Abstract:
  We present a multi-wavelength investigation of a C-class flaring activity
that occurred in the active region NOAA 12734 on 8 March 2019. The
investigation utilises data from AIA and HMI on board the SDO and the
Udaipur-CALLISTO solar radio spectrograph of the Physical Research Laboratory.
This low intensity C1.3 event is characterised by typical features of a long
duration event (LDE), viz. extended flare arcade, large-scale two-ribbon
structures and twin coronal dimmings. The eruptive event occurred in a coronal
sigmoid and displayed two distinct stages of energy release, manifested in
terms of temporal and spatial evolution. The formation of twin dimming regions
are consistent with the eruption of a large flux rope with footpoints lying in
the western and eastern edges of the coronal sigmoid. The metric radio
observations obtained from Udaipur-CALLISTO reveals a broad-band
($\approx$50-180 MHz), stationary plasma emission for $\approx$7 min during the
second stage of the flaring activity that resemble a type IV radio burst. A
type III decametre-hectometre radio bursts with starting frequency of
$\approx$2.5 MHz precedes the stationary type IV burst observed by
Udaipur-CALLISTO by $\approx$5 min. The synthesis of multi-wavelength
observations and Non-Linear Force Free Field (NLFFF) coronal modelling together
with magnetic decay index analysis suggests that the sigmoid flux rope
underwent a zipping-like uprooting from its western to eastern footpoints in
response to the overlying asymmetric magnetic field confinement. The
asymmetrical eruption of the flux rope also accounts for the observed
large-scale structures viz. apparent eastward shift of flare ribbons and post
flare loops along the polarity inversion line (PIL), and provides an evidence
for lateral progression of magnetic reconnection site as the eruption proceeds.


Abstract:
  Observations suggest that protoplanetary disks have moderate accretion rates
onto the central young star, especially at early stages (e.g. HL Tau),
indicating moderate disk turbulence. However, recent ALMA observations suggest
that dust is highly settled, implying weak turbulence. Motivated by such
tension, we carry out 3D stratified local simulations of self-gravitating
disks, focusing on settling of dust particles in actively accreting disks. We
find that gravitationally unstable disks can have moderately high accretion
rates while maintaining a relatively thin dust disk for two reasons. First,
accretion stress from the self-gravitating spirals (self-gravity stress) can be
stronger than the stress from turbulence (Reynolds stress) by a factor of 5-20.
Second, the strong gravity from the gas to the dust decreases the dust scale
height by another factor of $\sim 2$. Furthermore, the turbulence is slightly
anisotropic, producing a larger Reynolds stress than the vertical dust
diffusion coefficient. Thus, gravitoturbulent disks have unusually high
vertical Schmidt numbers ($Sc_z$) if we scale the total accretion stress with
the vertical diffusion coefficient (e.g. $Sc_z\sim$ 10-100). The reduction of
the dust scale height by the gas gravity, should also operate in
gravitationally stable disks ($Q>$1). Gravitational forces between particles
become more relevant for the concentration of intermediate dust sizes, forming
dense clouds of dust. After comparing with HL Tau observations, our results
suggest that self-gravity and gravity among different disk components could be
crucial for solving the conflict between the protoplanetary disk accretion and
dust settling, at least at the early stages.


Abstract:
  Non-Hermiticity enriches the 10-fold Altland-Zirnbauer symmetry class into
the 38-fold symmetry class, where critical behavior of the Anderson transitions
(ATs) has been extensively studied recently. Here, we establish a
correspondence of the universality classes of the ATs between Hermitian and
non-Hermitian systems. We demonstrate that the critical exponents of the length
scale in non-Hermitian systems coincide with the critical exponents in the
corresponding Hermitian systems with additional chiral symmetry. A remarkable
consequence is superuniversality, i.e., the ATs in some different symmetry
classes of non-Hermitian systems are characterized by the same critical
exponent. In addition to the comparisons between the known critical exponents
for non-Hermitian systems and their Hermitian counterparts, we obtain the
critical exponents in symmetry classes AI, AII, AII$^{\dagger}$,
CII$^{\dagger}$, and DIII in two and three dimensions. Our precise critical
exponents not only confirm the correspondence, but also predict the unknown
critical exponents in Hermitian systems, paving a way to study the ATs of
Hermitian systems by the corresponding non-Hermitian systems.


Abstract:
  We consider strongly convex-concave minimax problems in the federated
setting, where the communication constraint is the main bottleneck. When
clients are arbitrarily heterogeneous, a simple Minibatch Mirror-prox achieves
the best performance. As the clients become more homogeneous, using multiple
local gradient updates at the clients significantly improves upon Minibatch
Mirror-prox by communicating less frequently. Our goal is to design an
algorithm that can harness the benefit of similarity in the clients while
recovering the Minibatch Mirror-prox performance under arbitrary heterogeneity
(up to log factors). We give the first federated minimax optimization algorithm
that achieves this goal. The main idea is to combine (i) SCAFFOLD (an algorithm
that performs variance reduction across clients for convex optimization) to
erase the worst-case dependency on heterogeneity and (ii) Catalyst (a framework
for acceleration based on modifying the objective) to accelerate convergence
without amplifying client drift. We prove that this algorithm achieves our
goal, and include experiments to validate the theory.


Abstract:
  Thermal power generation plays a dominant role in the world's electricity
supply. It consumes large amounts of coal worldwide, and causes serious air
pollution. Optimizing the combustion efficiency of a thermal power generating
unit (TPGU) is a highly challenging and critical task in the energy industry.
We develop a new data-driven AI system, namely DeepThermal, to optimize the
combustion control strategy for TPGUs. At its core, is a new model-based
offline reinforcement learning (RL) framework, called MORE, which leverages
logged historical operational data of a TPGU to solve a highly complex
constrained Markov decision process problem via purely offline training. MORE
aims at simultaneously improving the long-term reward (increase combustion
efficiency and reduce pollutant emission) and controlling operational risks
(safety constraints satisfaction). In DeepThermal, we first learn a data-driven
combustion process simulator from the offline dataset. The RL agent of MORE is
then trained by combining real historical data as well as carefully filtered
and processed simulation data through a novel restrictive exploration scheme.
DeepThermal has been successfully deployed in four large coal-fired thermal
power plants in China. Real-world experiments show that DeepThermal effectively
improves the combustion efficiency of a TPGU. We also report and demonstrate
the superior performance of MORE by comparing with the state-of-the-art
algorithms on the standard offline RL benchmarks. To the best knowledge of the
authors, DeepThermal is the first AI application that has been used to solve
real-world complex mission-critical control tasks using the offline RL
approach.


Abstract:
  Pushed by market forces, software development has become fast-paced. As a
consequence, modern development projects are assembled from 3rd-party
components. Security & privacy assurance techniques once designed for large,
controlled updates over months or years, must now cope with small, continuous
changes taking place within a week, and happening in sub-components that are
controlled by third-party developers one might not even know they existed. In
this paper, we aim to provide an overview of the current software security
approaches and evaluate their appropriateness in the face of the changed nature
in software development. Software security assurance could benefit by switching
from a process-based to an artefact-based approach. Further, security
evaluation might need to be more incremental, automated and decentralized. We
believe this can be achieved by supporting mechanisms for lightweight and
scalable screenings that are applicable to the entire population of software
components albeit there might be a price to pay.


Abstract:
  We study the role of driving in an initial maximally entangled state evolving
under the presence of a structured environment in a weak and strong regime. We
focus on the enhancement and degradation of maximal Concurrence when the system
is driven on and out of resonance for a general evolution, as well as the
effect of adding a transverse coupling among the particles of the model. We
further investigate the role of driving in the acquisition of a geometric phase
for the maximally entangled state. As the model studied herein can be used to
model experimental situations such as hybrid quantum classical systems feasible
with current technologies, this knowledge can aid the search for physical
setups that best retain quantum properties under dissipative dynamics.


Abstract:
  Jacobi's triple product identity is proved from one of Euler's
$q$-exponential functions in an elementary way.


Abstract:
  The goal of regression is to recover an unknown underlying function that best
links a set of predictors to an outcome from noisy observations. In
non-parametric regression, one assumes that the regression function belongs to
a pre-specified infinite dimensional function space (the hypothesis space). In
the online setting, when the observations come in a stream, it is
computationally-preferable to iteratively update an estimate rather than
refitting an entire model repeatedly. Inspired by nonparametric sieve
estimation and stochastic approximation methods, we propose a sieve stochastic
gradient descent estimator (Sieve-SGD) when the hypothesis space is a Sobolev
ellipsoid. We show that Sieve-SGD has rate-optimal MSE under a set of simple
and direct conditions. We also show that the Sieve-SGD estimator can be
constructed with low time expense, and requires almost minimal memory usage
among all statistically rate-optimal estimators, under some conditions on the
distribution of the predictors.


Abstract:
  The objective of this study is to correlate the scaling factor of the
Standard Model (SM) like Higgs boson and the cross section ratio of the process
$e^+ e^- \rightarrow hhf\overline{f}$ where $f\neq t$, normalized to SM
predictions in the type I of the Two Higgs Doublet Model. All calculations have
been performed at $\sqrt{s}=500$ GeV and $1 \leq \tan{\beta} \leq 30$ for
masses $m_H = m_A = m_{H^\pm} = 300GeV$ and $m_H=300$ GeV, $m_A=m_{H^\pm}=500$
GeV. The working scenario is by taking without alignment limit, that is
$s_{\beta-\alpha}= 0.98$ and $s_{\beta-\alpha}= 0.99,$ $0.995$, which gives the
enhancement in the cross section, particularly a few times greater than the
predictions of the SM due to resonant-impacts of the additional heavy neutral
Higgs bosons. This shows that enhancement in cross section occurs on leaving
the alignment i.e., $s_{\beta-\alpha}=1$, at which all the higgs that couple to
vector bosons and fermions have the same values as in SM at tree level. A large
value of enhancement factor is obtained at $s_{\beta-\alpha}= 0.98$ compared to
$s_{\beta-\alpha}= 0.99,$ $0 .995$. Furthermore, the decrease in the
enhancement factor is observed for the case when $m_H=300$ GeV,
$m_A=m_{H^\pm}=500$ GeV. The behavior of the scaling factor with $\tan{\beta}$
is also studied, which shows that for large values of $\tan\beta$, the scaling
factor becomes equal to $s_{\beta-\alpha}$. Finally a convincing correlation is
achieved by taking into account, the experimental and theoretical constraints
e.g, perturbative unitarity, vacuum stability and electroweak oblique
parameters.


