Abstract:
  Studying the human factors that impact on software development, and assigning
individuals with specific competencies and qualities to particular software
roles, have been shown to aid software project performance. For instance, prior
evidence suggests that extroverted software project leaders are most
successful. Role assignment based on individuals' competencies and behaviors
may be especially relevant in distributed software development contexts where
teams are often affected by distance, cultural, and personality issues. Project
leaders in these environments need to possess high levels of inter-personal,
intra-personal and organizational competencies if they are to appropriately
manage such issues and maintain positive project performance. With a view to
understanding and explaining the specific competencies and behaviors that are
required of project leaders in these settings, we used psycholinguistic and
directed content analysis to study the way six successful IBM Rational Jazz
leaders operated while coordinating their three distributed projects. Contrary
to previous evidence reported in personality studies, our results did not
reveal universal competencies and behaviors among these Jazz leaders. Instead,
Jazz project leaders' competencies and behaviors varied with their project
portfolio of tasks. Our findings suggest that a pragmatic approach that
considers the nature of the software tasks being developed is likely to be a
more effective strategy for assigning leaders to distributed software teams, as
against a strategy that promotes a specific personality type. We discuss these
findings and outline implications for distributed software project governance.


Abstract:
  The extraction of filamentary structure from a point cloud is discussed. The
filaments are modeled as ridge lines or higher dimensional ridges of an
underlying density. We propose two novel algorithms, and provide theoretical
guarantees for their convergences. We consider the new algorithms as
alternatives to the Subspace Constraint Mean Shift (SCMS) algorithm that do not
suffer from a shortcoming of the SCMS that is also revealed in this paper.


Abstract:
  Video conferencing applications (VCAs) have become a critical Internet
application, even more so during the COVID-19 pandemic, as users worldwide now
rely on them for work, school, and telehealth. It is thus increasingly
important to understand the resource requirements of different VCAs and how
they perform under different network conditions, including: how much speed
(upstream and downstream throughput) a VCA needs to support high quality of
experience; how VCAs perform under temporary reductions in available capacity;
how they compete with themselves, with each other, and with other applications;
and how usage modality (e.g., number of participants) affects utilization. We
study three modern VCAs: Zoom, Google Meet, and Microsoft Teams. Answers to
these questions differ substantially depending on VCA. First, the average
utilization on an unconstrained link varies between 0.8 Mbps and 1.9 Mbps.
Given temporary reduction of capacity, some VCAs can take as long as 50 seconds
to recover to steady state. Differences in proprietary congestion control
algorithms also result in unfair bandwidth allocations: in constrained
bandwidth settings, one Zoom video conference can consume more than 75% of the
available bandwidth when competing with another VCA (e.g., Meet, Teams). For
some VCAs, client utilization can decrease as the number of participants
increases, due to the reduced video resolution of each participant's video
stream given a larger number of participants. Finally, one participant's
viewing mode (e.g., pinning a speaker) can affect the upstream utilization of
other participants.


Abstract:
  PSR J0740$+$6620 has a gravitational mass of $2.08\pm 0.07~M_\odot$, which is
the highest reliably determined mass of any neutron star. As a result, a
measurement of its radius will provide unique insight into the properties of
neutron star core matter at high densities. Here we report a radius measurement
based on fits of rotating hot spot patterns to Neutron Star Interior
Composition Explorer (NICER) and X-ray Multi-Mirror (XMM-Newton) X-ray
observations. We find that the equatorial circumferential radius of PSR
J0740$+$6620 is $13.7^{+2.6}_{-1.5}$ km (68%). We apply our measurement,
combined with the previous NICER mass and radius measurement of PSR
J0030$+$0451, the masses of two other $\sim 2~M_\odot$ pulsars, and the tidal
deformability constraints from two gravitational wave events, to three
different frameworks for equation of state modeling, and find consistent
results at $\sim 1.5-3$ times nuclear saturation density. For a given
framework, when all measurements are included the radius of a $1.4~M_\odot$
neutron star is known to $\pm 4$% (68% credibility) and the radius of a
$2.08~M_\odot$ neutron star is known to $\pm 5$%. The full radius range that
spans the $\pm 1\sigma$ credible intervals of all the radius estimates in the
three frameworks is $12.45\pm 0.65$ km for a $1.4~M_\odot$ neutron star and
$12.35\pm 0.75$ km for a $2.08~M_\odot$ neutron star.


Abstract:
  Sensor-based human activity recognition (HAR), i.e., the ability to discover
human daily activity patterns from wearable or embedded sensors, is a key
enabler for many real-world applications in smart homes, personal healthcare,
and urban planning. However, with an increasing number of applications being
deployed, an important question arises: how can a HAR system autonomously learn
new activities over a long period of time without being re-engineered from
scratch? This problem is known as continual learning and has been particularly
popular in the domain of computer vision, where several techniques to attack it
have been developed. This paper aims to assess to what extent such continual
learning techniques can be applied to the HAR domain. To this end, we propose a
general framework to evaluate the performance of such techniques on various
types of commonly used HAR datasets. We then present a comprehensive empirical
analysis of their computational cost and effectiveness of tackling HAR-specific
challenges (i.e., sensor noise and labels' scarcity). The presented results
uncover useful insights on their applicability and suggest future research
directions for HAR systems. Our code, models and data are available at
https://github.com/srvCodes/continual-learning-benchmark.


Abstract:
  Data protection across organizations is limiting the application of
centralized learning (CL) techniques. Federated learning (FL) enables multiple
participants to build a learning model without sharing data. Nevertheless,
there are very few research works on FL in intelligent manufacturing. This
paper presents the results of an empirical study on failure prediction in the
production line based on FL. This paper (1) designs Federated Support Vector
Machine (FedSVM) and Federated Random Forest (FedRF) algorithms for the
horizontal FL and vertical FL scenarios, respectively; (2) proposes an
experiment process for evaluating the effectiveness between the FL and CL
algorithms; (3) finds that the performance of FL and CL are not significantly
different on the global testing data, on the random partial testing data, and
on the estimated unknown Bosch data, respectively. The fact that the testing
data is heterogeneous enhances our findings. Our study reveals that FL can
replace CL for failure prediction.


Abstract:
  We propose a generalized Kuramoto model with relativistic effects and
investigate emergent asymptotic behaviors. The proposed generalized Kuramoto
model incorporates relativistic Kuramoto(RK) type models which can be derived
from the relativistic Cucker-Smale (RCS) on the unit sphere under suitable
approximations. We present several sufficient frameworks leading to complete
synchronization in terms of initial data and system parameters. For the
relativistic Kuramoto model, we show that it can be reduced to the Kuramoto
model in any finite time interval in a non-relativistic limit. We also provide
several numerical examples for two approximations of the relativistic Kuramoto
model, and compare them with analytical results.


Abstract:
  Pixel scaling effects have been a major issue for the development of
high-resolution color image sensors due to the reduced photoelectric signal and
the color crosstalk. Various structural color techniques have been proposed and
demonstrated the large freedom in color manipulation by the structure design.
However, the optical efficiency and the color distortion limit the practical
applications due to its intrinsic filtering mechanism. Instead, on-chip
full-color routing is quite charming for improving the signal-to-noise ratio.
In this paper, a single-layer quick response code like nanorouter is proposed
for the full-color light routing in a pixel level of image sensors. It shows
much higher routing efficiency than various planar lens schemes for signal
wavelength focusing. Moreover, over 60% signal enhancement with robust
polarization insensitivity is obtained in all three primary color bands with a
same nanorouter by a multi-objective optimization method. Negligible color
distortion is observed from the reconstructed color image. Such a simple
nanorouter structure is promising for the development of image sensor,
photovoltaics and display.


Abstract:
  The mechanism of atomic collisions in excited bands plays an important role
in the study of the orbital physics in optical lattices and simulation of
condensed matter physics. Atoms distributing in one excited bands of an optical
lattice would collide and decay to other bands through different scattering
channels. In excited bands of one dimensional lattice, due to lack of geometry,
there is no significant difference between cross section of scattering
channels. Here, we investigate the collisional scattering channels for atoms in
the excited bands of a triangular optical lattice and demonstrate a dominant
scattering channel in the experiment. A shortcut method is utilized to load
Bose-Einstein condensates of $^{87} {\rm Rb}$ atoms into the first D band with
zero quasi-momentum. After some time for evolution, the number of atoms
scattering to S band due to two-body collisions is around four times more than
that to the second most band. We reveal that the scattering channel to $ss$
band is dominant by theoretical calculation, which agrees with experimental
measurements. The appearance of dominant scattering channels in triangular
optical lattice is owing to geometric dimension coupling. This work is helpful
for the study of many-body systems and directional enhancement in optical
lattices.


Abstract:
  Accurate multi-class segmentation is a long-standing challenge in medical
imaging, especially in scenarios where classes share strong similarity.
Segmenting retinal blood vessels in retinal photographs is one such scenario,
in which arteries and veins need to be identified and differentiated from each
other and from the background. Intra-segment misclassification, i.e. veins
classified as arteries or vice versa, frequently occurs when arteries and veins
intersect, whereas in binary retinal vessel segmentation, error rates are much
lower. We thus propose a new approach that decomposes multi-class segmentation
into multiple binary, followed by a binary-to-multi-class fusion network. The
network merges representations of artery, vein, and multi-class feature maps,
each of which are supervised by expert vessel annotation in adversarial
training. A skip-connection based merging process explicitly maintains
class-specific gradients to avoid gradient vanishing in deep layers, to favor
the discriminative features. The results show that, our model respectively
improves F1-score by 4.4\%, 5.1\%, and 4.2\% compared with three
state-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV
data sets. Code: https://github.com/rmaphoh/Learning-AVSegmentation


Abstract:
  We combine ideas from uni-directional and bi-directional heuristic search,
and approximation algorithms for the Traveling Salesman Problem, to develop a
novel framework for a Multi-Goal Path Finding (MGPF) problem that provides a
2-approximation guarantee. MGPF aims to find a least-cost path from an origin
to a destination such that each node in a given set of goals is visited at
least once along the path. We present numerical results to illustrate the
advantages of our framework over conventional alternates in terms of the number
of expanded nodes and run time.


