Abstract:
  Common domain adaptation techniques assume that the source domain and the
target domain share an identical label space, which is problematic since when
target samples are unlabeled we have no knowledge on whether the two domains
share the same label space. When this is not the case, the existing methods
fail to perform well because the additional unknown classes are also matched
with the source domain during adaptation. In this paper, we tackle the open set
domain adaptation problem under the assumption that the source and the target
label spaces only partially overlap, and the task becomes when the unknown
classes exist, how to detect the target unknown classes and avoid aligning them
with the source domain. We propose to utilize an instance-level reweighting
strategy for domain adaptation where the weights indicate the likelihood of a
sample belonging to known classes and to model the tail of the entropy
distribution with Extreme Value Theory for unknown class detection. Experiments
on conventional domain adaptation datasets show that the proposed method
outperforms the state-of-the-art models.


Abstract:
  Animal interval timing is often studied through the peak interval (PI)
procedure. In this procedure, the animal is rewarded for the first response
after a fixed delay from the stimulus onset, but on some trials, the stimulus
remains and no reward is given. The common methods and models to analyse the
response pattern describe it as break-run-break, a period of low rate response
followed by rapid responding, followed by a low rate of response. The study of
the pattern has found correlations between start, stop, and duration of the run
period that hold across species and experiment.
  It is commonly assumed that in order to achieve the statistics with a
pacemaker accumulator model it is necessary to have start and stop thresholds.
In this paper we will develop a new model that varies response rate in relation
to the likelihood of event occurrence, as opposed to a threshold, for changing
the response rate. The new model reproduced the start and stop statistics that
have been observed in 14 different PI experiments from 3 different papers. The
developed model is also compared to the Time-adaptive Drift-diffusion Model
(TDDM), the latest accumulator model subsuming the scalar expectancy theory
(SET), on all 14 data-sets. The results show that it is unnecessary to have
explicit start and stop thresholds or an internal equivalent to break-run-break
states to reproduce the individual trials statistics and population behaviour
and get the same break-run-break analysis results. The new model also produces
more realistic individual trials compared to TDDM.


Abstract:
  Background: Spin-triplet ($S=1$) proton-neutron (pn) pairing in nuclei has
been under debate. It is well known that the dynamical pairing affects the
nuclear matrix element of the Gamow-Teller (GT) transition and the double beta
decay. Purpose: We investigate the effect of the pn-pair interaction in the
$T=0, S=1$ channel on the low-lying spin-dipole (SD) transition. We then aim at
clarifying the distinction of the role in between the SD and GT transitions.
Method: We perform a three-body model calculation for the transition
${}^{80}\mathrm{Ni}\to{}^{80}\mathrm{Cu}$, where ${}^{78}\mathrm{Ni}$ is taken
as a core. The strength of the pair interaction is varied to see the effect on
the SD transition-strength distribution. To fortify the finding obtained by the
three-body model, we employ the nuclear energy-density functional method for
the SD transitions in several nuclei, where one can expect a strong effect.
Results: The effect of the $S=1$ pn-pair interaction depends on the spatial
overlap of the pn pair and the angular momentum of the valence nucleons; the
higher the angular momentum of the orbitals, the more significant the effect.
Conclusions: The dynamical $S=1$ pairing is effective even for SD states
although the spatial overlap of the pn pair can be smaller than GT states. The
SD transition involving high-$\ell$ orbitals with the same principal quantum
number is strongly affected by the dynamical $S=1$ pairing.


Abstract:
  Graph Neural Networks (GNNs) are a new and increasingly popular family of
deep neural network architectures to perform learning on graphs. Training them
efficiently is challenging due to the irregular nature of graph data. The
problem becomes even more challenging when scaling to large graphs that exceed
the capacity of single devices. Standard approaches to distributed DNN
training, such as data and model parallelism, do not directly apply to GNNs.
Instead, two different approaches have emerged in the literature: whole-graph
and sample-based training.
  In this paper, we review and compare the two approaches. Scalability is
challenging with both approaches, but we make a case that research should focus
on sample-based training since it is a more promising approach. Finally, we
review recent systems supporting sample-based training.


Abstract:
  Many potential applications of reinforcement learning (RL) require guarantees
that the agent will perform well in the face of disturbances to the dynamics or
reward function. In this paper, we prove theoretically that standard maximum
entropy RL is robust to some disturbances in the dynamics and the reward
function. While this capability of MaxEnt RL has been observed empirically in
prior work, to the best of our knowledge our work provides the first rigorous
proof and theoretical characterization of the MaxEnt RL robust set. While a
number of prior robust RL algorithms have been designed to handle similar
disturbances to the reward function or dynamics, these methods typically
require adding additional moving parts and hyperparameters on top of a base RL
algorithm. In contrast, our theoretical results suggest that MaxEnt RL by
itself is robust to certain disturbances, without requiring any additional
modifications. While this does not imply that MaxEnt RL is the best available
robust RL method, MaxEnt RL does possess a striking simplicity and appealing
formal guarantees.


Abstract:
  The Internet of Value (IOV) with its distributed ledger technology (DLT)
underpinning has created new forms of lending markets. As an integral part of
the decentralised finance (DeFi) ecosystem, lending protocols are gaining
tremendous traction, holding an aggregate liquidity supply of over $40 billion
at the time of writing. In this paper, we enumerate the challenges of
traditional money markets led by banks and lending platforms, and present
advantageous characteristics of DeFi lending protocols that might help resolve
deep-rooted issues in the conventional lending environment. With the examples
of Maker, Compound and Aave, we describe in detail the mechanism of DeFi
lending protocols. We discuss the persisting reliance of DeFi lending on the
traditional financial system, and conclude with the outlook of the lending
market in the IOV era.


Abstract:
  Highly nonclassical character of optical quantum detectors, such as
single-photon detectors, is essential for preparation of quantum states of
light and a vast majority of applications in quantum metrology and quantum
information processing. Therefore, it is both fundamentally interesting and
practically relevant to investigate the nonclassical features of optical
quantum measurements. Here we propose and experimentally demonstrate a
procedure for direct certification of quantum non-Gaussianity and Wigner
function negativity, two crucial nonclassicality levels, of photonic quantum
detectors. Remarkably, we characterize the highly nonclassical properties of
the detector by probing it with only two classical thermal states and a vacuum
state. We experimentally demonstrate the quantum non-Gaussianity of
single-photon avalanche diode even under the presence of background noise, and
we also certify the negativity of the Wigner function of this detector. Our
results open the way for direct benchmarking of photonic quantum detectors with
a few measurements on classical states.


Abstract:
  Massive single-cell profiling efforts have accelerated our discovery of the
cellular composition of the human body, while at the same time raising the need
to formalise this new knowledge. Here, we review current cell ontology efforts
to harmonise and integrate different sources of annotations of cell types and
states. We illustrate with examples how a unified ontology can consolidate and
advance our understanding of cell types across scientific communities and
biological domains.


Abstract:
  A model of unified dark matter and dark energy based on a Dynamical Spacetime
Theory (DST) is studied. By introducing a Dynamical Spacetime vector field
$\chi_\mu$, a conservation of an energy momentum tensor $T^{\mu\nu}_{(\chi)}$
emerges. The action allows for two different potentials, while one represents a
dark energy. For constant potentials, the cosmological solution yields a non
singular bouncing solutions that rapidly approaches the $\Lambda$CDM model. The
Dynamical Time corresponds to the cosmic time as well. The theory fits with the
late time expansion data of the Universe. With higher dimensions a mechanism
for inflation and compactification appears, with exponential growth for some
dimensions and exponential contraction of the others. By demanding that the
Dynamical Spacetime vector field be a gradient of a scalar the DST becomes a
theory with diffusive interacting, which asymptotically returns to the
$\Lambda$CDM model as a stable point. These formulations lead to scenarios
which address our understanding about the origin of the Universe.


Abstract:
  With the COVID-19 pandemic entering a second phase and vaccination strategies
being applied by countries and governments worldwide, there is an increasing
expectation by people to return to normal life. There is currently a debate
about immunity passports, privacy, and the enablement of individuals to safely
enter everyday social life, workplace, and travel. Such digital immunity
passports and vaccination certificates should meet people's expectations for
privacy while enabling them to present to 3rd party verifiers tamper-evident
credentials. This paper provides a comprehensive answer to the technological,
ethical and security challenges, by proposing an architecture that provides to
individuals, employers, and government agencies, a digital, decentralized,
portable, immutable, and non-refutable health status cryptographic proof. It
can be used to evaluate the risk of allowing individuals to return to work,
travel, and public life activities.


Abstract:
  In this paper, we show that for sufficiently strong atomic interactions,
there exist analytical solutions of current-carrying nonlinear Bloch states at
the Brillouin zone edge to the model of spin-orbit-coupled Bose-Einstein
condensates (BECs) with symmetric spin interaction loaded into optical
lattices. These simple but generic exact solutions provide an analytical
demonstration of some intriguing properties which have neither an analog in the
regular BEC lattice systems nor in the uniform spin-orbit-coupled BEC systems.
It is an analytical example for understanding the superfluid and other related
properties of the spin-orbit-coupled BEC lattice systems.


Abstract:
  Insertion-deletion codes (insdel codes for short) play an important role in
synchronization error correction. The higher the minimum insdel distance, the
more insdel errors the code can correct. Haeupler and Shahrasbi established the
Singleton bound for insdel codes: the minimum insdel distance of any $[n,k]$
linear code over $\mathbb{F}_q$ satisfies $d\leq2n-2k+2.$ There have been some
constructions of insdel codes through Reed-Solomon codes with high
capabilities, but none has come close to this bound. Recently, Do Duc {\it et
al.} showed that the minimum insdel distance of any $[n,k]$ Reed-Solomon code
is no more than $2n-2k$ if $q$ is large enough compared to the code length $n$;
optimal codes that meet the new bound were also constructed explicitly. The
contribution of this paper is twofold. We first show that the minimum insdel
distance of any $[n,k]$ linear code over $\mathbb{F}_q$ satisfies $d\leq2n-2k$
if $n>k>1$. This result improves and generalizes the previously known results
in the literature. We then give a sufficient condition under which the minimum
insdel distance of a two-dimensional Reed-Solomon code of length $n$ over
$\mathbb{F}_q$ is exactly equal to $2n-4$. As a consequence, we show that the
sufficient condition is not hard to achieve; we explicitly construct an
infinite family of optimal two-dimensional Reed-Somolom codes meeting the
bound.