Abstract:
  In this paper, we propose a polar coding based scheme for set reconciliation
between two network nodes. The system is modeled as a well-known Slepian-Wolf
setting induced by a fixed number of deletions. The set reconciliation process
is divided into two phases: 1) a deletion polar code is employed to help one
node to identify the possible deletion indices, which may be larger than the
number of genuine deletions; 2) a lossless compression polar code is then
designed to feedback those indices with minimum overhead. Our scheme can be
viewed as a generalization of polar codes to some emerging network-based
applications such as the package synchronization in blockchains. Some
connections with the existing schemes based on the invertible Bloom lookup
tables (IBLTs) and network coding are also observed and briefly discussed.


Abstract:
  The two-dimensional regular and chaotic electro-convective flow states of a
dielectric liquid between two infinite parallel planar electrodes are
investigated using a two-relaxation-time lattice Boltzmann method. Positive
charges injected at the metallic planar electrode located at the bottom of the
dielectric liquid layer are transported towards the grounded upper electrode by
the synergy of the flow and the electric field. The various flow states can be
characterized by a non-dimensional parameter, the electric Rayleigh number.
Gradually increasing the electric Rayleigh number, the flow system sequentially
evolves via quasi-periodic, periodic, and chaotic flow states with five
identified bifurcations. The turbulence kinetic energy spectrum is shown to
follow the -3 law as the flow approaches turbulence. The spectrum is found to
follow a -5 law when the flow is periodic.


Abstract:
  We discuss possibilities to test physics beyond the Standard Model in
$\vert\Delta c\vert=\vert\Delta u\vert= 1$ semileptonic, hadronic and missing
energy decay modes. Clean null test observables such as angular observables,
CP-asymmetries and lepton universality tests are presented and
model-independent correlations as well as details within flavorful,
anomaly-free $Z^\prime$ models are worked out.


Abstract:
  Axion-CMB scenario is an interesting possibility to explain the temperature
anisotropy of the cosmic microwave background (CMB) by primordial fluctuations
of the QCD axion \cite{Iso:2020pzv}. In this scenario, fluctuations of
radiations are generated by an energy exchange between axions and radiations,
which results in the correlation between the primordial axion fluctuations and
the CMB anisotropies. Consequently, the cosmological observations stringently
constrain a model of the axion and the early history of the universe. In
particular, we need a large energy fraction $\Omega_A^{}$ of the axion at the
QCD phase transition, but it must become tiny at the present universe to
suppress the isocurvature power spectrum. One of natural cosmological scenarios
to realize such a situation is the thermal inflation which can sufficiently
dilute the axion abundance. Thermal inflation occurs in various models. In this
paper, we focus on a classically conformal (CC) $B$-$L$ model with a QCD axion.
In this model, the early universe undergoes a long supercooling era of the
$B$-$L$ and electroweak symmetries, and thermal inflation naturally occurs.
Thus it can be a good candidate for the axion-CMB scenario. But the axion
abundance at the QCD transition is shown to be insufficient in the original CC
$B$-$L$ model. To overcome the situation, we extend the model by introducing
$N$ scalar fields $S$ (either massive or massless) and consider a novel
cosmological history such that the $O(N)$ and the $B$-$L$ sectors evolve almost
separately in the early universe. We find that all the necessary conditions for
the axion-CMB scenario can be satisfied in some parameter regions for massless
$S$ fields, typically $N\sim 10^{19}$ and the mass of $B$-$L$ gauge boson
around $5-10$ TeV.


Abstract:
  We prove that every class of graphs $\mathscr C$ that is monadically stable
and has bounded twin-width can be transduced from some class with bounded
sparse twin-width. This generalizes analogous results for classes of bounded
linear cliquewidth and of bounded cliquewidth. It also implies that monadically
stable classes of bounded twin-widthare linearly $\chi$-bounded.


Abstract:
  We present a transformer-based image anomaly detection and localization
network. Our proposed model is a combination of a reconstruction-based approach
and patch embedding. The use of transformer networks helps to preserve the
spatial information of the embedded patches, which are later processed by a
Gaussian mixture density network to localize the anomalous areas. In addition,
we also publish BTAD, a real-world industrial anomaly dataset. Our results are
compared with other state-of-the-art algorithms using publicly available
datasets like MNIST and MVTec.


Abstract:
  Small-scale inhomogeneities in the baryon density around recombination have
been proposed as a solution to the tension between local and global
determinations of the Hubble constant. These baryon clumping models make
distinct predictions for the cosmic microwave background anisotropy power
spectra on small angular scales. We use recent data from the Atacama Cosmology
Telescope to test these predictions. No evidence for baryon clumping is found,
assuming a range of parameterizations for time-independent baryon density
probability distribution functions. The inferred Hubble constant remains in
significant tension with the SH0ES measurement.


Abstract:
  Spontaneous onset of a low temperature topologically ordered phase in a
2-dimensional (2D) lattice model of uniaxial liquid crystal (LC) was debated
extensively pointing to a suspected underlying mechanism affecting the RG flow
near the topological fixed point. A recent MC study clarified that a prior
crossover leads to a transition to nematic phase. The crossover was interpreted
as due to the onset of a perturbing relevant scaling field originating from the
extra spin degree of freedom. As a counter example and in support of this
hypothesis, we now consider V-shaped bent-core molecules with rigid rod-like
segments connected at an assigned angle. The two segments of the molecule
interact with the segments of all the nearest neighbours on a square lattice,
prescribed by a biquadratic interaction. We compute equilibrium averages of
different observables with Monte Carlo techniques as a function of temperature
and sample size. For the chosen molecular bend angle and symmetric
inter-segment interaction between neighbouirng molecules, the 2D system shows
two transitions as a function of T: the higher one at T1 leads to a topological
ordering of defects associated with the major molecular axis without a
crossover, imparting uniaxial symmetry to the medium described by the first
fundamental group of the order parameter space $\pi_{1}$= $Z_{2}$ (inversion
symmetry). The second at T2 leads to a medium displaying biaxial symmetry with
$\pi_{1}$ = Q (quaternion group). The biaxial phase shows a self-similar
microscopic structure with the three axes showing power law correlations with
vanishing exponents as the temperature decreases.


Abstract:
  WeChat is the largest social instant messaging platform in China, with 1.1
billion monthly active users. "Top Stories" is a novel friend-enhanced
recommendation engine in WeChat, in which users can read articles based on
preferences of both their own and their friends. Specifically, when a user
reads an article by opening it, the "click" behavior is private. Moreover, if
the user clicks the "wow" button, (only) her/his direct connections will be
aware of this action/preference. Based on the unique WeChat data, we aim to
understand user preferences and "wow" diffusion in Top Stories at different
levels. We have made some interesting discoveries. For instance, the "wow"
probability of one user is negatively correlated with the number of connected
components that are formed by her/his active friends, but the click probability
is the opposite. We further study to what extent users' "wow" and click
behavior can be predicted from their social connections. To address this
problem, we present a hierarchical graph representation learning based model
DiffuseGNN, which is capable of capturing the structure-based social
observations discovered above. Our experiments show that the proposed method
can significantly improve the prediction performance compared with alternative
methods.


Abstract:
  In recent experiments, electromagnetically induced transparency (EIT) were
observed with giant atoms, but nothing unconventional were found from the
transmission spectra. In this letter, we show that unconventional EIT does
exist in giant atoms, and indicate why it has not been observed so far.
Different from these existing works, this letter presents a consistent theory
including a real space method and a time delayed master equation for observing
unconventional EIT. We discover that this phenomenon is a quantum effect which
cannot be correctly described in a semi-classical way as those in recent works.
Our theory shows that it can be observed when the time delay between two
neighboring coupling points is comparable to the relaxation time of the atom,
which is crucial for a future experimental observation. This new phenomenon
results from inherent non-locality of the giant atom, which physically forces
propagating fields to be standing waves in space and the atom exhibiting
retardations in time. Our theory establishes a framework for application of
nonlocal systems to quantum information processing.


Abstract:
  We provide a generalization of the McMullen's algorithm to approximate the
Hausdorff dimension of the limit set for convex-cocompact subgroups of
isometries of the Complex Hyperbolic Plane.


Abstract:
  Bi$_2$O$_2$Se is a promising material for next-generation semiconducting
electronics. It exhibits premature metallicity on the introduction of a tiny
amount of electrons, the physics behind which remains elusive. Here we report
on transport and dielectric measurements in Bi$_2$O$_2$Se single crystals at
various carrier densities. The temperature-dependent resistivity ($\rho$)
indicates a smooth evolution from the semiconducting to the metallic state. The
critical concentration for the metal-insulator transition (MIT) to occur is
extraordinarily low ($n_\textrm{c}\sim10^{16}$ cm$^{-3}$). The relative
permittivity of the insulating sample is huge
($\epsilon_\textrm{r}\approx155(10)$) and varies slowly with temperature.
Combined with the light effective mass, a long effective Bohr radius
($a_\textrm{B}^*\approx36(2)$ $\textrm{nm}$) is derived, which provides a
reasonable interpretation of the metallic prematurity according to Mott's
criterion for MITs. The high electron mobility ($\mu$) at low temperatures may
result from the screening of ionized scattering centers due to the huge
$\epsilon_\textrm{r}$. Our findings shed light on the electron dynamics in two
dimensional (2D) Bi$_2$O$_2$Se devices.


Abstract:
  Discrete Morse theory, a cell complex-analog to smooth Morse theory, has been
developed over the past few decades since its original formulation by Robin
Forman in 1998. In particular, discrete gradient vector fields on simplicial
complexes capture important features of discrete Morse functions. We prove that
the characteristic polynomials of the Laplacian matrices of a simplicial
complex are generating functions for discrete gradient vector fields of
discrete Morse functions when the complex is either a graph or a triangulation
of an orientable manifold. Furthermore, we provide a full characterization of
the correspondence between rooted forests in higher dimensions and discrete
gradient vector fields.


