Abstract:
  We develop fractional buffer layers (FBLs) to absorb propagating waves
without reflection in bounded domains. Our formulation is based on
variable-order spatial fractional derivatives. We select a proper
variable-order function so that dissipation is induced to absorb the coming
waves in the buffer layers attached to the domain. In particular, we first
design proper FBsL for the one-dimensional one-way and two-way wave
propagation. Then, we extend our formulation to two-dimensional problems, where
we introduce a consistent variable-order fractional wave equation. In each
case, we obtain the fully discretized equations by employing a spectral
collocation method in space and Crank-Nicolson or Adams-Bashforth method in
time. We compare our results with the perfectly matched layer (PML) method and
show the effectiveness of FBL in accurately suppressing any erroneously
reflected waves, including corner reflections in two-dimensional rectangular
domains. FBLs can be used in conjunction with any discretization method
appropriate for fractional operators describing wave propagation in bounded or
truncated domains.


Abstract:
  This paper presents iBatch, a middleware system running on top of an
operational Ethereum network to enable secure batching of smart-contract
invocations against an untrusted relay server off-chain. iBatch does so at a
low overhead by validating the server's batched invocations in smart contracts
without additional states. The iBatch mechanism supports a variety of policies,
ranging from conservative to aggressive batching, and can be configured
adaptively to the current workloads. iBatch automatically rewrites smart
contracts to integrate with legacy applications and support large-scale
deployment.
  For cost evaluation, we develop a platform with fast and cost-accurate
transaction replaying, build real transaction benchmarks on popular Ethereum
applications, and build a functional prototype of iBatch on Ethereum. The
evaluation results show that iBatch saves 14.6%-59.1% Gas cost per invocation
with a moderate 2-minute delay and 19.06%-31.52% Ether cost per invocation with
a delay of 0.26-1.66 blocks.


Abstract:
  Dynamical systems that are subject to continuous uncertain fluctuations can
be modelled using Stochastic Differential Equations (SDEs). Controlling such
systems results in solving path constrained SDEs. Broadly, these problems fall
under the category of Stochastic Differential-Algebraic Equations (SDAEs). In
this article, the focus is on combining ideas from the local theory of
Differential-Algebraic Equations with that of Stochastic Differential
Equations. The question of existence and uniqueness of the solution for SDAEs
is addressed by using contraction mapping theorem in an appropriate Banach
space to arrive at a sufficient condition. From the geometric point of view, a
necessary condition is derived for the existence of the solution. It is
observed that there exists a class of high index SDAEs for which there is no
solution. Hence, computational methods to find approximate solution of high
index equations are presented. The techniques are illustrated in form of
algorithms with examples and numerical computations.


Abstract:
  It was recently demonstrated that the Matrix Action Key Exchange (MAKE)
algorithm, a new type of key exchange protocol using the semidirect product of
matrix groups, is vulnerable to a linear algebraic attack if the matrices are
over a commutative ring. In this note, we establish conditions under which
protocols using matrices over a non-commutative ring are also vulnerable to
this attack. We then demonstrate that group rings $R[G]$ are examples of
non-commutative rings that satisfy these conditions.


Abstract:
  Convolutional Neural Networks (CNNs) have proven to be a powerful
state-of-the-art method for image classification tasks. One drawback however is
the high computational complexity and high memory consumption of CNNs which
makes them unfeasible for execution on embedded platforms which are constrained
on physical resources needed to support CNNs. Quantization has often been used
to efficiently optimize CNNs for memory and computational complexity at the
cost of a loss of prediction accuracy. We therefore propose a method to
optimally quantize the weights, biases and activations of each layer of a
pre-trained CNN while controlling the loss in inference accuracy to enable
quantized inference. We quantize the 32-bit floating-point precision parameters
to low bitwidth fixed-point representations thereby finding optimal bitwidths
and fractional offsets for parameters of each layer of a given CNN. We quantize
parameters of a CNN post-training without re-training it. Our method is
designed to quantize parameters of a CNN taking into account how other
parameters are quantized because ignoring quantization errors due to other
quantized parameters leads to a low precision CNN with accuracy losses of up to
50% which is far beyond what is acceptable. Our final method therefore gives a
low precision CNN with accuracy losses of less than 1%. As compared to a method
used by commercial tools that quantize all parameters to 8-bits, our approach
provides quantized CNN with averages of 53% lower memory consumption and 77.5%
lower cost of executing multiplications for the two CNNs trained on the four
datasets that we tested our work on. We find that layer-wise quantization of
parameters significantly helps in this process.


Abstract:
  We introduce a functorial construction $\mathsf{C}$ which takes unitary
magmas $\mathcal{M}$ as input and produces operads. The obtained operads
involve configurations of chords labeled by elements of $\mathcal{M}$, called
$\mathcal{M}$-decorated cliques and generalizing usual configurations of
chords. By considering combinatorial subfamilies of $\mathcal{M}$-decorated
cliques defined, for instance, by limiting the maximal number of crossing
diagonals or the maximal degree of the vertices, we obtain suboperads and
quotients of $\mathsf{C} \mathcal{M}$. This leads to a new hierarchy of operads
containing, among others, operads on noncrossing configurations, Motzkin
configurations, forests, dissections of polygons, and involutions. Besides, the
construction $\mathsf{C}$ leads to alternative definitions of the operads of
simple and double multi-tildes, and of the gravity operad.


Abstract:
  We formalize and study the problem of optimal allocation strategies for a
(perfect) vaccine in the infinite-dimensional SIS model. The question may be
viewed as a bi-objective minimization problem, where one tries to minimize
simultaneously the cost of the vaccination, and a loss that may be either the
effective reproduction number, or the proportion of the infected population in
the endemic state. We prove the existence of Pareto optimal strategies,
describe the corresponding Pareto frontier in both cases, and study its
convexity and stability properties. We also show that vaccinating according to
the profile of the endemic state is a critical allocation, in the sense that,
if the initial reproduction number is larger than 1, then this vaccination
strategy yields an effective reproduction number equal to 1.


Abstract:
  Among other remote sensing technologies, synthetic aperture radar (SAR) has
become firmly established in the practice of oceanographic research. Despite
solid experience in this field, comprehensive knowledge and interpretation of
ocean/sea and vessel wave signatures on radar images are still very
challenging. Many technical parameters and scanning conditions vary for
different SAR platforms, which also imposes some restrictions on the
cross-analysis of their respective images. Numerical simulation of SAR images
allows the analysis of many radar imaging parameters including environmental,
ship, or platform related. In this paper, we present a universal simulation
framework for SAR imagery of the sea surface, which includes the superposition
of sea-ship waves. This paper is the first attempt to cover exhaustively all
SAR imaging effects for the sea waves and ship wakes scene. The study is based
on well proven concepts: the linear theory of sea surface modeling, Michell
thin-ship theory for Kelvin wake modeling, and ocean SAR imaging theory. We
demonstrate the role of two main factors that affect imaging of both types of
waves: (i) SAR parameters and (ii) Hydrodynamic related parameters such as wind
state and Froude number. The SAR parameters include frequency, signal
polarization, mean incidence angle, image resolution, variation by scanning
platform (airborne or spaceborne) of the range-to-velocity (R/V) ratio, and
velocity bunching with associated shifting, smearing and azimuthal cutoff
effects. We perform modeling for five wave frequency spectra and four ship
models. We also compare spectra in two aspects: with Cox and Munk's probability
density function (PDF), and with a novel proposed evaluation of ship wake
detectability. The simulation results agree well with SAR imaging theory. The
study gives a fuller understanding of radar imaging mechanisms for sea waves
and ship wakes.