Abstract:
  This paper examines the dynamics of saturated vapour in a corner formed by
two walls. It is shown that, if the angle $\phi$ between the walls is
sufficiently small, the vapor becomes unstable and spontaneous condensation
occurs near the corner, giving rise to an ever-growing liquid meniscus. The
diffuse-interface model and the lubrication approximation are used to
demonstrate that the condensation occurs if and only if $\phi+2\theta<\pi$,
where $\theta$ is the contact angle corresponding to the fluid-solid
combination under consideration. This criterion has a simple physical
explanation: if it holds, the meniscus surface is concave -- hence, the
so-called Kelvin effect causes condensation. If the near-vertex region of the
corner is smoothed, the instability can be triggered off only by a
\emph{finite}-size perturbation, with enough liquid to cover the smoothed aria
by a microscopically-thin liquid film.


Abstract:
  Motivated by a wide range of assemble-to-order systems and systems of the
collaborative economy applications, we introduce a stochastic matching model on
hypergraphs and multigraphs, extending the model introduced by Mairesse and
Moyal 2016. In this thesis, the stochastic matching model
$(\mathbb{S},\Phi,\mu)$ on general graph structures are defined as follows:
given a compatibility general graph structure
$\mathbb{S}=(\mathcal{V},\mathcal{S})$ which of a set of nodes denoted by
$\mathcal{V}$ that represent the classes of items and by a set of edges denoted
by $\mathcal{S}$ that allows matching between different classes of items. Items
arrive at the system at a random time, by a sequence (assumed to be $i.i.d.$)
that consists of different classes of $\mathcal{V}$, and request to be matched
due to their compatibility according to $\mathbb{S}$. The compatibility by
groups of two or more (hypergraphical cases) and by groups of two with
possibilities of matching between the items of the same classes (multigraphical
cases). The unmatched items are stored in the system and wait for a future
compatible item and as soon as they are matched they leave it together. Upon
arrival, an item may find several possible matches, the items that leave the
system depend on a matching policy $\Phi$ to be specified. We study the
stability of the stochastic matching model on hypergraphs, for different
hypergraphical topologies. Then, the stability of the stochastic matching model
on multigraphs using the maximal subgraph and minimal blow-up to distinguish
the zone of stability.


Abstract:
  A line-of-sight towards the Galactic Center (GC) offers the largest number of
potentially habitable systems of any direction in the sky. The Breakthrough
Listen program is undertaking the most sensitive and deepest targeted SETI
surveys towards the GC. Here, we outline our observing strategies with Robert
C. Byrd Green Bank Telescope (GBT) and Parkes telescope to conduct 600 hours of
deep observations across 0.7--93 GHz. We report preliminary results from our
survey for ETI beacons across 1--8 GHz with 7.0 and 11.2 hours of observations
with Parkes and GBT, respectively. With our narrowband drifting signal search,
we were able to place meaningful constraints on ETI transmitters across 1--4
GHz and 3.9--8 GHz with EIRP limits of $\geq$4$\times$10$^{18}$ W among 60
million stars and $\geq$5$\times$10$^{17}$ W among half a million stars,
respectively. For the first time, we were able to constrain the existence of
artificially dispersed transient signals across 3.9--8 GHz with EIRP
$\geq$1$\times$10$^{14}$ W/Hz with a repetition period $\leq$4.3 hours. We also
searched our 11.2 hours of deep observations of the GC and its surrounding
region for Fast Radio Burst-like magnetars with the DM up to 5000 pc cm$^{-3}$
with maximum pulse widths up to 90 ms at 6 GHz. We detected several hundred
transient bursts from SGR J1745$-$2900, but did not detect any new transient
burst with the peak luminosity limit across our observed band of
$\geq$10$^{31}$ erg s$^{-1}$ and burst-rate of $\geq$0.23 burst-hr$^{-1}$.
These limits are comparable to bright transient emission seen from other
Galactic radio-loud magnetars, constraining their presence at the GC.


Abstract:
  Systems for transport and processing of granular media are challenging to
analyse, operate and optimise. In the mining and mineral processing industries
these systems are chains of processes with complex interplay between the
equipment, control, and the processed material. The material properties have
natural variations that are usually only known at certain locations. Therefore,
we explore a material-oriented approach to digital twins with a particle
representation of the granular media. In digital form, the material is treated
as pseudo-particles, each representing a large collection of real particles of
various sizes, shapes and, mineral properties. Movements and changes in the
state of the material are determined by the combined data from control systems,
sensors, vehicle telematics, and simulation models at locations where no real
sensors can see. The particle-based representation enables material tracking
along the chain of processes. Each digital particle can act as a carrier of
observational data generated by the equipment as it interacts with the real
material. This makes it possible to better learn material properties from
process observations, and to predict the effect on downstream processes. We
test the technique on a mining simulator and demonstrate analysis that can be
performed using data from cross-system material tracking.


Abstract:
  We studied the creep motion of granular materials in a gradient potential
field that is created using a slow spin-up experiment device. Natural sand
confined in the acrylic box is spun up by a controlled turntable and the
surface flows are captured using video-based measurements. Various spin-up
accelerations were considered to understand the responses of creep motion on
different accelerating paths. Convergent behaviors in the morphological change
of sand surface were observed in the final steady state. To quantify the
quasi-static spin-up process, we examined the net flux and the surface slope as
a function of the spin rate and offset from the rotation axis. Evolution of
sand creep motion demonstrated behaviors similar to regolith migration in
numeric simulations, showing intermittency like general sheared granular
systems. We noticed the sand surface approaches criticality as the spin-up
proceeding, consistent with the observation that top-shaped asteroids near
limiting spin rate take on critical shape. Comparisons to large-scale numeric
simulations and analytical solutions reveal underlying similarities between our
experiments and the million-year evolution of asteroid regolith under YORP
acceleration, which raises the possibility of studying asteroid surface
processes in laboratory analogue experiments.


Abstract:
  Weakly-supervised text classification aims to induce text classifiers from
only a few user-provided seed words. The vast majority of previous work assumes
high-quality seed words are given. However, the expert-annotated seed words are
sometimes non-trivial to come up with. Furthermore, in the weakly-supervised
learning setting, we do not have any labeled document to measure the seed
words' efficacy, making the seed word selection process "a walk in the dark".
In this work, we remove the need for expert-curated seed words by first mining
(noisy) candidate seed words associated with the category names. We then train
interim models with individual candidate seed words. Lastly, we estimate the
interim models' error rate in an unsupervised manner. The seed words that yield
the lowest estimated error rates are added to the final seed word set. A
comprehensive evaluation of six binary classification tasks on four popular
datasets demonstrates that the proposed method outperforms a baseline using
only category name seed words and obtained comparable performance as a
counterpart using expert-annotated seed words.


Abstract:
  Quantum theory of a test field on a quantum cosmological spacetime may be
viewed as a theory of the test field on an emergent classical background. In
such a case, the resulting dressed metric for the field propagation is a
function of the quantum fluctuations of the original geometry. When the
backreaction is negligible, massive modes can experience an anisotropic Bianchi
type I background. The field modes propagating on such a
quantum-gravity-induced spacetime can then unveil interesting phenomenological
consequences of the super-Planckian scales, such as gravitational particle
production. The aim of this paper is to address the issue of gravitational
particle production associated to the massive modes in such an anisotropic
dressed spacetime. By imposing a suitable adiabatic condition on the vacuum
state and computing the energy density of the created particles, the
significance of the particle production on the dynamics of the universe in
Planck era is discussed.


Abstract:
  The compositions of planet-forming disks are set by a combination of material
inherited from the interstellar medium and material reprocessed during disk
formation and evolution. Indeed, comets and primitive meteorites exhibit
interstellar-like isotopic ratios and/or volatile compositions, supporting that
some pristine material was incorporated intact into icy planetesimals in the
Solar Nebula. To date, the survival of volatile interstellar material in the
disk stage has not been modeled using realistic disk physics. Here, we present
a modeling framework to track the destruction of interstellar ices on dust
grains undergoing transport processes within a disk, with a particular focus on
explaining the incorporation of pristine material into icy planetesimals. We
find it is difficult to explain inheritance through the local assembly of
comets, as ice destruction is rapid for small (<10um) grains in the inner few
tens of au. Instead, a plausible pathway to inheritance is to form pebbles at
larger disk radii, which then drift inwards to the comet-forming zone with
their ices mostly preserved. Small grains beyond ~100 au can experience ice
photodissociation at the tens of percent level, however little of the ice is
actually lost from the grain, likely making this a robust site for in situ ice
chemistry. Our models also indicate that many complex organic species should
survive passage through the disk intact. This raises the possibility that
organics synthesized in the interstellar medium can be delivered to terrestrial
planets by icy body impact and thus potentially participate in origins of life
chemistry.


Abstract:
  Pauli blocking of spontaneous emission is responsible for the stability of
atoms. Higher electronic orbitals cannot decay to lower-lying states if they
are already occupied -- this is Pauli blocking due to occupation of internal
states. Pauli blocking also occurs when free atoms scatter light elastically
(Rayleigh scattering) and the final external momentum states are already
occupied. A suppression of the total rate of light scattering requires a
quantum-degenerate Fermi gas with a Fermi energy larger than the photon recoil
energy. This has been predicted more than 30 years ago, but never realized.
Here we report the creation of a dense Fermi gas of ultracold lithium atoms and
show that at low temperatures light scattering is suppressed. We also explore
the suppression of inelastic light scattering when two colliding atoms emit
light shifted in frequency.


Abstract:
  It is shown how the extended conformal Einstein field equations and a gauge
based on the properties of conformal geodesics can be used to analyse the
non-linear stability of Einstein spaces with negative scalar curvature. This
class of spacetimes admits a smooth conformal extension with a spacelike
conformal boundary. Central to the analysis is the use of conformal Gaussian
systems to obtain a hyperbolic reduction of the conformal Einstein field
equations for which standard Cauchy stability results for symmetric hyperbolic
systems can be employed. The use of conformal methods allows to rephrase the
question of global existence of solutions to the Einstein field equations into
considerations of finite existence time for the conformal evolution system.


