \emph{variance-aware} confidence sets for linear
bandits and linear mixture Markov Decision Processes (MDPs). With the new
confidence sets, we obtain the follow regret bounds: For linear bandits, we
obtain an $\tilde{O}(poly(d)\sqrt{1 + \sum_{k=1}^{K}\sigma_k^2})$
data-dependent regret bound, where $d$ is the feature dimension, $K$ is the
number of rounds, and $\sigma_k^2$ is the \emph{unknown} variance of the reward
at the $k$-th round. This is the first regret bound that only scales with the
variance and the dimension but \emph{no explicit polynomial dependency on $K$}.
When variances are small, this bound can be significantly smaller than the
$\tilde{\Theta}\left(d\sqrt{K}\right)$ worst-case regret bound. For linear
mixture MDPs, we obtain an $\tilde{O}(poly(d, \log H)\sqrt{K})$ regret bound,
where $d$ is the number of base models, $K$ is the number of episodes, and $H$
is the planning horizon. This is the first regret bound that only scales
\emph{logarithmically} with $H$ in the reinforcement learning with linear
function approximation setting, thus \emph{exponentially improving} existing
results, and resolving an open problem in \citep{zhou2020nearly}. We develop
three technical ideas that may be of independent interest: 1) applications of
the peeling technique to both the input norm and the variance magnitude, 2) a
recursion-based estimator for the variance, and 3) a new convex potential lemma
that generalizes the seminal elliptical potential lemma.


Abstract:
  A theoretical framework and numerical simulations quantifying the impact of
atmospheric group velocity dispersion on wireless terahertz communication link
error rate were developed based upon experimental work. We present, for the
first time, predictions of symbol error rate as a function of link distance,
signal bandwidth, signal-to-noise ratio, and atmospheric conditions, revealing
that long-distance, broadband terahertz communication systems may be limited by
inter-symbol interference stemming from group velocity dispersion, rather than
attenuation. In such dispersion limited links, increasing signal strength does
not improve the symbol error rate and, consequently, theoretical predictions of
symbol error rate based only on signal-to-noise ratio are invalid for the
broadband case. This work establishes a new and necessary foundation for link
budget analysis in future long-distance terahertz communication systems that
accounts for the non-negligible effects of both attenuation and dispersion.


Abstract:
  We define a spatially-dependent fragmentation process, which involves
rectangles breaking up into progressively smaller pieces at rates that depend
on their shape. Long, thin rectangles are more likely to break quickly, and are
also more likely to split along their longest side. We are interested in how
the system evolves over time: how many fragments are there of different shapes
and sizes, and how did they reach that state? Our theorem gives an almost sure
growth rate along paths, which does not match the growth rate in expectation -
there are paths where the expected number of fragments of that shape and size
is exponentially large, but in reality no such fragments exist at large times
almost surely.


Abstract:
  WhatsApp is the most popular messaging app in the world. Due to its
popularity, WhatsApp has become a powerful and cheap tool for political
campaigning being widely used during the 2019 Indian general election, where it
was used to connect to the voters on a large scale. Along with the campaigning,
there have been reports that WhatsApp has also become a breeding ground for
harmful speech against various protected groups and religious minorities. Many
such messages attempt to instil fear among the population about a specific
(minority) community. According to research on inter-group conflict, such `fear
speech' messages could have a lasting impact and might lead to real offline
violence. In this paper, we perform the first large scale study on fear speech
across thousands of public WhatsApp groups discussing politics in India. We
curate a new dataset and try to characterize fear speech from this dataset. We
observe that users writing fear speech messages use various events and symbols
to create the illusion of fear among the reader about a target community. We
build models to classify fear speech and observe that current state-of-the-art
NLP models do not perform well at this task. Fear speech messages tend to
spread faster and could potentially go undetected by classifiers built to
detect traditional toxic speech due to their low toxic nature. Finally, using a
novel methodology to target users with Facebook ads, we conduct a survey among
the users of these WhatsApp groups to understand the types of users who consume
and share fear speech. We believe that this work opens up new research
questions that are very different from tackling hate speech which the research
community has been traditionally involved in.


Abstract:
  The quantification of uncertainties of computer simulations due to input
parameter uncertainties is paramount to assess a model's credibility. For
computationally expensive simulations, this is often feasible only via
surrogate models that are learned from a small set of simulation samples. The
surrogate models are commonly chosen and deemed trustworthy based on heuristic
measures, and substituted for the simulation in order to approximately
propagate the simulation input uncertainties to the simulation output. In the
process, the contribution of the uncertainties of the surrogate itself to the
simulation output uncertainties are usually neglected. In this work, we
specifically address the case of doubtful surrogate trustworthiness, i.e.
non-negligible surrogate uncertainties. We find that Bayesian probability
theory yields a natural measure of surrogate trustworthiness, and that
surrogate uncertainties can easily be included in simulation output
uncertainties. For a Gaussian likelihood for the simulation data, with unknown
surrogate variance and given a generalized linear surrogate model, the
resulting formulas reduce to simple matrix multiplications. The framework
contains Polynomial Chaos Expansions as a special case, and is easily extended
to Gaussian Process Regression. Additionally, we show a simple way to
implicitly include spatio-temporal correlations. Lastly, we demonstrate a
numerical example where surrogate uncertainties are in part negligible and in
part non-negligible.


Abstract:
  Classical regression has a simple geometric description in terms of a
projection of the training labels onto the column space of the design matrix.
However, for over-parameterized models -- where the number of fit parameters is
large enough to perfectly fit the training data -- this picture becomes
uninformative. Here, we present an alternative geometric interpretation of
regression that applies to both under- and over-parameterized models. Unlike
the classical picture which takes place in the space of training labels, our
new picture resides in the space of input features. This new feature-based
perspective provides a natural geometric interpretation of the double-descent
phenomenon in the context of bias and variance, explaining why it can occur
even in the absence of label noise. Furthermore, we show that adversarial
perturbations -- small perturbations to the input features that result in large
changes in label values -- are a generic feature of biased models, arising from
the underlying geometry. We demonstrate these ideas by analyzing three minimal
models for over-parameterized linear least squares regression: without basis
functions (input features equal model features) and with linear or nonlinear
basis functions (two-layer neural networks with linear or nonlinear activation
functions, respectively).