Abstract:
  Symbolic equations are at the core of scientific discovery. The task of
discovering the underlying equation from a set of input-output pairs is called
symbolic regression. Traditionally, symbolic regression methods use
hand-designed strategies that do not improve with experience. In this paper, we
introduce the first symbolic regression method that leverages large scale
pre-training. We procedurally generate an unbounded set of equations, and
simultaneously pre-train a Transformer to predict the symbolic equation from a
corresponding set of input-output-pairs. At test time, we query the model on a
new set of points and use its output to guide the search for the equation. We
show empirically that this approach can re-discover a set of well-known
physical equations, and that it improves over time with more data and compute.


Abstract:
  Owing to the pandemic caused by the coronavirus disease of 2019 (COVID-19),
several universities have closed their campuses for preventing the spread of
infection. Consequently, the university classes are being held over the
Internet, and students attend these classes from their homes. While the
COVID-19 pandemic is expected to be prolonged, the online-centric lifestyle has
raised concerns about secondary health issues caused by reduced physical
activity (PA). However, the actual status of PA among university students has
not yet been examined in Japan. Hence, in this study, we collected daily PA
data (including the data corresponding to the number of steps taken and the
data associated with six types of activities) by employing smartphones and
thereby analyzed the changes in the PA of university students. The PA data were
collected over a period of ten weeks from 305 first-year university students
who were attending a mandatory class of physical education at the university.
The obtained results indicate that compared to the average number of steps
taken before the COVID-19 pandemic (6474.87 steps), the average number of steps
taken after the COVID-19 pandemic (3522.5 steps) has decreased by 45.6%.
Furthermore, the decrease in commuting time (7 AM to 10 AM), classroom time,
and extracurricular activity time (11 AM to 12 AM) has led to a decrease in PA
on weekdays owing to reduced unplanned exercise opportunities and has caused an
increase in the duration of being in the stationary state in the course of
daily life.


Abstract:
  We consider a distributed function computation problem in which parties
observing noisy versions of a remote source facilitate the computation of a
function of their observations at a fusion center through public communication.
The distributed function computation is subject to constraints, including not
only reliability and storage but also privacy and secrecy. Specifically, 1) the
remote source should remain private from an eavesdropper and the fusion center,
measured in terms of the information leaked about the remote source; 2) the
function computed should remain secret from the eavesdropper, measured in terms
of the information leaked about the arguments of the function, to ensure
secrecy regardless of the exact function used. We derive the exact rate regions
for lossless and lossy single-function computation and illustrate the lossy
single-function computation rate region for an information bottleneck example,
in which the optimal auxiliary random variables are characterized for
binary-input symmetric-output channels. We extend the approach to lossless and
lossy asynchronous multiple-function computations with joint secrecy and
privacy constraints, in which case inner and outer bounds for the rate regions
differing only in the Markov chain conditions imposed are characterized.


Abstract:
  For a $d$-dimensional random vector $X$, let $p_{n, X}(\theta)$ be the
probability that the convex hull of $n$ independent copies of $X$ contains a
given point $\theta$. We provide several sharp inequalities regarding $p_{n,
X}(\theta)$ and $N_X(\theta)$ denoting the smallest $n$ for which $p_{n,
X}(\theta)\ge1/2$. As a main result, we derive the totally general inequality
$1/2 \le \alpha_X(\theta)N_X(\theta)\le 3d + 1$, where $\alpha_X(\theta)$
(a.k.a. the Tukey depth) is the minimum probability that $X$ is in a fixed
closed halfspace containing the point $\theta$. We also show several
applications of our general results: one is a moment-based bound on
$N_X(\mathbb{E}[X])$, which is an important quantity in randomized approaches
to cubature construction or measure reduction problem. Another application is
the determination of the canonical convex body included in a random convex
polytope given by independent copies of $X$, where our combinatorial approach
allows us to generalize existing results in random matrix community
significantly.


Abstract:
  Finding exact circuit size is a notorious optimization problem in practice.
Whereas modern computers and algorithmic techniques allow to find a circuit of
size seven in blink of an eye, it may take more than a week to search for a
circuit of size thirteen. One of the reasons of this behavior is that the
search space is enormous: the number of circuits of size $s$ is
$s^{\Theta(s)}$, the number of Boolean functions on $n$ variables is $2^{2^n}$.
  In this paper, we explore the following natural heuristic idea for decreasing
the size of a given circuit: go through all its subcircuits of moderate size
and check whether any of them can be improved by reducing to SAT. This may be
viewed as a local search approach: we search for a smaller circuit in a ball
around a given circuit. We report the results of experiments with various
symmetric functions.


Abstract:
  The orientational correlation scheme introduced earlier for tetrahedral
molecules is extended for being able to classify orientational correlations
between pairs of high symmetry molecules. While in the original algorithm a
given orientation of a pair of tetrahedral molecules is characterized
unambiguously by the number of ligand atoms that can be found between two
planes that contain each centre and perpendicular to the centre-centre
connecting line, in the generalized algorithm, the planes are replaced by
cones, whose apex angles are set according to the symmetry of each molecule. To
demonstrate the applicability of the method, the octahedral-shaped SF$_6$
molecule is studied in a wide range of phases (gaseous, supercritical fluid,
liquid and plastic crystalline) using classical molecular dynamics. By
analyzing the orientational correlations, a close-contact region in the first
coordination shell and a medium-range order behaviour are identified in the
non-crystalline phases. While the former is invariant to changes of the
density, the latter showed longer-ranged correlations as density is raised. In
the plastic crystalline state, fluorine atoms are oriented along the lattice
directions with higher probability. To test the method for icosahedral
symmetries, the crystalline structures of room temperature C$_{60}$ is
generated by three sets of potentials that produce different local
arrangements. The novel analysis provided quantitative result on preferred
arrangements.


Abstract:
  The oxDNA model of DNA has been applied widely to systems in biology,
biophysics and nanotechnology. It is currently available via two independent
open source packages. Here we present a set of clearly-documented exemplar
simulations that simultaneously provide both an introduction to simulating the
model, and a review of the model's fundamental properties. We outline how
simulation results can be interpreted in terms of -- and feed into our
understanding of -- less detailed models that operate at larger length scales,
and provide guidance on whether simulating a system with oxDNA is worthwhile.


Abstract:
  We study the problem of list-decodable mean estimation, where an adversary
can corrupt a majority of the dataset. Specifically, we are given a set $T$ of
$n$ points in $\mathbb{R}^d$ and a parameter $0< \alpha <\frac 1 2$ such that
an $\alpha$-fraction of the points in $T$ are i.i.d. samples from a
well-behaved distribution $\mathcal{D}$ and the remaining $(1-\alpha)$-fraction
are arbitrary. The goal is to output a small list of vectors, at least one of
which is close to the mean of $\mathcal{D}$. We develop new algorithms for
list-decodable mean estimation, achieving nearly-optimal statistical
guarantees, with running time $O(n^{1 + \epsilon_0} d)$, for any fixed
$\epsilon_0 > 0$. All prior algorithms for this problem had additional
polynomial factors in $\frac 1 \alpha$. We leverage this result, together with
additional techniques, to obtain the first almost-linear time algorithms for
clustering mixtures of $k$ separated well-behaved distributions,
nearly-matching the statistical guarantees of spectral methods. Prior
clustering algorithms inherently relied on an application of $k$-PCA, thereby
incurring runtimes of $\Omega(n d k)$. This marks the first runtime improvement
for this basic statistical problem in nearly two decades.
  The starting point of our approach is a novel and simpler near-linear time
robust mean estimation algorithm in the $\alpha \to 1$ regime, based on a
one-shot matrix multiplicative weights-inspired potential decrease. We
crucially leverage this new algorithmic framework in the context of the
iterative multi-filtering technique of Diakonikolas et al. '18, '20, providing
a method to simultaneously cluster and downsample points using one-dimensional
projections -- thus, bypassing the $k$-PCA subroutines required by prior
algorithms.


Abstract:
  A selection of intellectual goods produced by online communities - e.g. open
source software or knowledge bases like Wikipedia - are in daily use by a broad
audience, and thus their quality impacts the public at large. Yet, it is still
unclear what contributes to the effectiveness of such online peer production
systems: what conditions or social processes help them deliver quality
products. Specifically, while co-contribution (i.e. bipartite networks) are
often investigated in online collaboration, the role of interpersonal
communication in coordination of online peer-production is much less
investigated. To address this gap we have reconstructed networks of personal
communication (direct messaging) between Wikipedia editors gathered in so
called Wikiprojects - teams of contributors who focus on articles within
specific topical areas. We found that effective projects exchange larger volume
of direct messages and that their communication structure allows for complex
coordination: for sharing of information locally through selective ties, and at
the same time globally across the whole group. To verify how these network
measures relate to the subjective perception of importance of group
communication we conducted semi-structured interviews with members of selected
projects. Our interviewees used direct communication for providing feedback,
for maintaining close relations and for tapping on the social capital of the
Wikipedia community. Our results underscore the importance of communication
structure in online collaboration: online peer production communities rely on
interpersonal communication to coordinate their work and to maintain high
levels of engagement. Design of platforms for such communities should allow for
ample group level communication as well as for one-on-one interactions.


Abstract:
  A phenomenological Hamiltonian of a closed (i.e., unitary) quantum system is
assumed to have an $N$ by $N$ real-matrix form composed of a unperturbed
diagonal-matrix part $H^{(N)}_0$ and of a tridiagonal-matrix perturbation
$\lambda\,W^{(N)}(\lambda)$. The requirement of the unitarity of the evolution
of the system (i.e., of the diagonalizability and of the reality of the
spectrum) restricts, naturally, the variability of the matrix elements to a
"physical" domain ${\cal D}^{[N]} \subset \mathbb{R}^d$. We fix the unperturbed
matrix (simulating a non-equidistant, square-well-type unperturbed spectrum)
and we only admit the maximally non-Hermitian antisymmetric-matrix
perturbations. This yields the hiddenly Hermitian model with the measure of
perturbation $\lambda$ and with the $d=N$ matrix elements which are, inside
${\cal D}^{[N]}$, freely variable. Our aim is to describe the quantum
phase-transition boundary $\partial {\cal D}^{[N]}$ (alias exceptional-point
boundary) at which the unitarity of the system is lost. Our main attention is
paid to the strong-coupling extremes of stability, i.e., to the Kato's
exceptional points of order $N$ (EPN) and to the (sharply spiked) shape of the
boundary $\partial {\cal D}^{[N]}$ in their vicinity. The feasibility of our
constructions is based on the use of the high-precision arithmetics in
combination with the computer-assisted symbolic manipulations (including, in
particular, the Gr\"{o}bner basis elimination technique).