Abstract:
  The specific energy loss (dE/dx) resolution is critical for particle
identification and separation in TPC. In connection with the R&D for the TPC at
the Circular Electron Positron Collider (CEPC), we find the laser ionisation
obeys the Gaussian distribution theoretically and experimentally. Based on
this, we develop a novel method using a 266 nm UV laser to measure the dE/dx
resolution. Comparing to the traditional methods with particle beams or cosmic
rays, the novel method with 266 nm UV laser is more convenient and flexible. We
verify the method's feasibility with a TPC prototype and extrapolate the dE/dx
resolution to the CEPC TPC. The estimation of the dE/dx resolution is without
the magnetic field.


Abstract:
  Linear Kinematic Features (LKFs) are found everywhere in the Arctic sea ice
cover. They are strongly localized deformations often associated with the
formation of leads and pressure ridges. Viscous-plastic sea ice models start to
generate LKFs at high spatial grid resolution, typically with a grid spacing
below 5 km. Besides grid spacing, other aspects of a numerical implementation,
such as discretization details, may affect the number and definition of
simulated LKFs. To explore these effects, the solutions of sea ice models with
different grid spacings, mesh types, and numerical discretization techniques
are compared in an idealized configuration, which could also serve as a
benchmark problem in the future. The A, B, and C-grid discretizations of sea
ice dynamics on quadrilateral meshes leads to a similar number of LKFs as the
A-grid approximation on triangular meshes (with the same number of vertices).
The discretization on an Arakawa CD-grid on both structured quadrilateral and
triangular meshes resolves the same number of LKFs as conventional Arakawa
A-grid, B-grid, and C-grid approaches, but on two times coarser meshes. This is
due to the fact that the CD-grid approach has a higher number of degrees of
freedom to discretize the velocity field. Due to its enhanced resolving
properties, the CD-grid discretization is an attractive alternative to
conventional discretizations.


Abstract:
  To mitigate the spread of COVID-19 pandemic, decision-makers and public
authorities have announced various non-pharmaceutical policies. Analyzing the
causal impact of these policies in reducing the spread of COVID-19 is important
for future policy-making. The main challenge here is the existence of
unobserved confounders (e.g., vigilance of residents). Besides, as the
confounders may be time-varying during COVID-19 (e.g., vigilance of residents
changes in the course of the pandemic), it is even more difficult to capture
them. In this paper, we study the problem of assessing the causal effects of
different COVID-19 related policies on the outbreak dynamics in different
counties at any given time period. To this end, we integrate data about
different COVID-19 related policies (treatment) and outbreak dynamics (outcome)
for different United States counties over time and analyze them with respect to
variables that can infer the confounders, including the covariates of different
counties, their relational information and historical information. Based on
these data, we develop a neural network based causal effect estimation
framework which leverages above information in observational data and learns
the representations of time-varying (unobserved) confounders. In this way, it
enables us to quantify the causal impact of policies at different
granularities, ranging from a category of policies with a certain goal to a
specific policy type in this category. Besides, experimental results also
indicate the effectiveness of our proposed framework in capturing the
confounders for quantifying the causal impact of different policies. More
specifically, compared with several baseline methods, our framework captures
the outbreak dynamics more accurately, and our assessment of policies is more
consistent with existing epidemiological studies of COVID-19.


Abstract:
  Keystroke dynamics can be used to analyze the way that users type by
measuring various aspects of keyboard input. Previous work has demonstrated the
feasibility of user authentication and identification utilizing keystroke
dynamics. In this research, we consider a wide variety of machine learning and
deep learning techniques based on fixed-text keystroke-derived features, we
optimize the resulting models, and we compare our results to those obtained in
related research. We find that models based on extreme gradient boosting
(XGBoost) and multi-layer perceptrons (MLP)perform well in our experiments. Our
best models outperform previous comparable research.


Abstract:
  Transformer based knowledge tracing model is an extensively studied problem
in the field of computer-aided education. By integrating temporal features into
the encoder-decoder structure, transformers can processes the exercise
information and student response information in a natural way. However, current
state-of-the-art transformer-based variants still share two limitations. First,
extremely long temporal features cannot well handled as the complexity of
self-attention mechanism is O(n2). Second, existing approaches track the
knowledge drifts under fixed a window size, without considering different
temporal-ranges. To conquer these problems, we propose MUSE, which is equipped
with multi-scale temporal sensor unit, that takes either local or global
temporal features into consideration. The proposed model is capable to capture
the dynamic changes in users knowledge states at different temporal-ranges, and
provides an efficient and powerful way to combine local and global features to
make predictions. Our method won the 5-th place over 3,395 teams in the Riiid
AIEd Challenge 2020.


Abstract:
  We introduce a new concept of dissipative varifold solution to models of two
phase compressible viscous fluids. In contrast with the existing approach based
on the Young measure description, the new formulation is variational combining
the energy and momentum balance in a single inequality. We show the existence
of dissipative varifold solutions for a large class of general viscous fluids
with non--linear dependence of the viscous stress on the symmetric velocity
gradient.


Abstract:
  We formulate dynamical phase transitions in subsystems embedded in larger
quantum systems. Introducing the entanglement echo as an overlap of the initial
and instantaneous entanglement ground states, we show its analytic structure
after a quench provides natural definition of dynamical phase transitions in
the subsystem. These transitions come in two varieties, the entanglement-type
transitions and the bulk-type Loschmidt transitions. The entanglement-type
transitions arise from periodic reorganization of quantum correlations between
the subsystem and its environment, manifesting in instantaneous entanglement
ground state degeneracies. Furthermore, the entanglement echo distinguishes the
direction of the quench, resolves spatially distinct dynamical phase
transitions for non-uniform quenches and give rise to sharply-defined
transitions for mixed initial states. We propose an experimental probe to
identify entanglement-type transitions through temporal changes in subsystem
fluctuations.


Abstract:
  This work presents an ab-initio study of a few-layers hexagonal boron nitride
(hBN) and hBN-graphene heterostructure sandwiched between Ni(111) layers. The
aim of this study is to understand the electron transmission process through
the interface. Spin-polarized density functional theory calculations and
transmission probability calculations were conducted on Ni(111)/$n$hBN/Ni(111)
with $n$ = 2, 3, 4, and 5 as well as on Ni(111)/hBN-Gr-hBN/Ni(111). Slabs with
magnetic alignment in an anti-parallel configuration (APC) and parallel
configuration (PC) were considered. The pd-hybridizations at both the upper and
lower interfaces between the Ni slabs and hBN were found to stabilize the
system. The Ni/nhBN/Ni magnetic tunnel junction (MTJ) was found to exhibit a
high tunneling magnetoresistance (TMR) ratio at ~0.28 eV for $n$ = 2 and 0.34
eV for $n$ > 2, which are slightly higher than the Fermi energy. The observed
shifting of this high TMR ratio originates from the transmission of electrons
through the surface states of the $d_{z^2}$-orbital of Ni atoms at interfaces
which are hybridized with the $p_z$-orbital of N atoms. In the case of $n$ > 2,
the proximity effect causes an evanescent wave, contributing to decreasing
transmission probability but increasing the TMR ratio. However, TMR ratio, as
well as transmission probability, was found to be increased, by replacing the
unhybridized hBN layer of the Ni/3hBN/Ni MTJ with graphene, thus becoming
Ni/hBN-Gr-hBN/Ni. A TMR ratio as high as ~1200% was observed at an energy of
0.34 eV, which is higher than the Fermi energy. Furthermore, a design is
proposed for a device based on a new reading mechanism using the high TMR
observed just above the Fermi energy level.


Abstract:
  We give a detailed presentation of the theory and numerical implementation of
an expression for the adiabatic energy flux in extended systems, derived from
density-functional theory. This expression can be used to estimate the heat
conductivity from equilibrium ab initio molecular dynamics, using the
Green-Kubo linear response theory of transport coefficients. Our expression is
implemented in an open-source component of the QE suite of computer codes for
quantum mechanical materials modelling, which is being made publicly available.


Abstract:
  We propose a method to reveal axions and axion-like particles based on
interferometric measurement of neutron beams. We consider an interferometer in
which the neutron beam is split in two sub-beams propagating in regions with
differently oriented magnetic fields. The beam paths and the strength of the
magnetic fields are set in such a way that all the contributions to the phase
difference but the one due to axion-induced interactions are removed. The
resulting phase difference is directly related to the presence of axions. Our
results show that such a phase is in principle detectable with neutron
interferometry, possibly proving the existence of axions and axion-like
particles.


Abstract:
  We provide a combined theoretical and experimental study of the electronic
structure and the optical absorption edge of the orthorhombic perovskite
LaInO$_{3}$. Employing density-functional theory and many-body perturbation
theory, we predict a direct electronic quasiparticle band gap of about 5 eV and
an effective electron (hole) mass of 0.31 (0.48) m$_{0}$. We find the
lowest-energy excitation at 0.2 eV below the fundamental gap, reflecting a
sizeable electron-hole attraction. Since the transition from the valence band
maximum (VBM, $\Gamma$ point) is, however, dipole forbidden the onset is
characterized by weak excitations from transitions around it. The first intense
excitation appears about 0.32 eV above. Interestingly, this value coincides
with an experimental value obtained by ellipsometry (4.80 eV) which is higher
than the onset from optical absorption spectroscopy (4.35 eV). The latter
discrepancy is attributed to the fact that the weak transitions that define the
optical gap are not resolved by the ellipsometry measurement. The absorption
edge shows a strong dependency on the light polarization, reflecting the
character of the involved valence states. Temperature-dependent measurements
show a redshift of the optical gap by about 120 meV by increasing the
temperature from 5 to 300 K. Renormalization due to zero-point vibrations is
extrapolated from the latter measurement to amount to 150 meV. By adding the
excitonic binding energy of 0.2 eV obtained theoretically to the experimental
optical absorption onset, we determine the fundamental band gap at room
temperature to be 4.55 eV.


