Abstract:
  Autonomous vehicles (AVs) need to interact with other traffic participants
who can be either cooperative or aggressive, attentive or inattentive. Such
different characteristics can lead to quite different interactive behaviors.
Hence, to achieve safe and efficient autonomous driving, AVs need to be aware
of such uncertainties when they plan their own behaviors. In this paper, we
formulate such a behavior planning problem as a partially observable Markov
Decision Process (POMDP) where the cooperativeness of other traffic
participants is treated as an unobservable state. Under different
cooperativeness levels, we learn the human behavior models from real traffic
data via the principle of maximum likelihood. Based on that, the POMDP problem
is solved by Monte-Carlo Tree Search. We verify the proposed algorithm in both
simulations and real traffic data on a lane change scenario, and the results
show that the proposed algorithm can successfully finish the lane changes
without collisions.


Abstract:
  The perfect soliton crystal (PSC) was recently discovered as an extraordinary
Kerr soliton state with regularly distributed soliton pulses and enhanced comb
line power spaced by multiples of the cavity free spectral ranges (FSRs). The
modulation of continuous-wave excitation in optical microresonators and the
tunable repetition rate characteristic will significantly enhance and extend
the application potential of soliton microcombs for self-referencing comb
source, terahertz wave generation, and arbitrary waveform generation. However,
the reported PSC spectrum is generally narrow. Here, we demonstrate the
deterministic accessing of versatile perfect soliton crystals in the AlN
microresonators (FSR ~374 GHz), featuring a broad spectral range up to 0.96 of
an octave-span (1170-2300 nm) and terahertz repetition rates (up to ~1.87 THz).
The measured 60-fs short pulses and low-noise characteristics confirms the high
coherence of the PSCs


Abstract:
  Superconducting and topological states are two quantum phenomena attracting
much interest. Their coexistence may lead to topological superconductivity
sought-after for Majorana-based quantum computing. However, there is no causal
relationship between the two, since superconductivity is a many-body effect due
to electron-electron interaction while topology is a single-particle
manifestation of electron band structure. Here, we demonstrate a novel form of
Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) pairing, induced by topological Weyl
nodal lines in Ising Bardeen-Cooper-Schrieffer (IBCS) superconductors. Based on
first-principles calculations and analyses, we predict that the nonmagnetic
metals of MA$_2$Z$_4$ family, including ${\alpha}_1$-TaSi$_2$P$_4$,
${\alpha}_1$-TaSi$_2$N$_4$, ${\alpha}_1$-NbSi$_2$P$_4$,
${\alpha}_2$-TaGe$_2$P$_4$, and ${\alpha}_2$-NbGe$_2$P$_4$ monolayers, are all
superconductors. While the intrinsic IBCS paring arises in these
non-centrosymmetric systems, the extrinsic FFLO pairing is revealed to be
evoked by the Weyl nodal lines under magnetic field, facilitating the formation
of Cooper pairs with nonzero momentum in their vicinity. Moreover, we show that
the IBCS pairing alone will enhance the in-plane critical field $B_c$ to ~10-50
times of Pauli paramagnetic limit $B_p$, and additional FFLO pairing can
further triple the $B_c/B_p$ ratio. It therefore affords an effective approach
to enhance the robustness of superconductivity. Also, the topology induced
superconductivity renders naturally the possible existence of topological
superconducting state.


Abstract:
  Motion artifacts are a common occurrence in the Magnetic Resonance Imaging
(MRI) exam. Motion during acquisition has a profound impact on workflow
efficiency, often requiring a repeat of sequences. Furthermore, motion
artifacts may escape notice by technologists, only to be revealed at the time
of reading by the radiologists, affecting their diagnostic quality. Designing a
computer-aided tool for automatic motion detection and elimination can improve
the diagnosis, however, it needs a deep understanding of motion
characteristics. Motion artifacts in MRI have a complex nature and it is
directly related to the k-space sampling scheme. In this study we investigate
the effect of three conventional k-space samplers, including Cartesian, Uniform
Spiral and Radial on motion induced image distortion. In this regard, various
synthetic motions with different trajectories of displacement and rotation are
applied to T1 and T2-weighted MRI images, and a convolutional neural network is
trained to show the difficulty of motion classification. The results show that
the spiral k-space sampling method get less effect of motion artifact in image
space as compared to radial k-space sampled images, and radial k-space sampled
images are more robust than Cartesian ones. Cartesian samplers, on the other
hand, are the best in terms of deep learning motion detection because they can
better reflect motion.


Abstract:
  This is a commentary on, and critique of, Latif Salum's paper titled
"Tractability of One-in-three $\mathrm{3SAT}$: $\mathrm{P} = \mathrm{NP}$."
Salum purports to give a polynomial-time algorithm that solves the
$\mathrm{NP}$-complete problem $\mathrm{X3SAT}$, thereby claiming $\mathrm{P} =
\mathrm{NP}$. The algorithm, in short, fixes the polarity of a variable,
carries out simplifications over the resulting formula to decide whether to
keep the value assigned or flip the polarity, and repeats with the remaining
variables. One thing this algorithm does not do is backtrack. We give an
illustrative counterexample showing why the lack of backtracking makes this
algorithm flawed.


Abstract:
  The neural text generation suffers from the text degeneration issue such as
repetition. Traditional stochastic sampling methods only focus on truncating
the unreliable "tail" of the distribution, and do not address the "head" part,
which we show might contain tedious or even repetitive candidates with high
probability that lead to repetition loops. They also do not consider the issue
that human text does not always favor high-probability words. Inspired by
these, in this work we propose a heuristic sampling method. We propose to use
interquartile range of the predicted distribution to determine the "head" part,
then permutate and rescale the "head" with inverse probability. This aims at
decreasing the probability for the tedious and possibly repetitive candidates
with higher probability, and increasing the probability for the rational but
more surprising candidates with lower probability. The proposed algorithm
provides a reasonable permutation on the predicted distribution which enhances
diversity without compromising rationality of the distribution. We use
pre-trained language model to compare our algorithm with traditional methods.
Results show that our algorithm can effectively increase the diversity of
generated samples while achieving close resemblance to human text.


Abstract:
  On a smooth, closed Riemannian manifold, we study the question of
proportionality of components, also called synchronization, of vector-valued
solutions to nonlinear elliptic Schr\"odinger systems with constant
coefficients. In particular, we obtain bifurcation results showing the
existence of branches of non-synchronized solutions emanating from the constant
solutions.


Abstract:
  It is proved that for any mapping of a unit segment to a unit square, there
is a pair of points of the segment for which the square of the Euclidean
distance between their images exceeds the distance between them on the segment
by at least $3\frac58$ times. And the additional condition that the images of
the beginning and end of the segment belong to opposite sides of the square
increases the estimate to $4+\varepsilon$.


Abstract:
  The so-called aromatic infrared bands are attributed to emission of
polycyclic aromatic hydrocarbons. The observed variations toward different
regions in space are believed to be caused by contributions of different
classes of PAH molecules, i.e. with respect to their size, structure, and
charge state. Laboratory spectra of members of these classes are needed to
compare them to observations and to benchmark quantum-chemically computed
spectra of these species. In this paper we present the experimental infrared
spectra of three different PAH dications, naphthalene$^{2+}$,
anthracene$^{2+}$, and phenanthrene$^{2+}$, in the vibrational fingerprint
region 500-1700~cm$^{-1}$. The dications were produced by electron impact
ionization of the vapors with 70 eV electrons, and they remained stable against
dissociation and Coulomb explosion. The vibrational spectra were obtained by IR
predissociation of the PAH$^{2+}$ complexed with neon in a 22-pole cryogenic
ion trap setup coupled to a free-electron infrared laser at the Free-Electron
Lasers for Infrared eXperiments (FELIX) Laboratory. We performed anharmonic
density-functional theory calculations for both singly and doubly charged
states of the three molecules. The experimental band positions showed excellent
agreement with the calculated band positions of the singlet electronic ground
state for all three doubly charged species, indicating its higher stability
over the triplet state. The presence of several strong combination bands and
additional weaker features in the recorded spectra, especially in the
10-15~$\mu$m region of the mid-IR spectrum, required anharmonic calculations to
understand their effects on the total integrated intensity for the different
charge states. These measurements, in tandem with theoretical calculations,
will help in the identification of this specific class of doubly-charged PAHs
as carriers of AIBs.


Abstract:
  Distributed software development is more difficult than co-located software
development. One of the main reasons is that communication is more difficult in
distributed settings. Defined processes and artifacts help, but cannot cover
all information needs. Not communicating important project information,
decisions and rationales can result in duplicate or extra work, delays or even
project failure. Planning and managing a distributed project from an
information flow perspective helps to facilitate available communication
channels right from the start - beyond the documents and artifacts which are
defined for a given development process. In this paper we propose FLOW Mapping,
a systematic approach for planning and managing information flows in
distributed projects. We demonstrate the feasibility of our approach with a
case study in a distributed agile class room project. FLOW Mapping is
sufficient to plan communication and to measure conformance to the
communication strategy. We also discuss cost and impact of our approach.


Abstract:
  A new ultra-hard rhombohedral carbon rh-C4 (or hexagonal h-C12) is reported
as derived from 3R graphite through crystal chemistry construction and ground
state energy within the density functional theory. An extended hexagonal
three-dimensional network of h-C12 is formed of C4 tetrahedra alike in h-C4
lonsdaleite (hexagonal diamond). The electronic band structure of rh-C4 is
characteristic of insulator with Egap = 4 eV similarly to diamond. From the set
of elastic constants a larger value of bulk modulus versus lonsdaleite, and the
largest Vickers hardness (HV) versus both forms of diamond were derived.


Abstract:
  Multifunctional-therapeutic 3D scaffolds have been prepared. These
biomaterials are able to destroy the S. aureus bacteria biofilm and to allow
bone regeneration at the same time. The present study is focused on the design
of pH sensitive 3D hierarchical meso-macroporous scaffolds based on MGHA
nanocomposite formed by a mesostructured glassy network with embedded
hydroxyapatite nanoparticles, whose mesopores have been loaded with
levofloxacin as antibacterial agent. These 3D platforms exhibit controlled and
pH-dependent levofloxacin release, sustained over time at physiological pH
(7.4) and notably increased at infection pH (6.7 and 5.5), which is due to the
different interaction rate between diverse levofloxacin species and the silica
matrix. These 3D systems are able to inhibit the S. aureus growth and to
destroy the bacterial biofilm without cytotoxic effects on human osteoblasts
and allowing an adequate colonization and differentiation of preosteoblastic
cells on their surface. These findings suggest promising applications of these
hierarchical MGHA nanocomposite scaffolds for the treatment and prevention of
bone infection.