Abstract:
  In this paper, we propose a gradient boosting algorithm for large-scale
regression problems called \textit{Gradient Boosted Binary Histogram Ensemble}
(GBBHE) based on binary histogram partition and ensemble learning. From the
theoretical perspective, by assuming the H\"{o}lder continuity of the target
function, we establish the statistical convergence rate of GBBHE in the space
$C^{0,\alpha}$ and $C^{1,0}$, where a lower bound of the convergence rate for
the base learner demonstrates the advantage of boosting. Moreover, in the space
$C^{1,0}$, we prove that the number of iterations to achieve the fast
convergence rate can be reduced by using ensemble regressor as the base
learner, which improves the computational efficiency. In the experiments,
compared with other state-of-the-art algorithms such as gradient boosted
regression tree (GBRT), Breiman's forest, and kernel-based methods, our GBBHE
algorithm shows promising performance with less running time on large-scale
datasets.


Abstract:
  Over the years motor deficit in Parkinson's Disease (PD) patients was largely
studied, however, no consistent pattern of relations between quantitative
electroencephalography (qEEG) and motor scales emerged. There is a general lack
of information on the relation between EEG changes and scales related to
specific motor deficits. Therefore, the study aimed to investigate the relation
between brain oscillatory activity alterations (EEG power bands) and most used
PD-related motor deficit scales. A positive correlation was found between the
freezing of the gait questionnaire (FOGQ) and delta spectral power band
(rho=0.67; p=0.008), while a negative correlation with the same scale was
observed in the alpha spectral power band (rho=-0.59, p=0.027). Additionally,
motor scores measure by motor part of Unified Parkinson's Disease Rating Scale
(UPDRS) correlated directly with theta (rho=0.55, p=0.040) and inversely with
beta EEG power band (rho=-0.77, p=0.001). No significant correlation was found
between spectral powers and Hoehn and Yahr (H&Y), BERG (Berg K. et. al. 1995),
Modified Parkinson Activity Scale (MPAS), Six-Minute Walk Test (6MWT) and Timed
Up and Go Test (TUG). In conclusion, our study supports the earlier findings
suggesting a link between EEG slowing and motor decline, providing more insight
into the relation between EEG alteration and deficits in different motor
domains. These findings indicate that EEG assessment may be a useful biomarker
for objective monitoring of progression and neurophysiological effect of
rehabilitation approaches in PD's.


Abstract:
  One of the primary sources for the future space-based gravitational wave
detector, the Laser Interferometer Space Antenna, are the inspirals of small
compact objects into massive black holes in the centres of galaxies. The
gravitational waveforms from such Extreme Mass Ratio Inspiral (EMRI) systems
will provide measurements of their parameters with unprecedented precision, but
only if the waveforms are accurately modeled. Here we explore the impact of
transient orbital resonances which occur when the ratio of radial and polar
frequencies is a rational number. We introduce a new Effective Resonance Model,
which is an extension of the numerical kludge EMRI waveform approximation to
include the effect of resonances, and use it to explore the impact of
resonances on EMRI parameter estimation. For one-year inspirals, we find that
the few cycle dephasings induced by 3:2 resonances can lead to systematic
errors in parameter estimates, that are up to several times the typical
measurement precision of the parameters. The bias is greatest for 3:2
resonances that occur closer to the central black hole. By regarding them as
unknown model parameters, we further show that observations will be able to
constrain the size of the changes in the orbital parameters across the
resonance to a relative precision of 10% for a typical one-year EMRI
observation with signal-to-noise ratio of 20. Such measurements can be regarded
as tests of fundamental physics, by comparing the measured changes to those
predicted in general relativity.


Abstract:
  Statistical arbitrage identifies and exploits temporal price differences
between similar assets. We propose a unifying conceptual framework for
statistical arbitrage and develop a novel deep learning solution, which finds
commonality and time-series patterns from large panels in a data-driven and
flexible way. First, we construct arbitrage portfolios of similar assets as
residual portfolios from conditional latent asset pricing factors. Second, we
extract the time series signals of these residual portfolios with one of the
most powerful machine learning time-series solutions, a convolutional
transformer. Last, we use these signals to form an optimal trading policy, that
maximizes risk-adjusted returns under constraints. We conduct a comprehensive
empirical comparison study with daily large cap U.S. stocks. Our optimal
trading strategy obtains a consistently high out-of-sample Sharpe ratio and
substantially outperforms all benchmark approaches. It is orthogonal to common
risk factors, and exploits asymmetric local trend and reversion patterns. Our
strategies remain profitable after taking into account trading frictions and
costs. Our findings suggest a high compensation for arbitrageurs to enforce the
law of one price.


Abstract:
  Criticality experiments with $^{235}$U (metal and hydride) and $^{239}$Pu
(metal) were performed during the Manhattan Project. Results from these
experiments provided necessary information for the success of the Manhattan
Project. These experiments have been previously described in compilations made
after the Manhattan Project, but those works are either lacking in technical
details or are not publicly available. This work aims to provide detailed
information while showcasing the enduring impact of these experiments 75 years
after they were performed. Furthermore, we use modern computational methods
embodied in the MCNP6 code and ENDF data to analyze and interpret these
historic measurements. The world's first four criticality accidents are also
discussed, as lessons learned from these helped shape the field of criticality
experiments.


Abstract:
  Dietary supplements (DS) have been widely used by consumers, but the
information around the efficacy and safety of DS is disparate or incomplete,
thus creating barriers for consumers to find information effectively.
Conversational agent (CA) systems have been applied to the healthcare domain,
but there is no such a system to answer consumers regarding DS use, although
widespread use of DS. In this study, we develop the first CA system for DS use


Abstract:
  The functional object-oriented network (FOON) has been introduced as a
knowledge representation, which takes the form of a graph, for symbolic task
planning. To get a sequential plan for a manipulation task, a robot can obtain
a task tree through a knowledge retrieval process from the FOON. To evaluate
the quality of an acquired task tree, we compare it with a conventional form of
task knowledge, such as recipes or manuals. We first automatically convert task
trees to recipes, and we then compare them with the human-created recipes in
the Recipe1M+ dataset via a survey. Our preliminary study finds no significant
difference between the recipes in Recipe1M+ and the recipes generated from FOON
task trees in terms of correctness, completeness, and clarity.


Abstract:
  This paper studies model-based bandit and reinforcement learning (RL) with
nonlinear function approximations. We propose to study convergence to
approximate local maxima because we show that global convergence is
statistically intractable even for one-layer neural net bandit with a
deterministic reward. For both nonlinear bandit and RL, the paper presents a
model-based algorithm, Virtual Ascent with Online Model Learner (ViOlin), which
provably converges to a local maximum with sample complexity that only depends
on the sequential Rademacher complexity of the model class. Our results imply
novel global or local regret bounds on several concrete settings such as linear
bandit with finite or sparse model class, and two-layer neural net bandit. A
key algorithmic insight is that optimism may lead to over-exploration even for
two-layer neural net model class. On the other hand, for convergence to local
maxima, it suffices to maximize the virtual return if the model can also
reasonably predict the size of the gradient and Hessian of the real return.


Abstract:
  Learning-based stereo matching and depth estimation networks currently excel
on public benchmarks with impressive results. However, state-of-the-art
networks often fail to generalize from synthetic imagery to more challenging
real data domains. This paper is an attempt to uncover hidden secrets of
achieving domain robustness and in particular, discovering the important
ingredients of generalization success of stereo matching networks by analyzing
the effect of synthetic image learning on real data performance. We provide
evidence that demonstrates that learning of features in the synthetic domain by
a stereo matching network is heavily influenced by two "shortcuts" presented in
the synthetic data: (1) identical local statistics (RGB colour features)
between matching pixels in the synthetic stereo images and (2) lack of realism
in synthetic textures on 3D objects simulated in game engines. We will show
that by removing such shortcuts, we can achieve domain robustness in the
state-of-the-art stereo matching frameworks and produce a remarkable
performance on multiple realistic datasets, despite the fact that the networks
were trained on synthetic data, only. Our experimental results point to the
fact that eliminating shortcuts from the synthetic data is key to achieve
domain-invariant generalization between synthetic and real data domains.


Abstract:
  An increasing number of emerging applications in data science and engineering
are based on multidimensional and structurally rich data. The irregularities,
however, of high-dimensional data often compromise the effectiveness of
standard machine learning algorithms. We hereby propose the Rank-R Feedforward
Neural Network (FNN), a tensor-based nonlinear learning model that imposes
Canonical/Polyadic decomposition on its parameters, thereby offering two core
advantages compared to typical machine learning methods. First, it handles
inputs as multilinear arrays, bypassing the need for vectorization, and can
thus fully exploit the structural information along every data dimension.
Moreover, the number of the model's trainable parameters is substantially
reduced, making it very efficient for small sample setting problems. We
establish the universal approximation and learnability properties of Rank-R
FNN, and we validate its performance on real-world hyperspectral datasets.
Experimental evaluations show that Rank-R FNN is a computationally inexpensive
alternative of ordinary FNN that achieves state-of-the-art performance on
higher-order tensor data.


Abstract:
  Cooper pair splitters are promising candidates for generating spin-entangled
electrons. However, the splitting of Cooper pairs is a random and noisy
process, which hinders further synchronized operations on the entangled
electrons. To circumvent this problem, we here propose and analyze a dynamic
Cooper pair splitter that produces a noiseless and regular flow of
spin-entangled electrons. The Cooper pair splitter is based on a superconductor
coupled to quantum dots, whose energy levels are tuned in and out of resonance
to control the splitting process. We identify the optimal operating conditions
for which exactly one Cooper pair is split per period of the external drive and
the flow of entangled electrons becomes noiseless. To characterize the
regularity of the Cooper pair splitter in the time domain, we analyze the
$g^{(2)}$-function of the output currents and the distribution of waiting times
between split Cooper pairs. Our proposal is feasible using current technology,
and it paves the way for dynamic quantum information processing with
spin-entangled electrons.