Abstract:
  Stance detection concerns the classification of a writer's viewpoint towards
a target. There are different task variants, e.g., stance of a tweet vs. a full
article, or stance with respect to a claim vs. an (implicit) topic. Moreover,
task definitions vary, which includes the label inventory, the data collection,
and the annotation protocol. All these aspects hinder cross-domain studies, as
they require changes to standard domain adaptation approaches. In this paper,
we perform an in-depth analysis of 16 stance detection datasets, and we explore
the possibility for cross-domain learning from them. Moreover, we propose an
end-to-end unsupervised framework for out-of-domain prediction of unseen,
user-defined labels. In particular, we combine domain adaptation techniques
such as mixture of experts and domain-adversarial training with label
embeddings, and we demonstrate sizable performance gains over strong baselines,
both (i) in-domain, i.e., for seen targets, and (ii) out-of-domain, i.e., for
unseen targets. Finally, we perform an exhaustive analysis of the cross-domain
results, and we highlight the important factors influencing the model
performance.


Abstract:
  An inspiration at the origin of wavelet analysis (when Grossmann, Morlet,
Meyer and collaborators were interacting and exploring versions of multiscale
representations) was provided by the analysis of holomorphic signals, for which
the images of the phase of Cauchy wavelets were remarkable in their ability to
reveal intricate singularities or dynamic structures, such as instantaneous
frequency jumps in musical recordings. Our goal is to follow their seminal work
and introduce recent developments in nonlinear analysis. In particular we
sketch methods extending conventional Fourier analysis, exploiting both phase
and amplitudes of holomorphic functions. The Blaschke factors are a key
ingredient, in building analytic tools, starting with the Malmquist Takenaka
orthonormal bases of the Hardy space, continuing with "best" adapted bases
obtained through phase unwinding, and concluding with relations to composition
of Blaschke products and their dynamics. We also remark that the phase of a
Blaschke product is a one layer neural net with arctan as an activation sigmoid
and that the composition is a "Deep Neural Net" whose depth is the number of
compositions. Our results provide a wealth of related library of orthonormal
bases.


Abstract:
  In autonomous driving, perceiving the driving behaviors of surrounding agents
is important for the ego-vehicle to make a reasonable decision. In this paper,
we propose a neural network model based on trajectories information for driving
behavior recognition. Unlike existing trajectory-based methods that recognize
the driving behavior using the hand-crafted features or directly encoding the
trajectory, our model involves a Multi-Scale Convolutional Neural Network
(MSCNN) module to automatically extract the high-level features which are
supposed to encode the rich spatial and temporal information. Given a
trajectory sequence of an agent as the input, firstly, the Bi-directional Long
Short Term Memory (Bi-LSTM) module and the MSCNN module respectively process
the input, generating two features, and then the two features are fused to
classify the behavior of the agent. We evaluate the proposed model on the
public BLVD dataset, achieving a satisfying performance.


Abstract:
  High load latency that results from deep cache hierarchies and relatively
slow main memory is an important limiter of single-thread performance. Data
prefetch helps reduce this latency by fetching data up the hierarchy before it
is requested by load instructions. However, data prefetching has shown to be
imperfect in many situations. We propose cache-level prediction to complement
prefetchers. Our method predicts which memory hierarchy level a load will
access allowing the memory loads to start earlier, and thereby saves many
cycles. The predictor provides high prediction accuracy at the cost of just one
cycle added latency to L1 misses. Experimental results show speedup of 7.8\% on
generic, graph, and HPC applications over a baseline with aggressive
prefetchers.


Abstract:
  Buckling strength estimation of architected materials has mainly been
restricted to load cases oriented along symmetry axes. However, realistic load
scenarios normally exhibit more general stress distributions. In this paper we
propose a simple yet accurate method to estimate the buckling strength of
stretch-dominated lattice structures based on individual member analysis. As an
integral part of the method, the yield strength is also determined. This
simplified model is verified by rigorous numerical analysis. In particular, we
efficiently compute the complete buckling strength surfaces of an orthotropic
bulk modulus optimal plate lattice structure and isotropic stiffness optimal
plate and truss lattice structures subjected to rotated uni-axial loads, where
the ratio between the highest and lowest buckling strength is found to be 1.77,
2.11 and 2.41, respectively. For comparison, we also provide their yield
strength surfaces, where the corresponding ratios are 1.84, 1.16 and 1.79.
Furthermore, we use the knowledge gained from the simplified model to create a
new configuration of the isotropic plate lattice structure with a more
isotropic buckling strength surface and buckling strength ratio of 1.24,
without deterioration of the stiffness or yield strength. The proposed method
provides a valuable tool to quickly estimate the microstructural buckling
strength of stretch-dominated lattice structures, especially for applications
where the stress state is non-uniform such as infill in additive manufacturing.


Abstract:
  We analyze the phase diagram of a topological insulator model including
antiferromagnetic interactions in the form of an extended Su-Schrieffer Heeger
model. To this end, we employ a recently introduced operational definition of
topological order based on the ability of a system to perform topological error
correction. We show that the necessary error correction statistics can be
obtained efficiently using a Monte-Carlo sampling of a matrix product state
representation of the ground state wave function. Specifically, we identify two
distinct symmetry-protected topological phases corresponding to two different
fully dimerized reference states. Finally, we extend the notion of error
correction to classify thermodynamic phases to those exhibiting local order
parameters, finding a topologically trivial antiferromagnetic phase for
sufficiently strong interactions.


Abstract:
  Cyber-Physical System (CPS) has made a tremendous progress in recent years
and also disrupted many technical fields such as smart industries, smart
health, smart transportation etc. to flourish the nations economy. However, CPS
Security is still one of the concerns for wide adoption owing to high number of
devices connecting to the internet and the traditional security solutions may
not be suitable to protect the advanced, application specific attacks. This
paper presents a programmable device network layer architecture to combat
attacks and efficient network monitoring in heterogeneous environment CPS
applications. We leverage Industrial control systems (ICS) to discuss the
existing issues, highlighting the importance of advanced network layer for CPS.
The programmable data plane language (P4) is introduced to detect well known
HELLO Flood attack with minimal efforts in the network level and also used to
featuring the potential solutions for security.


Abstract:
  The news recommender systems are marked by a few unique challenges specific
to the news domain. These challenges emerge from rapidly evolving readers'
interests over dynamically generated news items that continuously change over
time. News reading is also driven by a blend of a reader's long-term and
short-term interests. In addition, diversity is required in a news recommender
system, not only to keep the reader engaged in the reading process but to get
them exposed to different views and opinions. In this paper, we propose a deep
neural network that jointly learns informative news and readers' interests into
a unified framework. We learn the news representation (features) from the
headlines, snippets (body) and taxonomy (category, subcategory) of news. We
learn a reader's long-term interests from the reader's click history,
short-term interests from the recent clicks via LSTMSs and the diversified
reader's interests through the attention mechanism. We also apply different
levels of attention to our model. We conduct extensive experiments on two news
datasets to demonstrate the effectiveness of our approach.


Abstract:
  Two-dimensional (2D) ZrS2 monolayer (ML) has emerged as a promising candidate
for thermoelectric (TE) device applications due to its high TE figure of merit,
which is mainly contributed by its inherently low lattice thermal conductivity.
This work investigates the effect of the lattice anharmonicity driven by
temperature-dependent phonon dispersions on thermal transport of ZrS2 ML. The
calculations are based on the self-consistent phonon (SCP) theory to calculate
the thermodynamic parameters along with the lattice thermal conductivity. The
higher- order (quartic) force constants were extracted by using an efficient
compressive sensing lattice dynamics technique, which estimates the necessary
data based on the emerging machine learning program as an alternative of
computationally expensive density functional theory calculations. Resolve of
the degeneracy and hardening of the vibrational frequencies of low-energy
optical modes were predicted upon including the quartic anharmonicity. As
compared to the conventional Boltzmann transport equation (BTE) approach, the
lattice thermal conductivity of the optimized ZrS2 ML unit cell within SCP +
BTE approach is found to be significantly enhanced (e.g., by 21% at 300 K).
This enhancement is due to the relatively lower value of phonon linewidth
contributed by the anharmonic frequency renormalization included in the SCP
theory. Mainly, the conventional BTE approach neglects the temperature
dependence of the phonon frequencies due to the consideration of harmonic
lattice dynamics and treats the normal process of three-phonon scattering
incorrectly due to the use of quasi-particle lifetimes. These limitations are
addressed in this work within the SCP + BTE approach, which signifies the
validity and accuracy of this approach.


Abstract:
  Let $R$ be a finite commutative ring with identity and $U(R)$ be its group of
units. In 2005, El-Kassar and Chehade presented a ring structure for $U(R)$ and
as a consequence they generalized this group of units to the generalized group
of units $U^{k}\left( R\right) $ defined iteratively as the group of the units
of $U^{k-1}(R)$, with $U^{1}\left( R\right) =U(R) $. In this paper, we examine
the structure of this group, when $R=\mathbb{Z}_{n}.$ We find a decomposition
of $U^{k}\left(\mathbb{Z}_{n}\right)$ as a direct product of cyclic groups for
the general case of any $k$, and we study when these groups are boolean and
trivial. We also show that this decomposition structure is directly related to
the Pratt Tree primes.


Abstract:
  Cluster-randomized experiments are widely used due to their logistical
convenience and policy relevance. To analyze them properly, we must address the
fact that the treatment is assigned at the cluster level instead of the
individual level. Standard analytic strategies are regressions based on
individual data, cluster averages, and cluster totals, which differ when the
cluster sizes vary. These methods are often motivated by models with strong and
unverifiable assumptions, and the choice among them can be subjective. Without
any outcome modeling assumption, we evaluate these regression estimators and
the associated robust standard errors from a design-based perspective where
only the treatment assignment itself is random and controlled by the
experimenter. We demonstrate that regression based on cluster averages targets
a weighted average treatment effect, regression based on individual data is
suboptimal in terms of efficiency, and regression based on cluster totals is
consistent and more efficient with a large number of clusters. We highlight the
critical role of covariates in improving estimation efficiency, and illustrate
the efficiency gain via both simulation studies and data analysis. Moreover, we
show that the robust standard errors are convenient approximations to the true
asymptotic standard errors under the design-based perspective. Our theory holds
even when the outcome models are misspecified, so it is model-assisted rather
than model-based. We also extend the theory to a wider class of weighted
average treatment effects.