Abstract:
  We investigate the coexistence of underlay spectrum sharing in cell-free
massive multiple-input multiple-output (MIMO) systems. A primary system with
geographically distributed primary access points (P-APs) serves a multitude of
primary users (PUs), while a secondary system serves a large number of
secondary users (SUs) in the same primary/licensed spectrum by exploiting the
underlay spectrum sharing. To mitigate the secondary co-channel interference
inflected at PUs, stringent secondary transmit power constraints are defined
for the secondary access points (S-APs). A generalized pilots sharing scheme is
used to locally estimate the uplink channels at P-APs/S-APs, and thereby,
conjugate precoders are adopted to serve PUs/SUs in the same time-frequency
resource element. Moreover, the effect of a user-centric AP clustering scheme
is investigated by assigning a suitable set of APs to a particular user. The
impact of estimated downlink (DL) channels at PUs/SUs via DL pilots beamformed
by P-APs/S-APs is investigated. The achievable primary/secondary rates at
PUs/SUs are derived for the statistical DL and estimated DL CSI cases.
User-fairness for PUs/SUs is achieved by designing efficient transmit power
control policies based on a multi-objective optimization problem formulation of
joint underlay spectrum sharing and max-min criteria. The proposed orthogonal
multiple-access based analytical framework is also extended to facilitate
non-orthogonal multiple-access. Our analysis and numerical results manifest
that the primary/secondary performance of underlay spectrum sharing can be
boosted by virtue of the average reduction of transmit powers/path-losses,
uniform coverage/service, and macro-diversity gains, which are inherent to
distributed transmissions/receptions of cell-free massive MIMO.


Abstract:
  The resistance to failure through tearing is a crucial mechanical property
for the application of different elastomers. In this work, graphene
nanoplatelets (GNPs) were introduced into a fluoroelastomer (FKM) matrix with
the aim of improving its tear resistance. The fracture energy through tearing
was evaluated using the pure shear test. It was found that the tearing energy
increased linearly with the volume fraction of the GNPs. At the maximum GNP
content, the tearing resistance was 3 times higher, suggesting efficient
toughening from the GNPs. Theoretical analysis of the micromechanics was
conducted by considering debonding and pull-out of the nanoplatelets as
possible toughening mechanisms. It was determined quantitatively that the main
toughening mechanism was debonding of the interface rather than pull-out. The
formation of cavities at flake ends during the deformation, as confirmed by
scanning electron microscopy, was found to contribute to the remarkably high
interfacial debonding energy (~1 kJ/m2).


Abstract:
  Recently, van der Waals heterostructure has attracted interest both
theoretically and experimentally for their potential applications in
photoelectronic devices, photovoltaic devices, plasmonic devices and
photocatalysis. Inspired by this, we design a lepidocrocite-type TiO2/GaSe
heterostructure. Via first-principles simulations, we show that such a
heterostructure is a direct bandgap semiconductor with a strong and broad
optical absorption, ranging from visible light to UV region, exhibiting its
potential application in photoelectronic and photovoltaic devices. With the
planar-averaged electron density difference and Bader charge analysis, the
heterostructure shows a strong capacity of enhancing the charge redistribution
especially at the interface, prolonging the lifetime of excitons, and hence
improving photocatalytic performance. By applying biaxial strain and interlayer
coupling, the heterostructure exhibits a direct-indirect bandgap transition and
shows a potential for mechanical sensors due to the smooth and linear variation
of bandgaps. Furthermore, our result indicates that a lower interlayer distance
leads to a stronger charge redistribution. The calculation of irradiating
ultrafast on the heterostructure further reveals a semiconductor-metal
transition for the heterostructure. Moreover, we find an enhanced induced
plasmonic current in the heterostructure under both x-polarized and z-polarized
laser, which is beneficial to plasmonic devices designs. Our research provides
valuable insight in applying the lepidocrocite-type TiO2/GaSe heterostructure
in photoelectronic, photovoltaic, photocatalytic, mechanical sensing and
plasmonic realms.


Abstract:
  In the first part of this paper we analyzed finite non-deterministic matrix
semantics for propositional non-normal modal logics as an alternative to the
standard Kripke's possible world semantics. This kind of modal systems
characterized by finite non-deterministic matrices was originally proposed by
Ju. Ivlev in the 70's. The aim of this second paper is to introduce a formal
non-deterministic semantical framework for the quantified versions of some
Ivlev-like non-normal modal logics. It will be shown that several well-known
controversial issues of quantified modal logics, relative to the identity
predicate, Barcan's formulas, and de dicto and de re modalities, can be tackled
from a new angle within the present framework.


Abstract:
  In this paper, we explore the effects of language variants, data sizes, and
fine-tuning task types in Arabic pre-trained language models. To do so, we
build three pre-trained language models across three variants of Arabic: Modern
Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a
fourth language model which is pre-trained on a mix of the three. We also
examine the importance of pre-training data size by building additional models
that are pre-trained on a scaled-down set of the MSA variant. We compare our
different models to each other, as well as to eight publicly available models
by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest
that the variant proximity of pre-training data to fine-tuning data is more
important than the pre-training data size. We exploit this insight in defining
an optimized system selection model for the studied tasks.


Abstract:
  Predicting the final ischaemic stroke lesion provides crucial information
regarding the volume of salvageable hypoperfused tissue, which helps physicians
in the difficult decision-making process of treatment planning and
intervention. Treatment selection is influenced by clinical diagnosis, which
requires delineating the stroke lesion, as well as characterising cerebral
blood flow dynamics using neuroimaging acquisitions. Nonetheless, predicting
the final stroke lesion is an intricate task, due to the variability in lesion
size, shape, location and the underlying cerebral haemodynamic processes that
occur after the ischaemic stroke takes place. Moreover, since elapsed time
between stroke and treatment is related to the loss of brain tissue, assessing
and predicting the final stroke lesion needs to be performed in a short period
of time, which makes the task even more complex. Therefore, there is a need for
automatic methods that predict the final stroke lesion and support physicians
in the treatment decision process. We propose a fully automatic deep learning
method based on unsupervised and supervised learning to predict the final
stroke lesion after 90 days. Our aim is to predict the final stroke lesion
location and extent, taking into account the underlying cerebral blood flow
dynamics that can influence the prediction. To achieve this, we propose a
two-branch Restricted Boltzmann Machine, which provides specialized data-driven
features from different sets of standard parametric Magnetic Resonance Imaging
maps. These data-driven feature maps are then combined with the parametric
Magnetic Resonance Imaging maps, and fed to a Convolutional and Recurrent
Neural Network architecture. We evaluated our proposal on the publicly
available ISLES 2017 testing dataset, reaching a Dice score of 0.38, Hausdorff
Distance of 29.21 mm, and Average Symmetric Surface Distance of 5.52 mm.


Abstract:
  The motion of Brownian particles in nonlinear baths, such as, e.g.,
viscoelastic fluids, is of great interest. We theoretically study a simple
model for such bath, where two particles are coupled via a sinusoidal
potential. This model, which is an extension of the famous Prandtl Tomlinson
model, has been found to reproduce some aspects of recent experiments, such as
shear-thinning and position oscillations [J. Chem. Phys. {\bf 154}, 184904
(2021)]. Analyzing this model in detail, we show that the predicted behavior of
position oscillations agrees qualitatively with experimentally observed trends;
(i) oscillations appear only in a certain regime of velocity and trap stiffness
of the confining potential, and (ii), the amplitude and frequency of
oscillations increase with driving velocity, the latter in a linear fashion.
Increasing the potential barrier height of the model yields a rupture
transition as a function of driving velocity, where the system abruptly changes
from a mildly driven state to a strongly driven state. The frequency of
oscillations scales as $(v_0-v_0^*)^{1/2}$ near the rupture velocity $v_0^*$,
found for infinite trap stiffness. Investigating the (micro-)viscosity for
different parameter ranges, we note that position oscillations leave their
signature by an additional (mild) plateau in the flow curves, suggesting that
oscillations influence the micro-viscosity. For a time-modulated driving, the
mean friction force of the driven particle shows a pronounced resonance
behavior, i.e, it changes strongly as a function of driving frequency. The
model has two known limits: For infinite trap stiffness, it can be mapped to
diffusion in a tilted periodic potential. For infinite bath friction, the
original Prandtl Tomlinson model is recovered. We find that the flow curve of
the model (roughly) crosses over between these two limiting cases.


Abstract:
  As a part of Advanced Driver Assistance Systems (ADASs), Consensus-based
Speed Advisory Systems (CSAS) have been proposed to recommend a common speed to
a group of vehicles for specific application purposes, such as emission control
and energy management. With Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure
(V2I) technologies and advanced control theories in place, state-of-the-art
CSAS can be designed to get an optimal speed in a privacy-preserving and
decentralized manner. However, the current method only works for specific cost
functions of vehicles, and its execution usually involves many algorithm
iterations leading long convergence time. Therefore, the state-of-the-art
design method is not applicable to a CSAS design which requires real-time
decision making. In this paper, we address the problem by introducing MPC-CSAS,
a Multi-Party Computation (MPC) based design approach for privacy-preserving
CSAS. Our proposed method is simple to implement and applicable to all types of
cost functions of vehicles. Moreover, our simulation results show that the
proposed MPC-CSAS can achieve very promising system performance in just one
algorithm iteration without using extra infrastructure for a typical CSAS.


Abstract:
  We study the gradient flow for a relaxed approximation to the
Kullback-Leibler (KL) divergence between a moving source and a fixed target
distribution. This approximation, termed the KALE (KL approximate lower-bound
estimator), solves a regularized version of the Fenchel dual problem defining
the KL over a restricted class of functions. When using a Reproducing Kernel
Hilbert Space (RKHS) to define the function class, we show that the KALE
continuously interpolates between the KL and the Maximum Mean Discrepancy
(MMD). Like the MMD and other Integral Probability Metrics, the KALE remains
well defined for mutually singular distributions. Nonetheless, the KALE
inherits from the limiting KL a greater sensitivity to mismatch in the support
of the distributions, compared with the MMD. These two properties make the KALE
gradient flow particularly well suited when the target distribution is
supported on a low-dimensional manifold. Under an assumption of sufficient
smoothness of the trajectories, we show the global convergence of the KALE
flow. We propose a particle implementation of the flow given initial samples
from the source and the target distribution, which we use to empirically
confirm the KALE's properties.