Abstract:
  Clustering artworks is difficult for several reasons. On the one hand,
recognizing meaningful patterns based on domain knowledge and visual perception
is extremely hard. On the other hand, applying traditional clustering and
feature reduction techniques to the highly dimensional pixel space can be
ineffective. To address these issues, in this paper we propose DELIUS: a DEep
learning approach to cLustering vIsUal artS. The method uses a pre-trained
convolutional network to extract features and then feeds these features into a
deep embedded clustering model, where the task of mapping the raw input data to
a latent space is jointly optimized with the task of finding a set of cluster
centroids in this latent space. Quantitative and qualitative experimental
results show the effectiveness of the proposed method. DELIUS can be useful for
several tasks related to art analysis, in particular visual link retrieval and
historical knowledge discovery in painting datasets.


Abstract:
  This paper describes the operation of the Coherent CAPTAIN-Mills (CCM)
detector located in the Lujan Neutron Science Center at Los Alamos National
Laboratory. CCM is a 10-ton liquid argon (LAr) detector located 20 meters from
a high flux neutron/neutrino source and is designed to search for sterile
neutrinos and light dark matter. An engineering run was performed in Fall 2019
to study the characteristics of the CCM120 detector by searching for signals
consistent with sterile neutrinos and light dark matter resulting from $\pi^+$
and $\pi^0$ decays in the tungsten target. New parameter space in a leptophobic
dark matter model was excluded for dark matter masses between $\sim2.5$ and 60
MeV. The lessons learned from this run have guided the development and
construction of the new CCM200 detector that will begin operations in 2021 and
significantly improve on these searches.


Abstract:
  Let $F:\mathcal{A}\to \mathcal{B}$ be a left adjoint between abelian
categories and let $Ch(F)$ be the induced left adjoint on chain complexes. If
the abelian categories $\mathcal{A}$ and $\mathcal{B}$ are equipped with
sufficiently nice cotorsion pairs, then we find model structures on their
categories of chain complexes. This paper gives novel criteria under which the
induced adjunction becomes a Quillen adjunction. In particular, our criteria
can be checked on the level of the underlying abelian categories instead of
chain complexes. We do the same for adjunctions of $n$ variables and we also
show how our results imply that the flat model structure can be used to compute
the derived tensor product.


Abstract:
  Existing algorithms with iterations as the principle for 3D inverse heat
conduction problems (IHCPs) are usually time-consuming. With the recent
advancements in deep learning techniques, it is possible to apply the neural
network to compute IHCPs. In this paper, a new framework based on
Convolutional-LSTM is introduced to predict the transient heat flux via
measured temperature. The inverse heat conduction models concerned in this work
have 3D complex structures with non-linear boundary conditions and
thermophysical parameters. In order to reach high precision, a forward solver
based on the finite element method is utilized to generate sufficient data for
training. The fully trained framework can provide accurate predictions
efficiently once the measured temperature and models are acquired. It is
believed that the proposed framework offers a new pattern for real-time heat
flux inversion.


Abstract:
  The origin of supermassive black holes (with $\gtrsim\!10^9\,M_{\odot}$) in
the early universe (redshift $z \sim 7$) remains poorly understood.
Gravitational collapse of a massive primordial gas cloud is a promising initial
process, but theoretical studies have difficulty growing the black hole fast
enough. We focus on the magnetic effects on star formation that occurs in an
atomic-cooling gas cloud. Using a set of three-dimensional magnetohydrodynamic
(MHD) simulations, we investigate the star formation process in the magnetized
atomic-cooling gas cloud with different initial magnetic field strengths. Our
simulations show that the primordial magnetic seed field can be quickly
amplified during the early accretion phase after the first protostar formation.
The strong magnetic field efficiently extracts angular momentum from accreting
gas and increases the accretion rate, which results in the high fragmentation
rate in the gravitationally unstable disk region. On the other hand, the
coalescence rate of fragments is also enhanced by the angular momentum transfer
due to the magnetic effects. Almost all the fragments coalesce to the primary
star, so the mass growth rate of the massive star increases due to the magnetic
effects. We conclude that the magnetic effects support the direct collapse
scenario of supermassive star formation.


Abstract:
  It has been recently shown how the tensorial nature of real-time path
integrals involving the Feynman-Vernon influence functional can be utilized
using matrix product states, taking advantage of the finite length of the
non-Markovian memory. Tensor networks promise to provide a new, unified
language to express the structure of path integral. Here, a generalized tensor
network is derived and implemented specifically incorporating the pairwise
interaction structure of the influence functional, allowing for a compact
representation and efficient evaluation. This pairwise connected tensor network
path integral (PCTNPI) is illustrated through applications to typical
spin-boson problems and explorations of the differences caused by the exact
form of the spectral density. The storage requirements and performance are
compared with iterative quasi-adiabatic propagator path integral and iterative
blip-summed path integral. Finally, the viability of using PCTNPI for
simulating multistate problems is demonstrated taking advantage of the
compressed representation.


Abstract:
  Deep neural networks often suffer from overconfidence which can be partly
remedied by improved out-of-distribution detection. For this purpose, we
propose a novel approach that allows for the generation of out-of-distribution
datasets based on a given in-distribution dataset. This new dataset can then be
used to improve out-of-distribution detection for the given dataset and machine
learning task at hand. The samples in this dataset are with respect to the
feature space close to the in-distribution dataset and therefore realistic and
plausible. Hence, this dataset can also be used to safeguard neural networks,
i.e., to validate the generalization performance. Our approach first generates
suitable representations of an in-distribution dataset using an autoencoder and
then transforms them using our novel proposed Soft Brownian Offset method.
After transformation, the decoder part of the autoencoder allows for the
generation of these implicit out-of-distribution samples. This newly generated
dataset then allows for mixing with other datasets and thus improved training
of an out-of-distribution classifier, increasing its performance.
Experimentally, we show that our approach is promising for time series using
synthetic data. Using our new method, we also show in a quantitative case study
that we can improve the out-of-distribution detection for the MNIST dataset.
Finally, we provide another case study on the synthetic generation of
out-of-distribution trajectories, which can be used to validate trajectory
prediction algorithms for automated driving.


Abstract:
  We establish mean convergence for multiple ergodic averages with iterates
given by distinct fractional powers of primes and related multiple recurrence
results. A consequence of our main result is that every set of integers with
positive upper density contains patterns of the form $\{m,m+[p_n^a],
m+[p_n^b]\}$, where $a,b$ are positive non-integers and $p_n$ denotes the
$n$-th prime, a property that fails if $a$ or $b$ is a natural number. Our
approach is based on a recent criterion for joint ergodicity of collections of
sequences and the bulk of the proof is devoted to obtaining good seminorm
estimates for the related multiple ergodic averages. The input needed from
number theory are upper bounds for the number of prime $k$-tuples that follow
from elementary sieve theory estimates and equidistribution results of
fractional powers of primes in the circle.