Abstract:
  We present BPFroid -- a novel dynamic analysis framework for Android that
uses the eBPF technology of the Linux kernel to continuously monitor events of
user applications running on a real device. The monitored events are collected
from different components of the Android software stack: internal kernel
functions, system calls, native library functions, and the Java API framework.
As BPFroid hooks these events in the kernel, a malware is unable to trivially
bypass monitoring. Moreover, using eBPF doesn't require any change to the
Android system or the monitored applications. We also present an analytical
comparison of BPFroid to other malware detection methods and demonstrate its
usage by developing novel signatures to detect suspicious behavior that are
based on it. These signatures are then evaluated using real apps. We also
demonstrate how BPFroid can be used to capture forensic artifacts for further
investigation. Our results show that BPFroid successfully alerts in real time
when a suspicious behavioral signature is detected, without incurring a
significant runtime performance overhead.


Abstract:
  Multi-label text classification (MLTC) aims to annotate documents with the
most relevant labels from a number of candidate labels. In real applications,
the distribution of label frequency often exhibits a long tail, i.e., a few
labels are associated with a large number of documents (a.k.a. head labels),
while a large fraction of labels are associated with a small number of
documents (a.k.a. tail labels). To address the challenge of insufficient
training data on tail label classification, we propose a Head-to-Tail Network
(HTTN) to transfer the meta-knowledge from the data-rich head labels to
data-poor tail labels. The meta-knowledge is the mapping from few-shot network
parameters to many-shot network parameters, which aims to promote the
generalizability of tail classifiers. Extensive experimental results on three
benchmark datasets demonstrate that HTTN consistently outperforms the
state-of-the-art methods. The code and hyper-parameter settings are released
for reproducibility


Abstract:
  We construct a CCCZ gate using six T gates, assisted by stabilizer operations
and classical feedback. More generally, we reduce the T cost of a $C^{n}Z$ gate
from $4n-4$ to $4n-6$, for $n > 2$.


Abstract:
  It is well understood that boundary conditions (BCs) may cause global radial
basis function (RBF) methods to become unstable for hyperbolic conservation
laws (CLs). Here we investigate this phenomenon and identify the strong
enforcement of BCs as the mechanism triggering such stability issues. Based on
this observation we propose a technique to weakly enforce BCs in RBF methods.
In the case of hyperbolic CLs, this is achieved by carefully building RBF
methods from the weak form of the CL, rather than the typically enforced strong
form. Furthermore, we demonstrate that global RBF methods may violate
conservation, yielding physically unreasonable solutions when the approximation
does not take into account these considerations. Numerical experiments validate
our theoretical results.


Abstract:
  The quantum illumination is examined by making use of the three-mode
maximally entangled Gaussian state, which involves one signal and two idler
beams. It is shown that the quantum Bhattacharyya bound between $\rho$ (state
for target absence) and $\sigma$ (state for target presence) is less than the
previous result derived by two-mode Gaussian state when $N_S$, average photon
number per signal, is less than $0.295$. This indicates that the quantum
illumination with three-mode Gaussian state gives less error probability
compared to that with two-mode Gaussian state when $N_S < 0.295$.


Abstract:
  Optimization in machine learning typically deals with the minimization of
empirical objectives defined by training data. However, the ultimate goal of
learning is to minimize the error on future data (test error), for which the
training data provides only partial information. In this view, the optimization
problems that are practically feasible are based on inexact quantities that are
stochastic in nature. In this paper, we show how probabilistic results,
specifically gradient concentration, can be combined with results from inexact
optimization to derive sharp test error guarantees. By considering
unconstrained objectives we highlight the implicit regularization properties of
optimization for learning.


Abstract:
  Predicting user intent and detecting the corresponding slots from text are
two key problems in Natural Language Understanding (NLU). In the context of
zero-shot learning, this task is typically approached by either using
representations from pre-trained multilingual transformers such as mBERT, or by
machine translating the source data into the known target language and then
fine-tuning. Our work focuses on a particular scenario where the target
language is unknown during training. To this goal, we propose a novel method to
augment the monolingual source data using multilingual code-switching via
random translations to enhance a transformer's language neutrality when
fine-tuning it for a downstream task. This method also helps discover novel
insights on how code-switching with different language families around the
world impact the performance on the target language. Experiments on the
benchmark dataset of MultiATIS++ yielded an average improvement of +4.2% in
accuracy for intent task and +1.8% in F1 for slot task using our method over
the state-of-the-art across 8 different languages. Furthermore, we present an
application of our method for crisis informatics using a new human-annotated
tweet dataset of slot filling in English and Haitian Creole, collected during
Haiti earthquake disaster.


Abstract:
  Measurements of the ac response represent a widely-used method for probing
the properties of superconductors. In the surface superconducting state (SSS),
increase of the current beyond the surface critical current $I_c$ leads to
breakdown of SSS and penetration of external magnetic field into the sample
bulk. An interesting free-of-bulk system in SSS is offered by thin-walled
superconducting cylinders. The critical state model (CSM) asserts the ac
susceptibility $\chi$ to exhibit jumps as a function of the external ac field
amplitude $H_{ac}$, because of the periodic destruction and restoration of SSS
in the cylinder wall. Here, we investigate experimentally the low-frequency
(128-8192\,Hz) ac response of thin-walled superconducting cylinders in
superimposed dc and ac magnetic fields applied parallel to the cylinder axis.
Distinct from the CSM predictions, experiments reveal that $\chi$ is a smooth
function of $H_{ac}$. For the explanation of our observations we propose a
phenomenological model of partial penetration of magnetic flux (PPMF). The PPMF
model implies that after a restoration of the superconducting state, the
magnetic fields inside and outside the cylinder are not equal, and the value of
the penetrating flux is random for each penetration. This model fits very well
to the experimental data on the temperature dependence of the first-harmonic
$\chi_1$ at any $H_{ac}$ and dc field magnitude. However, in a certain
temperature range the values of physical parameters deduced within the
framework of the PPMF model are questionable.


Abstract:
  For a relative equilibrium of a symmetric simple mechanical system, if the
Morse index of the corresponding amended potential is odd, whether the nullity
is zero or not, it is linearly unstable. We also provide a sufficient condition
for spectral instability.