Abstract:
  Photo- and thermally stimulated luminescence characteristics of
Gd3(Ga,Al)5O12:Ce single crystals co-doped with W and Mo are investigated in
the 85 - 510 K temperature range and compared with the corresponding
characteristics of the undoped and Ce3+ - doped Gd3(Ga,Al)5O12 single crystals
of similar composition. A strong effect of the W and Mo impurity ions appears
in the photoluminescence spectra and temperature dependences of the
photoluminescence intensity, afterglow intensity and decay kinetics, thermally
stimulated luminescence (TSL) intensity and TSL glow curves, excitation spectra
of the TSL glow curve peaks and activation energy of their creation. The
obtained results are explained by the enhancement of the intrinsic emission
contribution into the luminescence spectrum of Gd3(Ga,Al)5O12:Ce,W and
Gd3(Ga,Al)5O12:Ce,Mo due to a large concentration of various W - and Mo -
related electron traps in these crystals and, consequently, the O- - type hole
centers, as well as intrinsic crystal lattice defects (e.g., cation vacancies
needed for the excess positive charge compensation of the W6+ and Mo6+ ions).
The influence of the co-doping with W and Mo ions on the scintillation
characteristics of Gd3(Ga,Al)5O12:Ce is discussed.


Abstract:
  Domestic and service robots have the potential to transform industries such
as health care and small-scale manufacturing, as well as the homes in which we
live. However, due to the overwhelming variety of tasks these robots will be
expected to complete, providing generic out-of-the-box solutions that meet the
needs of every possible user is clearly intractable. To address this problem,
robots must therefore not only be capable of learning how to complete novel
tasks at run-time, but the solutions to these tasks must also be informed by
the needs of the user. In this paper we demonstrate how behaviour trees, a well
established control architecture in the fields of gaming and robotics, can be
used in conjunction with natural language instruction to provide a robust and
modular control architecture for instructing autonomous agents to learn and
perform novel complex tasks. We also show how behaviour trees generated using
our approach can be generalised to novel scenarios, and can be re-used in
future learning episodes to create increasingly complex behaviours. We validate
this work against an existing corpus of natural language instructions,
demonstrate the application of our approach on both a simulated robot solving a
toy problem, as well as two distinct real-world robot platforms which,
respectively, complete a block sorting scenario, and a patrol scenario.


Abstract:
  The many-body theory of interacting electrons poses an intrinsically
difficult problem that requires simplifying assumptions. For the determination
of electronic screening properties of the Coulomb interaction, the Random Phase
Approximation (RPA) provides such a simplification. Here, we explicitly show
that this approximation is justified for band structures with sizeable band
gaps. This is when the electronic states responsible for the screening are
energetically far away from the Fermi level, which is equivalent to a short
electronic propagation length of these states. The RPA contains exactly those
diagrams in which the classical Coulomb interaction covers all distances,
whereas neglected vertex corrections involve quantum tunneling through the
barrier formed by the band gap. Our analysis of electron-electron interactions
provides a real-space analogy to Migdal's theorem on the smallness of vertex
corrections in electron-phonon problems. An important application is the
increasing use of constrained Random Phase Approximation (cRPA) calculations of
effective interactions. We find that their usage of Kohn-Sham energies already
accounts for the leading local (excitonic) vertex correction in insulators.


Abstract:
  We consider the mathematical analysis and numerical approximation of a system
of nonlinear partial differential equations that arises in models that have
relevance to steady isochoric flows of colloidal suspensions. The symmetric
velocity gradient is assumed to be a monotone nonlinear function of the
deviatoric part of the Cauchy stress tensor. We prove the existence of a unique
weak solution to the problem, and under the additional assumption that the
nonlinearity involved in the constitutive relation is Lipschitz continuous we
also prove uniqueness of the weak solution. We then construct mixed finite
element approximations of the system using both conforming and nonconforming
finite element spaces. For both of these we prove the convergence of the method
to the unique weak solution of the problem, and in the case of the conforming
method we provide a bound on the error between the analytical solution and its
finite element approximation in terms of the best approximation error from the
finite element spaces. We propose first a Lions-Mercier type iterative method
and next a classical fixed-point algorithm to solve the finite-dimensional
problems resulting from the finite element discretisation of the system of
nonlinear partial differential equations under consideration and present
numerical experiments that illustrate the practical performance of the proposed
numerical method.


Abstract:
  This paper analyzes the global dynamics of 1-dimensional agent arrays with
nearest neighbor linear couplings. The equations of motion are second order
linear ODEs with constant coeffcients. The novel part of this research is that
the couplings are different for each distinct agent. We allow the forces to
depend on the positions and velocity (damping terms) but the magnitudes of both
the position and velocity couplings are different for each agent. We, also, do
not assume that the forces are "Newtonian" (i.e. the force due to A on B equals
the minus the force of B on A) as this assumption does not apply to certain
situations, such as traffic modeling. For example, driver A reacting to driver
B does not imply the opposite reaction in driver B. There are no known
analytical means to solve these systems, even though they are linear, and so
relatively little is known about them. This paper is a generalization of
previous work that computed the global dynamics of 1-dimensional sequences of
identical agents [3] assuming periodic boundary conditions. In this paper, we
push that method further, similar to [2], and use an extended periodic boundary
condition to to gain quantitative insights to the systems under consideration.
We find that we can approximate the global dynamics of such a system by
carefully analyzing the low-frequency behavior of the system with (generalized)
periodic boundary conditions.


Abstract:
  Deploying deep learning models in time-critical applications with limited
computational resources, for instance in edge computing systems and IoT
networks, is a challenging task that often relies on dynamic inference methods
such as early exiting. In this paper, we introduce a novel architecture for
early exiting based on the vision transformer architecture, as well as a
fine-tuning strategy that significantly increase the accuracy of early exit
branches compared to conventional approaches while introducing less overhead.
Through extensive experiments on image and audio classification as well as
audiovisual crowd counting, we show that our method works for both
classification and regression problems, and in both single- and multi-modal
settings. Additionally, we introduce a novel method for integrating audio and
visual modalities within early exits in audiovisual data analysis, that can
lead to a more fine-grained dynamic inference.


Abstract:
  The discovery of new multicomponent inorganic compounds can provide direct
solutions to many scientific and engineering challenges, yet the vast size of
the uncharted material space dwarfs current synthesis throughput. While the
computational crystal structure prediction is expected to mitigate this
frustration, the NP-hardness and steep costs of density functional theory (DFT)
calculations prohibit material exploration at scale. Herein, we introduce
SPINNER, a highly efficient and reliable structure-prediction framework based
on exhaustive random searches and evolutionary algorithms, which is completely
free from empiricism. Empowered by accurate neural network potentials, the
program can navigate the configuration space faster than DFT by more than
10$^{2}$-fold. In blind tests on 60 ternary compositions diversely selected
from the experimental database, SPINNER successfully identifies experimental
(or theoretically more stable) phases for ~80% of materials within 5000
generations, entailing up to half a million structure evaluations for each
composition. When benchmarked against previous data mining or DFT-based
evolutionary predictions, SPINNER identifies more stable phases in the majority
of cases. By developing a reliable and fast structure-prediction framework,
this work opens the door to large-scale, unbounded computational exploration of
undiscovered inorganic crystals.


Abstract:
  Training deep neural networks for automatic speech recognition (ASR) requires
large amounts of transcribed speech. This becomes a bottleneck for training
robust models for accented speech which typically contains high variability in
pronunciation and other semantics, since obtaining large amounts of annotated
accented data is both tedious and costly. Often, we only have access to large
amounts of unannotated speech from different accents. In this work, we leverage
this unannotated data to provide semantic regularization to an ASR model that
has been trained only on one accent, to improve its performance for multiple
accents. We propose Accent Pre-Training (Acc-PT), a semi-supervised training
strategy that combines transfer learning and adversarial training. Our approach
improves the performance of a state-of-the-art ASR model by 33% on average over
the baseline across multiple accents, training only on annotated samples from
one standard accent, and as little as 105 minutes of unannotated speech from a
target accent.


Abstract:
  Estimating health benefits of reducing fossil fuel use from improved air
quality provides important rationales for carbon emissions abatement.
Simulating pollution concentration is a crucial step of the estimation, but
traditional approaches often rely on complicated chemical transport models that
require extensive expertise and computational resources. In this study, we
develop a novel and succinct machine learning framework that is able to provide
precise and robust annual average fine particle (PM2.5) concentration
estimations directly from a high-resolution fossil energy use data set. The
accessibility and applicability of this framework show great potentials of
machine learning approaches for integrated assessment studies. Applications of
the framework with Chinese data reveal highly heterogeneous health benefits of
reducing fossil fuel use in different sectors and regions in China with a mean
of \$34/tCO2 and a standard deviation of \$84/tCO2. Reducing rural and
residential coal use offers the highest co-benefits with a mean of \$360/tCO2.
Our findings prompt careful policy designs to maximize cost-effectiveness in
the transition towards a carbon-neutral energy system.


Abstract:
  Recent advances in bioimaging have provided scientists a superior high
spatial-temporal resolution to observe dynamics of living cells as 3D
volumetric videos. Unfortunately, the 3D biomedical video analysis is lagging,
impeded by resource insensitive human curation using off-the-shelf 3D analytic
tools. Herein, biologists often need to discard a considerable amount of rich
3D spatial information by compromising on 2D analysis via maximum intensity
projection. Recently, pixel embedding-based cell instance segmentation and
tracking provided a neat and generalizable computing paradigm for understanding
cellular dynamics. In this work, we propose a novel spatial-temporal
voxel-embedding (VoxelEmbed) based learning method to perform simultaneous cell
instance segmenting and tracking on 3D volumetric video sequences. Our
contribution is in four-fold: (1) The proposed voxel embedding generalizes the
pixel embedding with 3D context information; (2) Present a simple multi-stream
learning approach that allows effective spatial-temporal embedding; (3)
Accomplished an end-to-end framework for one-stage 3D cell instance
segmentation and tracking without heavy parameter tuning; (4) The proposed 3D
quantification is memory efficient via a single GPU with 12 GB memory. We
evaluate our VoxelEmbed method on four 3D datasets (with different cell types)
from the ISBI Cell Tracking Challenge. The proposed VoxelEmbed method achieved
consistent superior overall performance (OP) on two densely annotated datasets.
The performance is also competitive on two sparsely annotated cohorts with
20.6% and 2% of data-set having segmentation annotations. The results
demonstrate that the VoxelEmbed method is a generalizable and memory-efficient
solution.