Abstract:
  We construct Schwinger functions as the superposition of Schwinger functions
which correspond to those of free fields with sharp masses $ m $. We prove that
all axioms of Osterwalder and Schrader are satisfied. This construction works
independently of the space-time dimension $ d $. The Schwinger functions under
consideration are the moments of a non-Gaussian measure on the space of
tempered distributions $ S'(\mathbb R^d) $.


Abstract:
  Given an unknown dynamic system such as a coupled harmonic oscillator with
$n$ springs and point masses. We are often interested in gaining insights into
its physical parameters, i.e. stiffnesses and masses, by observing trajectories
of motion. How do we achieve this from video frames or time-series data and
without the knowledge of the dynamics model? We present a neural framework for
estimating physical parameters in a manner consistent with the underlying
physics. The neural framework uses a deep latent variable model to disentangle
the system physical parameters from canonical coordinate observations. It then
returns a Hamiltonian parameterization that generalizes well with respect to
the discovered physical parameters. We tested our framework with simple
harmonic oscillators, $n=1$, and noisy observations and show that it discovers
the underlying system parameters and generalizes well with respect to these
discovered parameters. Our model also extrapolates the dynamics of the system
beyond the training interval and outperforms a non-physically constrained
baseline model. Our source code and datasets can be found at this URL:
https://github.com/gbarber94/ConSciNet.


Abstract:
  Traffic event cognition and reasoning in videos is an important task that has
a wide range of applications in intelligent transportation, assisted driving,
and autonomous vehicles. In this paper, we create a novel dataset,
SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA
based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs,
for benchmarking the cognitive capability of causal inference and event
understanding models in complex traffic scenarios. Specifically, we propose 6
challenging reasoning tasks corresponding to various traffic scenarios, so as
to evaluate the reasoning capability over different kinds of complex yet
practical traffic events. Moreover, we propose Eclipse, a novel Efficient
glimpse network via dynamic inference, in order to achieve
computation-efficient and reliable video reasoning. The experiments show that
our method achieves superior performance while reducing the computation cost
significantly. The project page: https://github.com/SUTDCV/SUTD-TrafficQA.


Abstract:
  The one-shot Person Re-ID scenario faces two kinds of uncertainties when
constructing the prediction model from $X$ to $Y$. The first is model
uncertainty, which captures the noise of the parameters in DNNs due to a lack
of training data. The second is data uncertainty, which can be divided into two
sub-types: one is image noise, where severe occlusion and the complex
background contain irrelevant information about the identity; the other is
label noise, where mislabeled affects visual appearance learning. In this
paper, to tackle these issues, we propose a novel Self-Paced Uncertainty
Estimation Network (SPUE-Net) for one-shot Person Re-ID. By introducing a
self-paced sampling strategy, our method can estimate the pseudo-labels of
unlabeled samples iteratively to expand the labeled samples gradually and
remove model uncertainty without extra supervision. We divide the pseudo-label
samples into two subsets to make the use of training samples more reasonable
and effective. In addition, we apply a Co-operative learning method of local
uncertainty estimation combined with determinacy estimation to achieve better
hidden space feature mining and to improve the precision of selected
pseudo-labeled samples, which reduces data uncertainty. Extensive comparative
evaluation experiments on video-based and image-based datasets show that
SPUE-Net has significant advantages over the state-of-the-art methods.


Abstract:
  High spin (S=3/2) organic triradicals may offer enhanced properties with
respect to several emerging technologies, but those synthesized to date
typically exhibit small doublet quartet energy gaps and/or possess limited
thermal stability and processability. We report a quartet ground state
triradical 3, synthesized by a Pd(0)-catalyzed radical-radical cross-coupling
reaction, which possesses two doublet-quartet energy gaps, DeltaEDQ ~ 0.2-0.3
kcal mol-1 and DealtaEDQ2 ~ 1.2-1.8 kcal mol-1. The triradical has a 70+%
population of the quartet ground state at room temperature, and good thermal
stability with onset of decomposition at > 160 {\deg}C under inert atmosphere.
Magnetic properties of 3 are characterized by SQUID magnetometry in polystyrene
glass and by quantitative EPR spectroscopy. Triradical 3 is evaporated under
ultra-high vacuum to form thin films of intact triradicals on silicon
substrate, as confirmed by high resolution X-ray photoelectron spectroscopy.
AFM and SEM images of the around 1 nm thick films indicate that the triradical
molecules form islands on the substrate. The films are stable under ultra-high
vacuum for at least 17 h but show onset of decomposition after 4 h at ambient
conditions. The drop-cast films are less prone to degradation in air and have
longer lifetime.


Abstract:
  Exemplar-based colourisation aims to add plausible colours to a grayscale
image using the guidance of a colour reference image. Most of the existing
methods tackle the task as a style transfer problem, using a convolutional
neural network (CNN) to obtain deep representations of the content of both
inputs. Stylised outputs are then obtained by computing similarities between
both feature representations in order to transfer the style of the reference to
the content of the target input. However, in order to gain robustness towards
dissimilar references, the stylised outputs need to be refined with a second
colourisation network, which significantly increases the overall system
complexity. This work reformulates the existing methodology introducing a novel
end-to-end colourisation network that unifies the feature matching with the
colourisation process. The proposed architecture integrates attention modules
at different resolutions that learn how to perform the style transfer task in
an unsupervised way towards decoding realistic colour predictions. Moreover,
axial attention is proposed to simplify the attention operations and to obtain
a fast but robust cost-effective architecture. Experimental validations
demonstrate efficiency of the proposed methodology which generates high quality
and visual appealing colourisation. Furthermore, the complexity of the proposed
methodology is reduced compared to the state-of-the-art methods.


Abstract:
  Total entropy generated by the Unruh effect is calculated within the
framework of information theory. In contrast to previous studies, here the
calculations are done for the finite time of existence of the non-inertial
reference frame. In this case only the finite number of particles is produced.
Dependence on mass of the emitted particles is taken into account. Analytic
expression for the entropy of radiated boson and fermion spectra is derived. We
study also its asymptotics corresponding to limiting cases of low and high
acceleration. The obtained results can be further generalized to other
intrinsic degrees of freedom of the emitted particles, such as spin and
electric charge.