Abstract:
  Face anti-spoofing approaches based on domain generalization (DG) have drawn
growing attention due to their robustness for unseen scenarios. Previous
methods treat each sample from multiple domains indiscriminately during the
training process, and endeavor to extract a common feature space to improve the
generalization. However, due to complex and biased data distribution, directly
treating them equally will corrupt the generalization ability. To settle the
issue, we propose a novel Dual Reweighting Domain Generalization (DRDG)
framework which iteratively reweights the relative importance between samples
to further improve the generalization. Concretely, Sample Reweighting Module is
first proposed to identify samples with relatively large domain bias, and
reduce their impact on the overall optimization. Afterwards, Feature
Reweighting Module is introduced to focus on these samples and extract more
domain-irrelevant features via a self-distilling mechanism. Combined with the
domain discriminator, the iteration of the two modules promotes the extraction
of generalized features. Extensive experiments and visualizations are presented
to demonstrate the effectiveness and interpretability of our method against the
state-of-the-art competitors.


Abstract:
  Quantum channels in free-space, an essential prerequisite for fundamental
tests of quantum mechanics and quantum technologies in open space, have so far
been based on direct line-of-sight because the predominant approaches for
photon-encoding, including polarization and spatial modes, are not compatible
with randomly scattered photons. Here we demonstrate a novel approach to
transfer and recover quantum coherence from scattered, non-line-of-sight
photons analyzed in a multimode and imaging interferometer for time-bins,
combined with photon detection based on a 8x8 single-photon-detector-array. The
observed time-bin visibility for scattered photons remained at a high $95\%$
over a wide scattering angle range of -45 degree to +45 degree, while the
individual pixels in the detector array resolve or track an image in its field
of view of ca. 0.5 degrees. Using our method we demonstrate the viability of
two novel applications. Firstly, using scattered photons as an indirect channel
for quantum communication thereby enabling non-line-of-sight quantum
communication with background suppression, and secondly, using the combined
arrival time and quantum coherence to enhance the contrast of low-light imaging
and laser ranging under high background light. We believe our method will
instigate new lines for research and development on applying photon coherence
from scattered signals to quantum sensing, imaging, and communication in
free-space environments.


Abstract:
  Aharonov-Bohm interferometry is the most direct probe of anyonic statistics
in the quantum Hall effect. The technique involves oscillations of the electric
current as a function of the magnetic field and is not applicable to Kitaev
spin liquids and other systems without charged quasiparticles. Here, we
establish a novel protocol, involving heat transport, for revealing fractional
statistics even in the absence of charged excitations, as is the case in
quantum spin liquids. Specifically, we demonstrate that heat transport in
Kitaev spin liquids through two distinct interferometer geometries, Fabry-Perot
and Mach-Zehnder, exhibits drastically different behaviors. Therefore, we
propose the use of heat transport interferometry as a probe of anyonic
statistics in charge insulators.


Abstract:
  We consider 5d supersymmetric gauge theories with unitary groups in the
$\Omega$-background and study codim-2/4 BPS defects supported on orthogonal
planes intersecting at the origin along a circle. The intersecting defects
arise upon implementing the most generic Higgsing (geometric transition) to the
parent higher dimensional theory, and they are described by pairs of 3d
supersymmetric gauge theories with unitary groups interacting through 1d matter
at the intersection. We explore the relations between instanton and generalized
vortex calculus, pointing out a duality between intersecting defects subject to
the $\Omega$-background and a deformation of supergroup gauge theories, the
exact supergroup point being achieved in the self-dual or unrefined limit.
Embedding our setup into refined topological strings and in the simplest case
when the parent 5d theory is Abelian, we are able to identify the supergroup
theory dual to the intersecting defects as the supergroup version of refined
Chern-Simons theory via open/closed duality. We also discuss the BPS/CFT side
of the correspondence, finding an interesting large rank duality with
super-instanton counting.


Abstract:
  We designed, constructed and have been operating a system based on
single-crystal synthetic diamond sensors, to monitor the beam losses at the
interaction region of the SuperKEKB asymmetric-energy electron-positron
collider. The system records the radiation dose-rates in positions close to the
inner detectors of the Belle II experiment, and protects both the detector and
accelerator components against destructive beam losses, by participating in the
beam-abort system. It also provides complementary information for the dedicated
studies of beam-related backgrounds. We describe the performance of the system
during the commissioning of the accelerator and during the first physics data
taking.


Abstract:
  We study a class of convex-concave saddle-point problems of the form
$\min_x\max_y \langle Kx,y\rangle+f_{\cal{P}}(x)-h^\ast(y)$ where $K$ is a
linear operator, $f_{\cal{P}}$ is the sum of a convex function $f$ with a
Lipschitz-continuous gradient and the indicator function of a bounded convex
polytope $\cal{P}$, and $h^\ast$ is a convex (possibly nonsmooth) function.
Such problem arises, for example, as a Lagrangian relaxation of various
discrete optimization problems. Our main assumptions are the existence of an
efficient linear minimization oracle ($lmo$) for $f_{\cal{P}}$ and an efficient
proximal map for $h^*$ which motivate the solution via a blend of proximal
primal-dual algorithms and Frank-Wolfe algorithms. In case $h^*$ is the
indicator function of a linear constraint and function $f$ is quadratic, we
show a $O(1/n^2)$ convergence rate on the dual objective, requiring $O(n \log
n)$ calls of $lmo$. If the problem comes from the constrained optimization
problem $\min_{x\in\mathbb R^d}\{f_{\cal{P}}(x)\:|\:Ax-b=0\}$ then we
additionally get bound $O(1/n^2)$ both on the primal gap and on the
infeasibility gap. In the most general case, we show a $O(1/n)$ convergence
rate of the primal-dual gap again requiring $O(n\log n)$ calls of $lmo$. To the
best of our knowledge, this improves on the known convergence rates for the
considered class of saddle-point problems. We show applications to labeling
problems frequently appearing in machine learning and computer vision.


Abstract:
  We study the optical properties of the solar gravitational lens (SGL) while
treating the Sun as an extended, axisymmetric and rotating body. The
gravitational field of the Sun is represented using a set of zonal harmonics.
We develop an analytical description of the intensity of light that is observed
in the image plane in the strong interference region of a realistic SGL. This
formalism makes it possible to model not only the point-spread function of
point sources, but also actual observables, images that form in the focal plane
of an imaging telescope positioned in the image plane. Perturbations of the
monopole gravitational field of the Sun are dominated by the solar quadrupole
moment, which results in forming an astroid caustic on the image plane.
Consequently, an imaging telescope placed inside the astroid caustic observes
four bright spots, forming the well-known pattern of an Einstein cross. The
relative intensities and positions of these spots change as the telescope is
moved in the image plane, with spots merging into bright arcs when the
telescope approaches the caustic boundary. Outside the astroid caustic, only
two spots remain and the observed pattern eventually becomes indistinguishable
from the imaging pattern of a monopole lens at greater distances from the
optical axis. We present results from extensive numerical simulations, forming
the basis of our ongoing study of prospective exoplanet imaging with the SGL.
These results are also applicable to describe a large class of gravitational
lensing scenarios involving axisymmetric lenses that can be represented using
zonal harmonics.


Abstract:
  Deep learning affords enormous opportunities to augment the armamentarium of
biomedical imaging, albeit its design and implementation have potential flaws.
Fundamentally, most deep learning models are driven entirely by data without
consideration of any prior knowledge, which dramatically increases the
complexity of neural networks and limits the application scope and model
generalizability. Here we establish a geometry-informed deep learning framework
for ultra-sparse 3D tomographic image reconstruction. We introduce a novel
mechanism for integrating geometric priors of the imaging system. We
demonstrate that the seamless inclusion of known priors is essential to enhance
the performance of 3D volumetric computed tomography imaging with ultra-sparse
sampling. The study opens new avenues for data-driven biomedical imaging and
promises to provide substantially improved imaging tools for various clinical
imaging and image-guided interventions.


Abstract:
  We present an exploratory case study describing the design and realisation of
a ''pure mixed reality'' application in a museum setting, where we investigate
the potential of using Microsoft's HoloLens for object-centred museum
mediation. Our prototype supports non-expert visitors observing a sculpture by
offering interpretation that is linked to visual properties of the museum
object. The design and development of our research prototype is based on a
two-stage visitor observation study and a formative study we conducted prior to
the design of the application. We present a summary of our findings from these
studies and explain how they have influenced our user-centred content creation
and the interaction design of our prototype. We are specifically interested in
investigating to what extent different constructs of initiative influence the
learning and user experience. Thus, we detail three modes of activity that we
realised in our prototype. Our case study is informed by research in the area
of human-computer interaction, the humanities and museum practice. Accordingly,
we discuss core concepts, such as gaze-based interaction, object-centred
learning, presence, and modes of activity and guidance with a transdisciplinary
perspective.


Abstract:
  The computational power increases over the past decades havegreatly enhanced
the ability to simulate chemical reactions andunderstand ever more complex
transformations. Tensor contractions are the fundamental computational building
block of these simulations. These simulations have often been tied to one
platform and restricted in generality by the interface provided to the user.
The expanding prevalence of accelerators and researcher demands necessitate a
more general approach which is not tied to specific hardware or requires
contortion of algorithms to specific hardware platforms. In this paper we
present COMET, a domain-specific programming language and compiler
infrastructure for tensor contractions targeting heterogeneous accelerators. We
present a system of progressive lowering through multiple layers of abstraction
and optimization that achieves up to 1.98X speedup for 30 tensor contractions
commonly used in computational chemistry and beyond.