Abstract:
  We investigate swampland conjectures for quantum gravity in the context of
M-theory compactified on Calabi-Yau threefolds which admit infinite sequences
of flops. Naively, the moduli space of such compactifications contains paths of
arbitrary geodesic length traversing an arbitrarily large number of K\"ahler
cones, along which the low-energy spectrum remains virtually unchanged. In
cases where the infinite chain of Calabi-Yau manifolds involves only a finite
number of isomorphism classes, the moduli space has an infinite discrete
symmetry which relates the isomorphic manifolds connected by flops. This is a
remnant of the 11D Poincare symmetry and consequently gauged, as it has to be
by the no-global symmetry conjecture. The apparent contradiction with the
swampland distance conjecture is hence resolved after dividing by this discrete
symmetry. If the flop sequence involves infinitely many non-isomorphic
manifolds, this resolution is no longer available. However, such a situation
cannot occur if the Kawamata-Morrison conjecture for Calabi-Yau threefolds is
true. Conversely, the swampland distance conjecture, when applied to infinite
flop chains, implies the Kawamata-Morrison conjecture under a plausible
assumption on the diameter of the K\"ahler cones.


Abstract:
  We consider entanglement of first-quantized identical particles by adopting
an algebraic approach. In particular, we investigate fermions whose wave
functions are given by the Slater determinants, as for singlet sectors of
one-matrix models. We show that the upper bounds of the general R\'enyi
entropies are $N \log 2$ for $N$ particles or an $N\times N$ matrix. We compute
the target space entanglement entropy and the mutual information in a free
one-matrix model. We confirm the area law: the single-interval entropy for the
ground state scales as $\frac{1}{3}\log N$ in the large $N$ model. We obtain an
analytical $\mathcal{O}(N^0)$ expression of the mutual information for two
intervals in the large $N$ expansion.


Abstract:
  Collaboration is identified as a required and necessary skill for students to
be successful in the fields of Science, Technology, Engineering and Mathematics
(STEM). However, due to growing student population and limited teaching staff
it is difficult for teachers to provide constructive feedback and instill
collaborative skills using instructional methods. Development of simple and
easily explainable machine-learning-based automated systems can help address
this problem. Improving upon our previous work, in this paper we propose using
simple temporal-CNN deep-learning models to assess student group collaboration
that take in temporal representations of individual student roles as input. We
check the applicability of dynamically changing feature representations for
student group collaboration assessment and how they impact the overall
performance. We also use Grad-CAM visualizations to better understand and
interpret the important temporal indices that led to the deep-learning model's
decision.


Abstract:
  For a group $G$, let $\mathcal{F}_{n}$ be the family of all the subgroups of
$G$ containing a subgroup isomorphic to $\mathbb{Z}^{r}$ for some
$r=0,1,2,\dots ,n$ of finite index. Joecken, Lafont and S\'anchez Salda\~na
computed the $\mathcal{F}_1$-dimension of 3-manifold groups. The goal of this
article is to compute the $\mathcal{F}_n$-geometric dimension of 3-manifold
groups for all $n\geq 2$.


Abstract:
  The Atoyac River is among the two most polluted in Mexico. Water quality in
the Upper Atoyac River Basin (UARB) has been devastated by industrial and
municipal wastewater, as well as from effluents from local dwellers, that go
through little to no treatment, affecting health, production, ecosystems and
property value. We did a systematic review and mapping of the costs that
pollution imposes on different sectors and localities in the UARB, and
initially found 358 studies, of which 17 were of our particular interest. We
focus on estimating the cost of pollution through different valuation methods
such as averted costs, hedonic pricing, and contingent valuation, and for that
we only use 10 studies. Costs range from less than a million to over $16
million dollars a year, depending on the sector, with agriculture, industry and
tourism yielding the highest costs. This exercise is the first of its kind in
the UARB that maps costs for sectors and localities affected, and sheds light
on the need of additional research to estimate the total cost of pollution
throughout the basin. This information may help design further research needs
in the region.


Abstract:
  Physics in schools is distinctly different from, and struggles to capture the
excitement of, university research-level work. Initiatives where students
engage in independent research linked to cutting-edge physics within their
school over several months might help mitigate this, potentially facilitating
the uptake of science in higher education. However, how such initiatives are
best supported remains unclear and understudied. This paper evaluates a
provision framework, `Physics Research in School Environments' (PRiSE), using
survey data from participating 14-18 year-old students and their teachers to
understand their experience of the programme. The results show that PRiSE
appears to provide much more positive experiences than typical university
outreach initiatives due to the nature of the opportunities afforded over
several months, which schools would not be able to provide without external
input. The intensive support offered is deemed necessary, with all elements
appearing equally important. Based on additional feedback from independent
researchers and engagement professionals, we also suggest the framework could
be adopted at other institutions and applied to their own areas of scientific
research, something which has already started to occur.


Abstract:
  The Stochastic Abacus is can employed to compute winning probabilities for
each vertex of a rooted tree in the game "Pass the Buck", with the starting
vertex being the root of the tree. For all but the simplest trees, the abacus
can't really be implemented due to the large number of steps needed for
completion. In this paper, a technique for anticipating the outcome is
introduced.


Abstract:
  In this paper we prove the generalized Kaplansky conjecture for the Jordan
algebras of the type $J_n$ in particular for self adjoint $2\times 2$ matrices
over $\R$, over $\C$, $\HH$ and $\Oct$. In fact, we prove that the image of
multilinear polynomial must be either $\{0\}$, $\mathbb{R}$, the space of pure
elements $V$, or $J_n$.


Abstract:
  Let $G$ be a linear algebraic group over an algebraically closed field $k$
and $Aut(G)$ the group of all algebraic group automorphisms of $G$. For every
$\varphi\in Aut(G)$ let $\mathcal{R}(\varphi)$ denote the set of all orbits of
the $\varphi$-twisted conjugacy action of $G$ on itself (given by $(g,x)\mapsto
gx\varphi(g^{-1})$, for all $g,x\in G$). We say that $G$ satisfies the
$R_\infty$-property if $\mathcal{R}(\varphi)$ is infinite for every $\varphi\in
Aut(G)$. In an earlier work we have shown that this property is satisfied by
every connected non-solvable algebraic group. From a theorem due to Steinberg
it follows that if a connected algebraic group $G$ has the $R_\infty$-property
then $G^\varphi$ is infinite for all $\varphi\in Aut(G)$. In this article we
show that the condition is also sufficient. In particular we deduce that a
Borel subgroup of any semisimple algebraic group has the $R_\infty$-property
and identify certain classes of solvable algebraic groups for which the
property fails.


Abstract:
  We perform a Hadamard point-split renormalization of the current density
$j_\nu$ of a charged scalar field in a Reissner-Nordstr\"om-de Sitter
spacetime. We compute numerically the expectation values of the components
$j_r$ and $j_t$ in the Unruh state in the exterior region and study their
dependence on the field parameters and the position.


Abstract:
  We investigate the concept of molecular-sized outward-swinging gate, which
allows for entropy decrease in an isolated system. The theoretical analysis,
the Monte Carlo simulation, and the direct solution of governing equations all
suggest that under the condition of local nonchaoticity, the probability of
particle crossing is asymmetric. It is demonstrated by an experiment on a
nanoporous membrane one-sidedly surface-grafted with bendable organic chains.
Remarkably, through the membrane, gas spontaneously and repeatedly flows from
the low-pressure side to the high-pressure side. While this phenomenon seems
counterintuitive, it is compatible with the principle of maximum entropy. The
locally nonchaotic gate interrupts the probability distribution of the local
microstates, and imposes additional constraints on the global microstates, so
that entropy reaches a nonequilibrium maximum. Such a mechanism is
fundamentally different from Maxwell's demon and Feynman's ratchet, and is
consistent with microscopic reversibility. It implies that useful work may be
produced in a cycle from a single thermal reservoir. A generalized form of the
second law of thermodynamics is proposed.


Abstract:
  We prove existence, uniqueness and Sobolev regularity of weak solution of the
Cauchy problem of the stochastic transport equation with drift in a large class
of singular vector fields containing, in particular, the $L^d$ class, the weak
$L^d$ class, as well as some vector fields that are not even in $L_{\rm
loc}^{2+\varepsilon}$ for any $\varepsilon>0$.


Abstract:
  Activity generation plays an important role in activity-based demand
modelling systems. While machine learning, especially deep learning, has been
increasingly used for mode choice and traffic flow prediction, much less
research exploiting the advantage of deep learning for activity generation
tasks. This paper proposes a novel activity pattern generation framework by
incorporating deep learning with travel domain knowledge. We model each
activity schedule as one primary activity tour and several secondary activity
tours. We then develop different deep neural networks with entity embedding and
random forest models to classify activity type, as well as to predict activity
times. The proposed framework can capture the activity patterns for individuals
in both training and validation sets. Results show high accuracy for the start
time and end time of work and school activities. The framework also replicates
the start time patterns of stop-before and stop-after primary work activity
well. This provides a promising direction to deploy advanced machine learning
methods to generate more reliable activity-travel patterns for transport demand
systems and their applications.


Abstract:
  The identification of rare diseases from clinical notes with Natural Language
Processing (NLP) is challenging due to the few cases available for machine
learning and the need of data annotation from clinical experts. We propose a
method using ontologies and weak supervision. The approach includes two steps:
(i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language
System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak
supervision based on customised rules and Bidirectional Encoder Representations
from Transformers (BERT) based contextual representations, and (ii)
UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease
Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a
case study, we show that the Text-to-UMLS process can be greatly improved with
weak supervision, without any annotated data from domain experts. Our analysis
shows that the overall pipeline processing discharge summaries can surface rare
disease cases, which are mostly uncaptured in manual ICD codes of the hospital
admissions.