Abstract:
  We investigate the role of higher-form symmetries and two kinds of 't Hooft
anomalies in non-equilibrium systems. To aid our investigation, we extend the
coset construction to account for $p$-form symmetries at zero and finite
temperature. One kind of anomaly arises when a $p$-form symmetry is
spontaneously broken: in a $d+1$-dimensional spacetime there often exists an
emergent $d-p-1$-form symmetry with mixed 't Hooft anomaly. That is, the
$p$-form and $d-p-1$-form symmetries cannot be gauged simultaneously. At the
level of the coset construction, this mixed anomaly prevents the Goldstones for
the $p$- and $d-p-1$-form symmetries from appearing in the same Maurer-Cartan
form. As a result, whenever such a mixed anomaly exists, we find the emergence
of dual theories -- one involving the $p$-form Goldstone and the other
involving the $d-p-1$-form Goldstone -- that are related to each other by a
kind of Legendre transform. Such an anomaly can exist at zero and finite
temperature. The other kind of 't Hooft anomaly can only arise in
non-equilibrium systems; we therefore term it the non-equilibrium 't Hoof
anomaly. In this case, an exact symmetry of the non-equilibrium effective
action fails to have a non-trivial, conserved Noether current. This anomalous
behavior arises when a global symmetry cannot be gauged in the non-equilibrium
effective action and can arise in both open and closed systems. We construct
actions for a number of systems including chemically reacting fluids,
Yang-Mills theory, Chern-Simons theory, magnetohydrodynamic systems, and dual
superfluid and solid theories. Finally, we find that the interplay of these two
kinds of anomalies has a surprising result: in non-equilibrium systems, whether
or not a symmetry appears spontaneously broken can depend on the time-scale
over which the system is observed.


Abstract:
  We present a brief review of Friedmann's impact on cosmology from a
historical and a physical perspective.


Abstract:
  Multi-agent behavior modeling aims to understand the interactions that occur
between agents. We present a multi-agent dataset from behavioral neuroscience,
the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists
of trajectory data of social interactions, recorded from videos of freely
behaving mice in a standard resident-intruder assay. To help accelerate
behavioral studies, the CalMS21 dataset provides benchmarks to evaluate the
performance of automated behavior classification methods in three settings: (1)
for training on large behavioral datasets all annotated by a single annotator,
(2) for style transfer to learn inter-annotator differences in behavior
definitions, and (3) for learning of new behaviors of interest given limited
training data. The dataset consists of 6 million frames of unlabeled tracked
poses of interacting mice, as well as over 1 million frames with tracked poses
and corresponding frame-level behavior annotations. The challenge of our
dataset is to be able to classify behaviors accurately using both labeled and
unlabeled tracking data, as well as being able to generalize to new settings.


Abstract:
  Synchronization, cooperation, and chaos are ubiquitous phenomena in nature.
In a population composed of many distinct groups of individuals playing the
prisoner's dilemma game, there exists a migration dilemma: No cooperator would
migrate to a group playing the prisoner's dilemma game lest it should be
exploited by a defector; but unless the migration takes place, there is no
chance of the entire population's cooperator-fraction to increase. Employing a
randomly rewired coupled map lattice of chaotic replicator maps, modelling
replication-selection evolutionary game dynamics, we demonstrate that the
cooperators -- evolving in synchrony -- overcome the migration dilemma to
proliferate across the population when altruism is mildly incentivized making
few of the demes play the leader game.


Abstract:
  We present large-eddy simulations (LESs) of riverine flow in a study reach in
the Sacramento River, California. The riverbed bathymetry was surveyed in
high-resolution using a multibeam echosounder to construct the computational
model of the study area, while the topographies were defined using aerial
photographs taken by an Unmanned Aircraft System (UAS). In a series of field
campaigns, we measured the flow field of the river using the acoustic Doppler
current profiler (ADCP) and estimated using large-scale particle velocimetry of
the videos taken during the operation UAS. We used the measured data of the
river flow field to evaluate the accuracy of the LES-computed hydrodynamics.
The propagation of uncertainties in the LES results due to the variations in
the effective roughness height of the riverbed and the inflow discharge of the
river was studied using uncertainty quantification (UQ) analyses. The
polynomial chaos expansion (PCE) method was used to develop a surrogate model,
which was randomly sampled sufficiently by the Monte Carlo Sampling (MCS)
method to generate confidence intervals for the LES-computed velocity field.
Also, Sobol indices derived from the PCE coefficients were calculated to help
understand the relative influence of different input parameters on the global
uncertainty of the results. The UQ analysis showed that uncertainties of LES
results in the shallow near bank regions of the river were mainly related to
the roughness, while the variation of inflow discharge leads to uncertainty in
the LES results throughout the river, indiscriminately.


Abstract:
  Directly manipulating the atomic structure to achieve a specific property is
a long pursuit in the field of materials. However, hindered by the disordered,
non-prototypical glass structure and the complex interplay between structure
and property, such inverse design is dauntingly hard for glasses. Here,
combining two cutting-edge techniques, graph neural networks and swap Monte
Carlo, we develop a data-driven, property-oriented inverse design route that
managed to improve the plastic resistance of Cu-Zr metallic glasses in a
controllable way. Swap Monte Carlo, as "sampler", effectively explores the
glass landscape, and graph neural networks, with high regression accuracy in
predicting the plastic resistance, serves as "decider" to guide the search in
configuration space. Via an unconventional strengthening mechanism, a
geometrically ultra-stable yet energetically meta-stable state is unraveled,
contrary to the common belief that the higher the energy, the lower the plastic
resistance. This demonstrates a vast configuration space that can be easily
overlooked by conventional atomistic simulations. The data-driven techniques,
structural search methods and optimization algorithms consolidate to form a
toolbox, paving a new way to the design of glassy materials.


Abstract:
  In this paper a novel non-negative finite volume discretization scheme is
proposed for certain first order nonlinear partial differential equations
describing conservation laws arising in traffic flow modelling. The spatially
discretized model is shown to preserve several fundamentally important
analytical properties of the conservation law (e.g., conservativeness,
capacity) giving rise to a set of (second order) polynomial ODEs. Furthermore,
it is shown that the discretized traffic flow model is formally kinetic and
that it can be interpreted in a compartmental context. As a consequence,
traffic networks can be represented as reaction graphs. It is shown that the
model can be equipped with on- and off- ramps in a physically meaningful way,
still preserving the advantageous properties of the discretization. Numerical
case studies include empirical convergence tests, and the stability analysis
presented in the paper paves the way to scalable observer and controller
design.


Abstract:
  We continue our study of initial-value problems for fully nonlinear systems
exhibiting strong or weak defects of hyperbolicity. We prove that, regardless
of the initial Sobolev regularity, the initial-value problem has no local $H^s$
solutions, for $s > s_0 + d/2,$ if the principal symbol has a strong, or even
weak, defect of hyperbolicity, and the purely imaginary eigenvalues of the
principal symbol are semi-simple and have constant multiplicity. The index $s_0
> 0$ depends on the severity of the defect of hyperbolicity. These results
recover and extend previous work from G. M\'etivier [{\it Remarks on the well
posedness of the nonlinear Cauchy problem,} 2005], N.Lerner, Y. Morimoto, C.-J.
Xu [{\it Instability of the Cauchy-Kovalevskaya solution for a class of
non-linear systems}, 2010] and N. Lerner, T. Nguyen, B. Texier, {\it The onset
of instability in first-order systems}, 2018]


Abstract:
  In this paper, we present a new objective prediction model for synthetic
speech naturalness. It can be used to evaluate Text-To-Speech or Voice
Conversion systems and works language independently. The model is trained
end-to-end and based on a CNN-LSTM network that previously showed to give good
results for speech quality estimation. We trained and tested the model on 16
different datasets, such as from the Blizzard Challenge and the Voice
Conversion Challenge. Further, we show that the reliability of deep
learning-based naturalness prediction can be improved by transfer learning from
speech quality prediction models that are trained on objective POLQA scores.
The proposed model is made publicly available and can, for example, be used to
evaluate different TTS system configurations.


Abstract:
  The (poset) cube Ramsey number $R(Q_n,Q_n)$ is defined as the least~$m$ such
that any 2-coloring of the $m$-dimensional cube $Q_m$ admits a monochromatic
copy of $Q_n$. The trivial lower bound $R(Q_n,Q_n)\ge 2n$ was improved by Cox
and Stolee, who showed $R(Q_n,Q_n)\ge 2n+1$ for $3\le n\le 8$ and $n\ge 13$
using a probabilistic existence proof. In this paper, we provide an explicit
construction that establishes $R(Q_n,Q_n)\ge 2n+1$ for all $n\ge 3$.


Abstract:
  In this article, we present a visual introduction to Gaussian Belief
Propagation (GBP), an approximate probabilistic inference algorithm that
operates by passing messages between the nodes of arbitrarily structured factor
graphs. A special case of loopy belief propagation, GBP updates rely only on
local information and will converge independently of the message schedule. Our
key argument is that, given recent trends in computing hardware, GBP has the
right computational properties to act as a scalable distributed probabilistic
inference framework for future machine learning systems.


Abstract:
  Materials property predictions have improved from advances in
machine-learning algorithms, delivering materials discoveries and novel
insights through data-driven models of structure-property relationships. Nearly
all available models rely on featurization of materials composition, however,
whether the exclusive use of structural knowledge in such models has the
capacity to make comparable predictions remains unknown. Here we employ a deep
neural network (DNN) model, deepKNet, to learn structure-property relationships
in crystalline materials without explicit chemical compositions, focusing on
classification of crystal systems, mechanical elasticity, electrical behavior,
and phase stability. The deepKNet model utilizes a three-dimensional (3D)
momentum space representation of structure from elastic X-ray scattering theory
and simultaneously exhibits rotation and permutation invariance. We find that
the spatial symmetry of the 3D point cloud, which reflects crystalline symmetry
operations, is more important than the point intensities contained within,
which correspond to various planar electron densities, for making a successful
metal-insulator classification. In contrast, the intensities are more important
for predicting bulk moduli. Phase stability, however, relies more upon chemical
composition information, where our structure-based model exhibits limited
predictability. We find learning the materials structural genome in the form of
a chemistry-agnostic DNN demonstrates that some crystal structures inherently
host high propensities for optimal materials properties, which enables the
decoupling of structure and composition for future co-design of
multifunctionality.