Abstract:
  Channel estimation has long been deemed as one of the most critical problems
in three-dimensional (3D) massive multiple-input multiple-output (MIMO), which
is recognized as the leading technology that enables 3D spatial signal
processing in the fifth-generation (5G) wireless communications and beyond.
Recently, by exploring the angular channel model and tensor decompositions, the
accuracy of single-user channel estimation for 3D massive MIMO communications
has been significantly improved given a limited number of pilot signals.
However, these existing approaches cannot be straightforwardly extended to the
multi-user channel estimation task, where the base station (BS) aims at
acquiring the channels of multiple users at the same time. The difficulty is
that the coupling among multiple users' channels makes the channel estimation
deviate from widely-used tensor decompositions. It gives a non-standard tensor
decomposition format that has not been well tackled. To overcome this
challenge, besides directly fitting the new tensor model for channel estimation
to the wireless data via block coordinate descent (BCD) method, which is prone
to the overfitting of noises or requires regularization parameter tuning, we
further propose a novel tuning-free channel estimation algorithm that can
automatically control the channel model complexity and thus effectively avoid
the overfitting. Numerical results are presented to demonstrate the excellent
performance of the proposed algorithm in terms of both estimation accuracy and
overfitting avoidance.


Abstract:
  Time plays a fundamental role in our ability to make sense of the physical
laws in the world around us. The nature of time has puzzled people -- from the
ancient Greeks to the present day -- resulting in a long running debate between
philosophers and physicists alike to whether time needs change to exist (the
so-called relatival theory), or whether time flows regardless of change (the
so-called substantival theory). One way to decide between the two is to attempt
to measure the flow of time with a stationary clock, since if time were
substantival, the flow of time would manifest itself in the experiment. Alas,
conventional wisdom suggests that in order for a clock to function, it cannot
be a static object, thus rendering this experiment seemingly impossible. Here
we show, with the aid of counterfactual measurements, the surprising result
that a quantum clock can measure the passage of time even while being switched
off, thus lending constructive support for the substantival theory of time.


Abstract:
  Let $G$ be a group. A function $l:G\rightarrow \lbrack 0,\infty )$ is called
a length function if
  (1) $l(g^{n})=|n|l(g)$ for any $g\in G$ and $n\in \mathbb{Z};$
  (2) $l(hgh^{-1})=l(g)$ for any $h,g\in G;$ and
  (3) $l(ab)\leq l(a)+l(b)$ for commuting elements $a,b.$
  Such length functions exist in many branches of mathematics, mainly as stable
word lengths, stable norms, smooth measure-theoretic entropy, translation
lengths on $\mathrm{CAT}(0)$ spaces and Gromov $\delta $% -hyperbolic spaces,
stable norms of quasi-cocycles, rotation numbers of circle homeomorphisms,
dynamical degrees of birational maps and so on. We study length functions on
Lie groups, Gromov hyperbolic groups, arithmetic subgroups, matrix groups over
rings and Cremona groups. As applications, we prove that every group
homomorphism from an arithmetic subgroup of a simple algebraic
$\mathbb{Q}$-group of $\mathbb{Q}$-rank at least $2,$ or a finite-index
subgroup of the elementary group $E_{n}(R)$ $(n\geq 3)$ over an associative
ring, or the Cremona group $\mathrm{Bir}(P_{\mathbb{C}}^{2})$ to any group $G$
having a purely positive length function must have its image finite. Here $G$
can be outer automorphism group $\mathrm{Out}(F_{n})$ of free groups, mapping
classes group $\mathrm{MCG}(\Sigma_{g})$, $\mathrm{CAT}% (0)$ groups or Gromov
hyperbolic groups, or the group $\mathrm{Diff}(\Sigma ,\omega )$ of
diffeomorphisms of a hyperbolic closed surface preserving an area form $\omega
.$


Abstract:
  We propose the novel numerical scheme for solution of the multidimensional
Fokker-Planck equation, which is based on the Chebyshev interpolation and the
spectral differentiation techniques as well as low rank tensor approximations,
namely, the tensor train decomposition and the multidimensional cross
approximation method, which in combination makes it possible to drastically
reduce the number of degrees of freedom required to maintain accuracy as
dimensionality increases. We demonstrate the effectiveness of the proposed
approach on a number of multidimensional problems, including Ornstein-Uhlenbeck
process and the dumbbell model. The developed computationally efficient solver
can be used in a wide range of practically significant problems, including
density estimation in machine learning applications.


Abstract:
  We present a set of idealised numerical experiments of a solstitial
aquaplanet ocean and examine the thermodynamic and dynamic implications of
surface gravity waves (SGWs) upon its mean state. The aquaplanet's oceanic
circulation is dominated by an equatorial zonal jet and four Ekman driven
meridional overturning circulation (MOC) cells aligned with the westerly
atmospheric jet streams and easterly trade winds in both hemispheres. Including
SGW parameterization (representing modulations of air-sea momentum fluxes,
Langmuir circulation and Stokes-Coriolis force) increases mixed layer vertical
momentum diffusivity by approx. 40% and dampens surface momentum fluxes by
approx. 4%. The correspondingly dampened MOC impacts the oceanic density
structure to 1 km depth by lessening the large-scale advective transports of
heat and salt, freshening the equatorial latitudes (where evaporation minus
precipitation [E-P] is negative) and increasing salinity in the subtropics
(where E-P is positive) by approx. 1%. The midlatitude pycnocline in both
hemispheres is deepened by the inclusion of SGWs. Including SGWs into the
aquaplanet ocean model acts to increase mixed layer depth by approx. 10% (up to
20% in the wintertime in midlatitudes), decrease vertical shear in the upper
200 m and alter local midlatitude buoyancy frequency. Generally, the impacts of
SGWs upon the aquaplanet ocean are found to be consistent across cooler and
warmer climates. We suggest that the implications of these simulations could be
relevant to understanding future projections of SGW climate, exoplanetary
oceans, and the dynamics of the Southern Ocean mixed layer.


Abstract:
  Network programmability is an area of research both defined by its potential
and its current limitations. While programmable hardware enables customization
of device operation, tailoring processing to finely tuned objectives, limited
resources stifle much of the capability and scalability desired for future
technologies. Current solutions to overcome these limitations simply shift the
problem, temporarily offloading memory needs or processing to other systems
while incurring both round-trip time and complexity costs. To overcome these
unnecessary costs, we introduce Infinity, a resource disaggregation method to
move processing to capable devices while continuing to forward as the original
owner, limiting unnecessary buffering and round-trip processing. By forwarding
both the processing need and associated data simultaneously we are able to
scale operation with minimal overhead and delay, improving both capability and
performance objectives for in-network processing.