Abstract:
  Graph colorings is a fundamental topic in graph theory that require an
assignment of labels (or colors) to vertices or edges subject to various
constraints. We focus on the harmonious coloring of a graph, which is a proper
vertex coloring such that for every two distinct colors i, j at most one pair
of adjacent vertices are colored with i and j. This type of coloring is
edge-distinguishing and has potential applications in transportation network,
computer network, airway network system.
  The results presented in this paper fall into two categories: in the first
part of the paper we are concerned with the computational aspects of finding a
minimum harmonious coloring and in the second part we determine the exact value
of the harmonious chromatic number for some particular graphs and classes of
graphs. More precisely, in the first part we show that finding a minimum
harmonious coloring for arbitrary graphs is APX-hard, the natural greedy
algorithm is a $\Omega(\sqrt{n})$-approximation, and, moreover, we show a
relationship between the vertex cover and the harmonious chromatic number. In
the second part we determine the exact value of the harmonious chromatic number
for all 3-regular planar graphs of diameter 3, some non-planar regular graphs
and cycle-related graphs.


Abstract:
  We derive thermodynamically consistent models for diblock copolymer solutions
coupled with the electric and magnetic field, respectively. These models
satisfy the second law of thermodynamics and therefore are therefore
thermodynamically consistent. We then design a set of 2nd order, linear,
semi-discrete schemes for the models using the energy quadratization method and
the supplementary variable method, respectively, which preserve energy
dissipation rates of the models. The spatial discretization is carried out
subsequently using 2nd order finite difference methods, leading to fully
discrete algorithms that preserve discrete energy-dissipation-rates of the
models so that the resulting fully discrete models are thermodynamically
consistent. Convergence rates are numerically confirmed through mesh refinement
tests and several numerical examples are given to demonstrate the role of the
mobility in pattern formation, defect removing effect of both electric and
magnetic fields as well as the hysteresis effect for applied external fields in
copolymer solutions.


Abstract:
  The year 2019 marked the quincentenary of the arrival in the southern
hemisphere of Ferdinand Magellan, the namesake of the Magellanic Clouds, our
nearest example of dwarf galaxies in the early stages of a minor merging event.
These galaxies have been firmly established as laboratories for the study of
variable stars, stellar evolution, and galaxy interaction, as well as being
anchors for the extragalactic distance scale. The goal of this conference was
to provide fertile ground for shaping future research related to the Magellanic
Clouds by combining state-of-the-art results based on advanced observational
programmes with discussions of the highly multiplexed wide-field spectroscopic
surveys that will come online in the 2020s.


Abstract:
  We present Low Distortion Local Eigenmaps (LDLE), a manifold learning
technique which constructs a set of low distortion local views of a dataset in
lower dimension and registers them to obtain a global embedding. The local
views are constructed using the global eigenvectors of the graph Laplacian and
are registered using Procrustes analysis. The choice of these eigenvectors may
vary across the regions. In contrast to existing techniques, LDLE can embed
closed and non-orientable manifolds into their intrinsic dimension by tearing
them apart. It also provides gluing instruction on the boundary of the torn
embedding to help identify the topology of the original manifold. Our
experimental results will show that LDLE largely preserved distances up to a
constant scale while other techniques produced higher distortion. We also
demonstrate that LDLE produces high quality embeddings even when the data is
noisy or sparse.


Abstract:
  New relations involving the Riemann, Ricci and Einstein tensors that have to
hold for a given geometry to admit Killing-Yano tensors are described. These
relations are then used to introduce novel conserved "currents" involving such
Killing-Yano tensors. For a particular current based on the Einstein tensor, we
discuss the issue of conserved charges and consider implications for matter
coupled to gravity. The condition on the background geometry to allow
asymptotic conserved charges for a current introduced by Kastor and Traschen is
found and a number of other new aspects of this current are commented on. In
particular we show that it vanishes for rank $(D-1)$ Killing-Yano tensors in
$D$ dimensions.


Abstract:
  This paper introduces SelfMatch, a semi-supervised learning method that
combines the power of contrastive self-supervised learning and consistency
regularization. SelfMatch consists of two stages: (1) self-supervised
pre-training based on contrastive learning and (2) semi-supervised fine-tuning
based on augmentation consistency regularization. We empirically demonstrate
that SelfMatch achieves the state-of-the-art results on standard benchmark
datasets such as CIFAR-10 and SVHN. For example, for CIFAR-10 with 40 labeled
examples, SelfMatch achieves 93.19% accuracy that outperforms the strong
previous methods such as MixMatch (52.46%), UDA (70.95%), ReMixMatch (80.9%),
and FixMatch (86.19%). We note that SelfMatch can close the gap between
supervised learning (95.87%) and semi-supervised learning (93.19%) by using
only a few labels for each class.


Abstract:
  This paper studies the essential normality of Bergman modules over the
intersection of complex ellipsoids, as well as their quotients by monomial
ideals.


Abstract:
  This paper studies an unmanned aerial vehicle (UAV)-assisted wireless
network, where a UAV is dispatched to gather information from ground sensor
nodes (SN) and transfer the collected data to the depot. The information
freshness is captured by the age of information (AoI) metric, whilst the energy
consumption of the UAV is seen as another performance criterion. Most
importantly, the AoI and energy efficiency are inherently competing metrics,
since decreasing the AoI requires the UAV returning to the depot more
frequently, leading to a higher energy consumption. To this end, we design UAV
paths that optimize these two competing metrics and reveal the Pareto frontier.
To formulate this problem, a multi-objective mixed integer linear programming
(MILP) is proposed with a flow-based constraint set and we apply Bender's
decomposition on the proposed formulation. The overall outcome shows that the
proposed method allows deriving non-dominated solutions for decision making for
UAV based wireless data collection. Numerical results are provided to
corroborate our study by presenting the Pareto front of the two objectives and
the effect on the UAV trajectory.


Abstract:
  We discuss the relation between the linear Tschebyshev-Pad\'e approximations
to analytic function $f$ and the diagonal type I Hermite-Pad\'e polynomials for
the tuple of functions $[1,f_1,f_2]$ where the pair of functions $f_1,f_2$
forms certain Nikishin system. An approach is proposed of how to extend the
seminal Stahl's Theory for Pad\'e approximations for multivalued analytic
functions to the Tschebyshev-Pad\'e approximations. The approach is based on
the relation between Tschebyshev-Pad\'e approximations and Hermite-Pad\'e
polynomials and also on a connection of Hermite-Pad\'e polynomials and
multipoint Pad\'e approximants.