Abstract:
  As a consequence of the classification of finite simple groups, the
classification of permutation groups of prime degree is complete, apart from
the question of when the natural degree $(q^n-1)/(q-1)$ of ${\rm PSL}_n(q)$ is
prime. We present heuristic arguments and computational evidence based on the
Bateman-Horn Conjecture to support a conjecture that for each prime $n\ge 3$
there are infinitely many primes of this form, even if one restricts to prime
values of $q$. Similar arguments and results apply to the parameters of the
simple groups ${\rm PSL}_n(q)$, ${\rm PSU}_n(q)$ and ${\rm PSp}_{2n}(q)$ which
arise in the work of Dixon and Zalesskii on linear groups of prime degree.


Abstract:
  It is shown that $c=-29/16$ is the unique rational number of smallest
denominator, and the unique rational number of smallest numerator, for which
the map $f_c(x) = x^2+c$ has a rational periodic point of period $3$. Several
arithmetic conditions on the set of all such rational numbers $c$ and the
rational orbits of $f_c(x)$ are proved. A graph on the numerators of the
rational $3$-periodic points of maps $f_c$ is considered which reflects
connections between solutions of norm equations from the cubic field of
discriminant $-23$.


Abstract:
  The emergence of deep learning has been accompanied by privacy concerns
surrounding users' data and service providers' models. We focus on private
inference (PI), where the goal is to perform inference on a user's data sample
using a service provider's model. Existing PI methods for deep networks enable
cryptographically secure inference with little drop in functionality; however,
they incur severe latency costs, primarily caused by non-linear network
operations (such as ReLUs). This paper presents Sphynx, a ReLU-efficient
network design method based on micro-search strategies for convolutional cell
design. Sphynx achieves Pareto dominance over all existing private inference
methods on CIFAR-100. We also design large-scale networks that support
cryptographically private inference on Tiny-ImageNet and ImageNet.


Abstract:
  Electronic health record (EHR) coding is the task of assigning ICD codes to
each EHR. Most previous studies either only focus on the frequent ICD codes or
treat rare and frequent ICD codes in the same way. These methods perform well
on frequent ICD codes but due to the extremely unbalanced distribution of ICD
codes, the performance on rare ones is far from satisfactory. We seek to
improve the performance for both frequent and rare ICD codes by using a
contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR
coding as a few-shot learning task. First, we construct a heterogeneous EHR
word-entity (HEWE) graph for each EHR, where the words and entities extracted
from an EHR serve as nodes and the relations between them serve as edges. Then,
CoGraph learns similarities and dissimilarities between HEWE graphs from
different ICD codes so that information can be transferred among them. In a
few-shot learning scenario, the model only has access to frequent ICD codes
during training, which might force it to encode features that are useful for
frequent ICD codes only. To mitigate this risk, CoGraph devises two graph
contrastive learning schemes, GSCL and GECL, that exploit the HEWE graph
structures so as to encode transferable features. GSCL utilizes the
intra-correlation of different sub-graphs sampled from HEWE graphs while GECL
exploits the inter-correlation among HEWE graphs at different clinical stages.
Experiments on the MIMIC-III benchmark dataset show that CoGraph significantly
outperforms state-of-the-art methods on EHR coding, not only on frequent ICD
codes, but also on rare codes, in terms of several evaluation indicators. On
frequent ICD codes, GSCL and GECL improve the classification accuracy and F1 by
1.31% and 0.61%, respectively, and on rare ICD codes CoGraph has more obvious
improvements by 2.12% and 2.95%.


Abstract:
  Semiconductor nanowire networks are essential elements for a variety of
gate-tunable quantum applications. Their relevance, however, depends critically
on the material quality. In this work we study selective area growth (SAG) of
highly lattice-mismatched InAs/In$_x$Ga$_{1-x}$As nanowires on insulating
GaAs(001) substrates and address two key challenges: crystalline quality and
compositional uniformity. We introduce optimization steps and show how misfit
dislocations are guided away from the InAs active region and how Ga-In
intermixing is kinetically limited with growth temperature. The optimization
process leads to a more than twofold increase in electron mobility and shows an
advancement toward realizing high quality gatable quantum wire networks.


Abstract:
  This article is mostly based on a talk I gave at the March 2021 meeting
(virtual) of the American Physical Society on the occasion of receiving the
Dannie Heineman prize for Mathematical Physics from the American Institute of
Physics and the American Physical Society. I am greatly indebted to many
colleagues for the results leading to this award. To name them all would take
up all the space allotted to this article. (I have had more than 200
collaborators so far), I will therefore mention just a few: Michael Aizenman,
Bernard Derrida, Shelly Goldstein, Elliott Lieb, Oliver Penrose, Errico
Presutti, Gene Speer and Herbert Spohn. I am grateful to all of my
collaborators, listed and unlisted. I would also like to acknowledge here long
time support form the AFOSR and the NSF.


Abstract:
  We investigate the possibility of gravitationally generated particle
production via the mechanism of nonminimal torsion--matter coupling. An
intriguing feature of this theory is that the divergence of the matter
energy--momentum tensor does not vanish identically. We explore the physical
and cosmological implications of the nonconservation of the energy--momentum
tensor by using the formalism of irreversible thermodynamics of open systems in
the presence of matter creation/annihilation. The particle creation rates,
pressure, and the expression of the comoving entropy are obtained in a
covariant formulation and discussed in detail. Applied together with the
gravitational field equations, the thermodynamics of open systems lead to a
generalization of the standard $\Lambda$CDM cosmological paradigm, in which the
particle creation rates and pressures are effectively considered as components
of the cosmological fluid energy--momentum tensor. We consider specific models,
and we show that cosmology with a torsion--matter coupling can almost perfectly
reproduce the $\Lambda$CDM scenario, while it additionally gives rise to
particle creation rates, creation pressures, and entropy generation through
gravitational matter production in both low and high redshift limits.


Abstract:
  We present the identification of the COCONUTS-2 system, composed of the M3
dwarf L 34-26 and the T9 dwarf WISEPA J075108.79$-$763449.6. Given their common
proper motions and parallaxes, these two field objects constitute a physically
bound pair with a projected separation of 594$"$ (6471 au). The primary star
COCONUTS-2A has strong stellar activity (H$\alpha$, X-ray, and UV emission) and
is rapidly rotating ($P_{\rm rot} = 2.83$ days), from which we estimate an age
of 150-800 Myr. Comparing equatorial rotational velocity derived from the TESS
light curve to spectroscopic $v\sin{i}$, we find COCONUTS-2A has a nearly
edge-on inclination. The wide exoplanet COCONUTS-2b has an effective
temperature of $T_{\rm eff}=434 \pm 9$ K, a surface gravity of $\log{g} =
4.11^{+0.11}_{-0.18}$ dex, and a mass of $M=6.3^{+1.5}_{-1.9}$ $M_{\rm Jup}$
based on hot-start evolutionary models, leading to a $0.016^{+0.004}_{-0.005}$
mass ratio for the COCONUTS-2 system. COCONUTS-2b is the second coldest (after
WD 0806$-$661B) and the second widest (after TYC 9486-927-1 b) exoplanet imaged
to date. Comparison of COCONUTS-2b's infrared photometry with ultracool model
atmospheres suggests the presence of both condensate clouds and non-equilibrium
chemistry in its photosphere. Similar to 51 Eri b, COCONUTS-2b has a
sufficiently low luminosity ($\log{(L_{\rm bol}/L_{\odot})} = -6.384 \pm 0.028$
dex) to be consistent with the cold-start process that may form gas-giant
(exo)planets, though its large separation means such formation would not have
occurred in situ. Finally, at a distance of 10.9 pc, COCONUTS-2b is the nearest
imaged exoplanet to Earth known to date.


Abstract:
  Magnetic insulators are important materials for a range of next generation
memory and spintronic applications. Structural constraints in this class of
devices generally require a clean heterointerface that allows effective
magnetic coupling between the insulating layer and the conducting layer.
However, there are relatively few examples of magnetic insulators which can be
synthesized with surface qualities that would allow these smooth interfaces and
precisely tuned interfacial magnetic exchange coupling which might be
applicable at room temperature. In this work, we demonstrate an example of how
the configurational complexity in the magnetic insulator layer can be used to
realize these properties. The entropy-assisted synthesis is used to create
single crystal (Mg0.2Ni0.2Fe0.2Co0.2Cu0.2)Fe2O4 films on substrates spanning a
range of strain states. These films show smooth surfaces, high resistivity, and
strong magnetic responses at room temperature. Local and global magnetic
measurements further demonstrate how strain can be used to manipulate magnetic
texture and anisotropy. These findings provide insight into how precise
magnetic responses can be designed using compositionally complex materials that
may find application in next generation magnetic devices.


Abstract:
  We characterize the essential spectrum of the plasmonic problem for polyhedra
in $\mathbb{R}^3$. The description is particularly simple for convex polyhedra
and permittivities $\epsilon < - 1$. The plasmonic problem is interpreted as a
spectral problem through a boundary integral operator, the direct value of the
double layer potential, also known as the Neumann--Poincar\'e operator. We
therefore study the spectral structure of the the double layer potential for
polyhedral cones and polyhedra.


Abstract:
  Lattice simulations of the QCD correlation functions in the Landau gauge have
established two remarkable facts. First, the coupling constant in the gauge
sector remains finite and moderate at all scales, suggesting that some kind of
perturbative description should be valid down to infrared momenta. Second, the
gluon propagator reaches a finite nonzero value at vanishing momentum,
corresponding to a gluon screening mass. We review recent studies which aim at
describing the long-distance properties of Landau gauge QCD by means of the
perturbative Curci-Ferrari model. The latter is the simplest deformation of the
Faddeev-Popov Lagrangian in the Landau gauge that includes a gluon screening
mass at tree-level. There are, by now, strong evidences that this approach
successfully describes many aspects of the infrared QCD dynamics. In
particular, several correlation functions were computed at one- and two-loop
orders and compared with {\it ab-initio} lattice simulations. The typical error
is of the order of ten percent for a one-loop calculation and drops to few
percents at two loops. We review such calculations in the quenched
approximation as well as in the presence of dynamical quarks. In the latter
case, the spontaneous breaking of the chiral symmetry requires to go beyond a
coupling expansion but can still be described in a controlled approximation
scheme in terms of small parameters. We also review applications of the
approach to nonzero temperature and chemical potential.