Abstract:
  The next generation of galaxy surveys will allow us to test some fundamental
aspects of the standard cosmological model, including the assumption of a
minimal coupling between the components of the dark sector. In this paper, we
present the Javalambre Physics of the Accelerated Universe Astrophysical Survey
(J-PAS) forecasts on a class of unified models where cold dark matter interacts
with a vacuum energy, considering future observations of baryon acoustic
oscillations, redshift-space distortions, and the matter power spectrum. After
providing a general framework to study the background and linear perturbations,
we focus on a concrete interacting model without momentum exchange by taking
into account the contribution of baryons. We compare the J-PAS results with
those expected for DESI and Euclid surveys and show that J-PAS is competitive
to them, especially at low redshifts. Indeed, the predicted errors for the
interaction parameter, which measures the departure from a $\Lambda$CDM model,
can be comparable to the actual errors derived from the current data of cosmic
microwave background temperature anisotropies.


Abstract:
  The scaling of different features of stream-wise normal stress profiles
$\langle uu\rangle^+(y^+)$ in turbulent wall-bounded flows, in particular in
truly parallel flows, such as channel and pipe flows, is the subject of a long
running debate. Particular points of contention are the scaling of the "inner"
and "outer" peaks of $\langle uu\rangle^+$ at $y^+\approxeq 15$ and $y^+
=\mathcal{O}(10^3)$, respectively, their infinite Reynolds number limit, and
the rate of logarithmic decay in the outer part of the flow. Inspired by the
landmark paper of Chen and Sreenivasan (2021), two terms of the inner
asymptotic expansion of $\langle uu\rangle^+$ in the small parameter
$Re_\tau^{-1/4}$ are extracted for the first time from a set of direct
numerical simulations (DNS) of channel flow. This inner expansion is completed
by a matching outer expansion, which not only fits the same set of channel DNS
within 1.5\% of the peak stress, but also provides a good match of laboratory
data in pipes and the near-wall part of boundary layers, up to the highest
$Re_\tau$'s of order $10^5$. The salient features of the new composite
expansion are first, an inner $\langle uu\rangle^+$ peak, which saturates at
11.3 and decreases as $Re_\tau^{-1/4}$, followed by a short "wall loglaw" with
a slope that becomes positive for $Re_\tau \gtrapprox 20'000$, leading up to an
outer peak, and an outer logarithmic overlap with a negative slope continuously
going to zero for $Re_\tau \to\infty$.


Abstract:
  In recent years, two-dimensional van der Waals materials have emerged as an
important platform for the observation of long-range ferromagnetic order in
atomically thin layers. Although heterostructures of such materials can be
conceived to harness and couple a wide range of magneto-optical and
magneto-electrical properties, technologically relevant applications require
Curie temperatures at or above room-temperature and the ability to grow films
over large areas. Here we demonstrate the large-area growth of single-crystal
ultrathin films of stoichiometric Fe5GeTe2 on an insulating substrate using
molecular beam epitaxy. Magnetic measurements show the persistence of soft
ferromagnetism up to room temperature, with a Curie temperature of 293 K, and a
weak out-of-plane magnetocrystalline anisotropy. Surface, chemical, and
structural characterizations confirm the layer-by-layer growth, 5:1:2 Fe:Ge:Te
stoichiometric elementary composition, and single crystalline character of the
films.


Abstract:
  \textbf{Background} Hydrogels are crosslinked polymer networks that can
absorb and retain a large fraction of liquid. Near a critical sliding velocity,
hydrogels pressed against smooth surfaces exhibit time-dependent frictional
behavior occurring over multiple timescales, yet the origin of these dynamics
is unresolved. \textbf{Objective} Here, we characterize this time-dependent
regime and show that it is consistent with two distinct molecular processes:
sliding-induced relaxation and quiescent recovery. \textbf{Methods} Our
experiments use a custom pin-on-disk tribometer to examine poly(acrylic acid)
hydrogels on smooth poly(methyl methacrylate) surfaces over a variety of
sliding conditions, from minutes to hours. \textbf{Results} We show that at a
fixed sliding velocity, the friction coefficient decays exponentially and
reaches a steady-state value. The time constant associated with this decay
varies exponentially with the sliding velocity, and is sensitive to any
precedent frictional shearing of the interface. This process is reversible;
upon cessation of sliding, the friction coefficient recovers to its original
state. We also show that the initial direction of shear can be imprinted as an
observable "memory", and is visible after 24 hrs of repeated frictional
shearing. \textbf{Conclusions} We attribute this behavior to nanoscale
extension and relaxation dynamics of the near-surface polymer network, leading
to a model of frictional relaxation and recovery with two parallel timescales.


Abstract:
  It is shown that the equalization of temperatures between our and mirror
sectors occurs during one Hubble time due to microscopic black hole production
and evaporation in particle collisions if the temperature of the Universe is
near the multidimensional Plank mass. This effect excludes the multidimensional
Planck masses smaller than the reheating temperature of the Universe
($\sim10^{13}$ GeV) in the mirror matter models, because the primordial
nucleosynthesis theory requires that the temperature of the mirror world should
be lower than ours. In particular, the birth of microscopic black holes in the
LHC is impossible if the dark matter of our Universe is represented by baryons
of mirror matter. It excludes some of the possible coexisting options in
particle physics and cosmology. Multidimensional models with flat additional
dimensions are already strongly constrained in maximum temperature due to the
effect of Kaluza-Klein mode (KK-mode) overproduction. In these models, the
reheating temperature should be significantly less than the multidimensional
Planck mass, so our restrictions in this case are not paramount. The new
constraints play a role in multidimensional models in which the spectrum of
KK-modes does not lead to their overproduction in the early Universe, for
example, in theories with hyperbolic additional space.


Abstract:
  Multilingual models have demonstrated impressive cross-lingual transfer
performance. However, test sets like XNLI are monolingual at the example level.
In multilingual communities, it is common for polyglots to code-mix when
conversing with each other. Inspired by this phenomenon, we present two strong
black-box adversarial attacks (one word-level, one phrase-level) for
multilingual models that push their ability to handle code-mixed sentences to
the limit. The former uses bilingual dictionaries to propose perturbations and
translations of the clean example for sense disambiguation. The latter directly
aligns the clean example with its translations before extracting phrases as
perturbations. Our phrase-level attack has a success rate of 89.75% against
XLM-R-large, bringing its average accuracy of 79.85 down to 8.18 on XNLI.
Finally, we propose an efficient adversarial training scheme that trains in the
same number of steps as the original model and show that it improves model
accuracy.


Abstract:
  We present a statistical study of the largest bibliographic compilation of
stellar and orbital parameters of W UMa stars derived by light curve synthesis
with Roche models. The compilation includes nearly 700 individually
investigated objects from over 450 distinct publications. Almost 70% of this
sample is comprised of stars observed in the last decade that have not been
considered in previous statistical studies. We estimate the ages of the
cataloged stars, model the distributions of their periods, mass ratios,
temperatures and other quantities, and compare them with the data from CRTS,
LAMOST and Gaia archives. As only a small fraction of the sample has radial
velocity curves, we examine the reliability of the photometric mass ratios in
totally and partially eclipsing systems and find that totally eclipsing W UMa
stars with photometric mass ratios have the same parameter distributions as
those with spectroscopic mass ratios. Most of the stars with reliable
parameters have mass ratios below 0.5 and orbital periods shorter than 0.5
days. Stars with longer periods and temperatures above 7000 K stand out as
outliers and shouldn't be labeled as W UMa binaries.
  The collected data is available as an online database at
https://wumacat.aob.rs.


Abstract:
  Among the models of disordered conduction and localization, models with $N$
orbitals per site are attractive both for their mathematical tractability and
for their physical realization in coupled disordered grains. However Wegner
proved that there is no Anderson transition and no localized phase in the $N
\rightarrow \infty$ limit, if the hopping constant $K$ is kept fixed. Here we
show that the localized phase is preserved in a different limit where $N$ is
taken to infinity and the hopping $K$ is simultaneously adjusted to keep $N \,
K$ constant. We support this conclusion with two arguments. The first is
numerical computations of the localization length showing that in the $N
\rightarrow \infty$ limit the site-diagonal-disorder model possesses a
localized phase if $N\,K$ is kept constant, but does not possess that phase if
$K$ is fixed. The second argument is a detailed analysis of the energy and
length scales in a functional integral representation of the gauge invariant
model. The analysis shows that in the $K$ fixed limit the functional integral's
spins do not exhibit long distance fluctuations, i.e. such fluctuations are
massive and therefore decay exponentially, which signals conduction. In
contrast the $N\,K$ fixed limit preserves the massless character of certain
spin fluctuations, allowing them to fluctuate over long distance scales and
cause Anderson localization.


Abstract:
  Mutation and drift play opposite roles in genetics. While mutation creates
diversity, drift can cause gene variants to disappear, especially when they are
rare. In the absence of natural selection and migration, the balance between
the drift and mutation in a well-mixed population defines its diversity. The
Moran model captures the effects of these two evolutionary forces and has a
counterpart in social dynamics, known as the Voter model with external opinion
influencers. Two extreme outcomes of the Voter model dynamics are consensus and
coexistence of opinions, which correspond to low and high diversity in the
Moran model. Here we use a Shannon's information-theoretic approach to
characterize the smooth transition between the states of consensus and
coexistence of opinions in the Voter model. Mapping the Moran into the Voter
model we extend the results to the mutation-drift balance and characterize the
transition between low and high diversity in finite populations. Describing the
population as a network of connected individuals we show that the transition
between the two regimes depends on the network topology of the population and
on the possible asymmetries in the mutation rates.


Abstract:
  Granulation of quantum matter -- the formation of persistent small-scale
patterns -- is realized in the images of quasi-one-dimensional Bose-Einstein
condensates perturbed by a periodically modulated interaction. Our present
analysis of a mean-field approximation suggests that granulation is caused by
the gradual transformation of phase undulations into density undulations. This
is achieved by a suitably large modulation frequency, while for low enough
frequencies the system exhibits a quasi-adiabatic regime. We show that the
persistence of granulation is a result of the irregular evolution of the phase
of the wavefunction representing an irreversible process. Our model predictions
agree with numerical solutions of the Schr\"odinger equation and experimental
observations. The numerical computations reveal the emergent many-body
correlations behind these phenomena via the multi-configurational
time-dependent Hartree theory for bosons (MCTDHB).