Abstract:
  Topic model evaluation, like evaluation of other unsupervised methods, can be
contentious. However, the field has coalesced around automated estimates of
topic coherence, which rely on the frequency of word co-occurrences in a
reference corpus. Contemporary neural topic models surpass classical ones
according to these metrics. At the same time, topic model evaluation suffers
from a validation gap: automated coherence, developed for classical models, has
not been validated using human experimentation for neural models. In addition,
a meta-analysis of topic modeling literature reveals a substantial
standardization gap in automated topic modeling benchmarks. To address the
validation gap, we compare automated coherence with the two most widely
accepted human judgment tasks: topic rating and word intrusion. To address the
standardization gap, we systematically evaluate a dominant classical model and
two state-of-the-art neural models on two commonly used datasets. Automated
evaluations declare a winning model when corresponding human evaluations do
not, calling into question the validity of fully automatic evaluations
independent of human judgments.


Abstract:
  Low rank matrix recovery problems, including matrix completion and matrix
sensing, appear in a broad range of applications. In this work we present GNMR
-- an extremely simple iterative algorithm for low rank matrix recovery, based
on a Gauss-Newton linearization. On the theoretical front, we derive recovery
guarantees for GNMR in both the matrix sensing and matrix completion settings.
A key property of GNMR is that it implicitly keeps the factor matrices
approximately balanced throughout its iterations. On the empirical front, we
show that for matrix completion with uniform sampling, GNMR performs better
than several popular methods, especially when given very few observations close
to the information limit.


Abstract:
  Novel Coronavirus disease (COVID-19) is a highly contagious respiratory
infection that has had devastating effects on the world. Recently, new COVID-19
variants are emerging making the situation more challenging and threatening.
Evaluation and quantification of COVID-19 lung abnormalities based on chest
Computed Tomography (CT) scans can help determining the disease stage,
efficiently allocating limited healthcare resources, and making informed
treatment decisions. During pandemic era, however, visual assessment and
quantification of COVID-19 lung lesions by expert radiologists become expensive
and prone to error, which raises an urgent quest to develop practical
autonomous solutions. In this context, first, the paper introduces an open
access COVID-19 CT segmentation dataset containing 433 CT images from 82
patients that have been annotated by an expert radiologist. Second, a Deep
Neural Network (DNN)-based framework is proposed, referred to as the
COVID-Rate, that autonomously segments lung abnormalities associated with
COVID-19 from chest CT scans. Performance of the proposed COVID-Rate framework
is evaluated through several experiments based on the introduced and external
datasets. The results show a dice score of 0:802 and specificity and
sensitivity of 0:997 and 0:832, respectively. Furthermore, the results indicate
that the COVID-Rate model can efficiently segment COVID-19 lesions in both 2D
CT images and whole lung volumes. Results on the external dataset illustrate
generalization capabilities of the COVID-Rate model to CT images obtained from
a different scanner.


Abstract:
  Scepticism towards childhood vaccines and genetically modified food has grown
despite scientific evidence of their safety. Beliefs about scientific issues
are difficult to change because they are entrenched within many related moral
concerns and beliefs about what others think. We propose a cognitive network
model which estimates the relationships, dissonance, and randomness between all
related beliefs to derive predictions of the circumstances under which beliefs
change. Using a probabilistic nationally representative longitudinal study, we
found support for our model's predictions: Randomness of the belief networks
decreased over time, for many participants their estimated dissonance related
positively to their self-reported dissonance, and individuals who had high
estimated dissonance of their belief network were more likely to change their
beliefs to reduce this dissonance. This study is the first to combine a
unifying predictive model with an experimental intervention and sheds light on
dynamics of dissonance reduction leading to belief change.


Abstract:
  The excitation of the filamentary gas structures surrounding giant elliptical
galaxies at the center of cool-core clusters, a.k.a BCGs (brightest cluster
galaxies), is key to our understanding of active galactic nucleus feedback, and
of the impact of environmental and local effects on star formation. We
investigate the contribution of the thermal radiation from the cooling flow
surrounding BCGs to the excitation of the filaments. We explore the effects of
small levels of extra-heating (turbulence), and of metallicity, on the optical
and infrared lines. Using the Cloudy code, we model the photoionization and
photodissociation of a slab of gas of optical depth AV{\leq}30mag at constant
pressure, in order to calculate self-consistently all of the gas phases, from
ionized gas to molecular gas. The ionizing source is the EUV and soft X-ray
radiation emitted by the cooling gas. We test these models comparing their
predictions to the rich multi-wavelength observations, from optical to
submillimeter. These models reproduce most of the multi-wavelength spectra
observed in the nebulae surrounding the BCGs, not only the LINER-like optical
diagnostics: [O iii]{\lambda} 5007 {\AA}/H\b{eta}, [N ii]{\lambda} 6583
{\AA}/H{\alpha} and ([S ii]{\lambda} 6716 {\AA}+[S ii]{\lambda} 6731
{\AA})/H{\alpha} but also the infrared emission lines from the atomic gas. The
modeled ro-vib H2 lines also match observations, which indicates that near and
mid-IR H2 lines are mostly excited by collisions between H2 molecules and
secondary electrons produced naturally inside the cloud by the interaction
between the X-rays and the cold gas in the filament. However, there is still
some tension between ionized and molecular line tracers (i.e. CO), which
requires to optimize the cloud structure and the density of the molecular zone.


Abstract:
  Considering the energy-efficient emergency response, subject to a given set
of constraints on emergency communication networks (ECN), this article proposes
a hybrid device-to-device (D2D) and device-to-vehicle (D2V) network for
collecting and transmitting emergency information. First, we establish the D2D
network from the perspective of complex networks by jointly determining the
optimal network partition (ONP) and the temporary data caching centers (TDCC),
and thus emergency data can be forwarded and cached in TDCCs. Second, based on
the distribution of TDCCs, the D2V network is established by unmanned aerial
vehicles (UAV)-based waypoint and motion planning, which saves the time for
wireless transmission and aerial moving. Finally, the amount of time for
emergency response and the total energy consumption are simultaneously
minimized by a multiobjective evolutionary algorithm based on decomposition
(MOEA/D), subject to a given set of minimum signal-to-interference-plus-noise
ratio (SINR), number of UAVs, transmit power, and energy constraints.
Simulation results show that the proposed method significantly improves
response efficiency and reasonably controls the energy, thus overcoming
limitations of existing ECNs. Therefore, this network effectively solves the
key problem in the rescue system and makes great contributions to post-disaster
decision-making.


Abstract:
  In this paper, we deal with (angular cut-off) Boltzmann equation with soft
potential ($-3<\gamma<0$). In particular, we construct a unique global solution
in $L^\infty_{x,v}$ which converges to global equilibrium asymptotically
provided that initial data has a large amplitude but with sufficiently small
relative entropy. Because frequency multiplier is not uniformly positive
anymore, unlike hard potential case, time-involved velocity weight will be used
to derive sub-exponential decay of the solution. Motivated by recent
development of $L^2\mbox{-}L^\infty$ approach also, we introduce some modified
estimates of quadratic nonlinear terms. Linearized collision kernel will be
treated in a subtle manner to control singularity of soft potential kernel.


Abstract:
  The lack of explainability of a decision from an Artificial Intelligence (AI)
based "black box" system/model, despite its superiority in many real-world
applications, is a key stumbling block for adopting AI in many high stakes
applications of different domain or industry. While many popular Explainable
Artificial Intelligence (XAI) methods or approaches are available to facilitate
a human-friendly explanation of the decision, each has its own merits and
demerits, with a plethora of open challenges. We demonstrate popular XAI
methods with a mutual case study/task (i.e., credit default prediction),
analyze for competitive advantages from multiple perspectives (e.g., local,
global), provide meaningful insight on quantifying explainability, and
recommend paths towards responsible or human-centered AI using XAI as a medium.
Practitioners can use this work as a catalog to understand, compare, and
correlate competitive advantages of popular XAI methods. In addition, this
survey elicits future research directions towards responsible or human-centric
AI systems, which is crucial to adopt AI in high stakes applications.


Abstract:
  Coronal holes are well accepted to be source regions of the fast solar wind.
As one of the common structures in coronal holes, coronal plumes might
contribute to the origin of the nascent solar wind. To estimate the
contribution of coronal plumes to the nascent solar wind, we make the first
attempt to estimate their populations in solar polar coronal holes. By
comparing the observations viewed from two different angles taken by the twin
satellites of STEREO and the results of Monte Carlo simulations, we estimate
about 16--27 plumes rooted in an area of $4\times10^4$ arcsec$^2$ of the polar
coronal holes near the solar minimum, which occupy about 2--3.4% of the area.
Based on these values, the contribution of coronal plumes to the nascent solar
wind has also been discussed. A further investigation indicates that more
precise number of coronal plumes can be worked out with observations from three
or more viewing angles.


Abstract:
  Borrowing from the transformer models that revolutionized the field of
natural language processing, self-supervised feature learning for visual tasks
has also seen state-of-the-art success using these extremely deep, isotropic
networks. However, the typical AI researcher does not have the resources to
evaluate, let alone train, a model with several billion parameters and
quadratic self-attention activations. To facilitate further research, it is
necessary to understand the features of these huge transformer models that can
be adequately studied by the typical researcher. One interesting characteristic
of these transformer models is that they remove most of the inductive biases
present in classical convolutional networks. In this work, we analyze the
effect of these and more inductive biases on small to moderately-sized
isotropic networks used for unsupervised visual feature learning and show that
their removal is not always ideal.


Abstract:
  App builders commonly use security challenges, a form of step-up
authentication, to add security to their apps. However, the ethical
implications of this type of architecture has not been studied previously. In
this paper, we present a large-scale measurement study of running an existing
anti-fraud security challenge, Boxer, in real apps running on mobile devices.
We find that although Boxer does work well overall, it is unable to scan
effectively on devices that run its machine learning models at less than one
frame per second (FPS), blocking users who use inexpensive devices. With the
insights from our study, we design Daredevil, anew anti-fraud system for
scanning payment cards that work swell across the broad range of performance
characteristics and hardware configurations found on modern mobile devices.
Daredevil reduces the number of devices that run at less than one FPS by an
order of magnitude compared to Boxer, providing a more equitable system for
fighting fraud. In total, we collect data from 5,085,444 real devices spread
across 496 real apps running production software and interacting with real
users.