Abstract:
  Two-band model works well for Hall effect in topological insulators. It turns
out to be non-Hermitian when the system is subjected to environments, and its
topology characterized by Chern numbers has received extensive studies in the
past decades. However, how a non-Hermitian system responses to an electric
field and what is the connection of the response to the Chern number defined
via the non-Hermitian Hamiltonian remain barely explored. In this paper,
focusing on a k-dependent decay rate, we address this issue by studying the
response of such a non-Hermitian Chern insulator to an external electric field.
To this aim, we first derive an effective non-Hermitian Hamiltonian to describe
the system and give a specific form of k-dependent decay rate. Then we
calculate the response of the non-Hermitian system to a constant electric
field. We observe that the environment leads the Hall conductance to be a
weighted integration of curvature of the ground band and hence the conductance
is no longer quantized in general. And the environment induces a delay in the
response of the system to the electric field. A discussion on the validity of
the non-Hermitian model compared with the master equation description is also
presented.


Abstract:
  An appearance or disappearance of QPOs associated with the variation of X-ray
flux can be used to decipher the accretion ejection mechanism of black hole
X-ray sources. We searched and studied such rapid transitions in H1743-322
using RXTE archival data and found eight such events, where QPO vanishes
suddenly along with the variation of X-ray flux. The appearance/disappearance
of QPOs were associated to the four events exhibiting type-B QPOs at $\sim$ 4.5
Hz, one with type-A QPO at $\nu$ $\sim$ 3.5 Hz, and the remaining three were
connected to type-C QPOs at $\sim$ 9.5 Hz. Spectral studies of the data
unveiled that an inner disk radius remained at the same location around 2-9
r$_g$ , depending on the used model but power-law indices were varying,
indicating that either corona or jet is responsible for the events. The
probable ejection radii of corona were estimated to be around 4.2-15.4 r$_g$
based on the plasma ejection model. Our X-ray and quasi-simultaneous radio
correlation studies suggest that the type-B QPOs are probably related to the
precession of a weak jet though a small and weak corona is present at its base
and the type-C QPOs are associated to the base of a relatively strong jet which
is acting like a corona.


Abstract:
  Solutions to the $\mu$ problem in supersymmetry based on the Kim-Nilles
mechanism naturally feature a Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) axion
with decay constant of order the geometric mean of the Planck and TeV scales,
consistent with astrophysical limits. We investigate minimal models of this
type with two gauge-singlet fields that break a Peccei-Quinn symmetry, and
extensions with extra vectorlike quark and lepton supermultiplets consistent
with gauge coupling unification. We show that there are many anomaly-free
discrete symmetries, depending on the vectorlike matter content, that protect
the Peccei-Quinn symmetry to sufficiently high order to solve the strong CP
problem. We study the axion couplings in this class of models. Models of this
type that are automatically free of the domain wall problem require at least
one pair of strongly interacting vectorlike multiplets with mass at the
intermediate scale, and predict axion couplings that are greatly enhanced
compared to the minimal supersymmetric DFSZ models, putting them within reach
of proposed axion searches.


Abstract:
  In this paper, we focus on the $(\si,\t)$-derivation theory of Lie conformal
superalgebras. Firstly, we study the fundamental properties of conformal
$(\si,\t)$-derivations. Secondly, we mainly research the interiors of conformal
$G$-derivations. Finally, we discuss the relationships between the conformal
$(\si,\t)$-derivations and some generalized conformal derivations of Lie
conformal superalgebras.


Abstract:
  The thermodynamic activities of all components in Ga-In-Tl system have been
predicted at 1073, 1173 and 1273 K, using Molecular interaction volume model
(MIVM). The infinite dilute activity coefficients for Ga-In and Ga-Tl binary
subsystems, which were needed for the determination of thermodynamic activities
of all components in Ga-In-Tl, have been predicted by using a method that is
based on Complex formation model for liquid alloys. The computed thermodynamic
activities of Ga in Ga-In-Tl were observed to satisfactorily agree with the
available experimental data when the newly computed coefficients were applied
in MIVM. The satisfactory prediction of the activity of Ga led to the
prediction of the activities of the remaining two components (In and Tl).
Iso-activities of all components (Ga, In and Tl) were plotted, and they reveal
the dependence of the nature of chemical short range order in Ga-In-Tl system
on composition.


Abstract:
  The Ni-based superalloy Alloy 718 is used in aircraft engines as
high-pressure turbine discs and must endure challenging demands on
high-temperature yield strength, creep-, and oxidation-resistance. Nanoscale
$\gamma^{\prime}$- and $\gamma^{\prime \prime}$-precipitates commonly found in
duplet and triplet co-precipitate morphologies provide high-temperature
strength under these harsh operating conditions. Direct ageing of Alloy 718 is
an attractive alternative manufacturing route known to increase the yield
strength at 650 $^{\deg}$C by at least +10 $\%$, by both retaining high
dislocation densities and changing the nanoscale co-precipitate morphology.
However, the detailed nucleation and growth mechanisms of the duplet and
triplet co-precipitate morphologies of $\gamma^{\prime}$ and $\gamma^{\prime
\prime}$ during the direct ageing process remain unknown. We provide a
correlative high-resolution microscopy approach using transmission electron
microscopy, high-angle annular dark-field imaging, and atom probe microscopy to
reveal the early stages of precipitation during direct ageing of Alloy 718.
Quantitative stereological analyses of the $\gamma^{\prime}$- and
$\gamma^{\prime \prime}$-precipitate dispersions as well as their chemical
compositions have allowed us to propose a qualitative model of the
microstructural evolution. It is shown that fine $\gamma^{\prime}$- and
$\gamma^{\prime \prime}$-precipitates nucleate homogeneously and grow
coherently. However, $\gamma^{\prime \prime}$-precipitates also nucleate
heterogeneously on dislocations and experience accelerated growth due to Nb
pipe diffusion. Moreover, the co-precipitation reactions are largely influenced
by solute availability and the potential for enrichment of Nb and rejection of
Al+Ti.


Abstract:
  We study systems of String Equations where block variables need to be
assigned strings so that their concatenation gives a specified target string.
We investigate this problem under a multivariate complexity framework,
searching for tractable special cases such as systems of equations with few
block variables or few equations. Our main results include a polynomial-time
algorithm for size-2 equations, and hardness for size-3 equations, as well as
hardness for systems of two equations, even with tight constraints on the block
variables. We also study a variant where few deletions are allowed in the
target string, and give XP algorithms in this setting when the number of block
variables is constant.


Abstract:
  The inverse renormalization group is studied based on the image
super-resolution using the deep convolutional neural networks. We consider the
improved correlation configuration instead of spin configuration for the spin
models, such as the two-dimensional Ising and three-state Potts models. We
propose a block-cluster transformation as an alternative to the block-spin
transformation in dealing with the improved estimators. In the framework of the
dual Monte Carlo algorithm, the block-cluster transformation is regarded as a
transformation in the graph degrees of freedom, whereas the block-spin
transformation is that in the spin degrees of freedom. We demonstrate that the
renormalized improved correlation configuration successfully reproduces the
original configuration at all the temperatures by the super-resolution scheme.
Using the rule of enlargement, we repeatedly make inverse renormalization
procedure to generate larger correlation configurations. To connect
thermodynamics, an approximate temperature rescaling is discussed. The enlarged
systems generated using the super-resolution satisfy the finite-size scaling.


Abstract:
  Although much progress has been made on the physics of magic angle twisted
bilayer graphene at integer fillings, little attention has been given to
fractional fillings. Here we show that the three-peak structure of Wannier
orbitals, dictated by the symmetry and topology of flat bands, facilitates the
emergence of a novel state at commensurate fractional filling of $\nu = n \pm
1/3$. We dub this state a "fractional correlated insulator". Specifically for
the filling of $\pm 1/3$ electrons per moir\'{e} unit cell, we show that
short-range interactions alone imply an approximate extensive entropy due to
the "breathing" degree of freedom of an irregular honeycomb lattice that
emerges through defect lines. The leading further-range interaction lifts this
degeneracy and selects a novel ferromagnetic nematic state that breaks AB/BA
sublattice symmetry. The proposed fractional correlated insulating state might
underlie the suppression of superconductivity at $\nu = 2-1/3$ filling observed
in arXiv:2004.04148. Further investigation of the proposed fractional
correlated insulating state would open doors to new regimes of correlation
effects in MATBG.


Abstract:
  Self-supervised learning presents a remarkable performance to utilize
unlabeled data for various video tasks. In this paper, we focus on applying the
power of self-supervised methods to improve semi-supervised action proposal
generation. Particularly, we design an effective Self-supervised
Semi-supervised Temporal Action Proposal (SSTAP) framework. The SSTAP contains
two crucial branches, i.e., temporal-aware semi-supervised branch and
relation-aware self-supervised branch. The semi-supervised branch improves the
proposal model by introducing two temporal perturbations, i.e., temporal
feature shift and temporal feature flip, in the mean teacher framework. The
self-supervised branch defines two pretext tasks, including masked feature
reconstruction and clip-order prediction, to learn the relation of temporal
clues. By this means, SSTAP can better explore unlabeled videos, and improve
the discriminative abilities of learned action features. We extensively
evaluate the proposed SSTAP on THUMOS14 and ActivityNet v1.3 datasets. The
experimental results demonstrate that SSTAP significantly outperforms
state-of-the-art semi-supervised methods and even matches fully-supervised
methods. Code is available at https://github.com/wangxiang1230/SSTAP.


Abstract:
  We consider the Keller-Segel system of consumption type coupled with an
incompressible fluid equation. The system describes the dynamics of oxygen and
bacteria densities evolving within a fluid. We establish local well-posedness
of the system in Sobolev spaces for partially inviscid and fully inviscid
cases. In the latter, additional assumptions on the initial data are required
when either the oxygen or bacteria density touches zero. Even though the oxygen
density satisfies a maximum principle due to consumption, we prove finite time
blow-up of its $C^{2}$--norm with certain initial data.