Abstract:
  We investigate the adversarial robustness of CNNs from the perspective of
channel-wise activations. By comparing \textit{non-robust} (normally trained)
and \textit{robustified} (adversarially trained) models, we observe that
adversarial training (AT) robustifies CNNs by aligning the channel-wise
activations of adversarial data with those of their natural counterparts.
However, the channels that are \textit{negatively-relevant} (NR) to predictions
are still over-activated when processing adversarial data. Besides, we also
observe that AT does not result in similar robustness for all classes. For the
robust classes, channels with larger activation magnitudes are usually more
\textit{positively-relevant} (PR) to predictions, but this alignment does not
hold for the non-robust classes. Given these observations, we hypothesize that
suppressing NR channels and aligning PR ones with their relevances further
enhances the robustness of CNNs under AT. To examine this hypothesis, we
introduce a novel mechanism, i.e., \underline{C}hannel-wise
\underline{I}mportance-based \underline{F}eature \underline{S}election (CIFS).
The CIFS manipulates channels' activations of certain layers by generating
non-negative multipliers to these channels based on their relevances to
predictions. Extensive experiments on benchmark datasets including CIFAR10 and
SVHN clearly verify the hypothesis and CIFS's effectiveness of robustifying
CNNs. \url{https://github.com/HanshuYAN/CIFS}


Abstract:
  Purpose: Recent studies suggest ultra-high dose rate (FLASH) irradiation can
spare normal tissues from radiotoxicity, while efficiently controlling the
tumor, and this is known as the FLASH effect. This study performed theoretical
analyses about the impact of radiolytic oxygen depletion (ROD) on the cellular
responses after FLASH irradiation. Methods: Monte Carlo simulation was used to
model the ROD process, determine the DNA damage, and calculate the amount of
oxygen depleted (LROD) during FLASH exposure. A mathematical model was applied
to analyze oxygen tension (pO2) distribution in human tissues and the recovery
of pO2 after FLASH irradiation. DNA damage and cell survival fractions (SFs)
after FLASH irradiation were calculated. The impact of initial cellular pO2,
FLASH pulse number, pulse interval, and radiation quality of the source
particles on ROD and subsequent cellular responses were systematically
evaluated. Results: The simulated electron LROD range was 0.38-0.43 {\mu}M/Gy
when pO2 ranged from 7.5-160 mmHg. The calculated DNA damage and SFs show that
radioprotective effect is only evident in cells with a lower pO2. Different
irradiation setups alter the cellular responses by modifying the pO2. Single
pulse delivery or multi-pulse delivery with pulse intervals shorter than 10-50
ms resulted in fewer DNA damages and higher SFs. Source particles with a low
radiation quality have a higher capacity to deplete oxygen, and thus, lead to a
more conspicuous radioprotective effect. Conclusions: The FLASH radioprotective
effect due to ROD may only be observed in cells with a low pO2. Single pulse
delivery or multi-pulse delivery with short pulse intervals are suggested for
FLASH irradiation to avoid oxygen tension recovery during pulse intervals.
Source particles with low radiation quality are preferred for their conspicuous
radioprotective effects.


Abstract:
  Particles kicked by external forces to produce mobility distinct from thermal
diffusion are an iconic feature of the active matter problem. Here, we map this
onto a minimal model for experiment and theory covering the wide time and
length scales of usual active matter systems. A particle diffusing in a
harmonic potential generated by an optical trap is kicked by programmed forces
with time correlation at random intervals following the Poisson process. The
model's generic simplicity allows us to find conditions for which displacements
are Gaussian (or not), how diffusion is perturbed (or not) by kicks, and
quantifying heat dissipation to maintain the non-equilibrium steady state in an
active bath. The model reproduces experimental results of tracer mobility in an
active bath of swimming algal cells. It can be used as a stochastic dynamic
simulator for Brownian objects in various active baths without mechanistic
understanding, owing to the generic framework of the protocol.


Abstract:
  Let $(M_j)_{j=1}^\infty\in\mathbb{N}$ and $(r_j)_{j=1}^\infty\in\mathbb{R}^+$
be increasing sequences satisfying some mild rate of growth conditions. We
prove that there is an entire function $f: \mathbb{C} \rightarrow\mathbb{C}$
whose behavior in the large annuli $\{ z\in\mathbb{C} :
r_{j}\cdot\exp(\pi/M_{j})\leq|z|\leq r_{j+1}\}$ is given by a perturbed
rescaling of $z\mapsto z^{M_j}$, such that the only singular values of $f$ are
rescalings of $\pm r_j^{M_j}$. We describe several applications to the dynamics
of entire functions.


Abstract:
  A novel compressed matrix format is proposed that combines an adaptive
hierarchical partitioning of the matrix with low-rank approximation. One
typical application is the approximation of discretized functions on
rectangular domains; the flexibility of the format makes it possible to deal
with functions that feature singularities in small, localized regions. To deal
with time evolution and relocation of singularities, the partitioning can be
dynamically adjusted based on features of the underlying data. Our format can
be leveraged to efficiently solve linear systems with Kronecker product
structure, as they arise from discretized partial differential equations
(PDEs). For this purpose, these linear systems are rephrased as linear matrix
equations and a recursive solver is derived from low-rank updates of such
equations. We demonstrate the effectiveness of our framework for stationary and
time-dependent, linear and nonlinear PDEs, including the Burgers' and
Allen-Cahn equations.


Abstract:
  In the paper a simple explicit formula for an arbitrary $3j$-symbol for the
Lie algebra $\mathfrak{gl}_3$ is given. More precise necessary conditions for
non-vanishing of a $3j$-symbol are given, in the case when these conditions
hold we give an explicit expression for a $3j$-symbol. It is expressed through
a value of an $A$-hypergeometric function when one substitutes $1$ instead of
all it's arguments. The problem of calculation of an arbitrary $3j$-symbol is
equivalent to the problem of calculation of an arbitrary Clebsh-Gordan
coefficient for the algebra $\mathfrak{gl}_3$. These coefficients play an
important role in quantum mechanics in the theory of quarks.


Abstract:
  We prove several consistency results in choiceless set theory ZF+DC regarding
countable chromatic numbers of various algebraic hypergraphs on Euclidean
spaces.


Abstract:
  We develop an algorithm to train individually fair learning-to-rank (LTR)
models. The proposed approach ensures items from minority groups appear
alongside similar items from majority groups. This notion of fair ranking is
based on the definition of individual fairness from supervised learning and is
more nuanced than prior fair LTR approaches that simply ensure the ranking
model provides underrepresented items with a basic level of exposure. The crux
of our method is an optimal transport-based regularizer that enforces
individual fairness and an efficient algorithm for optimizing the regularizer.
We show that our approach leads to certifiably individually fair LTR models and
demonstrate the efficacy of our method on ranking tasks subject to demographic
biases.


Abstract:
  In this work, we present EvoK, a new way of sharing one's heart rate with
feedback from their close contacts to alleviate social isolation and
loneliness. EvoK consists of a pair of wearable prototype devices (i.e., sender
and receiver). The sender is designed as a headband enabling continuous sensing
of heart rate with aesthetic designs to maximize social acceptance. The
receiver is designed as a wristwatch enabling unobtrusive receiving of the
loved one's continuous heart rate with multi-modal notification systems.


Abstract:
  Detailed chemistry-based computational fluid dynamics (CFD) simulations are
computationally expensive due to the solution of the underlying chemical
kinetics system of ordinary differential equations (ODEs). Here, we introduce a
novel open-source library aiming at speeding up such reactive flow simulations
using OpenFOAM, an open-source C++ software for CFD. First, our dynamic load
balancing model DLBFoam (Tekg\"ul et al., 2021) is utilized to mitigate the
computational imbalance due to chemistry solution in multiprocessor reactive
flow simulations. Then, the individual (cell-based) chemistry solutions are
optimized by implementing an analytical Jacobian formulation using the
open-source library pyJac, and by increasing the efficiency of the ODE solvers
by utilizing the linear algebra package LAPACK. We demonstrate the speed-up
capabilities of this new library on various combustion problems. These test
problems include a 2D turbulent reacting shear layer and 3D stratified
combustion to highlight the favorable scaling aspects of the library on
ignition/flame front initiation setups for dual-fuel combustion. Furthermore,
two fundamental 3D demonstrations are provided on non-premixed and partially
premixed flames, namely the ECN Spray A and the Sandia flame D experimental
configurations. The novel model offers up to two orders of magnitude speed-up
for most of the investigated cases. The openly shared code along with the test
case setups represent a radically new enabler for reactive flow simulations in
the OpenFOAM framework.


Abstract:
  We have performed cough detection based on measurements from an accelerometer
attached to the patient's bed. This form of monitoring is less intrusive than
body-attached accelerometer sensors, and sidesteps privacy concerns encountered
when using audio for cough detection. For our experiments, we have compiled a
manually-annotated dataset containing the acceleration signals of approximately
6000 cough and 68000 non-cough events from 14 adult male patients in a
tuberculosis clinic. As classifiers, we have considered convolutional neural
networks (CNN), long-short-term-memory (LSTM) networks, and a residual neural
network (Resnet50). We find that all classifiers are able to distinguish
between the acceleration signals due to coughing and those due to other
activities including sneezing, throat-clearing and movement in the bed with
high accuracy. The Resnet50 performs the best, achieving an area under the ROC
curve (AUC) exceeding 0.98 in cross-validation experiments. We conclude that
high-accuracy cough monitoring based only on measurements from the
accelerometer in a consumer smartphone is possible. Since the need to gather
audio is avoided and therefore privacy is inherently protected, and since the
accelerometer is attached to the bed and not worn, this form of monitoring may
represent a more convenient and readily accepted method of long-term patient
cough monitoring.


Abstract:
  We introduce DatasetGAN: an automatic procedure to generate massive datasets
of high-quality semantically segmented images requiring minimal human effort.
Current deep networks are extremely data-hungry, benefiting from training on
large-scale datasets, which are time consuming to annotate. Our method relies
on the power of recent GANs to generate realistic images. We show how the GAN
latent code can be decoded to produce a semantic segmentation of the image.
Training the decoder only needs a few labeled examples to generalize to the
rest of the latent space, resulting in an infinite annotated dataset generator!
These generated datasets can then be used for training any computer vision
architecture just as real datasets are. As only a few images need to be
manually segmented, it becomes possible to annotate images in extreme detail
and generate datasets with rich object and part segmentations. To showcase the
power of our approach, we generated datasets for 7 image segmentation tasks
which include pixel-level labels for 34 human face parts, and 32 car parts. Our
approach outperforms all semi-supervised baselines significantly and is on par
with fully supervised methods, which in some cases require as much as 100x more
annotated data as our method.


Abstract:
  Histopathological characterization of colorectal polyps allows to tailor
patients' management and follow up with the ultimate aim of avoiding or
promptly detecting an invasive carcinoma. Colorectal polyps characterization
relies on the histological analysis of tissue samples to determine the polyps
malignancy and dysplasia grade. Deep neural networks achieve outstanding
accuracy in medical patterns recognition, however they require large sets of
annotated training images. We introduce UniToPatho, an annotated dataset of
9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-slide
images, meant for training deep neural networks for colorectal polyps
classification and adenomas grading. We present our dataset and provide
insights on how to tackle the problem of automatic colorectal polyps
characterization.