Abstract:
  The statistical significance that characterizes a discrepancy between a
measurement and theoretical prediction is usually calculated assuming that the
statistical and systematic uncertainties are known. Many types of systematic
uncertainties are, however, estimated on the basis of approximate procedures
and thus the values of the assigned errors are themselves uncertain. Here the
impact of the uncertainty {\it on the assigned uncertainty} is investigated in
the context of the muon $g-2$ anomaly. The significance of the observed
discrepancy between the Standard Model prediction of the muon's anomalous
magnetic moment and measured values are shown to decrease substantially if the
relative uncertainty in the uncertainty assigned to the Standard Model
prediction exceeds around 30\%. The reduction in sensitivity increases for
higher significance, so that establishing a $5\sigma$ effect will require not
only small uncertainties but the uncertainties themselves must be estimated
accurately to correspond to one standard deviation.


Abstract:
  First principles simulations are conducted to shed light on the question of
whether a two-dimensional topological insulator (2DTI) phase may be obtained at
the interface between InAs and GaSb. To this end, the InAs/GaSb interface is
compared and contrasted with the HgTe/CdTe interface. Density functional theory
(DFT) simulations of these interfaces are performed using a machine-learned
Hubbard U correction [npj Comput. Mater. 6, 180 (2020)]. For the HgTe/CdTe
interface our simulations show that band crossing is achieved and an inverted
gap is obtained at a critical thickness of 5.1 nm of HgTe, in agreement with
experiment and previous DFT calculations. In contrast, for InAs/GaSb the gap
narrows with increasing thickness of InAs; however the gap does not close for
interfaces with up to 50 layers (about 15 nm) of each material. When an
external electric field is applied across the InAs/GaSb interface, the
GaSb-derived valence band maximum is shifted up in energy with respect to the
InAs-derived conduction band minimum until eventually the bands cross and an
inverted gap opens. Our results show that it may be possible to reach the
topological regime at the InAs/GaSb interface under the right conditions.
However, it may be challenging to realize these conditions experimentally,
which explains the difficulty of experimentally demonstrating an inverted gap
in InAs/GaSb.


Abstract:
  Graphical models are a key class of probabilistic models for studying the
conditional independence structure of a set of random variables. Circular
variables are special variables, characterized by periodicity, arising in
several contexts and fields. However, models for studying the
dependence/independence structure of circular variables are under-explored.
This paper analyses three multivariate circular distributions, the von Mises,
the Wrapped Normal and the Inverse Stereographic distributions, focusing on
their properties concerning conditional independence. For each one of these
distributions, we discuss the main properties related to conditional
independence and introduce suitable classes of graphical models. The usefulness
of the proposed models is shown by modelling the conditional independence among
dihedral angles characterizing the three-dimensional structure of some
proteins.


Abstract:
  This paper develops an integer programming approach on two-sided many-to-one
matching by investigating stable integral matchings of a fictitious continuum
market induced from the original matching market. Each stable integral matching
of the continuum market corresponds to a stable matching of the original
matching market. We show that a stable matching exists in the original matching
market when firms' preference profile satisfies a unimodularity condition. Our
result indicates that a stable matching is guaranteed to exist with various
forms of complementary preferences.


Abstract:
  We examine the effect of boson-fermion interaction in a one-dimensional
Bose-Fermi mixture by using the density matrix renormalization group method. We
show that the boson superfluidity is enhanced by fermions for a weak
boson-fermion coupling at an approximate integer boson filling factor (e.g.,
$0.935\le \rho_b \le 1.0$), and this enhancement is produced both in a fermion
metallic state and in a fermion insulating state. A metal-insulator phase
transition of fermions induced by boson-fermion interaction is observed even
though there is no fermion-fermion interaction in the parent Hamiltonian.
Furthermore, we find that the boson superfluid order and density wave order can
coexist in a deep fermion Mott region. All these features could be measured in
future experiments and open up the possibility of detecting the new physical
effect in the Bose-Fermi mixture.


Abstract:
  We build on recent work of Yeats, Courtiel, and others involving connected
chord diagrams. We first derive from a Hopf-algebraic foundation a class of
tree-like functional equations and prove that they are solved by weighted
generating functions of two different subsets of weighted connected chord
diagrams: arbitrary diagrams and diagrams forbidding so-called top cycle
subdiagrams. These equations generalize the classic specification for
increasing ordered trees and their solution uses a novel decomposition,
simplifying and generalizing previous results. The resulting tree perspective
on chord diagrams leads to new enumerative insights through the study of novel
diagram classes. We present a recursive bijection between connected
top-cycle-free diagrams with $n$ chords and triangulations of a disk with $n+1$
vertices, thereby counting the former. This connects to combinatorial maps,
Catalan intervals, and uniquely sorted permutations, leading to new conjectured
bijective relationships between diagram classes defined by forbidding graphical
subdiagrams and imposing connectedness properties and a variety of other rich
combinatorial objects. We conclude by exhibiting and studying a direct
bijection between diagrams of size $n$ with a single terminal chord and
diagrams of size $n-1$.


Abstract:
  We formulate a comprehensive hydrodynamic theory of two-dimensional liquid
crystals with generic $p-$fold rotational symmetry, also known as $p-$atics, of
which mematics $(p=2)$ and hexatics $(p=6)$ are the two best known examples.
Previous hydrodynamic theories of $p-$atics are characrerized by continuous
${\rm O}(2)$ rotational symmetry, which is higher than the discrete rotational
symmetry of $p-$atic phases. By contrast, here we demonstrate that the discrete
rotational symmetry allows the inclusion of additional terms in the
hydrodynamic equations, which, in turn, lead to novel phenomena, such as the
possibility of flow alignment at high shear rates, even for $p>2$. Furthermore,
we show that any finite imposed shear will induce long-ranged orientational
order in any $p-$atic liquid crystal, in contrast to the quasi-long-ranged
order that occurs in the absence of shear. The induced order parameter scales
like a non-universal power of the applied shear rate at small shear rates.