Abstract:
  Magnetic impurities in $s$-wave superconductors lead to spin-polarized
Yu-Shiba-Rusinov (YSR) in-gap states. Chains of magnetic impurities offer one
of the most viable routes for the realization of Majorana bound states which
hold a promise for topological quantum computing. However, this ambitious goal
looks distant since no quantum coherent degrees of freedom have yet been
identified in these systems. To fill this gap we propose an effective two-level
system, a YSR qubit, stemming from two nearby impurities. Using a
time-dependent wave-function approach, we derive an effective Hamiltonian
describing the YSR qubit evolution as a function of distance between the
impurity spins, their relative orientations, and their dynamics. We show that
the YSR qubit can be controlled and read out using the state-of-the-art
experimental techniques for manipulation of the spins. Finally, we address the
effect of the spin noises on the coherence properties of the YSR qubit, and
show a robust behaviour for a wide range of experimentally relevant parameters.
Looking forward, the YSR qubit could facilitate the implementation of a
universal set of quantum gates in hybrid systems where they are coupled to
topological Majorana qubits.


Abstract:
  We consider the problem of finding, through adaptive sampling, which of $n$
options (arms) has the largest mean. Our objective is to determine a rule which
identifies the best arm with a fixed minimum confidence using as few
observations as possible, i.e. this is a fixed-confidence (FC) best arm
identification (BAI) in multi-armed bandits. We study such problems under the
Bayesian setting with both Bernoulli and Gaussian arms. We propose to use the
classical "vector at a time" (VT) rule, which samples each remaining arm once
in each round. We show how VT can be implemented and analyzed in our Bayesian
setting and be improved by early elimination. Our analysis show that these
algorithms guarantee an optimal strategy under the prior. We also propose and
analyze a variant of the classical "play the winner" (PW) algorithm. Numerical
results show that these rules compare favorably with state-of-art algorithms.


Abstract:
  The dust production in debris discs by grinding collisions of planetesimals
requires their orbits to be stirred. However, stirring levels remain largely
unconstrained, and consequently the stirring mechanisms as well. This work
shows how the sharpness of the outer edge of discs can be used to constrain the
stirring levels. Namely, the sharper the edge is the lower the eccentricity
dispersion must be. For a Rayleigh distribution of eccentricities ($e$), I find
that the disc surface density near the outer edge can be parametrised as
$\tanh[(r_{\max}-r)/l_{\rm out}]$, where $r_{\max}$ approximates the maximum
semi-major axis and $l_{\rm out}$ defines the edge smoothness. If the
semi-major axis distribution has sharp edges $e_\mathrm{rms}$ is roughly $1.2
l_{\rm out}/r_{\max}$, or $e_\mathrm{rms}=0.77 l_{\rm out}/r_{\max}$ if
semi-major axes have diffused due to self-stirring. This model is fitted to
ALMA data of five wide discs: HD 107146, HD 92945, HD 206893, AU Mic and HR
8799. The results show that HD 107146, HD 92945 and AU Mic have the sharpest
outer edges, corresponding to $e_\mathrm{rms}$ values of $0.121\pm0.05$,
$0.15^{+0.07}_{-0.05}$ and $0.10\pm0.02$ if their discs are self-stirred,
suggesting the presence of Pluto-sized objects embedded in the disc. Although
these stirring values are larger than typically assumed, the radial stirring of
HD 92945 is in good agreement with its vertical stirring constrained by the
disc height. HD 206893 and HR~8799, on the other hand, have smooth outer edges
that are indicative of scattered discs since both systems have massive inner
companions.


Abstract:
  Entangled quantum states play an important role in quantum information
science and also in quantum mechanics fundamental investigations.
Implementation and characterization of techniques allowing for easy preparation
of entangled states are important steps in such fields. Here we generated
entangled quantum states encoded in photons transversal paths, obtained by
pumping a non-linear crystal with multiple transversal Gaussian beams. Such
approach allows us to generate entangled states of two qubits and two qutrits
encoded in Gaussian transversal path of twin photons. We make a theoretical
analyses of this source, considering the influence of the pump angular spectrum
on the generated states, further characterizing those by their purity and
entanglement degree. Our experimental results reveals that the generated states
presents both high purity and entanglement, and the theoretical analysis
elucidates how the pump beams profile can be used to manipulate such photonic
states.


Abstract:
  The logic of Bunched Implications (BI) combines both additive and
multiplicative connectives, which include two primitive intuitionistic
implications. As a consequence, contexts in the sequent presentation are not
lists, nor multisets, but rather tree-like structures called bunches. This
additional complexity notwithstanding, the logic has a well-behaved metatheory
admitting all the familiar forms of semantics and proof systems. However, the
presentation of an effective proof-search procedure has been elusive since the
logic's debut. We show that one can reduce the proof-search space for any given
sequent to a primitive recursive set, the argument generalizing Gentzen's
decidability argument for classical propositional logic and combining key
features of Dyckhoff's contraction-elimination argument for intuitionistic
logic. An effective proof-search procedure, and hence decidability of
provability, follows as a corollary.


Abstract:
  Cell-free translational strategies are needed to accelerate the repair of
mineralised tissues, particularly large bone defects, using minimally invasive
approaches. Regenerative bone scaffolds should ideally mimic aspects of the
tissue's ECM over multiple length scales and enable surgical handling and
fixation during implantation in vivo. Leveraging the knowledge gained with
bioactive self-assembling peptides (SAPs) and SAP-enriched electrospun fibres,
we presented a cell free approach for promoting mineralisation via apatite
deposition and crystal growth, in vitro, of SAP-enriched nonwoven scaffolds.
The nonwoven scaffold was made by electrospinning poly(epsilon-caprolactone)
(PCL) in the presence of either peptide P11-4 (Ac-QQRFEWEFEQQ-Am) or P11-8
(Ac-QQRFOWOFEQQ-Am), in light of the polymer's fibre forming capability and its
hydrolytic degradability as well as the well-known apatite nucleating
capability of SAPs. The 11-residue family of peptides (P11-X) has the ability
to self-assemble into beta-sheet ordered structures at the nano-scale and to
generate hydrogels at the macroscopic scale, some of which are capable of
promoting biomineralisation due to their apatite-nucleating capability. Both
variants of SAP-enriched nonwoven used in this study were proven to be
biocompatible with murine fibroblasts and supported nucleation and growth of
apatite minerals in simulated body fluid (SBF) in vitro. The fibrous nonwoven
provided a structurally robust scaffold, with the capability to control SAP
release behaviour. Up to 75% of P11-4 and 45% of P11-8 were retained in the
fibres after 7-day incubation in aqueous solution at pH 7.4. The encapsulation
of SAP in a nonwoven system with apatite-forming as well as localised and
long-term SAP delivery capabilities is appealing as a potential means of
achieving cost-effective bone repair therapy for critical size defects.


Abstract:
  Min-max saddle point games have recently been intensely studied, due to their
wide range of applications, including training Generative Adversarial Networks
(GANs). However, most of the recent efforts for solving them are limited to
special regimes such as convex-concave games. Further, it is customarily
assumed that the underlying optimization problem is solved either by a single
machine or in the case of multiple machines connected in centralized fashion,
wherein each one communicates with a central node. The latter approach becomes
challenging, when the underlying communications network has low bandwidth. In
addition, privacy considerations may dictate that certain nodes can communicate
with a subset of other nodes. Hence, it is of interest to develop methods that
solve min-max games in a decentralized manner. To that end, we develop a
decentralized adaptive momentum (ADAM)-type algorithm for solving min-max
optimization problem under the condition that the objective function satisfies
a Minty Variational Inequality condition, which is a generalization to
convex-concave case. The proposed method overcomes shortcomings of recent
non-adaptive gradient-based decentralized algorithms for min-max optimization
problems that do not perform well in practice and require careful tuning. In
this paper, we obtain non-asymptotic rates of convergence of the proposed
algorithm (coined DADAM$^3$) for finding a (stochastic) first-order Nash
equilibrium point and subsequently evaluate its performance on training GANs.
The extensive empirical evaluation shows that DADAM$^3$ outperforms recently
developed methods, including decentralized optimistic stochastic gradient for
solving such min-max problems.


Abstract:
  Artificial Intelligence (AI), along with the recent progress in biomedical
language understanding, is gradually changing medical practice. With the
development of biomedical language understanding benchmarks, AI applications
are widely used in the medical field. However, most benchmarks are limited to
English, which makes it challenging to replicate many of the successes in
English for other languages. To facilitate research in this direction, we
collect real-world biomedical data and present the first Chinese Biomedical
Language Understanding Evaluation (CBLUE) benchmark: a collection of natural
language understanding tasks including named entity recognition, information
extraction, clinical diagnosis normalization, single-sentence/sentence-pair
classification, and an associated online platform for model evaluation,
comparison, and analysis. To establish evaluation on these tasks, we report
empirical results with the current 11 pre-trained Chinese models, and
experimental results show that state-of-the-art neural models perform by far
worse than the human ceiling. Our benchmark is released at
\url{https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us}.


Abstract:
  For $m, d \in {\mathbb N}$, a jittered sampling point set $P$ having $N =
m^d$ points in $[0,1)^d$ is constructed by partitioning the unit cube $[0,1)^d$
into $m^d$ axis-aligned cubes of equal size and then placing one point
independently and uniformly at random in each cube. We show that there are
constants $c \ge 0$ and $C$ such that for all $d$ and all $m \ge d$ the
expected non-normalized star discrepancy of a jittered sampling point set
satisfies \[c \,dm^{\frac{d-1}{2}} \sqrt{1 + \log(\tfrac md)} \le {\mathbb E}
D^*(P) \le C\, dm^{\frac{d-1}{2}} \sqrt{1 + \log(\tfrac md)}.\]
  This discrepancy is thus smaller by a factor of
$\Theta\big(\sqrt{\frac{1+\log(m/d)}{m/d}}\,\big)$ than the one of a uniformly
distributed random point set of $m^d$ points. This result improves both the
upper and the lower bound for the discrepancy of jittered sampling given by
Pausinger and Steinerberger (Journal of Complexity (2016)). It also removes the
asymptotic requirement that $m$ is sufficiently large compared to $d$.