Abstract:
  Automated skin lesion analysis for simultaneous detection and recognition is
still challenging for inter-class homogeneity and intra-class heterogeneity,
leading to low generic capability of a Single Convolutional Neural Network
(CNN) with limited datasets. This article proposes an end-to-end deep CNN-based
framework for simultaneous detection and recognition of the skin lesions, named
Dermo-DOCTOR, consisting of two encoders. The feature maps from two encoders
are fused channel-wise, called Fused Feature Map (FFM). The FFM is utilized for
decoding in the detection sub-network, concatenating each stage of two
encoders' outputs with corresponding decoder layers to retrieve the lost
spatial information due to pooling in the encoders. For the recognition
sub-network, the outputs of three fully connected layers, utilizing feature
maps of two encoders and FFM, are aggregated to obtain a final lesion class. We
train and evaluate the proposed Dermo-Doctor utilizing two publicly available
benchmark datasets, such as ISIC-2016 and ISIC-2017. The achieved segmentation
results exhibit mean intersection over unions of 85.0 % and 80.0 % respectively
for ISIC-2016 and ISIC-2017 test datasets. The proposed Dermo-DOCTOR also
demonstrates praiseworthy success in lesion recognition, providing the areas
under the receiver operating characteristic curves of 0.98 and 0.91
respectively for those two datasets. The experimental results show that the
proposed Dermo-DOCTOR outperforms the alternative methods mentioned in the
literature, designed for skin lesion detection and recognition. As the
Dermo-DOCTOR provides better-results on two different test datasets, even with
limited training data, it can be an auspicious computer-aided assistive tool
for dermatologists.


Abstract:
  Within the past two decades, Gaussian process regression has been
increasingly used for modeling dynamical systems due to some beneficial
properties such as the bias variance trade-off and the strong connection to
Bayesian mathematics. As data-driven method, a Gaussian process is a powerful
tool for nonlinear function regression without the need of much prior
knowledge. In contrast to most of the other techniques, Gaussian Process
modeling provides not only a mean prediction but also a measure for the model
fidelity. In this article, we give an introduction to Gaussian processes and
its usage in regression tasks of dynamical systems. Try Gaussian process
regression yourself: https://gpr.tbeckers.com


Abstract:
  The non-trivial magnetic texture in real space gives rise to the intriguing
phenomenon of topological Hall effect (THE), which is relatively less explored
in topological semimetals. Here, we report large THE in the antiferromagnetic
(AFM) state in single crystals of EuAgAs, an AFM Dirac semimetal. EuAgAs hosts
AFM ground state below $T_N$ = 12 K with a weak ferromagnetic component. The
in-plane isothermal magnetization below $T_N$ exhibits a weak metamagnetic
transition. We also observe chiral anomaly induced positive longitudinal
magnetoconductivity which indicates a Weyl fermion state under applied magnetic
field. The first-principles calculations reveal that EuAgAs is an AFM Dirac
semimetal with a pair of Dirac cones, and therefore, a Weyl semimetailic state
can be realized under time-reversal symmetry breaking via an applied magnetic
field. Our study establishes that EuAgAs is a novel system for exploiting the
interplay of band topology and the topology of the magnetic texture.


Abstract:
  This chapter presents a quantum computing-based approach to study and harness
neuronal correlates of mental activity for the development of Brain-Computer
Interface (BCI) systems. It introduces the notion of a logic of the mind, where
neurophysiological data are encoded as logical expressions representing mental
activity. Effective logical expressions are likely to be extensive, involving
dozens of variables. Large expressions require considerable computational power
to be processed. This is problematic for BCI applications because they require
fast reaction times to execute sequences of commands. Quantum computers hold
much promise in terms of processing speed for some problems, including those
involving logical expressions. Hence, we propose to use quantum computers to
process the logic of the mind. The chapter begins with an introduction to BCI
and the electroencephalogram, which is the neurophysiological signal that is
normally used in BCI. Then, it briefly discusses how the EEG corresponds to
mental states, followed by an introduction to the logic of the mind. After
that, there is an overview of quantum computing, focusing on the basics deemed
necessary to understand how it processes logical expressions. An example of a
BCI system is presented. In a nutshell, the system reads the EEG and builds
logical expressions, which are sent to a quantum computer to solve them. In
turn, the system converts the results into sounds by means of a bespoke
synthesiser. Essentially, the BCI here is a musical instrument controlled by
the mind of the player. Our BCI is a proof-of-concept aimed at demonstrating
how quantum computing may support the development of sophisticated BCI systems.
The remaining of the chapter is devoted to technical and practical
considerations on the limitations of current quantum computing hardware
technology and scalability of the system.


Abstract:
  The high solubility of oxygen in Ti, Zr and Hf makes it difficult to
stabilize the protective oxide scales on their surfaces as the subsurface
regions can serve as boundless sinks that continuously dissolve oxygen.
Alloying elements are crucial to reduce the oxygen solubility and diffusivity
within early transition metals. Past studies have shown that substitutional
alloying additions to titanium repel interstitial oxygen. Here we use
first-principles calculations to show that this repulsion is short ranged and
identify a variety of factors that are likely responsible for the repulsive
interaction. We identify a unique hybridization phenomenon between dissolved
substitutional elements and interstitial oxygen within hcp Ti that leads to a
repulsive interaction at short distances, similar to that between closed-shell
atoms. Calculations of Bader charges also suggest the existence of short-range
Coulomb interactions due to the accumulation of charge on the substitutional
solute and interstitial oxygen that is drawn from the Ti host.


Abstract:
  We study the 3-\textsc{Coloring} problem in graphs with small diameter. In
2013, Mertzios and Spirakis showed that for $n$-vertex diameter-2 graphs this
problem can be solved in subexponential time $2^{\mathcal{O}(\sqrt{n \log
n})}$. Whether the problem can be solved in polynomial time remains a
well-known open question in the area of algorithmic graphs theory.
  In this paper we present an algorithm that solves 3-\textsc{Coloring} in
$n$-vertex diameter-2 graphs in time $2^{\mathcal{O}(n^{1/3} \log^{2} n)}$.
This is the first improvement upon the algorithm of Mertzios and Spirakis in
the general case, i.e., without putting any further restrictions on the
instance graph.
  In addition to standard branchings and reducing the problem to an instance of
2-\textsc{Sat}, the crucial building block of our algorithm is a combinatorial
observation about 3-colorable diameter-2 graphs, which is proven using a
probabilistic argument.
  As a side result, we show that 3-\textsc{Coloring} can be solved in time
$2^{\mathcal{O}( (n \log n)^{2/3})}$ in $n$-vertex diameter-3 graphs. We also
generalize our algorithms to the problem of finding a list homomorphism from a
small-diameter graph to a cycle.


Abstract:
  We give a general framework for the universality classes of $ \sigma $-finite
measures in penalisation problems with multiplicative weights. We discuss
penalisation problems for Brownian motions, L\'evy processes and Langevin
processes in our framework.


Abstract:
  The approximation of functions in Orlicz space by multivariate operators on
simplex is considered. The convergence rate is given by using modulus of
smoothness.


Abstract:
  In 1999, Katona and Kierstead conjectured that if a $k$-uniform hypergraph
$\cal H$ on $n$ vertices has minimum co-degree $\lfloor
\frac{n-k+3}{2}\rfloor$, i.e., each set of $k-1$ vertices is contained in at
least $\lfloor \frac{n-k+3}{2}\rfloor$ edges, then it has a Hamiltonian cycle.
R\"{o}dl, Ruci\'{n}ski and Szemer\'{e}di in 2011 proved that the conjecture is
true when $k=3$ and $n$ is large. We show that this Katona-Kierstead conjecture
holds if $k=4$, $n$ is large, and $V({\cal H})$ has a partition $A$, $B$ such
that $|A|=\lceil n/2\rceil$, $|\{e\in E({\cal H}):|e \cap A|=2\}| <\epsilon
n^4$.


Abstract:
  We propose a novel method to reconstruct volumetric flows from sparse views
via a global transport formulation. Instead of obtaining the space-time
function of the observations, we reconstruct its motion based on a single
initial state. In addition we introduce a learned self-supervision that
constrains observations from unseen angles. These visual constraints are
coupled via the transport constraints and a differentiable rendering step to
arrive at a robust end-to-end reconstruction algorithm. This makes the
reconstruction of highly realistic flow motions possible, even from only a
single input view. We show with a variety of synthetic and real flows that the
proposed global reconstruction of the transport process yields an improved
reconstruction of the fluid motion.


Abstract:
  In a typical supervised machine learning setting, the predictions on all test
instances are based on a common subset of features discovered during model
training. However, using a different subset of features that is most
informative for each test instance individually may not only improve prediction
accuracy, but also the overall interpretability of the model. At the same time,
feature selection methods for classification have been known to be the most
effective when many features are irrelevant and/or uncorrelated. In fact,
feature selection ignoring correlations between features can lead to poor
classification performance. In this work, a Bayesian network is utilized to
model feature dependencies. Using the dependency network, a new method is
proposed that sequentially selects the best feature to evaluate for each test
instance individually, and stops the selection process to make a prediction
once it determines that no further improvement can be achieved with respect to
classification accuracy. The optimum number of features to acquire and the
optimum classification strategy are derived for each test instance. The
theoretical properties of the optimum solution are analyzed, and a new
algorithm is proposed that takes advantage of these properties to implement a
robust and scalable solution for high dimensional settings. The effectiveness,
generalizability, and scalability of the proposed method is illustrated on a
variety of real-world datasets from diverse application domains.


Abstract:
  Maximum likelihood estimation is widely used in training Energy-based models
(EBMs). Training requires samples from an unnormalized distribution, which is
usually intractable, and in practice, these are obtained by MCMC algorithms
such as Langevin dynamics. However, since MCMC in high-dimensional space
converges extremely slowly, the current understanding of maximum likelihood
training, which assumes approximate samples from the model can be drawn, is
problematic. In this paper, we try to understand this training procedure by
replacing Langevin dynamics with deterministic solutions of the associated
gradient descent ODE. Doing so allows us to study the density induced by the
dynamics (if the dynamics are invertible), and connect with GANs by treating
the dynamics as generator models, the initial values as latent variables and
the loss as optimizing a critic defined by the very same energy that determines
the generator through its gradient. Hence the term - self-adversarial loss. We
show that reintroducing the noise in the dynamics does not lead to a
qualitative change in the behavior, and merely reduces the quality of the
generator. We thus show that EBM training is effectively a self-adversarial
procedure rather than maximum likelihood estimation.