Abstract:
  Previous research on nonlinear oscillator networks has shown that chaos
synchronization is attainable for identical oscillators but deteriorates in the
presence of parameter mismatches. Here, we identify regimes for which the
opposite occurs and show that oscillator heterogeneity can synchronize chaos
for conditions under which identical oscillators cannot. This effect is not
limited to small mismatches and is observed for random oscillator heterogeneity
on both homogeneous and heterogeneous network structures. The results are
demonstrated experimentally using networks of Chua's oscillators and are
further supported by numerical simulations and theoretical analysis. In
particular, we propose a general mechanism based on heterogeneity-induced mode
mixing that provides insights into the observed phenomenon. Since individual
differences are ubiquitous and often unavoidable in real systems, it follows
that such imperfections can be an unexpected source of synchronization
stability.


Abstract:
  We report the results of tests of data transmission and signal stability in
time of two different wide-band multiplexing (MUX) schemes, each in a
point-to-point configuration, based on electromagnetic waves carrying Orbital
Angular Momentum (OAM) in noisy real-world settings. Each radio link
transmitted two high definition wide--band analog TV channels in the same
frequency band with FM-carrier centered at $2.414$ GHz and $27$ MHz bandwidth,
encoded with different OAM modes in the same polarization state,
uninterruptedly for $5$ months during the world exhibition
``Globale--Digitale'' at ZKM in Karlsruhe and in other $2$ months time slots
taken in the following $4$ years, $24$ hours per day. We show the practical
feasibility of the use of stable OAM radio/TV links in the real world for a
long time, paving the way for for secure and efficient communication schemes
also under electromagnetic jamming conditions.


Abstract:
  Modeling real-world phenomena is a focus of many science and engineering
efforts, such as ecological modeling and financial forecasting, to name a few.
Building an accurate model for complex and dynamic systems improves
understanding of underlying processes and leads to resource efficiency. Towards
this goal, knowledge-driven modeling builds a model based on human expertise,
yet is often suboptimal. At the opposite extreme, data-driven modeling learns a
model directly from data, requiring extensive data and potentially generating
overfitting. We focus on an intermediate approach, model revision, in which
prior knowledge and data are combined to achieve the best of both worlds. In
this paper, we propose a genetic model revision framework based on
tree-adjoining grammar (TAG) guided genetic programming (GP), using the TAG
formalism and GP operators in an effective mechanism to incorporate prior
knowledge and make data-driven revisions in a way that complies with prior
knowledge. Our framework is designed to address the high computational cost of
evolutionary modeling of complex systems. Via a case study on the challenging
problem of river water quality modeling, we show that the framework efficiently
learns an interpretable model, with higher modeling accuracy than existing
methods.


Abstract:
  In this letter we use the Carath\'{e}odory's approach to thermodynamics, to
construct the thermodynamic manifold of the Hayward black hole. The Pfaffian
form representing the infinitesimal heat exchange reversibly is considered to
be $\delta Q_{\mathrm{rev}}\equiv {\rm d}r_s-\mathcal{F}_H {\rm d}l$,
previously obtained by Molina \& Villanueva \cite{fmv20}, where $r_s$ is the
Schwarzschild radius, $l$ is the Hayward's parameter responsible for the
possible regularization of the Schwarzschild black hole, and $\mathcal{F}_H$ is
the intensive variable called the Hayward's force. By solving the associated
Cauchy problem, the adiabatic paths are confined to the non-extremal manifold,
and therefore, the status of the second and third laws are preserved.
Consequently, the extremal sub-manifold corresponds to the {adiabatically
disconnected} boundary of the manifold. In addition, the merger of two extremal
Hayward black holes is analyzed.


Abstract:
  Conventional transfer learning leverages weights of pre-trained networks, but
mandates the need for similar neural architectures. Alternatively, knowledge
distillation can transfer knowledge between heterogeneous networks but often
requires access to the original training data or additional generative
networks. Knowledge transfer between networks can be improved by being agnostic
to the choice of network architecture and reducing the dependence on original
training data. We propose a knowledge transfer approach from a teacher to a
student network wherein we train the student on an independent transferal
dataset, whose annotations are generated by the teacher. Experiments were
conducted on five state-of-the-art networks for semantic segmentation and seven
datasets across three imaging modalities. We studied knowledge transfer from a
single teacher, combination of knowledge transfer and fine-tuning, and
knowledge transfer from multiple teachers. The student model with a single
teacher achieved similar performance as the teacher; and the student model with
multiple teachers achieved better performance than the teachers. The salient
features of our algorithm include: 1)no need for original training data or
generative networks, 2) knowledge transfer between different architectures, 3)
ease of implementation for downstream tasks by using the downstream task
dataset as the transferal dataset, 4) knowledge transfer of an ensemble of
models, trained independently, into one student model. Extensive experiments
demonstrate that the proposed algorithm is effective for knowledge transfer and
easily tunable.


Abstract:
  The thermodynamic uncertainty relations (TURs) provide lower bounds on the
entropy production (EP) of a system in terms of the statistical precision of an
arbitrary current in that system. All conventional TURs derived so far have
concerned a single physical system, differing from one another in what
properties they require the system to have. However, many physical scenarios of
interest involve multiple interacting systems, e.g. organelles within a
biological cell. Here we show how to extend the conventional TURs to those
scenarios. A common feature of these extended versions of the TURs is that they
bound the global EP, jointly generated by the set of interacting systems, in
terms of a weighted sum of the precisions of the local currents generated
within those systems -- plus an information-theoretic correction term.
Importantly, these extended TURs can bound the global EP even when the global
system does not meet any of the requirements of the conventional TURs. After
deriving these extended TURs we use them to obtain bounds that do not involve
the global EP, but instead relate the local EPs of the individual systems and
the statistical coupling among the currents generated within those systems. We
derive such bounds for both scalar-valued and vector-valued currents within
each system. We illustrate our results with numerical experiments.


Abstract:
  Current neural architecture search (NAS) algorithms still require expert
knowledge and effort to design a search space for network construction. In this
paper, we consider automating the search space design to minimize human
interference, which however faces two challenges: the explosive complexity of
the exploration space and the expensive computation cost to evaluate the
quality of different search spaces. To solve them, we propose a novel
differentiable evolutionary framework named AutoSpace, which evolves the search
space to an optimal one with following novel techniques: a differentiable
fitness scoring function to efficiently evaluate the performance of cells and a
reference architecture to speedup the evolution procedure and avoid falling
into sub-optimal solutions. The framework is generic and compatible with
additional computational constraints, making it feasible to learn specialized
search spaces that fit different computational budgets. With the learned search
space, the performance of recent NAS algorithms can be improved significantly
compared with using previously manually designed spaces. Remarkably, the models
generated from the new search space achieve 77.8% top-1 accuracy on ImageNet
under the mobile setting (MAdds < 500M), out-performing previous SOTA
EfficientNet-B0 by 0.7%. All codes will be made public.