Abstract:
  Magnetic field evolution in neutron-star crusts is driven by the Hall effect
and Ohmic dissipation, for as long as the crust is sufficiently strong to
absorb Maxwell stresses exerted by the field and thus make the momentum
equation redundant. For the strongest neutron-star fields, however, stresses
build to the point of crustal failure, at which point the standard evolution
equations are no longer valid. Here, we study the evolution of the magnetic
field of the crust up to and beyond crustal failure, whence the crust begins to
flow plastically. We perform global axisymmetric evolutions, exploring
different types of failure affecting a limited region of the crust. We find
that a plastic flow does not simply suppress the Hall effect even in the regime
of a low plastic viscosity, but it rather leads to non-trivial evolution -- in
some cases even overreacting and enhancing the impact of the Hall effect. Its
impact is more pronouced in the toroidal field, with the differences on the
poloidal field being less substantial. We argue that both the nature of
magnetar bursts and their spindown evolution will be affected by plastic flow,
so that observations of these phenomena may help to constrain the way the crust
fails.


Abstract:
  We study best approximations to compact operators between Banach spaces and
Hilbert spaces, from the point of view of Birkhoff-James orthogonality and
semi-inner-products. As an application of the present study, some distance
formulae are presented in the space of compact operators. The special case of
bounded linear functionals as compact operators is treated separately and some
applications to best approximations in reflexive, strictly convex and smooth
Banach spaces are discussed. An explicit example is presented in $ \ell_p^{n} $
spaces, where $ 1 < p < \infty, $ to illustrate the applicability of the
methods developed in this article. A comparative analysis of the results
presented in this article with the well-known classical duality principle in
approximation theory is conducted to demonstrate the advantage in the former
case, from a computational point of view.


Abstract:
  We study Krasnoselskii-Mann style iterative algorithms for approximating
fixpoints of asymptotically weakly contractive mappings, with a focus on
providing generalised convergence proofs along with explicit rates of
convergence. More specifically, we define a new notion of being asymptotically
$\psi$-weakly contractive with modulus, and present a series of abstract
convergence theorems which both generalise and unify known results from the
literature. Rates of convergence are formulated in terms of our modulus of
contractivity, in conjunction with other moduli and functions which form
quantitative analogues of additional assumptions that are required in each
case. Our approach makes use of ideas from proof theory, in particular our
emphasis on abstraction and on formulating our main results in a quantitative
manner. As such, the paper can be seen as a contribution to the proof mining
program.


Abstract:
  From nutrient uptake, to chemoreception, to synaptic transmission, many
systems in cell biology depend on molecules diffusing and binding to membrane
receptors. Mathematical analysis of such systems often neglects the fact that
receptors process molecules at finite kinetic rates. A key example is the
celebrated formula of Berg and Purcell for the rate that cell surface receptors
capture extracellular molecules. Indeed, this influential result is only valid
if receptors transport molecules through the cell wall at a rate much faster
than molecules arrive at receptors. From a mathematical perspective, ignoring
receptor kinetics is convenient because it makes the diffusing molecules
independent. In contrast, including receptor kinetics introduces correlations
between the diffusing molecules since, for example, bound receptors may be
temporarily blocked from binding additional molecules. In this work, we present
a modeling framework for coupling bulk diffusion to surface receptors with
finite kinetic rates. The framework uses boundary homogenization to couple the
diffusion equation to nonlinear ordinary differential equations on the
boundary. We use this framework to derive an explicit formula for the cellular
uptake rate and show that the analysis of Berg and Purcell significantly
overestimates uptake in some typical biophysical scenarios. We confirm our
analysis by numerical simulations of a many particle stochastic system.


Abstract:
  Minimizing the bending energy within knot classes leads to the concept of
elastic knots which has been initiated in [von der Mosel, Asymptot. Anal.
1998]. Motivated by numerical experiments in arxiv:1804.02206
(doi:10.1090/mcom/3633) we prescribe dihedral symmetry and establish existence
of dihedrally symmetric elastic knots for knot classes admitting this type of
symmetry. Among other results we prove that the dihedral elastic trefoil is the
union of two circles that form a (planar) figure-eight. We also discuss some
generalizations and limitations regarding other symmetries and knot classes.


Abstract:
  We employed the log-periodic power law singularity (LPPLS) methodology to
systematically investigate the 2020 stock market crash in the U.S. equities
sectors with different levels of total market capitalizations through four
major U.S. stock market indexes, including the Wilshire 5000 Total Market
index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index,
representing the stocks overall, the large capitalization stocks, the middle
capitalization stocks and the small capitalization stocks, respectively. During
the 2020 U.S. stock market crash, all four indexes lost more than a third of
their values within five weeks, while both the middle capitalization stocks and
the small capitalization stocks have suffered much greater losses than the
large capitalization stocks and stocks overall. Our results indicate that the
price trajectories of these four stock market indexes prior to the 2020 stock
market crash have clearly featured the obvious LPPLS bubble pattern and were
indeed in a positive bubble regime. Contrary to the popular belief that the
COVID-19 led to the 2020 stock market crash, the 2020 U.S. stock market crash
was endogenous, stemming from the increasingly systemic instability of the
stock market itself. We also performed the complementary post-mortem analysis
of the 2020 U.S. stock market crash. Our analyses indicate that the 2020 U.S.
stock market crash originated from a bubble which began to form as early as
September 2018; and the bubbles in stocks with different levels of total market
capitalizations have significantly different starting time profiles. This study
not only sheds new light on the making of the 2020 U.S. stock market crash but
also creates a novel pipeline for future real-time crash detection and
mechanism dissection of any financial market and/or economic index.


Abstract:
  It is important to estimate the errors of probabilistic inference algorithms.
Existing diagnostics for Markov chain Monte Carlo methods assume inference is
asymptotically exact, and are not appropriate for approximate methods like
variational inference or Laplace's method. This paper introduces a diagnostic
based on repeatedly simulating datasets from the prior and performing inference
on each. The central observation is that it is possible to estimate a symmetric
KL-divergence defined over these simulations.


Abstract:
  Although distance learning presents a number of interesting educational
advantages as compared to in-person instruction, it is not without its
downsides. We first assess the educational challenges presented by distance
learning as a whole and identify 4 main challenges that distance learning
currently presents as compared to in-person instruction: the lack of social
interaction, reduced student engagement and focus, reduced comprehension and
information retention, and the lack of flexible and customizable instructor
resources. After assessing each of these challenges in-depth, we examine how
AR/VR technologies might serve to address each challenge along with their
current shortcomings, and finally outline the further research that is required
to fully understand the potential of AR/VR technologies as they apply to
distance learning.


Abstract:
  An important goal of medical imaging is to be able to precisely detect
patterns of disease specific to individual scans; however, this is challenged
in brain imaging by the degree of heterogeneity of shape and appearance.
Traditional methods, based on image registration to a global template,
historically fail to detect variable features of disease, as they utilise
population-based analyses, suited primarily to studying group-average effects.
In this paper we therefore take advantage of recent developments in generative
deep learning to develop a method for simultaneous classification, or
regression, and feature attribution (FA). Specifically, we explore the use of a
VAE-GAN translation network called ICAM, to explicitly disentangle class
relevant features from background confounds for improved interpretability and
regression of neurological phenotypes. We validate our method on the tasks of
Mini-Mental State Examination (MMSE) cognitive test score prediction for the
Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age
prediction, for both neurodevelopment and neurodegeneration, using the
developing Human Connectome Project (dHCP) and UK Biobank datasets. We show
that the generated FA maps can be used to explain outlier predictions and
demonstrate that the inclusion of a regression module improves the
disentanglement of the latent space. Our code is freely available on Github
https://github.com/CherBass/ICAM.


Abstract:
  It is well-known that there is no spherical/topologically spherical
gravitational waves in vacuum space in general relativity. We show that a
deviation from general relativity leads to exact vacuum spherical gravitational
waves, no matter how tiny this deviation is. We also discuss the related
topics, including Vaidya-like metric in $f(R)$ gravity. We demonstrate that the
existence of spherical gravitational wave is a non perturbative property for
gravities. We investigate energy carried by this nonperturbative wave. We first
find the wave solution from investigations of Vaidya-like metric in $f(R)$
gravity, which has only one longitude polarization. We further extend it to a
metric with two transverse polarizations by directly solving the field
equation.


Abstract:
  We introduce a novel primal-dual flow for affine constrained convex
optimization problem. As a modification of the standard saddle-point system,
our primal-dual flow is proved to possesses the exponential decay property, in
terms of a tailored Lyapunov function. Then a class of primal-dual methods for
the original optimization problem are obtained from numerical discretizations
of the continuous flow, and with a unified discrete Lyapunov function,
nonergodic convergence rates are established. Among those algorithms, we can
recover the (linearized) augmented Lagrangian method and the quadratic penalty
method with continuation technique. Also, new methods with a special inner
problem, that is a linear symmetric positive definite system or a nonlinear
equation which may be solved efficiently via the semi-smooth Newton method,
have been proposed as well. Especially, numerical tests on the linearly
constrained $l_1$-$l_2$ minimization show that our method outperforms the
accelerated linearized Bregman method.


Abstract:
  We present 3 mm and 2 mm band simultaneously spectroscopic observations of
HCN 1-0, HCO$^{+}$ 1-0, HNC 1-0, and CS 3-2 with the IRAM 30 meter telescope,
toward a sample of 70 sources as nearby galaxies with infrared luminosities
ranging from several 10$^{5}L_{\odot}$ to more than 10$^{12}L_{\odot}$. After
combining HCN 1-0, HCO$^{+}$ 1-0 and HNC 1-0 data from literature with our
detections, relations between luminosities of dense gas tracers (HCN 1-0,
HCO$^{+}$ 1-0 and HNC 1-0) and infrared luminosities are derived, with tight
linear correlations for all tracers. Luminosities of CS 3-2 with only our
observations also show tight linear correlation with infrared luminosities. No
systematic difference is found for tracing dense molecular gas among these
tracers. Star formation efficiencies for dense gas with different tracers also
do not show any trend along different infrared luminosities. Our study also
shows that HCN/HCO$^{+}$ line ratio might not be a good indicator to diagnose
obscured AGN in galaxies.


Abstract:
  We investigate a model of one-to-one matching with transferable utility and
general unobserved heterogeneity. Under a separability assumption that
generalizes Choo and Siow (2006), we first show that the equilibrium matching
maximizes a social gain function that trades off exploiting complementarities
in observable characteristics and matching on unobserved characteristics. We
use this result to derive simple closed-form formulae that identify the joint
matching surplus and the equilibrium utilities of all participants, given any
known distribution of unobserved heterogeneity. We provide efficient algorithms
to compute the stable matching and to estimate parametric versions of the
model. Finally, we revisit Choo and Siow's empirical application to illustrate
the potential of our more general approach.