Abstract:
  In this work we investigate the boundedness of Fourier multipliers on
Triebel-Lizorkin spaces associated to positive Rockland operators on a graded
Lie group. The found criterion is expressed in terms of the H\"ormander-Mihlin
condition on the global symbol of a Fourier multiplier.


Abstract:
  We study Chern insulators from the point of view of K\"ahler geometry, i.e.
the geometry of smooth manifolds equipped with a compatible triple consisting
of a symplectic form, an integrable almost complex structure and a Riemannian
metric. The Fermi projector, i.e. the projector onto the occupied bands,
provides a map to a K\"ahler manifold. The quantum metric and Berry curvature
of the occupied bands are then related to the Riemannian metric and symplectic
form, respectively, on the target space of quantum states. We find that the
minimal volume of a parameter space with respect to the quantum metric is $\pi
|\mathcal{C}|$, where $\mathcal{C}$ is the first Chern number. We determine the
conditions under which the minimal volume is achieved both for the Brillouin
zone and the twist-angle space. The minimal volume of the Brillouin zone,
provided the quantum metric is everywhere non-degenerate, is achieved when the
latter is endowed with the structure of a K\"ahler manifold inherited from the
one of the space of quantum states. If the quantum volume of the twist-angle
torus is minimal, then both parameter spaces have the structure of a K\"ahler
manifold inherited from the space of quantum states. These conditions turn out
to be related to the stability of fractional Chern insulators. For two-band
systems, the volume of the Brillouin zone is naturally minimal provided the
Berry curvature is everywhere non-negative or nonpositive, and we additionally
show how the latter, which in this case is proportional to the quantum volume
form, necessarily has zeros due to topological constraints.


Abstract:
  Finding efficient, easily implementable differentially private (DP)
algorithms that offer strong excess risk bounds is an important problem in
modern machine learning. To date, most work has focused on private empirical
risk minimization (ERM) or private population loss minimization. However, there
are often other objectives--such as fairness, adversarial robustness, or
sensitivity to outliers--besides average performance that are not captured in
the classical ERM setup. To this end, we study a completely general family of
convex, Lipschitz loss functions and establish the first known DP excess risk
and runtime bounds for optimizing this broad class. We provide similar bounds
under additional assumptions of smoothness and/or strong convexity. We also
address private stochastic convex optimization (SCO). While $(\epsilon,
\delta)$-DP ($\delta > 0$) has been the focus of much recent work in private
SCO, proving tight population loss bounds and runtime bounds for $(\epsilon,
0)$-DP remains a challenging open problem. We provide the tightest known
$(\epsilon, 0)$-DP population loss bounds and fastest runtimes under the
presence of (or lack of) smoothness and strong convexity. Our methods extend to
the $\delta > 0$ setting, where we offer the unique benefit of ensuring
differential privacy for arbitrary $\epsilon > 0$ by incorporating a new form
of Gaussian noise. Finally, we apply our theory to two learning frameworks:
tilted ERM and adversarial learning. In particular, our theory quantifies
tradeoffs between adversarial robustness, privacy, and runtime. Our results are
achieved using perhaps the simplest DP algorithm: output perturbation. Although
this method is not novel conceptually, our novel implementation scheme and
analysis show that the power of this method to achieve strong privacy, utility,
and runtime guarantees has not been fully appreciated in prior works.


Abstract:
  We compute a formula for the discriminant of tautological bundles on
symmetric powers of a complex smooth projective curve. It follows that the
Bogomolov inequality does not give a new restriction to stability of these
tautological bundles. It only rules out tautological bundles which are already
known to have the structure sheaf as a destabilising subbundle.


Abstract:
  Analysis of competing risks data plays an important role in the lifetime data
analysis. Recently Feizjavadian and Hashemi (Computational Statistics and Data
Analysis, vol. 82, 19-34, 2015) provided a classical inference of a competing
risks data set using four-parameter Marshall-Olkin bivariate Weibull
distribution when the failure of an unit at a particular time point can happen
due to more than one cause. The aim of this paper is to provide the Bayesian
analysis of the same model based on a very flexible Gamma-Dirichlet prior on
the scale parameters. It is observed that the Bayesian inference has certain
advantages over the classical inference in this case. We provide the Bayes
estimates of the unknown parameters and the associated highest posterior
density credible intervals based on Gibbs sampling technique. We further
consider the Bayesian inference of the model parameters assuming partially
ordered Gamma-Dirichlet prior on the scale parameters when one cause is more
severe than the other cause. We have extended the results for different
censoring schemes also.


Abstract:
  With availability of huge amounts of labeled data, deep learning has achieved
unprecedented success in various object detection tasks. However, large-scale
annotations for medical images are extremely challenging to be acquired due to
the high demand of labour and expertise. To address this difficult issue, in
this paper we propose a novel semi-supervised deep metric learning method to
effectively leverage both labeled and unlabeled data with application to
cervical cancer cell detection. Different from previous methods, our model
learns an embedding metric space and conducts dual alignment of semantic
features on both the proposal and prototype levels. First, on the proposal
level, we generate pseudo labels for the unlabeled data to align the proposal
features with learnable class proxies derived from the labeled data.
Furthermore, we align the prototypes generated from each mini-batch of labeled
and unlabeled data to alleviate the influence of possibly noisy pseudo labels.
Moreover, we adopt a memory bank to store the labeled prototypes and hence
significantly enrich the metric learning information from larger batches. To
comprehensively validate the method, we construct a large-scale dataset for
semi-supervised cervical cancer cell detection for the first time, consisting
of 240,860 cervical cell images in total. Extensive experiments show our
proposed method outperforms other state-of-the-art semi-supervised approaches
consistently, demonstrating efficacy of deep semi-supervised metric learning
with dual alignment on improving cervical cancer cell detection performance.


Abstract:
  Several recent works [40, 24] observed an interesting phenomenon in neural
network pruning: A larger finetuning learning rate can improve the final
performance significantly. Unfortunately, the reason behind it remains elusive
up to date. This paper is meant to explain it through the lens of dynamical
isometry [42]. Specifically, we examine neural network pruning from an unusual
perspective: pruning as initialization for finetuning, and ask whether the
inherited weights serve as a good initialization for the finetuning? The
insights from dynamical isometry suggest a negative answer. Despite its
critical role, this issue has not been well-recognized by the community so far.
In this paper, we will show the understanding of this problem is very important
-- on top of explaining the aforementioned mystery about the larger finetuning
rate, it also unveils the mystery about the value of pruning [5, 30]. Besides a
clearer theoretical understanding of pruning, resolving the problem can also
bring us considerable performance benefits in practice.


Abstract:
  Knowledge graph completion refers to predicting missing triples. Most
approaches achieve this goal by predicting entities, given an entity and a
relation. We predict missing triples via the relation prediction. To this end,
we frame the relation prediction problem as a multi-label classification
problem and propose a shallow neural model (SHALLOM) that accurately infers
missing relations from entities. SHALLOM is analogous to C-BOW as both
approaches predict a central token (p) given surrounding tokens ((s,o)). Our
experiments indicate that SHALLOM outperforms state-of-the-art approaches on
the FB15K-237 and WN18RR with margins of up to $3\%$ and $8\%$ (absolute),
respectively, while requiring a maximum training time of 8 minutes on these
datasets. We ensure the reproducibility of our results by providing an
open-source implementation including training and evaluation scripts at
{\url{https://github.com/dice-group/Shallom}.}


Abstract:
  Recently, we obtained the simple metrics of a spherically symmetric black
hole in a dark matter halo, and extended to the case of rotation. As the
characteristic sound of black holes, quasinormal modes (QNMs) are one of the
important means to understand black holes currently. Based on these two metrics
of a spherically symmetric black hole, we study the QNMs of cold dark matter
(CDM) and scalar field dark matter (SFDM) models using the methods of the
material field perturbations and the gravitational perturbation, and make
comparisons with the Schwarzschild black hole. Our results show that black hole
QNMs of CDM and SFDM in a dark matter halo are different from the Schwarzschild
black hole, unlike a Schwarzschild black hole with a prominent power-law tail.
The different kinds models of dark matter can be distinguished by their QNMs.
The time of QNMs ringing and frequencies increase with increasing parameter
$l$. The overall QNMs of CDM are stronger than that of SFDM in the same
condition, which is easier to be detected. In addition, QNMs frequencies using
the sixth-order WKB method and the Prony method are in good agreement.


Abstract:
  We study the collision of a kink and an antikink in the double sine-Gordon
model with and without the excited vibrational mode. In the latter case, we
find that there is a limited range of the parameters where the resonance
windows exist, despite the existence of a vibrational mode. Still, when the
vibrational mode is initially excited, its energy can turn into translational
energy after the collision. This creates one-bounce as well as a rich structure
of higher-bounce resonance windows that depend on the wobbling phase being in
or out of phase at the collision and the wobbling amplitude being sufficiently
large. When the vibrational mode is excited, the modified structure of
one-bounce windows is observed in the whole range of the model's parameters,
and the resonant interval with higher-bounce windows gradually increases with
the wobbling amplitude. We estimated the center of the one-bounce windows using
a simple analytical approximation for the wobbling evolution. The kinks' final
wobbling frequency is Lorentz contracted, which is simply derived from our
equations. We also report that the maximum energy density value always has a
smooth behavior in the resonance windows.


Abstract:
  We demonstrate ferroelectric (FE) memory transistors on a crystalline silicon
channel with endurance exceeding $10^{10}$ cycles. The ferroelectric
transistors (FeFETs) incorporate a high-$\kappa$ interfacial layer (IL) of
thermally grown silicon nitride (SiN$_x$) and a thin 4.5 nm layer of Zr-doped
FE-HfO$_2$ on a $\sim$30 nm SOI channel. The device shows a $\sim$ 1V memory
window in a DC sweep of just $\pm$ 2.5V, and can be programmed and erased with
voltage pulses of $V_G= \pm$ 3V at a pulse width of 250 ns. The device also
shows very good retention behavior. These results indicate that appropriate
engineering of the IL layer could substantially improve FeFET device
performance and reliability.