Abstract:
  This paper focuses on the analysis of a free energy functional, that models a
dilute suspension of magnetic nanoparticles in a two-dimensional nematic well.
The {\it first part} of the article is devoted to the asymptotic analysis of
global energy minimizers in the limit of vanishing elastic constant, $\ell
\rightarrow 0$ where the re-scaled elastic constant $\ell$ is inversely
proportional to the domain area. The first results concern the strong
$H^1$-convergence and a $\ell$-independent $H^2$-bound for the global
minimizers on smooth bounded 2D domains, with smooth boundary and topologically
trivial Dirichlet conditions. The {\it second part} focuses on the discrete
approximation of regular solutions of the corresponding non-linear system of
partial differential equations with cubic non-linearity and non-homogeneous
Dirichlet boundary conditions. We establish (i) the existence and local
uniqueness of the discrete solutions using fixed point argument, (ii) a best
approximation result in energy norm, (iii) error estimates in the energy and
$L^2$ norms with $\ell $- discretization parameter dependency for the
conforming finite element method. Finally, the theoretical results are
complemented by numerical experiments on the discrete solution profiles, the
numerical convergence rates that corroborates the theoretical estimates,
followed by plots that illustrate the dependence of the discretization
parameter on $\ell$.


Abstract:
  We consider the coded caching problem with shared caches where several users
share a cache, but each user has access to only a single cache. For this
network, the fundamental limits of coded caching are known for centralized and
decentralized settings under uncoded placement. In the centralized case, to
achieve the gains offered by coded caching, one requires a sub-packetization
which increases exponentially with the number of caches. The dedicated cache
networks had a similar issue, and placement delivery arrays (PDAs) were
introduced as a solution to it. Using the PDA framework, we propose a procedure
to obtain new coded caching schemes for shared caches with lower
sub-packetization requirements. The advantage of this procedure is that we can
transform all the existing PDA structures into coded caching schemes for shared
caches, thus resulting in low sub-packetization schemes. We also show that the
optimal scheme given by Parrinello, Unsal and Elia (Fundamental Limits of Coded
Caching with Multiple Antennas, Shared Caches and Uncoded Prefetching) can be
recovered using a Maddah-Ali Niesen PDA.


Abstract:
  We introduce a machine-learning density-functional-theory formalism for the
spinless Hubbard model in one dimension at both zero and finite temperature. In
the zero-temperature case this establishes a one-to-one relation between the
site occupation and the total energy, which is then minimised at the
ground-state occupation. In contrast, at finite temperature the same relation
is defined between the Helmholtz free energy and the equilibrium site
occupation. Most importantly, both functionals are semi-local, so that they are
independent from the size of the system under investigation and can be
constructed over exact data for small systems. These 'exact' functionals are
numerically defined by neural networks. We also define additional neural
networks for finite-temperature thermodynamical quantities, such as the entropy
and heat capacity. These can be either a functional of the ground-state site
occupation or of the finite-temperature equilibrium site occupation. In the
first case their equilibrium value does not correspond to an extremal point of
the functional, while it does in the second case. Our work gives us access to
finite-temperature properties of many-body systems in the thermodynamic limit.


Abstract:
  Let $\phi(x, y)\colon \mathbb{R}^d\times \mathbb{R}^d\to \mathbb{R}$ be a
function. We say $\phi$ is a Mattila--Sj\"{o}lin type function of index
$\gamma$ if $\gamma$ is the smallest number satisfying the property that for
any compact set $E\subset \mathbb{R}^d$, $\phi(E, E)$ has a non-empty interior
whenever $\dim_H(E)>\gamma$. The usual distance function, $\phi(x, y)=|x-y|$,
is conjectured to be a Mattila--Sj\"{o}lin type function of index
$\frac{d}{2}$. In the setting of finite fields $\mathbb{F}_q$, this definition
is equivalent to the statement that $\phi(E, E)=\mathbb{F}_q$ whenever $|E|\gg
q^{\gamma}$. The main purpose of this paper is to prove the existence of such
functions with index $\frac{d}{2}$ in the vector space $\mathbb{F}_q^d$.


Abstract:
  We perform a detailed study of the phase transitions and mechanisms of
electron localization in the extended Hubbard model using the dynamical cluster
approximation on a $2\times 2$ cluster. We explore the interplay of charge
order and Mott physics. We find that a nearest-neighbor Coulomb interaction $V$
causes "screening" effects close to the Mott phase transition, pushing the
phase boundary to larger values of $U$. We also demonstrate the different
effects of $V$ on correlations in metallic and insulating regimes and document
the different correlation aspects of charge order and Mott states.


Abstract:
  The malfunction or interruption of wireless coverage services has been shown
to increase the mortality rate during natural disasters. Wireless coverage by
an unmanned aerial vehicle (UAV) provides network coverage to ground user
devices during and post-disaster events. The relay hops receive wireless
coverage and can be forwarded to user devices that are out of coverage allowing
reliable connectivity for large-scale user devices. This work evaluates the
optimal relay hops performance to improve wireless coverage services and
establish connectivity in post-disaster scenarios. The results demonstrate the
UAV line of sights understanding to select an optimal relay for improving
wireless coverage services. The path loss probability and system capacity were
all affected by the user device distance and relay densities. The optimal relay
hop distance and the UAV positions static are also investigated to improve
coverage likelihood which could be especially useful for UAV deployment design.
It is found that the dense relays node in UAV systems enhances the capacity
coverage area and energy efficiency by decentralized connectivity through a
multihop device to device wireless network.


Abstract:
  Using the Carlson-Simpson theorem, we give a new general condition for a
structure in a finite binary relational language to have finite big Ramsey
degrees


Abstract:
  A promising and cost-effective method for numerical simulation of high Re
wall-bounded flows is wall-modeled large-eddy simulation. Most wall models are
formulated from the Reynolds-averaged Navier-Stokes equations (RANS). These
RANS-based wall models are calibrated using mean turbulence data and make no
use of the current vast knowledge on turbulent flow structure. Moreover,
RANS-based models are limited to predicting the mean velocity profile, and the
mean wall shear stress. Using the knowledge of the wall-normal self-similarity
of high-$Re_\tau$ wall-bounded turbulent flows, we present a coupling between a
near-wall patch of DNS resolution fixed in inner-units and an outer LES flow
field. The near-wall patch captures the near-wall self-sustaining cycle as well
as the lower portion of the self-similar hierarchy of eddies. Given that both
the near-wall patch and the LES capture separate portions of the self-similar
hierarchy of eddies an instantaneous top boundary condition for the patch is
formulated. The near-wall model is capable of predicting subgrid-scale
quantities such as the wall stress, velocity fluctuations, kinetic energy
spectra, and flow structure across the entire near-wall layer.