Abstract:
  We introduce a discriminative multimodal descriptor based on a pair of sensor
readings: a point cloud from a LiDAR and an image from an RGB camera. Our
descriptor, named MinkLoc++, can be used for place recognition, re-localization
and loop closure purposes in robotics or autonomous vehicles applications. We
use late fusion approach, where each modality is processed separately and fused
in the final part of the processing pipeline. The proposed method achieves
state-of-the-art performance on standard place recognition benchmarks. We also
identify dominating modality problem when training a multimodal descriptor. The
problem manifests itself when the network focuses on a modality with a larger
overfit to the training data. This drives the loss down during the training but
leads to suboptimal performance on the evaluation set. In this work we describe
how to detect and mitigate such risk when using a deep metric learning approach
to train a multimodal neural network. Our code is publicly available on the
project website: https://github.com/jac99/MinkLocMultimodal.


Abstract:
  Liquid xenon offers several features, which make it suitable for applications
in nuclear medicine, such as high scintillation yield and fast scintillation
decay time. Moreover, being a continuous medium with a uniform response, liquid
xenon allows one to avoid most of the geometrical distortions of conventional
detectors based on scintillating crystals. In this paper, we describe how these
properties have motivated the development of a novel concept for positron
emission tomography scanners with Time-Of-Flight measurement, which combines a
liquid xenon scintillating volume and silicon photomultipliers as sensors. A
Monte Carlo investigation has pointed out that this technology would provide an
excellent intrinsic time resolution, down to 70 ps. Also, the transparency of
liquid xenon to UV and blue wavelengths opens the possibility of exploiting
both scintillation and Cherenkov light for a high-sensitivity positron emission
tomography scanner with Time-Of-Flight capabilities. Monte Carlo simulations
point to a time resolution of 30-50 ps obtained using Cherenkov light. A
prototype is being built to demonstrate the high resolution in energy, time and
reconstruction of spatial coordinates of this concept, using a ring of 30 cm
internal diameter and a depth of 3 cm instrumented with VUV-sensitive silicon
photomultipliers.


Abstract:
  Over-parametrized deep neural networks trained by stochastic gradient descent
are successful in performing many tasks of practical relevance. One aspect of
over-parametrization is the possibility that the student network has a larger
expressivity than the data generating process. In the context of a
student-teacher scenario, this corresponds to the so-called over-realizable
case, where the student network has a larger number of hidden units than the
teacher. For on-line learning of a two-layer soft committee machine in the
over-realizable case, we find that the approach to perfect learning occurs in a
power-law fashion rather than exponentially as in the realizable case. All
student nodes learn and replicate one of the teacher nodes if teacher and
student outputs are suitably rescaled.


Abstract:
  Fractures occur in the shoulder area, which has a wider range of motion than
other joints in the body, for various reasons. To diagnose these fractures,
data gathered from Xradiation (X-ray), magnetic resonance imaging (MRI), or
computed tomography (CT) are used. This study aims to help physicians by
classifying shoulder images taken from X-ray devices as fracture / non-fracture
with artificial intelligence. For this purpose, the performances of 26 deep
learning-based pretrained models in the detection of shoulder fractures were
evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble
learning models (EL1 and EL2) were developed. The pretrained models used are
ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully
connected (Spinal FC) versions. In the EL1 and EL2 models developed using
pretrained models with the best performance, test accuracy was 0.8455,0.8472,
Cohens kappa was 0.6907, 0.6942 and the area that was related with fracture
class under the receiver operating characteristic (ROC) curve (AUC) was
0.8862,0.8695. As a result of 28 different classifications in total, the
highest test accuracy and Cohens kappa values were obtained in the EL2 model,
and the highest AUC value was obtained in the EL1 model.


Abstract:
  Given the voluminous nature of the multimedia sensed data, the Multimedia
Internet of Things (MIoT) devices and networks will present several limitations
in terms of power and communication overhead. One traditional solution to cope
with the large-size data challenge is to use lossy compression. However,
current lossy compression schemes require low compression rate to guarantee
acceptable perceived image quality, which results in a low reduction of the
communicated data size and consequently a low reduction in the energy and
bandwidth consumption. Thus, an efficient compression solution is required for
striking a good balance between data size (and consequently communication
overhead) and visual degradation. In this paper, a Deep-Learning (DL)
super-resolution model is applied to recuperate high quality images (at the
application server side) given as input degraded images with a high compression
ratio (at the sender side). The experimental analysis shows the effectiveness
of the proposed solution in enhancing the visual quality of the compressed and
down-scaled images. Consequently, the proposed solution reduces the overall
communication overhead and power consumption of limited MIoT devices.


Abstract:
  Volume of fluid(VOF) method is a sharp interface method employed for
simulations of two phase flows. Interface in VOF is usually represented using
piecewise linear line segments in each computational grid based on the volume
fraction field. While VOF for cartesian coordinates conserve mass exactly,
existing algorithms do not show machine-precision mass conservation for
axisymmetric coordinate systems. In this work, we propose analytic formulae for
interface reconstruction in axisymmetric coordinates, similar to those proposed
by Scardovelli and Zaleski (J. Comput. Phys. 2000) for cartesian coordinates.
We also propose modifications to the existing advection schemes in VOF for
axisymmetric coordinates to obtain higher accuracy in mass conservation


Abstract:
  We investigate the uniform boundedness of the fronts of the solutions to the
randomized Fisher-KPP equation and to its linearization, the parabolic Anderson
model. It has been known that for the standard (i.e. deterministic) Fisher-KPP
equation, as well as for the special case of a randomized Fisher-KPP equation
with so-called ignition type nonlinearity, one has a uniformly bounded (in
time) transition front. Here, we show that this property of having a uniformly
bounded transition front fails to hold for the general randomized Fisher-KPP
equation. Nevertheless, we establish that this property does hold true for the
parabolic Anderson model.


Abstract:
  Memory effects can have a profound impact on the resistivity of semiconductor
systems, resulting in giant negative magnetoresistance and MIRO phenomena. This
work opens the discussion of the memory effects in 3D conducting systems
featured by the presence of the extended one-dimensional defects, such as screw
dislocations or static charge stripes. We demonstrate that accounting for the
memory effect, that is the capture of electrons on collisionless spiral
trajectories winding around extended defects, leads to the strong negative
magnetoresistance in case when the external magnetic field direction becomes
parallel to the defects axis. This effect gives rise to a significant
magnetoresistance anisotropy already for an isotropic Fermi surface and no
spin-orbit effects. The proposed resistivity feature can be used to detect
one-dimensional scattering defects in these systems.


Abstract:
  The Partial Least Square Regression (PLSR) algorithm exhibits exceptional
competence for predicting continuous variables from inter-correlated brain
recordings in brain-computer interfaces, which achieved successful prediction
from epidural electrocorticography of macaques to three-dimensional continuous
hand trajectories recently. Nevertheless, PLSR is in essence formulated based
on the least square criterion, thus, being non-robust with respect to
complicated noises consequently. The aim of the present study is to propose a
robust version of PLSR. To this end, the maximum correntropy criterion is
adopted to structure a new robust variant of PLSR, namely Partial Maximum
Correntropy Regression (PMCR). Half-quadratic optimization technique is
utilized to calculate the robust latent variables. We assess the proposed PMCR
on a synthetic example and the public Neurotycho dataset. Compared with the
conventional PLSR and the state-of-the-art variant, PMCR realized superior
prediction competence on three different performance indicators with
contaminated training set. The proposed PMCR was demonstrated as an effective
approach for robust decoding from noisy brain measurements, which could reduce
the performance degradation resulting from adverse noises, thus, improving the
decoding robustness of brain-computer interfaces.