Abstract:
  Graph filtering is a fundamental tool in graph signal processing. Polynomial
graph filters (PGFs), defined as polynomials of a fundamental graph operator,
can be implemented in the vertex domain, and usually have a lower complexity
than frequency domain filter implementations. In this paper, we focus on the
design of filters for graphs with graph Fourier transform (GFT) corresponding
to a discrete trigonometric transform (DTT), i.e., one of 8 types of discrete
cosine transforms (DCT) and 8 discrete sine transforms (DST). In this case, we
show that multiple sparse graph operators can be identified, which allows us to
propose a generalization of PGF design: multivariate polynomial graph filter
(MPGF). First, for the widely used DCT-II (type-2 DCT), we characterize a set
of sparse graph operators that share the DCT-II matrix as their common
eigenvector matrix. This set contains the well-known connected line graph.
These sparse operators can be viewed as graph filters operating in the DCT
domain, which allows us to approximate any DCT graph filter by a MPGF, leading
to a design with more degrees of freedom than the conventional PGF approach.
Then, we extend those results to all of the 16 DTTs as well as their 2D
versions, and show how their associated sets of multiple graph operators can be
determined. We demonstrate experimentally that ideal low-pass and exponential
DCT/DST filters can be approximated with higher accuracy with similar runtime
complexity. Finally, we apply our method to transform-type selection in a video
codec, AV1, where we demonstrate significant encoding time savings, with a
negligible compression loss.


Abstract:
  We derive Boltzmann equations for massive spin-1/2 fermions with local and
nonlocal collision terms from the Kadanoff--Baym equation in the
Schwinger--Keldysh formalism, properly accounting for the spin degrees of
freedom. The Boltzmann equations are expressed in terms of matrix-valued spin
distribution functions, which are the building blocks for the quasi-classical
parts of the Wigner functions. Nonlocal collision terms appear at
next-to-leading order in $\hbar$ and are sources for the polarization part of
the matrix-valued spin distribution functions. The Boltzmann equations for the
matrix-valued spin distribution functions pave the way for simulating
spin-transport processes involving spin-vorticity couplings from first
principles.


Abstract:
  There is growing concern about image privacy due to the popularity of social
media and photo devices, along with increasing use of face recognition systems.
However, established image de-identification techniques are either too subject
to re-identification, produce photos that are insufficiently realistic, or
both. To tackle this, we present a novel approach for image obfuscation by
manipulating latent spaces of an unconditionally trained generative model that
is able to synthesize photo-realistic facial images of high resolution. This
manipulation is done in a way that satisfies the formal privacy standard of
local differential privacy. To our knowledge, this is the first approach to
image privacy that satisfies $\varepsilon$-differential privacy \emph{for the
person.}


Abstract:
  It turns out that the standard application of the four-vector SR formalism
does not include the concept of relative velocity. Only the absolute velocity
is described by the four-vector, and even the Lorentz transformation parameters
is described by the three-dimensional velocity.
  This gap in the development of the SR formalism reflects the lack of some
significant velocity subtraction operations. The differential application of
these operations leads to a relativistic acceleration.


Abstract:
  We study the problem of off-policy evaluation in the multi-armed bandit model
with bounded rewards, and develop minimax rate-optimal procedures under three
settings. First, when the behavior policy is known, we show that the Switch
estimator, a method that alternates between the plug-in and importance sampling
estimators, is minimax rate-optimal for all sample sizes. Second, when the
behavior policy is unknown, we analyze performance in terms of the competitive
ratio, thereby revealing a fundamental gap between the settings of known and
unknown behavior policies. When the behavior policy is unknown, any estimator
must have mean-squared error larger -- relative to the oracle estimator
equipped with the knowledge of the behavior policy -- by a multiplicative
factor proportional to the support size of the target policy. Moreover, we
demonstrate that the plug-in approach achieves this worst-case competitive
ratio up to a logarithmic factor. Third, we initiate the study of the partial
knowledge setting in which it is assumed that the minimum probability taken by
the behavior policy is known. We show that the plug-in estimator is optimal for
relatively large values of the minimum probability, but is sub-optimal when the
minimum probability is low. In order to remedy this gap, we propose a new
estimator based on approximation by Chebyshev polynomials that provably
achieves the optimal estimation error. Numerical experiments on both simulated
and real data corroborate our theoretical findings.


Abstract:
  Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between
variables recorded at different frequencies. However, as the number of series
and high-frequency observations per low-frequency period grow, MF-VARs suffer
from the "curse of dimensionality". We curb this curse through a regularizer
that permits various hierarchical sparsity patterns by prioritizing the
inclusion of coefficients according to the recency of the information they
contain. Additionally, we investigate the presence of nowcasting relations by
sparsely estimating the MF-VAR error covariance matrix. We study predictive
Granger causality relations in a MF-VAR for the U.S. economy and construct a
coincident indicator of GDP growth.


Abstract:
  An indoor localization approach uses Wi-Fi Access Points (APs) to estimate
the Direction of Arrival (DoA) of the WiFi signals. This paper demonstrates
FIND, a tool for Fine INDoor localization based on a software-defined radio,
which receives Wi-Fi frames in the 80 MHz band with four antennas. To the best
of our knowledge, it is the first-ever prototype that extracts from such frames
data in both frequency and time domains to calculate the DoA of Wi-Fi signals
in real-time. Apart from other prototypes, we retrieve from frames
comprehensive information that could be used to DoA estimation: all preamble
fields in the time domain, Channels State Information, and signal-to-noise
ratio. Using our device, we collect a dataset for comparing different
algorithms estimating the angle of arrival in the same scenario. Furthermore,
we propose a novel calibration method, eliminating the constant phase shift
between receiving paths caused by hardware imperfections. All calibration data,
as well as a gathered dataset with various DoA in an anechoic chamber and in a
classroom, are provided to facilitate further research in the area of indoor
localization, intelligence surfaces, and multi-user transmissions in dense
deployments.


Abstract:
  In this paper we describe a computational model for the simulation of
fluid-structure interaction problems based on a fictitious domain approach. We
summarize the results presented over the last years when our research evolved
from the Finite Element Immersed Boundary Method (FE-IBM) to the actual Finite
Element Distributed Lagrange Multiplier method (FE-DLM). We recall the
well-posedness of our formulation at the continuous level in a simplified
setting. We describe various time semi-discretizations that provide
unconditionally stable schemes. Finally we report the stability analysis for
the finite element space discretization where some improvements and
generalizations of the previous results are obtained.


Abstract:
  Defects in solid state materials provide an ideal, robust platform for
quantum sensing. To deliver maximum sensitivity, a large ensemble of
non-interacting defects hosting coherent quantum states are required. Control
of such an ensemble is challenging due to the spatial variation in both the
defect energy levels and in any control field across a macroscopic sample. In
this work we experimentally demonstrate that we can overcome these challenges
using Floquet theory and optimal control optimization methods to efficiently
and coherently control a large defect ensemble, suitable for sensing. We apply
our methods experimentally to a spin ensemble of up to 4 $\times$ 10$^9$
nitrogen vacancy (NV) centers in diamond. By considering the physics of the
system and explicitly including the hyperfine interaction in the optimization,
we design shaped microwave control pulses that can outperform conventional
($\pi$-) pulses when applied to sensing of temperature or magnetic field, with
a potential sensitivity improvement between 11 and 78\%. Through dynamical
modelling of the behaviour of the ensemble, we shed light on the physical
behaviour of the ensemble system and propose new routes for further
improvement.


Abstract:
  t-distributed stochastic neighbor embedding (t-SNE) is a well-established
visualization method for complex high-dimensional data. However, the original
t-SNE method is nonparametric, stochastic, and often cannot well prevserve the
global structure of data as it emphasizes local neighborhood. With t-SNE as a
reference, we propose to combine the deep neural network (DNN) with the
mathematical-grounded embedding rules for high-dimensional data embedding. We
first introduce a deep embedding network (DEN) framework, which can learn a
parametric mapping from high-dimensional space to low-dimensional embedding.
DEN has a flexible architecture that can accommodate different input data
(vector, image, or tensor) and loss functions. To improve the embedding
performance, a recursive training strategy is proposed to make use of the
latent representations extracted by DEN. Finally, we propose a two-stage loss
function combining the advantages of two popular embedding methods, namely,
t-SNE and uniform manifold approximation and projection (UMAP), for optimal
visualization effect. We name the proposed method Deep Recursive Embedding
(DRE), which optimizes DEN with a recursive training strategy and two-stage
losse. Our experiments demonstrated the excellent performance of the proposed
DRE method on high-dimensional data embedding, across a variety of public
databases. Remarkably, our comparative results suggested that our proposed DRE
could lead to improved global structure preservation.


Abstract:
  The quiet solar corona consists of myriads of loop-like features, with
magnetic fields originating from network and internetwork regions on the solar
surface. The continuous interaction between these different magnetic patches
leads to transient brightenings or bursts that might contribute to the heating
of the solar atmosphere. However, it remains unclear whether such transients,
which are mostly observed in the EUV, play a significant role in atmospheric
heating. We revisit the open question of these bursts as a prelude to the new
high-resolution EUV imagery expected from the recently launched Solar Orbiter.
We use EUV images recorded by the SDO/AIA to investigate statistical properties
of the bursts. We detect the bursts in the 171 {\AA} filter images of AIA in an
automated way through a pixel-wise analysis by imposing different intensity
thresholds. By exploiting the high cadence (12 s) of the AIA observations, we
find that the distribution of lifetimes of these events peaks at about 120 s.
The sizes of the detected bursts are limited by the spatial resolution, which
indicates that a larger number of events might be hidden in the AIA data. We
estimate that about 100 new bursts appear per second on the whole Sun. The
detected bursts have nanoflare-like energies of $10^{24}$\,erg per event. Based
on this, we estimate that at least 100 times more events of a similar nature
would be required to account for the energy that is required to heat the
corona. When AIA observations are considered alone, the EUV bursts discussed
here therefore play no significant role in the coronal heating of the quiet
Sun. If the coronal heating of the quiet Sun is mainly bursty, then the
high-resolution EUV observations from Solar Orbiter may be able to reduce the
deficit in the number of EUV bursts seen with SDO/AIA at least partly by
detecting more such events.