Abstract:
  Asset health monitoring continues to be of increasing importance on
productivity, reliability, and cost reduction. Early Fault detection is a
keystone of health management as part of the emerging Prognostics and Health
Management (PHM) philosophy. This paper proposes a Hidden Markov Model (HMM) to
assess the machine health degradation. using Principal Component Analysis (PCA)
to enhance features extracted from vibration signals is considered. The
enhanced features capture the second order structure of the data. The
experimental results based on a bearing test bed show the plausibility of the
proposed method.


Abstract:
  Quasi one-dimensional (1D) vanadium tetrasulfide ($VS_4$) nanowires (NWs) are
synthetic semiconductors which combine with each other through Van der Waals
interactions to form bulk phases. However, the properties of these individual
nanowires remain unknown. Nevertheless, our calculations of their stability
indicate that $VS_4$) NWs can be separated from their bulk structures.
Accordingly, we theoretically investigated the geometrical, electronic, and
magnetic properties of bulk phase and isolated $VS_4$ NWs. Our results indicate
that both bulk phase and isolated $VS_4$ NWs are semiconductors with band gaps
of 2.24 and 2.64 eV, respectively, and that they prefer the antiferromagnetic
(AFM) ground state based on DFT calculations. These calculations also suggested
that isolated $VS_4$ NWs show half-metallic antiferromagnetism upon electron
and hole doping because carrier doping splits the spin degeneracy to induce
local spin polarisation. As a result, spin polarisation currents in isolated
$VS_4$ NWs can be manipulated with locally applied gate voltage. Therefore,
these 1D AFM materials have a high potential for advancing both fundamental
research and spintronic applications because they are more resistant to
magnetic perturbation than their 1D ferromagnetic counterparts.


Abstract:
  Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction
and visual SLAM. Existing deep learning-based approaches formulate the problem
by either recovering absolute pose scales from two consecutive frames or
predicting a depth map from a single image, both of which are ill-posed
problems. In contrast, we propose to revisit the problem of deep two-view SfM
by leveraging the well-posedness of the classic pipeline. Our method consists
of 1) an optical flow estimation network that predicts dense correspondences
between two frames; 2) a normalized pose estimation module that computes
relative camera poses from the 2D optical flow correspondences, and 3) a
scale-invariant depth estimation network that leverages epipolar geometry to
reduce the search space, refine the dense correspondences, and estimate
relative depth maps. Extensive experiments show that our method outperforms all
state-of-the-art two-view SfM methods by a clear margin on KITTI depth, KITTI
VO, MVS, Scenes11, and SUN3D datasets in both relative pose and depth
estimation.


Abstract:
  In this paper, we define both the upper and lower order of a sense-preserving
harmonic mapping in $\mathbb{D}$. We generalize to the harmonic case some known
results about holomorphic functions with positive lower order and we show some
consequences of a function having finite upper order. In addition, we improve a
related result in the case when there is equality in a known distortion theorem
for harmonic mappings with finite upper order. Some examples are provided to
illustrate the developed theory.


Abstract:
  Lithium is an important element for the understanding of ultracool dwarfs
because it is lost to fusion at masses above $\sim 68\, M_{\rm J}$. Hence, the
presence or absence of atomic Li has served as an indicator of the nearby
H-burning boundary at about $75\,M_{\rm J}$ between brown-dwarfs and very
low-mass stars. Historically the "Lithium test", a search for the presence and
strength of the Li line at 670.8 nm, has been a marker if an object has a
substellar mass with stellar-like spectral energy distribution (e.g., a
late-type M dwarf). While the Li test could in principle also be used to
distinguish masses of later-type L-T dwarfs, Li is predominantly no longer
found as an atomic gas, but rather a molecular species such as LiH, LiF, LiOH,
and LiCl in their cooler atmospheres. L- and T-type brown dwarfs are also quite
faint at 670 nm and thus challenging targets for high resolution spectroscopy.
But only recently have experimental molecular line lists become available for
the molecular Li species, allowing molecular Li mass discrimination. In this
study, we generated the latest opacity of each of these Li-bearing molecules
and performed thermochemical equilibrium atmospheric composition calculation of
the abundance of these molecules. Finally, we computed thermal emission spectra
for a series of radiative-convective equilibrium models of cloudy and cloudless
brown dwarf atmospheres (with $T_{\rm eff}=$ 500--2400~K, and $\log g$=4.0,
4.5, 5.0) to understand where the presence or absence of atmospheric
lithium-bearing species is most easily detected as a function of brown dwarf
mass and age. After atomic Li, the best spectral signatures were found to be
LiF at $10.5-12.5$~\micron and LiCl at $14.5-18.5$ $\micron$. LiH also shows a
narrow feature at $\sim 9.38$ $\micron$.


Abstract:
  In complex industrial settings, it is common practice to monitor the
operation of machines in order to detect undesired states, adjust maintenance
schedules, optimize system performance or collect usage statistics of
individual machines. In this work, we focus on estimating the power output of a
Combined Heat and Power (CHP) machine of a medium-sized company facility by
analyzing the total facility power consumption. We formulate the problem as a
time-series classification problem where the class label represents the CHP
power output. As the facility is fully instrumented and sensor measurements
from the CHP are available, we generate the training labels in an automated
fashion from the CHP sensor readings. However, sensor failures result in
mislabeled training data samples which are hard to detect and remove from the
dataset. Therefore, we propose a novel multi-task deep learning approach that
jointly trains a classifier and an autoencoder with a shared embedding
representation. The proposed approach targets to gradually correct the
mislabelled data samples during training in a self-supervised fashion, without
any prior assumption on the amount of label noise. We benchmark our approach on
several time-series classification datasets and find it to be comparable and
sometimes better than state-of-the-art methods. On the real-world use-case of
predicting the CHP power output, we thoroughly evaluate the architectural
design choices and show that the final architecture considerably increases the
robustness of the learning process and consistently beats other recent
state-of-the-art algorithms in the presence of unstructured as well as
structured label noise.


Abstract:
  Controlling false discovery rate (FDR) while leveraging the side information
of multiple hypothesis testing is an emerging research topic in modern data
science. Existing methods rely on the test-level covariates while ignoring
possible hierarchy among the covariates. This strategy may not be optimal for
complex large-scale problems, where hierarchical information often exists among
those test-level covariates. We propose NeurT-FDR which boosts statistical
power and controls FDR for multiple hypothesis testing while leveraging the
hierarchy among test-level covariates. Our method parametrizes the test-level
covariates as a neural network and adjusts the feature hierarchy through a
regression framework, which enables flexible handling of high-dimensional
features as well as efficient end-to-end optimization. We show that NeurT-FDR
has strong FDR guarantees and makes substantially more discoveries in synthetic
and real datasets compared to competitive baselines.