Abstract:
  The body of experimental measurements of intermediate-energy reactions that
remove a single nucleon from a secondary beam of neutron- or proton-rich nuclei
continues to grow. These data have been analysed consistently using an
approximate, eikonal-model treatment of the reaction dynamics combined with
appropriate shell-model descriptions of the projectile initial state, the bound
final states spectrum of the reaction residue and single-particle removal
strengths computed from their wave-function overlaps. The systematics of the
ratio $R_s$ of the measured inclusive cross-section to all bound final states
and the calculated cross-section to bound shell-model states -- in different
regions of the nuclear chart and involving both very weakly-bound and
strongly-bound valence nucleons -- is important in relating the empirically
deduced orbital occupancies to those from the best available shell-model
predictions. Importantly, several new higher-energy measurements, for which the
sudden-approximation aspect of the dynamical description is placed on an even
stronger footing, now supplement the previously-analysed measurements. These
additional data sets are discussed. Their $R_s$ values are shown to conform to
and reinforce the earlier-observed systematics, with no indication that the
approximately linear reduction in $R_s$ with increasing nucleon separation
energy is a consequence of a breakdown of the sudden approximation.


Abstract:
  We search for ultra-luminous Quasi-Stellar Objects (QSOs) at high redshift
using photometry from the SkyMapper Southern Survey Data Release 3 (DR3), in
combination with 2MASS, VHS DR6, VIKING DR5, AllWISE, and CatWISE2020, as well
as parallaxes and proper motions from Gaia DR2 and eDR3. We report 142 newly
discovered Southern QSOs at $3.8<z<5.5$, of which 126 have $M_{145} <-27$ ABmag
and are found in a search area of 14,486 deg$^2$. This Southern sample,
utilising the Gaia astrometry to offset wider photometric colour criteria,
achieves unprecedented completeness for an ultra-luminous QSO search at high
redshift. In combination with already known QSOs, we construct a sample that is
$>80$ per cent complete for $M_{145}<-27.33$ ABmag at $z=4.7$ and for
$M_{145}<-27.73$ ABmag at $z=5.4$. We derive the bright end of the QSO
luminosity function at restframe 145 nm for $z=4.7-5.4$ and measure its slope
to be $\beta = -3.60\pm0.37$ and $\beta = -3.38\pm0.32$ for two different
estimates of the faint-end QSO density adopted from the literature. We also
present the first $z\sim 5$ QSO luminosity function at restframe 300 nm.


Abstract:
  A single-hole ground state ansatz for the two-dimensional t-J model has been
recently studied by variational Monte Carlo (VMC) method. Such a doped hole
behaves like a ``twisted'' non-Landau quasiparticle characterized by an
emergent quantum number in agreement with exact numerics. In this work, we
further investigate the ground state of two holes by VMC. It is found that the
two holes strongly attract each other to form a pairing state with a new
quantum number the same as obtained by the numerical exact diagonalization (ED)
and density matrix renormalization group (DMRG) calculations. A unique feature
of this pairing state is a dichotomy in the pairing symmetry, i.e., a d-wave in
terms of the electron c-operators and an s-wave in terms of the new
quasiparticles, as explicitly illustrated in the ground state wave function. A
similar VMC study of a two-hole wave function for the t-J two-leg ladder also
yields a good agreement with the DMRG result. We demonstrate that the pairing
mechanism responsible for the strong binding here is not due to the long-range
antiferromagnetic nor the resonating-valence-bound pairing in the spin
background, but is the consequence of the quantum phase strings created by the
hopping of holes. The resulting spin current pattern mediating the pairing
force is explicitly illustrated in the VMC calculation. Physical implications
to superconductivity at finite doping will be also discussed.


Abstract:
  Author name disambiguation results are often evaluated by measures such as
Cluster-F, K-metric, Pairwise-F, Splitting & Lumping Error, and B-cubed.
Although these measures have distinctive evaluation schemes, this paper shows
that they can be calculated in a single framework by a set of common steps that
compare truth and predicted clusters through two hash tables recording
information about name instances with their predicted cluster indices and
frequencies of those indices per truth cluster. This integrative calculation
reduces greatly calculation runtime, which is scalable to a clustering task
involving millions of name instances within a few seconds. During the
integration process, B-cubed and K-metric are shown to produce the same
precision and recall scores. In this framework, especially, name instance pairs
for Pairwise-F are counted using a heuristic, surpassing a state-of-the-art
algorithm in speedy calculation. Details of the integrative calculation are
described with examples and pseudo-code to assist scholars to implement each
measure easily and validate the correctness of implementation. The integrative
calculation will help scholars compare similarities and differences of multiple
measures before they select ones that characterize best the clustering
performances of their disambiguation methods.


Abstract:
  The possibility of two interaction regions (IRs) is a design requirement for
the Electron Ion Collider (the EIC). There is also a significant interest from
the nuclear physics community in a 2nd IR with measurements capabilities
complementary to those of the first IR. While the 2nd IR will be in operation
over the entire energy range of ~20GeV to ~140GeV center of mass (CM). The 2nd
IR can also provide an acceptance coverage complementary to that of the first.
We present a brief overview and the current progress of the 2nd IR design in
terms of the parameters, magnet layout, and beam dynamics.


Abstract:
  A quiet point, an operating point of pump-resonance detuning that minimizes
frequency fluctuation due to nonlinear effects inside a resonator, has been
employed for phase noise reduction of a soliton Kerr microresonator frequency
comb (microcomb). Naturally, it is expected that the use of the point will also
improve performances of a microcomb in terms of frequency stability and
faithfulness in a phase locked loop. In this study, we experimentally
investigate the effect in a microcomb with a repetition frequency of 300~GHz.
We obtain a lowest fractional frequency instability at a quiet point of
$1.5\times 10^{-9}$ at 1 second, which is 44 times lower than free-running
instability. Phase-locking of a microcomb to a stabilized fiber comb is
demonstrated to evaluate performance in a feedback loop, where in-loop-limited
relative fractional frequency instability between the microcomb and the fiber
comb of $6.8 \times 10^{-13}$ is obtained as an indicator of the stability
limitation.