Abstract:
  Group testing can help maintain a widespread testing program using fewer
resources amid a pandemic. In group testing, we are given $n$ samples, one per
individual. These samples are arranged into $m < n$ pooled samples, where each
pool is obtained by mixing a subset of the $n$ individual samples. Infected
individuals are then identified using a group testing algorithm. In this paper,
we use side information (SI) collected from contact tracing (CT) within
nonadaptive/single-stage group testing algorithms. We generate CT SI data by
incorporating characteristics of disease spread between individuals. These data
are fed into two signal and measurement models for group testing, and numerical
results show that our algorithms provide improved sensitivity and specificity.
We also show how to incorporate CT SI into the design of the pooling matrix.
That said, our numerical results suggest that the utilization of SI in the
pooling matrix design does not yield significant performance gains beyond the
incorporation of SI in the group testing algorithm.


Abstract:
  A search for pair production of third-generation scalar leptoquarks decaying
into a top quark and a $\tau$-lepton is presented. The search is based on a
dataset of $pp$ collisions at $\sqrt{s}=13$ TeV recorded with the ATLAS
detector during Run 2 of the Large Hadron Collider, corresponding to an
integrated luminosity of 139 fb$^{-1}$. Events are selected if they have one
light lepton (electron or muon) and at least one hadronically decaying
$\tau$-lepton, or at least two light leptons. In addition, two or more jets, at
least one of which must be identified as containing $b$-hadrons, are required.
Six final states, defined by the multiplicity and flavour of lepton candidates,
are considered in the analysis. Each of them is split into multiple event
categories to simultaneously search for the signal and constrain several
leading backgrounds. The signal-rich event categories require at least one
hadronically decaying $\tau$-lepton candidate and exploit the presence of
energetic final-state objects, which is characteristic of signal events. No
significant excess above the Standard Model expectation is observed in any of
the considered event categories, and 95% CL upper limits are set on the
production cross section as a function of the leptoquark mass, for different
assumptions about the branching fractions into $t\tau$ and $b\nu$. Scalar
leptoquarks decaying exclusively into $t\tau$ are excluded up to masses of 1.43
TeV while, for a branching fraction of 50% into $t\tau$, the lower mass limit
is 1.22 TeV.


Abstract:
  We describe a robust method for determining Pipek-Mezey (PM) Wannier
functions (WF), recently introduced by J\'onsson et al. (J. Chem. Theor. Chem.
2017, 13, 460), which provide some formal advantages over the more common Boys
(also known as maximally-localized) Wannier functions. The
Broyden-Fletcher-Goldfarb-Shanno (BFGS) based PMWF solver is demonstrated to
yield dramatically faster convergence compared to the alternatives (steepest
ascent and conjugate gradient) in a variety of 1-, 2-, and 3-dimensional solids
(including some with vanishing gaps), and can be used to obtain Wannier
functions robustly in supercells with thousands of atoms. Evaluation of the PM
functional and its gradient in periodic LCAO representation used a particularly
simple definition of atomic charges obtained by Moore-Penrose pseudoinverse
projection onto the minimal atomic orbital basis. An automated "Canonicalize
Phase then Randomize" (CPR) method for generating the initial guess for WFs
contributes significantly to the robustness of the solver.


Abstract:
  The system under study is the $\Lambda$-Kantowski-Sachs universe. Its
canonical quantization is provided based on a recently developed method: the
singular minisuperspace Lagrangian describing the system, is reduced to a
regular (by inserting into the dynamical equations the lapse dictated by the
quadratic constraint) possessing an explicit (though arbitrary) time
dependence; thus a time-covariant Schr\"{o}dinger equation arises.
Additionally, an invariant (under transformations $t=f(\tilde{t})$) decay
probability is defined and thus ``observers'' which correspond to different
gauge choices obtain, by default, the same results. The time of decay for a
Gaussian wave packet localized around the point $a=0$ (where $a$ the radial
scale factor) is calculated to be of the order $\sim
10^{-42}-10^{-41}\mathrm{s}$. The acquired value is near the end of the Planck
era (when comparing to a FLRW universe), during which the quantum effects are
most prominent. Some of the results are compared to those obtained by following
the well known canonical quantization of cosmological systems, i.e. the
solutions of the Wheeler-DeWitt equation.


Abstract:
  A class of analytical solutions of axially symmetric vacuum initial data for
a self-gravitating system has been found. The active region of the constructed
gravitational wave is a thin torus around which the solution is conformally
flat. For higher values of gravitational wave amplitude the resulting
hypersurface contains apparent horizons.


Abstract:
  Automatic garbage collection (GC) prevents certain kinds of bugs and reduces
programming overhead. GC techniques for sequential programs are based on
reachability analysis. However, testing reachability from a root set is
inadequate for determining whether an actor is garbage: Observe that an
unreachable actor may send a message to a reachable actor. Instead, it is
sufficient to check termination (sometimes also called quiescence): an actor is
terminated if it is not currently processing a message and cannot receive a
message in the future. Moreover, many actor frameworks provide all actors with
access to file I/O or external storage; without inspecting an actor's internal
code, it is necessary to check that the actor has terminated to ensure that it
may be garbage collected in these frameworks. Previous algorithms to detect
actor garbage require coordination mechanisms such as causal message delivery
or nonlocal monitoring of actors for mutation. Such coordination mechanisms
adversely affect concurrency and are therefore expensive in distributed
systems. We present a low-overhead reference listing technique (called DRL) for
termination detection in actor systems. DRL is based on asynchronous local
snapshots and message-passing between actors. This enables a decentralized
implementation and transient network partition tolerance. The paper provides a
formal description of DRL, shows that all actors identified as garbage have
indeed terminated (safety), and that all terminated actors--under certain
reasonable assumptions--will eventually be identified (liveness).


Abstract:
  Single-molecule memory device based on a single-molecule magnet (SMM) is one
of the ultimate goals of semiconductor nanofabrication technologies. Here, we
study how to manipulate and readout the SMM's two spin-state of stored
information that characterized by the maximum and minimum average value of the
$Z$-component of the total spin of the SMM and the conduction-electron, which
are recognized as the information bits "$1$" and "$0$". We demonstrate that the
switching time depends on both the sequential tunneling gap $\varepsilon_{se}$
and the spin-selection-rule allowed transition-energy $\varepsilon_{trans}$,
which can be tuned by the gate voltage. In particular, when the external bias
voltage is turned off, in the cases of the unoccupied and doubly-occupied
ground eigenstates, the time derivative of the transport current can be used to
read out the SMM's two spin-state of stored information. Moreover, the
tunneling strength of and the asymmetry of the SMM-electrode coupling have a
strong influence on the switching time, but that have a slight influence on the
readout time that being on the order of nanoseconds. Our results suggest a
SMM-based memory device, and provide fundamental insight into the electrical
controllable manipulation and readout of the SMM's two spin-state of stored
information.


Abstract:
  The plastic deformation mechanisms of tungsten carbide at room and elevated
temperatures influence the wear and fracture properties of WC-Co hardmetal
composite materials. The relationship between residual defect structures,
including glissile and sessile dislocations and stacking faults, and the slip
deformation activity, which produce slip traces, is not clear. Part 1 of this
study showed that {10-10} was the primary slip plane at all measured
temperatures and orientations, but secondary slip on the basal plane was
activated at 600 {\deg}C, which suggests that <a> dislocations can cross-slip
onto the basal plane at 600 {\deg}C. In the present work, Part 2, lattice
rotation axis analysis of deformed WC micropillar mid-sections was used to
discriminate <a> prismatic slip from multiple <c+a> prismatic slip in WC, which
enabled the dislocation types contributing to plastic slip to be distinguished,
independently of TEM residual defect analysis. Prismatic-oriented micropillars
deformed primarily by multiple <c+a> prismatic slip at room temperature, but by
<a> prismatic slip at 600 {\deg}C. Deformation in the near-basal oriented
pillar at 600 {\deg}C can be modelled as prismatic slip along <c> constrained
by the indenter face and pillar base. Secondary <a> basal slip, which was
observed near the top of the pillar, was activated to maintain deformation
compatibility with the indenter face. The lattice rotations, buckled pillar
shape, mechanical data, and slip traces observed in the pillar are all
consistent with this model.


Abstract:
  Video classification and analysis is always a popular and challenging field
in computer vision. It is more than just simple image classification due to the
correlation with respect to the semantic contents of subsequent frames brings
difficulties for video analysis. In this literature review, we summarized some
state-of-the-art methods for multi-label video classification. Our goal is
first to experimentally research the current widely used architectures, and
then to develop a method to deal with the sequential data of frames and perform
multi-label classification based on automatic content detection of video.


Abstract:
  The generation-defining Vera C. Rubin Observatory will make state-of-the-art
measurements of both the static and transient universe through its Legacy
Survey for Space and Time (LSST). With such capabilities, it is immensely
challenging to optimize the LSST observing strategy across the survey's wide
range of science drivers. Many aspects of the LSST observing strategy relevant
to the LSST Dark Energy Science Collaboration, such as survey footprint
definition, single visit exposure time and the cadence of repeat visits in
different filters, are yet to be finalized. Here, we present metrics used to
assess the impact of observing strategy on the cosmological probes considered
most sensitive to survey design; these are large-scale structure, weak lensing,
type Ia supernovae, kilonovae and strong lens systems (as well as photometric
redshifts, which enable many of these probes). We evaluate these metrics for
over 100 different simulated potential survey designs. Our results show that
multiple observing strategy decisions can profoundly impact cosmological
constraints with LSST; these include adjusting the survey footprint, ensuring
repeat nightly visits are taken in different filters and enforcing regular
cadence. We provide public code for our metrics, which makes them readily
available for evaluating further modifications to the survey design. We
conclude with a set of recommendations and highlight observing strategy factors
that require further research.


Abstract:
  This paper reexamines the seminal Lagrange multiplier test for cross-section
independence in a large panel model where both the number of cross-sectional
units n and the number of time series observations T can be large. The first
contribution of the paper is an enlargement of the test with two extensions:
firstly the new asymptotic normality is derived in a simultaneous limiting
scheme where the two dimensions (n, T) tend to infinity with comparable
magnitudes; second, the result is valid for general error distribution (not
necessarily normal). The second contribution of the paper is a new test
statistic based on the sum of the fourth powers of cross-section correlations
from OLS residuals, instead of their squares used in the Lagrange multiplier
statistic. This new test is generally more powerful, and the improvement is
particularly visible against alternatives with weak or sparse cross-section
dependence. Both simulation study and real data analysis are proposed to
demonstrate the advantages of the enlarged Lagrange multiplier test and the
power enhanced test in comparison with the existing procedures.