Abstract:
  The objective of the present paper is to investigate the decay of solutions
for a laminated Timoshenko beam with interfacial slip in the whole space R
subject to a thermal effect acting only on one component modelled by either
Fourier or Cattaneo law. When the thermal effect is acting via the second or
third component of the laminated Timoshenko beam (rotation angle displacement
or dynamic of the slip), we obtain that both systems, Timoshenko-Fourier and
Timoshenko-Cattaneo systems, satisfy the same polynomial stability estimates in
the L2 -norm of the solution and its higher order derivatives with respect of
the space variable. The decay rate depends on the regularity of the initial
data. In addition, the presence and absence of the regularity-loss type
property are determined by some relations between the parameters of systems.
However, when the thermal effect is acting via the first comoponent of the
system (transversal displacement), a new stability condition is introduced for
both TimoshenkoFourier and Timoshenko-Cattaneo systems. This stability
condition is in the form of threshold between polynomial stability and
convergence to zero. To prove our results, we use the energy method in Fourier
space combined with judicious choices of weight functions to build appropriate
Lyapunov functionals.


Abstract:
  We consider a two-component Fermi gas with a contact interaction from the BCS
regime to the unitary limit. Starting from the idea that many-body effects
should not depend on short-distance or high-momentum physics which is encoded
in the s-wave scattering length, but only on momentum scales of the order of
the Fermi momentum, we build effective low-momentum interactions that reproduce
the scattering phase shifts of the contact interaction below some momentum
cutoff. Inspired from recent successes of this method in nuclear structure
theory, we use these interactions to describe the equation of state of the
Fermi gas in the framework of Hartree-Fock-Bogliubov theory with perturbative
corrections. In the BCS regime, there is a range of cutoffs where we obtain
fully converged results. Near unitarity, convergence is not yet reached, but we
obtain promising results for the ground-state energies close to the
experimental ones. Limitations and possible extensions of the approach are
discussed.


Abstract:
  We prove the persistence of boundary smoothness of vortex patches for a
non-linear transport equation in $\mathbb{R}^n$ with velocity field given by
convolution of the density with an odd kernel, homogeneous of degree $-(n-1)$
and of class $C^2(\mathbb{R}^n\setminus\{0\}, \mathbb{R}^n).$ This allows the
velocity field to have non-trivial divergence. The quasi-geostrophic equation
in $\mathbb{R}^3$ and the Cauchy transport equation in the plane are examples.


Abstract:
  We found evidence for critical internal heat values during significant
geophysical transitions in the inner solar system planetary bodies in
association with volcanism. From a simple rocky planet thermal evolution model,
we could infer critical surface heat flux values during peak phases (~1.2 W/m2)
and cessation phases (~ 0.092 W/m2) of major volcanism in Earth, Moon, Mars,
Venus and Mercury. The above phases of volcanism are accompanied by significant
geophysical transitions like growth and decay of global planetary magnetic
fields which is likely to be related to systematic changes in core-mantle
boundary heat flux values. The above results suggest the that planets are
possibly self-organised physical systems with strong core-mantle-crust coupling
. The present study will have implications on the search for habitable
extrasolar planets.


Abstract:
  Chain conditions are one of the major tools used in the theory of forcing. We
say that a partial order has the countable chain condition if every antichain
(in the sense of forcing) is countable. Without the axiom of choice antichains
tend to be of little use, for various reasons, and in this short note we study
a number of conditions which in ZFC are equivalent to the countable chain
condition.


Abstract:
  In this paper, an intelligent reflecting surface (IRS) assisted spectrum
sharing underlay cognitive radio (CR) wiretap channel (WTC) is studied, and we
aim at enhancing the secrecy rate of secondary user in this channel subject to
total power constraint at secondary transmitter (ST), interference power
constraint (IPC) at primary receiver (PR) as well as unit modulus constraint at
IRS. Due to extra IPC and eavesdropper (Eve) are considered, all the existing
solutions for enhancing secrecy rate of IRS-assisted non-CR WTC as well as
enhancing transmission rate in IRS-assisted CR channel without eavesdropper
fail in this work. Therefore, we propose new numerical solutions to optimize
the secrecy rate of this channel under full primary, secondary users' channel
state information (CSI) and three different cases of Eve's CSI: full CSI,
imperfect CSI with bounded estimation error, and no CSI. Simulation results
show that our proposed solutions for the IRS-assisted design greatly enhance
the secrecy performance compared with the existing numerical solutions with and
without IRS under full and imperfect Eve's CSI. And positive secrecy rate can
be achieved by our proposed AN aided approach given most channel realizations
under no Eve's CSI case so that secure communication also can be guaranteed.
All of the proposed AO algorithms are guaranteed to monotonic convergence.


Abstract:
  Fisher forecasts are a common tool in cosmology with applications ranging
from survey planning to the development of new cosmological probes. While
frequently adopted, they are subject to numerical instabilities that need to be
carefully investigated to ensure accurate and reproducible results. This
research note discusses these challenges using the example of a weak lensing
data vector and proposes procedures that can help in their solution.


Abstract:
  The influence of hypothetical new interactions beyond the Standard Model on
atomic spectra has attracted recent interest. In the present work,
interelectronic photon-exchange corrections and radiative quantum
electrodynamic corrections to the hypothetical contribution to the energy
levels of few-electron ions from a new interaction are calculated. The $1s$,
$2s$ and $2p_{1/2}$ ground states of H-like, Li-like and B-like ions are
considered, as motivated by proposals to use isotope shift spectroscopy of
few-electron ions in order to set stringent constraints on hypothetical new
interactions. It is shown that, for light Li-like and B-like ions,
photon-exchange corrections are comparable to or even larger, by up to several
orders of magnitude, than the leading one-electron contribution from the new
interaction, when the latter is mediated by heavy bosons.


Abstract:
  Located in the Large Magellanic cloud and mostly irradiated by a massive-star
cluster R$\,$136, 30 Doradus is an ideal target to test the leading theory of
the grain alignment and rotational disruption by RAdiative Torques (RATs).
Here, we use publicly available polarized thermal dust emission observations of
30 Doradus at 89, 154, and 214$\,\mu$m using SOFIA/HAWC+. We analyse the
variation of the dust polarization degree ($p$) with the total emission
intensity ($I$), the dust temperature ($T_{\rm d}$), and the gas column density
($N_{\rm H}$) constructed from ${\it Herschel}$ data. The 30 Doradus complex is
divided into two main regions relative to R$\,$136, namely North and South. In
the North, we find that the polarization degree first decreases and then
increases before decreasing again when the dust temperature increases toward
the irradiating cluster R$\,$136. The first depolarization likely arises from
the decrease of grain alignment efficiency toward the dense medium due to the
attenuation of the interstellar radiation field and the increase of the gas
density. The second trend (the increase of $p$ with $T_{\rm d}$) is consistent
with the RAT alignment theory. The final trend (the decrease of $p$ with
$T_{\rm d}$) is consistent with the RAT alignment theory only when the grain
rotational disruption by RATs is taken into account. In the South, we find that
the polarization degree is nearly independent of the dust temperature, while
the grain alignment efficiency is higher around the peak of the gas column
density and decreases toward the radiation source. The latter feature is also
consistent with the prediction of the rotational disruption by RATs.


Abstract:
  Predicting the next interaction of a short-term interaction session is a
challenging task in session-based recommendation. Almost all existing works
rely on item transition patterns, and neglect the impact of user historical
sessions while modeling user preference, which often leads to non-personalized
recommendation. Additionally, existing personalized session-based recommenders
capture user preference only based on the sessions of the current user, but
ignore the useful item-transition patterns from other user's historical
sessions. To address these issues, we propose a novel Heterogeneous Global
Graph Neural Networks (HG-GNN) to exploit the item transitions over all
sessions in a subtle manner for better inferring user preference from the
current and historical sessions. To effectively exploit the item transitions
over all sessions from users, we propose a novel heterogeneous global graph
that contains item transitions of sessions, user-item interactions and global
co-occurrence items. Moreover, to capture user preference from sessions
comprehensively, we propose to learn two levels of user representations from
the global graph via two graph augmented preference encoders. Specifically, we
design a novel heterogeneous graph neural network (HGNN) on the heterogeneous
global graph to learn the long-term user preference and item representations
with rich semantics. Based on the HGNN, we propose the Current Preference
Encoder and the Historical Preference Encoder to capture the different levels
of user preference from the current and historical sessions, respectively. To
achieve personalized recommendation, we integrate the representations of the
user current preference and historical interests to generate the final user
preference representation. Extensive experimental results on three real-world
datasets show that our model outperforms other state-of-the-art methods.


Abstract:
  The surface plasmon-polaritons (SPPs) switch is the key element of the
integrated devices in optical computation and terahertz (THz) communications.
In this paper, we propose a novel design of THz SPPs switch based on quantum
engineering. Due to the robustness of coherent quantum control technique, our
switch is very robust against with perturbations of geometrical parameters and
presents a good performance at on-state (and off-state) from 0.5 THz to 0.7
THz. The on-state and off-state of our device can be controlled by the external
voltage. We believe this finding will be the great improvement for the
integrated optical computing and THz communications.


Abstract:
  Here we report on studying the electronic and optical material properties of
the technologically-relevant material indium tin oxide (ITO) as a function of
thermal annealing. In this work, ITO powder has been prepared utilizing
solid-state reaction methods. An electron beam gun technology has been used to
prepare a ITO film (325 nm). The ITO window layer has been investigated at
various temperatures. The effects of absolute temperature on the structural,
optical, and electrical properties of the prepared ITO thin film layer are
investigated. The energy band type corresponding to the orbital transitions has
been determined, and the energies of the orbital transitions have been
calculated in the Tauc region, HOMO/LUMO gap, and charge transfer gap. In
additions, the exciton and Urbach energies have been computed. It has been
found that these energies increase with increasing the annealing temperature,
except for Urbach's energies which behave differently. Thin-film quality
coefficient, surface resistance, and thermal emission in addition to the angle
of refraction as a function of wavelength, have been determined.


Abstract:
  The Kochen-Specker theorem is one of the fundamental no-go theorems in
quantum theory. It has far-reaching consequences for all attempts trying to
give an interpretation of the quantum formalism. In this work, we examine the
hypotheses that, at the ontological level, lead to the KochenSpecker
contradiction. We emphasize the role of the assumptions about identity and
distinguishability of quantum objects in the argument.