Abstract:
  Production high-performance computing systems continue to grow in complexity
and size. As applications struggle to make use of increasingly heterogeneous
compute nodes, maintaining high efficiency (performance per watt) for the whole
platform becomes a challenge. Alongside the growing complexity of scientific
workloads, this extreme heterogeneity is also an opportunity: as applications
dynamically undergo variations in workload, due to phases or data/compute
movement between devices, one can dynamically adjust power across compute
elements to save energy without impacting performance. With an aim toward an
autonomous and dynamic power management strategy for current and future HPC
architectures, this paper explores the use of control theory for the design of
a dynamic power regulation method. Structured as a feedback loop, our
approach-which is novel in computing resource management-consists of
periodically monitoring application progress and choosing at runtime a suitable
power cap for processors. Thanks to a preliminary offline identification
process, we derive a model of the dynamics of the system and a
proportional-integral (PI) controller. We evaluate our approach on top of an
existing resource management framework, the Argo Node Resource Manager,
deployed on several clusters of Grid'5000, using a standard memory-bound HPC
benchmark.


Abstract:
  We present a data-driven optimization framework for redesigning police patrol
zones in an urban environment. The objectives are to rebalance police workload
among geographical areas and to reduce response time to emergency calls. We
develop a stochastic model for police emergency response by integrating
multiple data sources, including police incidents reports, demographic surveys,
and traffic data. Using this stochastic model, we optimize zone redesign plans
using mixed-integer linear programming. Our proposed design was implemented by
the Atlanta Police Department in March 2019. By analyzing data before and after
the zone redesign, we show that the new design has reduced the response time to
high priority 911 calls by 5.8\% and the imbalance of police workload among
different zones by 43\%.


Abstract:
  We present a novel class of projected methods, to perform statistical
analysis on a data set of probability distributions on the real line, with the
2-Wasserstein metric. We focus in particular on Principal Component Analysis
(PCA) and regression. To define these models, we exploit a representation of
the Wasserstein space closely related to its weak Riemannian structure, by
mapping the data to a suitable linear space and using a metric projection
operator to constrain the results in the Wasserstein space. By carefully
choosing the tangent point, we are able to derive fast empirical methods,
exploiting a constrained B-spline approximation. As a byproduct of our
approach, we are also able to derive faster routines for previous work on PCA
for distributions. By means of simulation studies, we compare our approaches to
previously proposed methods, showing that our projected PCA has similar
performance for a fraction of the computational cost and that the projected
regression is extremely flexible even under misspecification. Several
theoretical properties of the models are investigated and asymptotic
consistency is proven. Two real world applications to Covid-19 mortality in the
US and wind speed forecasting are discussed.


Abstract:
  Task-agnostic knowledge distillation, a teacher-student framework, has been
proved effective for BERT compression. Although achieving promising results on
NLP tasks, it requires enormous computational resources. In this paper, we
propose Extract Then Distill (ETD), a generic and flexible strategy to reuse
the teacher's parameters for efficient and effective task-agnostic
distillation, which can be applied to students of any size. Specifically, we
introduce two variants of ETD, ETD-Rand and ETD-Impt, which extract the
teacher's parameters in a random manner and by following an importance metric
respectively. In this way, the student has already acquired some knowledge at
the beginning of the distillation process, which makes the distillation process
converge faster. We demonstrate the effectiveness of ETD on the GLUE benchmark
and SQuAD. The experimental results show that: (1) compared with the baseline
without an ETD strategy, ETD can save 70\% of computation cost. Moreover, it
achieves better results than the baseline when using the same computing
resource. (2) ETD is generic and has been proven effective for different
distillation methods (e.g., TinyBERT and MiniLM) and students of different
sizes. The source code will be publicly available upon publication.


Abstract:
  We present the results regarding the analysis of the fast X-ray/infrared (IR)
variability of the black-hole transient MAXI J1535$-$571. The data studied in
this work consist of two strictly simultaneous observations performed with
XMM-Newton (X-rays: 0.7$-$10 keV), VLT/HAWK-I ($K_{\rm s}$ band, 2.2 $\mu$m)
and VLT/VISIR ($M$ and $PAH2$_$2$ bands, 4.85 and 11.88 $\mu$m respectively).
The cross-correlation function between the X-ray and near-IR light curves shows
a strong asymmetric anti-correlation dip at positive lags. We detect a near-IR
QPO (2.5 $\sigma$) at $2.07\pm0.09$ Hz simultaneously with an X-ray QPO at
approximately the same frequency ($f_0=2.25\pm0.05$). From the cross-spectral
analysis a lag consistent with zero was measured between the two oscillations.
We also measure a significant correlation between the average near-IR and
mid-IR fluxes during the second night, but find no correlation on short
timescales. We discuss these results in terms of the two main scenarios for
fast IR variability (hot inflow and jet powered by internal shocks). In both
cases, our preliminary modelling suggests the presence of a misalignment
between disk and jet.


Abstract:
  The paper contains an application of van Kampen theorem for groupoids for
computation of homotopy types of certain class of non-compact foliated surfaces
obtained by gluing at most countably many strips $\mathbb{R}\times(0,1)$ with
boundary intervals in $\mathbb{R}\times\{\pm1\}$ along some of those intervals.


Abstract:
  We present randUBV, a randomized algorithm for matrix sketching based on the
block Lanzcos bidiagonalization process. Given a matrix $\bf{A}$, it produces a
low-rank approximation of the form ${\bf UBV}^T$, where $\bf{U}$ and $\bf{V}$
have orthonormal columns in exact arithmetic and $\bf{B}$ is block bidiagonal.
In finite precision, the columns of both ${\bf U}$ and ${\bf V}$ will be close
to orthonormal. Our algorithm is closely related to the randQB algorithms of
Yu, Gu, and Li (2018) in that the entries of $\bf{B}$ are incrementally
generated and the Frobenius norm approximation error may be efficiently
estimated. Our algorithm is therefore suitable for the fixed-accuracy problem,
and so is designed to terminate as soon as a user input error tolerance is
reached. Numerical experiments suggest that the block Lanczos method is
generally competitive with or superior to algorithms that use power iteration,
even when $\bf{A}$ has significant clusters of singular values.


Abstract:
  We study the transport property of Gaussian measures on Sobolev spaces of
periodic functions under the dynamics of the one-dimensional cubic fractional
nonlinear Schr\"{o}dinger equation. For the case of second-order dispersion or
greater, we establish an optimal regularity result for the quasi-invariance of
these Gaussian measures, following the approach by Debussche and Tsutsumi [15].
Moreover, we obtain an explicit formula for the Radon-Nikodym derivative and,
as a corollary, a formula for the two-point function arising in wave turbulence
theory. We also obtain improved regularity results in the weakly dispersive
case, extending those in [20]. Our proof combines the approach introduced by
Planchon, Tzvetkov and Visciglia [47] and that of Debussche and Tsutsumi [15].


Abstract:
  We report on preliminary results of a statistical study of student
performance in more than a decade of calculus-based introductory physics
courses. Treating average homework and test grades as proxies for student
effort and comprehension respectively, we plot comprehension versus effort in
an academic version of the astronomical Hertzsprung-Russell diagram (which
plots stellar luminosity versus temperature). We study the evolution of this
diagram with time, finding that the "academic main sequence" has begun to break
down in recent years as student achievement on tests has become decoupled from
homework grades. We present evidence that this breakdown is likely related to
the emergence of easily accessible online solutions to most textbook problems,
and discuss possible responses and strategies for maintaining and enhancing
student learning in the online era.


Abstract:
  A method for creating a vision-and-language (V&L) model is to extend a
language model through structural modifications and V&L pre-training. Such an
extension aims to make a V&L model inherit the capability of natural language
understanding (NLU) from the original language model. To see how well this is
achieved, we propose to evaluate V&L models using an NLU benchmark (GLUE). We
compare five V&L models, including single-stream and dual-stream models,
trained with the same pre-training. Dual-stream models, with their higher
modality independence achieved by approximately doubling the number of
parameters, are expected to preserve the NLU capability better. Our main
finding is that the dual-stream scores are not much different than the
single-stream scores, contrary to expectation. Further analysis shows that
pre-training causes the performance drop in NLU tasks with few exceptions.
These results suggest that adopting a single-stream structure and devising the
pre-training could be an effective method for improving the maintenance of
language knowledge in V&L extensions.


Abstract:
  We address a state-of-the-art reinforcement learning (RL) control approach to
automatically configure robotic prosthesis impedance parameters to enable
end-to-end, continuous locomotion intended for transfemoral amputee subjects.
Specifically, our actor-critic based RL provides tracking control of a robotic
knee prosthesis to mimic the intact knee profile. This is a significant advance
from our previous RL based automatic tuning of prosthesis control parameters
which have centered on regulation control with a designer prescribed robotic
knee profile as the target. In addition to presenting the complete tracking
control algorithm based on direct heuristic dynamic programming (dHDP), we
provide an analytical framework for the tracking controller with constrained
inputs. We show that our proposed tracking control possesses several important
properties, such as weight convergence of the learning networks, Bellman
(sub)optimality of the cost-to-go value function and control input, and
practical stability of the human-robot system under input constraint. We
further provide a systematic simulation of the proposed tracking control using
a realistic human-robot system simulator, the OpenSim, to emulate how the dHDP
enables level ground walking, walking on different terrains and at different
paces. These results show that our proposed dHDP based tracking control is not
only theoretically suitable, but also practically useful.


Abstract:
  The high energy Operator Product Expansion for the product of two
electromagnetic currents is extended to the sub-eikonal level in a rigorous
way. I calculate the impact factors for polarized and unpolarized structure
functions, define new distribution functions, and derive the evolution
equations for unpolarized and polarized structure functions in the flavor
singlet and non-singlet case.


Abstract:
  We investigate the diurnal modulation of the event rate for dark matter
scattering on solid targets arising from the directionally dependent defect
creation threshold energy. In particular, we quantify how this effect would
help in separating dark matter signal from the neutrino background. We perform
a benchmark analysis for a germanium detector and compute how the reach of the
experiment is affected by including the timing information of the scattering
events. We observe that for light dark matter just above the detection
threshold the magnitude of the annual modulation is enhanced. In this mass
range using either the annual or diurnal modulation information provides a
similar gain in the reach of the experiment, while the additional reach from
using both effects remains modest. Furthermore, we demonstrate that if the
background contains a feature exhibiting an annual modulation similar to the
one observed by DAMA experiment, the diurnal modulation provides for an
additional handle to separate dark matter signal from the background.