Abstract:
  A spherical conical metric $g$ on a surface $\Sigma$ is a metric of constant
curvature $1$ with finitely many isolated conical singularities. The
uniformization problem for such metrics remains largely open when at least one
of the cone angles exceeds $2\pi$. The eigenfunctions of the Friedrichs
Laplacian $\Delta_g$ with eigenvalue $\lambda=2$ play a special role in this
problem, as they represent local obstructions to deformations of the metric $g$
in the class of spherical conical metrics. In the present paper we apply the
theory of multivalued harmonic maps to spheres to the question of existence of
such eigenfunctions. In the first part we establish a new criterion for the
existence of $2$-eigenfunctions, given in terms of a certain meromorphic data
on $\Sigma$. As an application we give a description of all $2$-eigenfunctions
for metrics on the sphere with at most three conical singularities. The second
part is an algebraic construction of metrics with large number of
$2$-eigenfunctions via the deformation of multivalued harmonic maps. We provide
new explicit examples of metrics with many $2$-eigenfunctions via both
approaches, and describe the general algorithm to find metrics with arbitrarily
large number of $2$-eigenfunctions.


Abstract:
  State estimation problems without absolute position measurements routinely
arise in navigation of unmanned aerial vehicles, autonomous ground vehicles,
etc., whose proper operation relies on accurate state estimates and reliable
covariances. Unaware of absolute positions, these problems have immanent
unobservable directions. Traditional causal estimators, however, usually gain
spurious information on the unobservable directions, leading to over-confident
covariance inconsistent with actual estimator errors. The consistency problem
of fixed-lag smoothers (FLSs) has only been attacked by the first estimate
Jacobian (FEJ) technique because of the complexity to analyze their
observability property. But the FEJ has several drawbacks hampering its wide
adoption. To ensure the consistency of a FLS, this paper introduces the right
invariant error formulation into the FLS framework. To our knowledge, we are
the first to analyze the observability of a FLS with the right invariant error.
Our main contributions are twofold. As the first novelty, to bypass the
complexity of analysis with the classic observability matrix, we show that
observability analysis of FLSs can be done equivalently on the linearized
system. Second, we prove that the inconsistency issue in the traditional FLS
can be elegantly solved by the right invariant error formulation without
artificially correcting Jacobians. By applying the proposed FLS to the
monocular visual inertial simultaneous localization and mapping (SLAM) problem,
we confirm that the method consistently estimates covariance similarly to a
batch smoother in simulation and that our method achieved comparable accuracy
as traditional FLSs on real data.


Abstract:
  This work introduces a concept of explanations with respect to the violation
of safe behaviours within software defined networks (SDNs) expressible in
NetKAT. The latter is a network programming language based on a well-studied
mathematical structure, namely, Kleene Algebra with Tests (KAT). Amongst
others, the mathematical foundation of NetKAT gave rise to a sound and complete
equational theory. In our setting, a safe behaviour is characterised by a
NetKAT policy, or program, which does not enable forwarding packets from an
ingress i to an undesirable egress e. We show how explanations for safety
violations can be derived in an equational fashion, according to a modification
of the existing NetKAT axiomatisation. We propose an approach based on the
Maude system for actually computing the undesired behaviours witnessing the
forwarding of packets from i to e as above. SDN-SafeCheck is a tool based on
Maude equational theories satisfying important properties such as Church-Rosser
and termination. SDN-SafeCheck automatically identifies all the undesired
behaviours leading to e, covering forwarding paths up to a user specified size.


Abstract:
  The Standard Model Neutrino Effective Field Theory (SMNEFT) is the Standard
Model Effective Field Theory (SMEFT) augmented with right-handed neutrinos.
Building on our previous work, arXiv:2010.12109, we calculate the Yukawa
coupling contributions to the one-loop anomalous dimension matrix for the 11
dimension-six four-fermion SMNEFT operators. We also present the new
contributions to the anomalous dimension matrix for the 14 four-fermion SMEFT
operators that mix with the SMNEFT operators through the Yukawa couplings of
the right-handed neutrinos.


Abstract:
  We present a spinning black hole solution in $d$ dimensions with a maximal
number of rotation parameters in the context of the Einstein-Maxwell-Dilaton
theory. An interesting feature of such a solution is that it accommodates
Lifshitz black holes when the rotation parameters are set to zero. We verify
the rotating nature of the black hole solution by performing the quasi-local
analysis of conserved charges and defining the corresponding angular momenta.
In addition, we perform the thermodynamical analysis of the black hole
configuration, show that the first law of thermodynamics is completely
consistent, and obtain a Smarr-like formula. We further study the thermodynamic
stability of the constructed solution from a local viewpoint, by computing the
associated specific heats, and from a global perspective, by using the
so-called new thermodynamic geometry. We finally make some comments related to
a pathology found in the causal structure of the obtained rotating black hole
spacetime and compute some of its curvature invariants.


Abstract:
  In this paper we address the large-scale regularity theory for the stationary
Navier-Stokes equations in highly oscillating bumpy John domains. These domains
are very rough, possibly with fractals or cusps, at the microscopic scale, but
are amenable to the mathematical analysis of the Navier-Stokes equations. We
prove: (i) a large-scale Calder\'on-Zygmund estimate, (ii) a large-scale
Lipschitz estimate, (iii) large-scale higher-order regularity estimates,
namely, $C^{1,\gamma}$ and $C^{2,\gamma}$ estimates. These nice regularity
results are inherited only at mesoscopic scales, and clearly fail in general at
the microscopic scales. We emphasize that the large-scale $C^{1,\gamma}$
regularity is obtained by using first-order boundary layers constructed via a
new argument. The large-scale $C^{2,\gamma}$ regularity relies on the
construction of second-order boundary layers, which allows for certain boundary
data with linear growth at spatial infinity. To the best of our knowledge, our
work is the first to carry out such an analysis. In the wake of many works in
quantitative homogenization, our results strongly advocate in favor of
considering the boundary regularity of the solutions to fluid equations as a
multiscale problem, with improved regularity at or above a certain scale.


Abstract:
  Sparse Bayesian learning (SBL) can be implemented with low complexity based
on the approximate message passing (AMP) algorithm. However, it does not work
well for a generic measurement matrix, which may cause AMP to diverge. Damped
AMP has been used for SBL to alleviate the problem at the cost of reducing
convergence speed. In this work, we propose a new SBL algorithm based on
structured variational inference, leveraging AMP with a unitary transformation
(UAMP). Both single measurement vector and multiple measurement vector problems
are investigated. It is shown that, compared to state-of-the-art AMP-based SBL
algorithms, the proposed UAMP-SBL is more robust and efficient, leading to
remarkably better performance.


Abstract:
  Charge order has recently been identified as a leading competitor of
high-temperature superconductivity in moderately doped cuprates. We provide a
survey of universal and materials-specific aspects of this phenomenon, with
emphasis on results obtained by scattering methods. In particular, we discuss
the structure, periodicity, and stability range of the charge-ordered state,
its response to various external perturbations, the influence of disorder, the
coexistence and competition with superconductivity, as well as collective
charge dynamics. In the context of this journal issue which honors Roger
Cowley's legacy, we also discuss the connection of charge ordering with lattice
vibrations and the central-peak phenomenon. We end the review with an outlook
on research opportunities offered by new synthesis methods and experimental
platforms, including cuprate thin films and superlattices.


Abstract:
  Neural networks are a prominent tool for identifying and modeling complex
patterns, which are otherwise hard to detect and analyze. While machine
learning and neural networks have been finding applications across many areas
of science and technology, their use in decoding ultrafast dynamics of quantum
systems driven by strong laser fields has been limited so far. Here we use deep
neural networks to analyze simulated noisy spectra of highly nonlinear optical
response of a 2-dimensional gapped graphene crystal to intense few-cycle laser
pulses. We show that a computationally simple 1-dimensional system provides a
useful "nursery school" for our neural network, allowing it to be easily
retrained to treat more complex systems, recovering the band structure and
spectral phases of the incident few-cycle pulse with high accuracy, in spite of
significant amplitude noise and phase jitter. Our results both offer a new tool
for attosecond spectroscopy of quantum dynamics in solids and also open a route
to developing all-solid-state devices for complete characterization of
few-cycle pulses, including their nonlinear chirp and the carrier envelope
phase.


Abstract:
  We probe the effect of diffeomorphism symmetry on the critical exponents
values for massive O($N$) $\lambda\phi^{4}$ scalar field theories in curved
spacetime. We apply field-theoretic renormalization group tools, where we use
only momentum coordinates as opposed to the earlier standard procedure with
both space and momentum coordinates, for computing analytically the critical
exponents up to three-loop order for one of them and up to two-loop level for
the remaining ones. We found that the curved spacetime critical exponents are
the same as their flat spacetime counterparts, at least up to the level
considered. At the conclusions we present the physical interpretation for the
results.


Abstract:
  A set $D$ of vertices in $G$ is a disjunctive dominating set in $G$ if every
vertex not in $D$ is adjacent to a vertex of $D$ or has at least two vertices
in $D$ at distance $2$ from it in $G$. The disjunctive domination number,
$\gamma^{d}_2(G)$, of $G$ is the minimum cardinality of a disjunctive
dominating set in $G$. In this paper, we show that if $G$ be a graph of order
at least $3$, $\delta(G)\geq 2$ and with no component isomorphic to any of
eight forbidden graphs, then $\gamma^{d}_2(G)\leq \frac{|G|}{3}$. Moreover, we
provide an infinite family of graphs attaining this bound. In addition, we also
study the case that $G$ is a claw-free graph with minimum degree at least two.