Abstract:
  Recent work has highlighted roles for thermodynamic phase behavior in diverse
cellular processes. Proteins and nucleic acids can phase separate into
three-dimensional liquid droplets in the cytoplasm and nucleus and the plasma
membrane of animal cells appears tuned close to a two-dimensional liquid-liquid
critical point. In some examples, cytoplasmic proteins aggregate at plasma
membrane domains, forming structures such as the post-synaptic density and
diverse signaling clusters. Here we examine the physics of these surface
densities, employing minimal simulations of co-acervating polymers coupled to
an Ising membrane surface in conjunction with a complementary Landau theory. We
argue that these surface densities are a novel phase reminiscent of
pre-wetting, in which a molecularly thin three-dimensional liquid forms on a
usually solid surface. However, in surface densities the solid surface is
replaced by a membrane with an independent propensity to phase separate. We
show that proximity to criticality in the membrane dramatically increases the
parameter regime in which a pre-wetting-like transition occurs, leading to a
broad region where coexisting surface phases can form even when a bulk phase is
unstable. Our simulations naturally exhibit three surface phase coexistence
even though both the membrane and the polymer bulk can only display two phase
coexistence on their own. We argue that the physics of these surface densities
enables diverse functions seen in Eukaryotic cells.


Abstract:
  Colexification refers to the phenomenon of multiple meanings sharing one word
in a language. Cross-linguistic lexification patterns have been shown to be
largely predictable, as similar concepts are often colexified. We test a recent
claim that, beyond this general tendency, communicative needs play an important
role in shaping colexification patterns. We approach this question by means of
a series of human experiments, using an artificial language communication game
paradigm. Our results across four experiments match the previous
cross-linguistic findings: all other things being equal, speakers do prefer to
colexify similar concepts. However, we also find evidence supporting the
communicative need hypothesis: when faced with a frequent need to distinguish
similar pairs of meanings, speakers adjust their colexification preferences to
maintain communicative efficiency, and avoid colexifying those similar meanings
which need to be distinguished in communication. This research provides further
evidence to support the argument that languages are shaped by the needs and
preferences of their speakers.


Abstract:
  To improve the efficiency of flood early warning systems (FEWS), it is
important to understand the interactions between natural and social systems.
The high level of trust in authorities and experts is necessary to improve the
likeliness of individuals to take preparedness actions responding to warnings.
Despite a lot of efforts to develop the dynamic model of human and water in
socio-hydrology, no socio-hydrological models explicitly simulate social
collective trust in FEWS. Here we develop the stylized model to simulate the
interactions of flood, social collective memory, social collective trust in
FEWS, and preparedness actions responding to warnings by extending the existing
socio-hydrological model. We realistically simulate the cry wolf effect, in
which many false alarms undermine the credibility of the early warning systems
and make it difficult to induce preparedness actions. We found (1) considering
the dynamics of social collective trust in FEWS is more important in the
technological society with infrequent flood events than in the green society
with frequent flood events; (2) as the natural scientific skill to predict
flood events is improved, the efficiency of FEWS gets more sensitive to the
behavior of social collective trust, so that forecasters need to determine
their warning threshold by considering the social aspects.


Abstract:
  Designing novel protein sequences for a desired 3D topological fold is a
fundamental yet non-trivial task in protein engineering. Challenges exist due
to the complex sequence--fold relationship, as well as the difficulties to
capture the diversity of the sequences (therefore structures and functions)
within a fold. To overcome these challenges, we propose Fold2Seq, a novel
transformer-based generative framework for designing protein sequences
conditioned on a specific target fold. To model the complex sequence--structure
relationship, Fold2Seq jointly learns a sequence embedding using a transformer
and a fold embedding from the density of secondary structural elements in 3D
voxels. On test sets with single, high-resolution and complete structure inputs
for individual folds, our experiments demonstrate improved or comparable
performance of Fold2Seq in terms of speed, coverage, and reliability for
sequence design, when compared to existing state-of-the-art methods that
include data-driven deep generative models and physics-based RosettaDesign. The
unique advantages of fold-based Fold2Seq, in comparison to a structure-based
deep model and RosettaDesign, become more evident on three additional
real-world challenges originating from low-quality, incomplete, or ambiguous
input structures. Source code and data are available at
https://github.com/IBM/fold2seq.


Abstract:
  We exploit the relationship between the stochastic Koopman operator and the
Kolmogorov backward equation to construct importance sampling schemes for
stochastic differential equations. Specifically, we propose using
eigenfunctions of the stochastic Koopman operator to approximate the Doob
transform for an observable of interest (e.g., associated with a rare event)
which in turn yields an approximation of the corresponding zero-variance
importance sampling estimator. Our approach is broadly applicable and
systematic, treating non-normal systems, non-gradient systems, and systems with
oscillatory dynamics or rank-deficient noise in a common framework. In
nonlinear settings where the stochastic Koopman eigenfunctions cannot be
derived analytically, we use dynamic mode decomposition (DMD) methods to
compute them numerically, but the framework is agnostic to the particular
numerical method employed. Numerical experiments demonstrate that even coarse
approximations of a few eigenfunctions, where the latter are built from
non-rare trajectories, can produce effective importance sampling schemes for
rare events.


Abstract:
  This is a sequel to [Xu18] on the $L$-packets of quasisplit general
symplectic and even orthogonal groups. We show the existence of global
$L$-packets and establish the functoriality of endoscopic transfer for them in
many cases.


Abstract:
  The Ramsey sequence is a canonical example of a quantum phase measurement for
a spin qubit. In Ramsey measurements, the measurement efficiency can be
optimized through careful selection of settings for the phase accumulation time
setting, $\tau$. This paper implements a sequential Bayesian experiment design
protocol for the phase accumulation time in low-fidelity Ramsey measurements,
and performance is compared to both a previously reported adaptive heuristic
protocol and random setting choices. A workflow allowing measurements and
design calculations to run concurrently largely eliminates computation time
from measurement overhead. When precession frequency is the lone parameter to
estimate, the Bayesian design is faster by factors of 2 and 4 relative to the
adaptive heuristic and random protocols respectively.


Abstract:
  Hybrid spin-optomechanical quantum systems offer high flexibility,
integrability and applicability for quantum science and technology.
Particularly, on-chip surface acoustic waves (SAWs) can efficiently drive spin
transitions in the ground states (GSs) of atomic-scale, color centre qubits,
which are forbidden in case of the more frequently used electromagnetic fields.
Here, we demonstrate that strain-induced spin interactions within their
optically excited state (ES) can exceed by two orders of magnitude the ones
within the GS. This gives rise to novel physical phenomena, such as the
acoustically induced coherent spin trapping (CST) unvealed here. The CST
manifests itself as the spin preservation along one particular direction under
the coherent drive of the GS and ES by the same acoustic field. Our findings
provide new opportunities for the coherent control of spin qubits with
dynamically generated strain fields that can lead towards the realization of
future spin-acoustic quantum devices.