Abstract:
  Ensemble and auxiliary tasks are both well known to improve the performance
of machine learning models when data is limited. However, the interaction
between these two methods is not well studied, particularly in the context of
deep reinforcement learning. In this paper, we study the effects of ensemble
and auxiliary tasks when combined with the deep Q-learning algorithm. We
perform a case study on ATARI games under limited data constraint. Moreover, we
derive a refined bias-variance-covariance decomposition to analyze the
different ways of learning ensembles and using auxiliary tasks, and use the
analysis to help provide some understanding of the case study. Our code is open
source and available at https://github.com/NUS-LID/RENAULT.


Abstract:
  Attention is a key component of the now ubiquitous pre-trained language
models. By learning to focus on relevant pieces of information, these
Transformer-based architectures have proven capable of tackling several tasks
at once and sometimes even surpass their single-task counterparts. To better
understand this phenomenon, we conduct a structural analysis of a new
all-purpose question answering model that we introduce. Surprisingly, this
model retains single-task performance even in the absence of a strong transfer
effect between tasks. Through attention head importance scoring, we observe
that attention heads specialize in a particular task and that some heads are
more conducive to learning than others in both the multi-task and single-task
settings.


Abstract:
  We report the spatial beam self-cleaning in bi-tapered conventional multimode
fibers (MMFs) with different tapered lengths. Through the introduction of the
bi-tapered structure in MMFs, the input beam with poor beam quality from a
high-power fiber laser can be converted to a centered, bell-shaped beam in a
short length, due to the strengthened nonlinear modes coupling. It is found
that the bi-tapered MMF with longer tapered length at the same waist diameter
shows better beam self-cleaning effect and larger spectral broadening. The
obtained results offer a new method to improve the beam quality of high-power
laser at low cost. Besides, it may be interesting for manufacturing bi-tapered
MMF-based devices to obtain the quasi-fundamental mode beam in spatiotemporal
mode-locked fiber lasers.


Abstract:
  This paper presents the application of a meta model and single underlying
model on an applied avionics system design use case. System models, safety
assurance cases and safety requirements are maintained in a central repository.
This enables to link these data which are originally developed in unrelated
tools. By having such a central repository, traceability can be established,
and consistency can be ensured, which leads to less errors and a shorter
development time. A meta model was constructed which matches the central
repository to enable bidirectional synchronization with an external authoring
tool.


Abstract:
  The massive screened expansion for pure SU(3) Yang-Mills theory is extended
to finite temperature in the Landau gauge. All thermal integrals are evaluated
analytically up to an external one-dimensional integration, yielding explicit
integral representations of analytic functions which can be continued to the
whole complex plane. The gluon propagator is first explored in the Euclidean
space by making use of parameters obtained from first principles, which were
already found to accurately reproduce the lattice data at zero temperature.
Within such a scheme, the agreement with the lattice at $T\neq 0$ turns out to
be only qualitative. The description improves provided that the parameters are
tuned in a temperature-dependent way by a fit to the data, carried out
separately for each component of the propagator; in particular, the transverse
component closely follows the lattice data, while the agreement of the
longitudinal component with the data is poor at small momenta and moderately
high temperatures. The dispersion relations of the quasi-gluon are then
extracted from the pole trajectory in the complex plane using the fitted
parameters. A crossover is found for the mass, suppressed by temperature like
an order parameter in the confined phase, while increasing like an ordinary
thermal mass in the deconfined phase.


Abstract:
  As the Covid-19 outbreaks rapidly all over the world day by day and also
affects the lives of million, a number of countries declared complete lock-down
to check its intensity. During this lockdown period, social media plat-forms
have played an important role to spread information about this pandemic across
the world, as people used to express their feelings through the social
networks. Considering this catastrophic situation, we developed an experimental
approach to analyze the reactions of people on Twitter taking into ac-count the
popular words either directly or indirectly based on this pandemic. This paper
represents the sentiment analysis on collected large number of tweets on
Coronavirus or Covid-19. At first, we analyze the trend of public sentiment on
the topics related to Covid-19 epidemic using an evolutionary classification
followed by the n-gram analysis. Then we calculated the sentiment ratings on
collected tweet based on their class. Finally, we trained the long-short term
network using two types of rated tweets to predict sentiment on Covid-19 data
and obtained an overall accuracy of 84.46%.


Abstract:
  Sentiment analysis as a sub-field of natural language processing has received
increased attention in the past decade enabling organisations to more
effectively manage their reputation through online media monitoring. Many
drivers impact reputation, however, this thesis focuses only the aspect of
financial performance and explores the gap with regards to financial sentiment
analysis in a South African context. Results showed that pre-trained sentiment
analysers are least effective for this task and that traditional lexicon-based
and machine learning approaches are best suited to predict financial sentiment
of news articles. The evaluated methods produced accuracies of 84\%-94\%. The
predicted sentiments correlated quite well with share price and highlighted the
potential use of sentiment as an indicator of financial performance. A main
contribution of the study was updating an existing sentiment dictionary for
financial sentiment analysis. Model generalisation was less acceptable due to
the limited amount of training data used. Future work includes expanding the
data set to improve general usability and contribute to an open-source
financial sentiment analyser for South African data.


Abstract:
  The isospin splitting of the in-medium $NN\rightarrow N\Delta$ cross sections
in asymmetric nuclear medium are investigated in the framework of the one-boson
exchange model by including $\delta$ and $\rho$ mesons. Our results show that
the medium correction factors $R=\sigma_{ NN\rightarrow
N\Delta}^*/\sigma_{NN\rightarrow N\Delta}^{\text{free}}$ have $R_{pp \to
n\Delta ^{++}} < R_{nn \to p\Delta ^{-}}$ and $R_{NN \to N\Delta ^{+}} <R_{NN
\to N\Delta ^{0}}$ by using the without-$\delta$ sets. By including the
$\delta$ meson, it appears the totally opposite results in the $R$ for
different channels, i.e., $R_{pp \to n\Delta ^{++}} > R_{nn \to p\Delta ^{-}}$
and $R_{NN \to N\Delta ^{+}} >R_{NN \to N\Delta ^{0}}$.