Abstract:
  Quasielastic scattering excitation function at large backward angle has been
measured for the weakly bound system, $^{7}$Li+$^{159}$Tb at energies around
the Coulomb barrier. The corresponding quasielastic barrier distribution has
been derived from the excitation function, both including and excluding the
$\alpha$-particles produced in the reaction. The centroid of the barrier
distribution obtained after inclusion of $\alpha$-particles was found to be
shifted higher in energy, compared to the distribution excluding the $\alpha
$-particles. The quasielastic data, excluding the $\alpha$-particles, have been
analyzed in the framework of continuum discretized coupled channel
calculations. The quasielastic barrier distribution for $^{7}$Li+$^{159}$Tb,
has also been compared with the fusion barrier distribution for the system.


Abstract:
  Rectification of interacting Brownian particles is investigated in a
two-dimensional asymmetric channel in the presence of an external periodic
driving force. The periodic driving force can break the thermodynamic
equilibrium and induces rectification of particles (or finite average
velocity). The spatial variation in the shape of the channel leads to entropic
barriers, which indeed control the rectification of particles. We find that by
simply tunning the driving frequency, driving amplitude, and shape of the
asymmetric channel, the average velocity can be reversed. Moreover, a short
range interaction force between the particles further enhances the
rectification of particles greatly. This interaction force is modeled as the
lubrication interaction. Interestingly, it is observed that there exists a
characteristic critical frequency $\Omega_c$ below which the rectification of
particles greatly enhances in the positive direction with increasing the
interaction strength; whereas, for the frequency above this critical value, it
greatly enhances in the negative direction with increasing the interaction
strength. Further, there exists an optimal value of the asymmetric parameter of
the channel for which the rectification of interacting particles is maximum.
These findings are useful in sorting out the particles and understanding the
diffusive behavior of small particles or molecules in microfluidic channels,
membrane pores, etc.


Abstract:
  Modern deep neural networks (DNNs) achieve highly accurate results for many
recognition tasks on overhead (e.g., satellite) imagery. One challenge however
is visual domain shifts (i.e., statistical changes), which can cause the
accuracy of DNNs to degrade substantially and unpredictably when tested on new
sets of imagery. In this work we model domain shifts caused by variations in
imaging hardware, lighting, and other conditions as non-linear pixel-wise
transformations; and we show that modern DNNs can become largely invariant to
these types of transformations, if provided with appropriate training data
augmentation. In general, however, we do not know the transformation between
two sets of imagery. To overcome this problem, we propose a simple real-time
unsupervised training augmentation technique, termed randomized histogram
matching (RHM). We conduct experiments with two large public benchmark datasets
for building segmentation and find that RHM consistently yields comparable
performance to recent state-of-the-art unsupervised domain adaptation
approaches despite being simpler and faster. RHM also offers substantially
better performance than other comparably simple approaches that are widely-used
in overhead imagery.


Abstract:
  Feature-based dynamic pricing is an increasingly popular model of setting
prices for highly differentiated products with applications in digital
marketing, online sales, real estate and so on. The problem was formally
studied as an online learning problem [Javanmard & Nazerzadeh, 2019] where a
seller needs to propose prices on the fly for a sequence of $T$ products based
on their features $x$ while having a small regret relative to the best --
"omniscient" -- pricing strategy she could have come up with in hindsight. We
revisit this problem and provide two algorithms (EMLP and ONSP) for stochastic
and adversarial feature settings, respectively, and prove the optimal
$O(d\log{T})$ regret bounds for both. In comparison, the best existing results
are $O\left(\min\left\{\frac{1}{\lambda_{\min}^2}\log{T},
\sqrt{T}\right\}\right)$ and $O(T^{2/3})$ respectively, with $\lambda_{\min}$
being the smallest eigenvalue of $\mathbb{E}[xx^T]$ that could be arbitrarily
close to $0$. We also prove an $\Omega(\sqrt{T})$ information-theoretic lower
bound for a slightly more general setting, which demonstrates that
"knowing-the-demand-curve" leads to an exponential improvement in feature-based
dynamic pricing.


Abstract:
  The interplay of different electronic phases underlies the physics of
unconventional superconductors. One of the most intriguing examples is a
high-Tc superconductor FeTe1-xSex: it undergoes both a topological transition,
linked to the electronic band inversion, and an electronic nematic phase
transition, associated with rotation symmetry breaking, around the same
critical composition xc where superconducting Tc peaks. At this regime, nematic
fluctuations and symmetry-breaking strain could have an enormous impact, but
this is yet to be fully explored. Using spectroscopic-imaging scanning
tunneling microscopy, we study the electronic nematic transition in FeTe1-xSex
as a function of composition. Near xc, we reveal the emergence of electronic
nematicity in nanoscale regions. Interestingly, we discover that
superconductivity is drastically suppressed in areas where static nematic order
is the strongest. By analyzing atomic displacement in STM topographs, we find
that small anisotropic strain can give rise to these strongly nematic localized
regions. Our experiments reveal a tendency of FeTe1-xSex near x~0.45 to form
puddles hosting static nematic order, suggestive of nematic fluctuations pinned
by structural inhomogeneity, and demonstrate a pronounced effect of anisotropic
strain on superconductivity in this regime.


Abstract:
  The near vanishing of the cosmological constant is one of the most puzzling
open problems in theoretical physics. We consider a system, the so-called
framid, that features a technically similar problem. Its stress-energy tensor
has a Lorentz-invariant expectation value on the ground state, yet there are no
standard, symmetry-based selection rules enforcing this, since the ground state
spontaneously breaks boosts. We verify the Lorentz invariance of the
expectation value in question with explicit one-loop computations. These,
however, yield the expected result only thanks to highly nontrivial
cancellations, which are quite mysterious from the low-energy effective theory
viewpoint.


Abstract:
  We demonstrate experimentally a method of varying the degree of
directionality in laser-induced molecular rotation. To control the ratio
between the number of clockwise and counter-clockwise rotating molecules (with
respect to a fixed laboratory axis), we change the polarization ellipticity of
the laser field of an optical centrifuge. The experimental data, supported by
the numerical simulations, show that the degree of rotational directionality
can be varied in a continuous fashion between unidirectional and bidirectional
rotation. The control can be executed with no significant loss in the total
number of rotating molecules. The technique could be used for studying the
effects of orientation of the molecular angular momentum on molecular
collisions and chemical reactions. It could also be utilized for controlling
magnetic and optical properties of gases, as well as for the enantioselective
detection of chiral molecules.


Abstract:
  Based on a general transport theory for non-reciprocal non-Hermitian systems
and a topological model that encompasses a wide range of previously studied
models, we (i) provide conditions for effects such as reflectionless and
transparent transport, lasing, and coherent perfect absorption, (ii) identify
which effects are compatible and linked with each other, and (iii) determine by
which levers they can be tuned independently. For instance, the directed
amplification inherent in the non-Hermitian skin effect does not enter the
spectral conditions for reflectionless transport, lasing, or coherent perfect
absorption, but allows to adjust the transparency of the system. In addition,
in the topological model the conditions for reflectionless transport depend on
the topological phase, but those for coherent perfect absorption do not. This
then allows us to establish a number of distinct transport signatures of
non-Hermitian, nonreciprocal, and topological behaviour, in particular (I)
reflectionless transport in a direction that depends on the topological phase,
(II) invisibility coinciding with the skin-effect phase transition of
topological edge states, and (III) coherent perfect absorption in a system that
is transparent when probed from one side.


Abstract:
  We obtain an upper bound for the number of critical points of the systole
function on $\mathcal{M}_g$. Besides, we obtain an upper bound for the number
of those critical points whose systole is smaller than a constant.


Abstract:
  We study the multimessenger signals from the merger of a black hole with a
magnetized neutron star using resistive magnetohydrodynamics simulations
coupled to full general relativity. We focus on a case with a 5:1 mass ratio,
where only a small amount of the neutron star matter remains post-merger, but
we nevertheless find that significant electromagnetic radiation can be powered
by the interaction of the neutron star's magnetosphere with the black hole. In
the lead-up to merger, strong twisting of magnetic field lines from the
inspiral leads to plasmoid emission and results in a luminosity in excess of
that expected from unipolar induction. We find that the strongest emission
occurs shortly after merger during a transitory period in which magnetic loops
form and escape the central region. The remaining magnetic field collimates
around the spin axis of the remnant black hole before dissipating, an
indication that, in more favorable scenarios (higher black hole spin/lower mass
ratio) with larger accretion disks, a jet would form.


Abstract:
  As a unique perovskite transparent oxide semiconductor, high-mobility
La-doped BaSnO3 films have been successfully synthesized by molecular beam
epitaxy and pulsed laser deposition. However, it remains a big challenge for
magnetron sputtering, a widely applied technique suitable for large-scale
fabrication, to grow high-mobility La-doped BaSnO3 films. Here, we developed a
method to synthesize high-mobility epitaxial La-doped BaSnO3 films (mobility up
to 121 cm2V-1s-1 at the carrier density ~ 4.0 x 10^20 cm-3 at room temperature)
directly on SrTiO3 single crystal substrates using high-pressure magnetron
sputtering. The structural and electrical properties of the La-doped BaSnO3
films were characterized by combined high-resolution X-ray diffraction, X-ray
photoemission spectroscopy, and temperature-dependent electrical transport
measurements. The room temperature electron mobility of La-doped BaSnO3 films
in this work is 2 to 4 times higher than the reported values of the films grown
by magnetron sputtering. Moreover, in the high carrier density range (n > 3 x
10^20 cm-3), the electron mobility value of 121 cm2V-1s-1 in our work is among
the highest values for all reported doped BaSnO3 films. It is revealed that
high argon pressure during sputtering plays a vital role in stabilizing the
fully relaxed films and inducing oxygen vacancies, which benefit the high
mobility at room temperature. Our work provides an easy and economical way to
massively synthesize high-mobility transparent conducting films for transparent
electronics.


Abstract:
  The 16-year old Blaise Pascal found a way to determine if 6 points lie on a
conic using a straightedge. Nearly 400 years later, we develop a method that
uses a straightedge to check whether 10 points lie on a plane cubic curve.