Abstract:
  In this paper, we design a deep learning based resource allocation framework,
in the form of an auction, for simultaneous information and power transfer from
a hybrid access point (AP) to information devices and energy harvesting
devices, respectively. Using Myerson's lemma and the concept of virtual welfare
maximization, we develop an optimal dominant-strategy incentive-compatible
mechanism for the AP to maximize its expected revenue, based on the devices'
bid profiles, valuation distributions, demand profiles, and channel state
information. In so doing, we formulate the revenue maximization problem, which
is a mixed-integer non-linear program, and propose an efficient
Branch-and-Bound (BnB) algorithm to solve the problem using semidefinite
relaxation technique in each branch. Since the problem has exponential time
complexity, using BnB algorithms can be impractical for real-time applications.
To circumvent this, a deep neural network (DNN) is proposed, and trained to
predict the optimal mechanism for beamforming the data and the energy towards
the information and energy devices, respectively. We use the BnB algorithm to
solve the problem offline and populate the training dataset. The proposed DNN
architecture is indeed a multi-layer perceptron, which is trained well to map
the heterogeneous input to the desired output with high accuracy. Furthermore,
we propose a heuristic iterative solution whose accuracy performance is
comparable to that of the DNN-based solution. The heuristic solution has
polynomial time complexity whereas the DNN-based solution has linear time
complexity.


Abstract:
  Einstein-Maxwell dilaton-axion gravity is a string-inspired model arising
from the low energy effective action of heterotic string theory and an
important candidate as alternative to General Relativity. Recently, some
authors have explored its astrophysical implications in the spectra of
accreting black holes and inferred the constraint $r_2 < 0.1$, where $r_2 \ge
0$ is the black hole dilaton charge and General Relativity is recovered for
$r_2 = 0$. In the present paper, we study the impact of a non-vanishing black
hole dilaton charge on the reflection spectrum of the disk. From the analysis
of a NuSTAR spectrum of the black hole binary EXO 1846-031, we find the
constraint $r_2 < 0.011$ (90% CL), which is an order of magnitude more
stringent.


Abstract:
  By globally analyzing nuclear Drell-Yan data including all incident energies,
the nuclear effects of nPDFs and initial-state parton energy loss are
investigated. Based on Landau-Pomeranchuk-Migdal (LPM) regime, the calculations
are carried out by means of the analytic parametrizations of quenching weights
derived from the Baier-Dokshitzer-Mueller-Peign$\acute{e}$-Schiff (BDMPS)
formalism and using the new EPPS16 nPDFs. It is found that the results are in
good agreement with the data and the role of the energy loss effect on the
suppression of Drell-Yan ratios is prominent, especially for low-mass Drell-Yan
measurements. The nuclear effects of nPDFs becomes more obvious with the
nuclear mass number A, the same as the energy loss effect. By global fit, the
transport coefficient extracted is $\hat{q}=0.26\pm0.04$ GeV$^{2}$/fm. In
addition, to avoid diminishing the QCD NLO correction on the data form of
Drell-Yan ratios, the separate calculations about the Compton differential
cross section ratios $R_{Fe(W)/C}(x_{F})$ at 120GeV are performed, which
provides a feasible way to better distinguish the gluon energy loss in Compton
scattering. It is found that the role of the initial-state gluon energy loss on
the suppression of Compton scattering rations is not very important and becomes
disappear with the increase of $x_{F}$.


Abstract:
  Colloidal semiconductor nanoplatelets combine weak lateral confinement with
strong Coulomb interactions, enhanced by dielectric confinement. When the
platelets are charged with carriers of the same sign, this results in severe
Coulomb repulsions which shape the electronic structure. To illustrate this
point, the shell filling of type-I (CdSe/CdS) and type-II (CdSe/CdTe)
core/crown nanoplatelets with up to 4 electrons or holes is investigated
theoretically. We find that Coulomb repulsions enable addition energies
exceeding room temperature thermal energy and promote the occupation of
high-spin states. For charged excitons and biexcitons in CdSe/CdTe
nanoplatelets, the repulsions further give rise to multi-peaked emission
spectra with widely tunable (over 100 meV) energy, and a transition from
type-II to quasi-type-II band profile as the number of electrons confined in
the core increases. We conclude that the number of excess carriers injected in
nanoplatelets is a versatile degree of freedom to modulate their magnetic and
optoelectronic properties.


Abstract:
  While the twin prime conjecture is still famously open, it holds true in the
setting of finite fields: There are infinitely many pairs of monic irreducible
polynomials over $\mathbb{F}_q$ that differ by a fixed constant, for each $q
\geq 3$. Elementary, constructive proofs were given for different cases by Hall
and Pollack. In the same spirit, we discuss the construction of a further
infinite family of twin prime tuples of odd degree, and its relations to the
existence of certain Wieferich primes and to arithmetic properties of the
combinatorial Bell numbers.


Abstract:
  Gaussian processes (GPs) are frequently used in machine learning and
statistics to construct powerful models. However, when employing GPs in
practice, important considerations must be made, regarding the high
computational burden, approximation of the posterior, choice of the covariance
function and inference of its hyperparmeters. To address these issues, Hensman
et al. (2015) combine variationally sparse GPs with Markov chain Monte Carlo
(MCMC) to derive a scalable, flexible and general framework for GP models.
Nevertheless, the resulting approach requires intractable likelihood
evaluations for many observation models. To bypass this problem, we propose a
pseudo-marginal (PM) scheme that offers asymptotically exact inference as well
as computational gains through doubly stochastic estimators for the intractable
likelihood and large datasets. In complex models, the advantages of the PM
scheme are particularly evident, and we demonstrate this on a two-level GP
regression model with a nonparametric covariance function to capture
non-stationarity.


Abstract:
  We present OrbNet Denali, a machine learning model for electronic structure
that is designed as a drop-in replacement for ground-state density functional
theory (DFT) energy calculations. The model is a message-passing neural network
that uses symmetry-adapted atomic orbital features from a low-cost quantum
calculation to predict the energy of a molecule. OrbNet Denali is trained on a
vast dataset of 2.3 million DFT calculations on molecules and geometries. This
dataset covers the most common elements in bio- and organic chemistry (H, Li,
B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, I) as well as charged
molecules. OrbNet Denali is demonstrated on several well-established benchmark
datasets, and we find that it provides accuracy that is on par with modern DFT
methods while offering a speedup of up to three orders of magnitude. For the
GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of
7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets,
which contain chemical problems that are not present in the training set,
OrbNet Denali produces a mean absolute error comparable to those of DFT
methods. For the Hutchison conformers benchmark set, OrbNet Denali has a median
correlation coefficient of R^2=0.90 compared to the reference DLPNO-CCSD(T)
calculation, and R^2=0.97 compared to the method used to generate the training
data (wB97X-D3/def2-TZVP), exceeding the performance of any other method with a
similar cost. Similarly, the model reaches chemical accuracy for non-covalent
interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali
reproduces the torsion profiles of wB97X-D3/def2-TZVP with an average MAE of
0.12 kcal/mol for the potential energy surfaces of the diverse fragments in the
TorsionNet500 dataset.


Abstract:
  Multi-agent systems are designed to concurrently accomplish a diverse set of
tasks at unprecedented scale. Here, the central problems faced by a system
operator are to decide (i) how to divide available resources amongst the agents
assigned to tasks and (ii) how to coordinate the behavior of the agents to
optimize the efficiency of the resulting collective behavior. The focus of this
paper is on problem (i), where we seek to characterize the impact of the
division of resources on the best-case efficiency of the resulting collective
behavior. Specifically, we focus on a team Colonel Blotto game where there are
two sub-colonels competing against a common adversary in a two battlefield
environment. Here, each sub-colonel is assigned a given resource budget and is
required to allocate these resources independent of the other sub-colonel.
However, their success is dependent on the allocation strategy of both
sub-colonels. The central focus of this manuscript is on how to divide a common
pool of resources among the two sub-colonels to optimize the resulting
best-case efficiency guarantees. Intuitively, one would imagine that the more
balanced the division of resources, the worse the performance, as such
divisions restrict the sub-colonels' ability to employ joint randomized
strategies that tend to be necessary for optimizing performance guarantees.
However, the main result of this paper demonstrates that this intuition is
actually incorrect. A more balanced division of resources can offer better
performance guarantees than a more centralized division. Hence, this paper
demonstrates that the resource division problem is highly non-trivial in such
enmeshed environments and worthy of significant future research efforts.


Abstract:
  Most clinical trials involve the comparison of a new treatment to a control
arm (e.g., the standard of care) and the estimation of a treatment effect.
External data, including historical clinical trial data and real-world
observational data, are commonly available for the control arm. Borrowing
information from external data holds the promise of improving the estimation of
relevant parameters and increasing the power of detecting a treatment effect if
it exists. In this paper, we propose to use Bayesian additive regression trees
(BART) for incorporating external data into the analysis of clinical trials,
with a specific goal of estimating the conditional or population average
treatment effect. BART naturally adjusts for patient-level covariates and
captures potentially heterogeneous treatment effects across different data
sources, achieving flexible borrowing. Simulation studies demonstrate that BART
compares favorably to a hierarchical linear model and a normal-normal
hierarchical model. We illustrate the proposed method with an acupuncture
trial.


Abstract:
  A pair of triply charmed baryons, $\Omega_{ccc}\Omega_{ccc}$, is studied as
an ideal dibaryon system by (2+1)-flavor lattice QCD with nearly physical
light-quark masses and the relativistic heavy quark action with the physical
charm quark mass. The spatial baryon-baryon correlation is related to their
scattering parameters on the basis of the HAL QCD method. The
$\Omega_{ccc}\Omega_{ccc}$ in the ${^1S_0}$ channel taking into account the
Coulomb repulsion with the charge form factor of $\Omega_{ccc}$ leads to the
scattering length $a^{\rm C}_0\simeq -19~\text{fm}$ and the effective range
$r^{\rm C}_{\mathrm{eff}}\simeq 0.45~\text{fm}$. The ratio $r^{\rm
C}_{\mathrm{eff}}/a^{\rm C}_0 \simeq -0.024$, whose magnitude is considerably
smaller than that of the dineutron ($-0.149$), indicates that
$\Omega_{ccc}\Omega_{ccc}$ is located in the unitary regime.