Abstract:
  A growing interest in designing high-alumina MgO-bonded refractory castables
has been identified in recent years due to the magnesia ability to react: (i)
with water at the initial processing stages of these materials (inducing the
precipitation of brucite phase) or (ii) with alumina, giving rise to in situ
MgAl2O4 generation at high temperatures. Nevertheless, despite the great
potential of caustic magnesia to be used as a binder in such systems due to its
high reactivity, it is still a challenge to control the hydration reaction rate
of this oxide and the negative effects derived from the expansive feature of
Mg(OH)2 formation. Thus, this work evaluated the incorporation of different
contents of aluminum hydroxyl lactate (AHL) into caustic magnesia-bonded
castables, aiming to control the brucite precipitation during the curing and
drying steps of the prepared samples, resulting in crack-free refractories. The
designed compositions were characterized via flowability, setting behavior,
X-ray diffraction, cold flexural strength, porosity, permeability and
thermogravimetric measurements. According to the results, instead of Mg(OH)2,
hydrotalcite-like phases [Mg6Al2(OH)16(OH)2.4.5H2O and Mg6Al2(OH)16(CO3).4H2O]
were the main hydrated phases identified in the AHL-containing compositions.
The addition of 1.0 wt.% of aluminum hydroxyl lactate to the designed castable
proved to be, so far, the best option for this magnesia source, resulting in
the development of a crack-free refractory with enhanced properties and greater
spalling resistance under heating.


Abstract:
  Rapid miniaturization of electronic devices and circuits demands profound
understanding of fluctuation phenomena at the nanoscale. Superconducting
nanowires -- serving as important building blocks for such devices -- may
seriously suffer from fluctuations which tend to destroy long-range order and
suppress superconductivity. In particular, quantum phase slips (QPS)
proliferating at low temperatures may turn a quasi-one-dimensional
superconductor into a resistor or an insulator. Here, we introduce a physical
concept of QPS-controlled localization of Cooper pairs that may occur even in
uniform nanowires without any dielectric barriers being a fundamental
manifestation of the flux-charge duality in superconductors. We demonstrate --
both experimentally and theoretically -- that deep in the "insulating" state
such nanowires actually exhibit non-trivial superposition of superconductivity
and weak Coulomb blockade of Cooper pairs generated by quantum tunneling of
magnetic fluxons across the wire.


Abstract:
  Up to date, only lower and upper bounds for the optimal configuration of a
Square Array (A2) Group Testing (GT) algorithm are known. We establish exact
analytical formulae and provide a couple of applications of our result. First,
we compare the A2 GT scheme to several other classical GT schemes in terms of
the gain per specimen attained at optimal configuration. Second, operating
under objective Bayesian framework with the loss designed to attain minimum at
optimal GT configuration, we suggest the preferred choice of the group size
under natural minimal assumptions: the prior information regarding the
prevalence suggests that grouping and application of A2 is better than
individual testing. The same suggestion is provided for the Minimax strategy.


Abstract:
  Let $ \mathcal{H}(\mathbb{D}) $ be the linear space of analytic functions on
the unit disk $ \mathbb{D}=\{z\in\mathbb{C}: |z|<1\} $ and let $
\mathcal{B}=\{w\in \mathcal{H}(\mathbb{D}: |w(z)|<1)\} $. The classical Bohr's
inequality states that if a power series $ f(z)=\sum_{n=0}^{\infty}a_nz^n $
converges in $ \mathbb{D} $ and $ |f(z)|<1 $ for $ z\in\mathbb{D} $, then
\begin{equation*}
  \sum_{n=0}^{\infty}|a_n|r^n\leq 1\;\;\mbox{for}\;\; r\leq \frac{1}{3}
\end{equation*} and the constant $ 1/3 $ is the best possible. The constant $
1/3 $ is known as Bohr radius. A function $ f : \mathbb{D}\rightarrow\mathbb{C}
$ is said to be log-harmonic if there is a $ w\in\mathcal{B} $ such that $ f $
is a non-constant solution of the non-linear elliptic partial differential
equation \begin{equation*}
  \bar{f}_{\bar{z}}(z)/\bar{f}(z)=w(z)f_{z}(z)/f(z). \end{equation*} The class
of log-harmonic mappings is denoted by $ \mathcal{S}_{LH} $. The set of all
starlike log-harmonic mapping is defined by \begin{equation*}
\mathcal{ST}_{LH}=\bigg\{f\in\mathcal{S}_{LH}:\frac{\partial}{\partial\theta}{\rm
Arg}(f(e^{i\theta}))={\rm
Re}\left(\frac{zf_{z}-\bar{z}f_{\bar{z}}}{f}\right)>0\;\; \mbox{in}\;\;
\mathbb{D}\bigg\}. \end{equation*} In this paper, we study several improved
Bohr radius for the class $ \mathcal{ST}^{0}_{LH} $, a subclass of $
\mathcal{ST}_{LH} $, consisting of functions $ f\in\mathcal{ST}_{LH} $ which
map the unit disk $ \mathbb{D} $ onto a starlike domain (with respect to the
origin).


Abstract:
  Jellyfish (Medusozoa) have been deemed the most energy-efficient animals in
the world. Their bell morphology and relatively simple nervous systems make
them attractive to robotocists. Although, the science community has devoted
much attention to understanding their swimming performance, there is still much
to be learned about the jet propulsive locomotive gait displayed by prolate
jellyfish. Traditionally, computational scientists have assumed uniform duty
cycle kinematics when computationally modeling jellyfish locomotion. In this
study we used fluid-structure interaction modeling to determine possible
enhancements in performance from shuffling different duty cycles together
across multiple Reynolds numbers and contraction frequencies. Increases in
speed and reductions in cost of transport were observed as high as 80% and 50%,
respectively. Generally, the net effects were greater for cases involving lower
contraction frequencies. Overall, robust duty cycle combinations were
determined that led to enhanced or impeded performance.


Abstract:
  The Turing Award is recognized as the most influential and prestigious award
in the field of computer science(CS). With the rise of the science of science
(SciSci), a large amount of bibliographic data has been analyzed in an attempt
to understand the hidden mechanism of scientific evolution. These include the
analysis of the Nobel Prize, including physics, chemistry, medicine, etc. In
this article, we extract and analyze the data of 72 Turing Award laureates from
the complete bibliographic data, fill the gap in the lack of Turing Award
analysis, and discover the development characteristics of computer science as
an independent discipline. First, we show most Turing Award laureates have
long-term and high-quality educational backgrounds, and more than 61% of them
have a degree in mathematics, which indicates that mathematics has played a
significant role in the development of computer science. Secondly, the data
shows that not all scholars have high productivity and high h-index; that is,
the number of publications and h-index is not the leading indicator for
evaluating the Turing Award. Third, the average age of awardees has increased
from 40 to around 70 in recent years. This may be because new breakthroughs
take longer, and some new technologies need time to prove their influence.
Besides, we have also found that in the past ten years, international
collaboration has experienced explosive growth, showing a new paradigm in the
form of collaboration. It is also worth noting that in recent years, the
emergence of female winners has also been eye-catching. Finally, by analyzing
the personal publication records, we find that many people are more likely to
publish high-impact articles during their high-yield periods.


Abstract:
  The Jacobian group (also known as the critical group or sandpile group) is an
important invariant of a finite, connected graph $X$; it is a finite abelian
group whose cardinality is equal to the number of spanning trees of $X$
(Kirchhoff's Matrix Tree Theorem). A specific type of covering graph, called a
derived graph, that is constructed from a voltage graph with voltage group $G$
is the object of interest in this paper. Towers of derived graphs are studied
by using aspects of classical Iwasawa Theory (from number theory). Formulas for
the orders of the Sylow $p$-subgroups of Jacobians in an infinite voltage
$p$-tower, for any prime $p$, are obtained in terms of classical $\mu$ and
$\lambda$ invariants by using the decomposition of a finitely generated module
over the Iwasawa Algebra.


Abstract:
  We propose an experiment that the entanglement between two macroscopic
mirrors suspended at the end of an equal-arm interferometer is destroyed by the
noise of gravitons through bremsstrahlung. By calculating the correlation
function of the noise, we obtain the decoherence time from the decoherence
functional. We estimate that the decoherence time induced by the noise of
gravitons in squeezed states stemming from inflation is approximately 20
seconds for 40 km long arms and 40 kg mirrors. Our analysis shows that
observation of the decoherence time of quantum entanglement has the potential
to detect gravitons indirectly. This indirect detection of gravitons would give
strong evidence of quantum gravity.


Abstract:
  In general, graph representation learning methods assume that the train and
test data come from the same distribution. In this work we consider an
underexplored area of an otherwise rapidly developing field of graph
representation learning: The task of out-of-distribution (OOD) graph
classification, where train and test data have different distributions, with
test data unavailable during training. Our work shows it is possible to use a
causal model to learn approximately invariant representations that better
extrapolate between train and test data. Finally, we conclude with synthetic
and real-world dataset experiments showcasing the benefits of representations
that are invariant to train/test distribution shifts.


Abstract:
  A low noise linearly frequency modulated continuous wave laser source with a
wide frequency bandwidth is demonstrated. By two-dimensional thermal tuning,
the laser source shows 42 GHz continuous frequency tuning with 49.86 Hz
intrinsic linewidth under static operation. For dynamical FMCW, the laser
source has 10.25 GHz frequency bandwidth at 100 Hz chirped frequency and 5.56
GHz at 1 kHz chirped frequency. With pre-distortion linearization, it can
distinguish 3 m length difference at 45 km distance in the fibre length
measured experiment, which demonstrate a potential for application in
ultra-long fibre sensing and FMCW LiDAR.


Abstract:
  The temporal coincidences of events detected in four neutrino detectors and
two gravitational antennas still remains among the most puzzling phenomena
associated with SN1987A. The coincidences form a six-hour signal approximately
coincident in time with the well-known LSD signal at 2h52m UT on 23/02/1987.
After 30 years of research, the characteristics and the shape of the six-hour
signal have been studied quite well, but the mechanisms of its formation have
not been fully understood as of yet. Here we suggest that data obtained from
another technology, radioactive decays, might provide new insights into the
origin of signals previously seen in neutrino detectors and gravity wave
detectors. On August 17, 2017, at 12h41m UT, the GW170817 signal was detected
by LIGO and Virgo. At the same time, an approximately 7-hour long signal
coincident with GW170817 was detected in the Si/Cl experiment on precision
measurement of the $^{32}$Si half-life. We show that the Si/Cl signal is
unexpectedly similar to the six-hour signal from SN1987A. In addition, we
establish that the sources of the coinciding events are similar to those of the
Si/Cl signal. To explain the surprising similarities in both signals, we
present a mechanism which could in principle account for this phenomenon in
terms of a local increase in the density of axionic dark matter induced by a
gravity wave.