Abstract:
  We propose new mathematical models of inventory management in a reverse
logistics system. The proposed models extend the model introduced by Nahmias
and Rivera with the assumption that the demand for newly produced and repaired
(remanufacturing) items are not the same. We derive two mathematical models and
formulate unconstrained and constrained optimization problems to optimize the
holding cost. We also introduce the solution procedures of the proposed
problems. The exactness of the proposed solutions has been tested by numerical
experiments. Nowadays, it is an essential commitment for industries to reduce
greenhouse gas (GHG) emissions as well as energy consumption during the
production and remanufacturing processes. This paper also extends along this
line of research, and therewith develops a three-objective mathematical model
and provides an algorithm to obtain the Pareto solution.


Abstract:
  The boundaries identification of Kelvin-Helmholtz vortices in observational
data has been addressed by searching for single-spacecraft small-scale
signatures. A recent hybrid Vlasov-Maxwell simulation of Kelvin-Helmholtz
instability has pointed out clear kinetic features which uniquely characterize
the vortex during both the nonlinear and turbulent stage of the instability. We
compare the simulation results with \textit{in situ} observations of
Kelvin-Helmholtz vortices by the Magnetospheric MultiScale satellites. We find
good agreement between simulation and observations. In particular, the edges of
the vortex are associated with strong current sheets, while the center is
characterized by a low value for the magnitude of the total current density and
strong deviation of the ion distribution function from a Maxwellian
distribution. We also find a significant temperature anisotropy parallel to the
magnetic field inside the vortex region and strong agyrotropies near the edges.
We suggest that these kinetic features can be useful for the identification of
Kelvin-Helmholtz vortices in \textit{in situ} data.


Abstract:
  This paper aims at studying the sample complexity of graph convolutional
networks (GCNs), by providing tight upper bounds of Rademacher complexity for
GCN models with a single hidden layer. Under regularity conditions, theses
derived complexity bounds explicitly depend on the largest eigenvalue of graph
convolution filter and the degree distribution of the graph. Again, we provide
a lower bound of Rademacher complexity for GCNs to show optimality of our
derived upper bounds. Taking two commonly used examples as representatives, we
discuss the implications of our results in designing graph convolution filters
an graph distribution.


Abstract:
  Named entity recognition (NER) is a fundamental component in many
applications, such as Web Search and Voice Assistants. Although deep neural
networks greatly improve the performance of NER, due to the requirement of
large amounts of training data, deep neural networks can hardly scale out to
many languages in an industry setting. To tackle this challenge, cross-lingual
NER transfers knowledge from a rich-resource language to languages with low
resources through pre-trained multilingual language models. Instead of using
training data in target languages, cross-lingual NER has to rely on only
training data in source languages, and optionally adds the translated training
data derived from source languages. However, the existing cross-lingual NER
methods do not make good use of rich unlabeled data in target languages, which
is relatively easy to collect in industry applications. To address the
opportunities and challenges, in this paper we describe our novel practice in
Microsoft to leverage such large amounts of unlabeled data in target languages
in real production settings. To effectively extract weak supervision signals
from the unlabeled data, we develop a novel approach based on the ideas of
semi-supervised learning and reinforcement learning. The empirical study on
three benchmark data sets verifies that our approach establishes the new
state-of-the-art performance with clear edges. Now, the NER techniques reported
in this paper are on their way to become a fundamental component for Web
ranking, Entity Pane, Answers Triggering, and Question Answering in the
Microsoft Bing search engine. Moreover, our techniques will also serve as part
of the Spoken Language Understanding module for a commercial voice assistant.
We plan to open source the code of the prototype framework after deployment.


Abstract:
  The randomized Gauss--Seidel method and its extension have attracted much
attention recently and their convergence rates have been considered
extensively. However, the convergence rates are usually determined by upper
bounds, which cannot fully reflect the actual convergence. In this paper, we
make a detailed analysis of their convergence behaviors. The analysis shows
that the larger the singular value of $A$ is, the faster the error decays in
the corresponding singular vector space, and the convergence directions are
mainly driven by the large singular values at the beginning, then gradually
driven by the small singular values, and finally by the smallest nonzero
singular value. These results explain the phenomenon found in the extensive
numerical experiments appearing in the literature that these two methods seem
to converge faster at the beginning. Numerical examples are provided to confirm
the above findings.


Abstract:
  We explore strain-modulated helimagnetism in highly crystalline MnP nanorod
films grown on Si(100) substrates using molecular beam epitaxy. The strained
MnP film exhibits a paramagnetic to ferromagnetic (PM-FM) phase transition at
TC ~ 279 K, and the FM to helical phase transition at TN ~ 110 K. The value of
TN is greater than TN ~ 47 K for the MnP single crystal, indicating strong
strain-modulated helimagnetic states in the MnP nanorod film. The presence of
significant thermal hysteresis in the helical phase indicates coexistence of
competing magnetic interactions, leading to the first-order metamagnetic
transition. Similar to its single crystal counterpart, an anisotropic magnetic
effect is observed in the MnP film, which is independently confirmed by
magnetic hysteresis loop and radio-frequency transverse susceptibility (TS)
measurements. The evolution of screw (SCR) to CONE and FAN phase is precisely
tracked from magnetization versus magnetic field/temperature measurements. The
temperature dependence of the anisotropy fields, extracted from the TS spectra,
yields further insight into the competing nature of the magnetic phases.
Unfolding of the different helical phases at T < 120 K (~TN) is analyzed by the
temperature- and field-dependent magnetic entropy change. Based on these
findings, the comprehensive magnetic phase diagrams of the MnP nanorod film are
constructed for the first time for both the in-plane and out-of-plane magnetic
field directions, revealing emergent strain/dimensionality-driven helical
magnetic features that are absent in the magnetic phase diagram of the MnP
single crystal.


Abstract:
  Photons radiated from an evaporating black hole in principle provide complete
information on the particle spectrum of nature up to the Planck scale. If an
evaporating black hole were to be observed, it would open a unique window onto
models beyond the Standard Model of particle physics. To demonstrate this, we
compute the limits that could be placed on the size of a dark sector. We find
that observation of an evaporating black hole at a distance of 0.01 parsecs
could probe dark sector models containing one or more copies of the Standard
Model particles, with any mass scale up to 300 TeV.


Abstract:
  Gaussian processes (GPs) are nonparametric Bayesian models that have been
applied to regression and classification problems. One of the approaches to
alleviate their cubic training cost is the use of local GP experts trained on
subsets of the data. In particular, product-of-expert models combine the
predictive distributions of local experts through a tractable product
operation. While these expert models allow for massively distributed
computation, their predictions typically suffer from erratic behaviour of the
mean or uncalibrated uncertainty quantification. By calibrating predictions via
a tempered softmax weighting, we provide a solution to these problems for
multiple product-of-expert models, including the generalised product of experts
and the robust Bayesian committee machine. Furthermore, we leverage the optimal
transport literature and propose a new product-of-expert model that combines
predictions of local experts by computing their Wasserstein barycenter, which
can be applied to both regression and classification.


Abstract:
  We present the cumulative star-formation histories (SFHs) of >15000 dwarf
galaxies ($M_{*}=10^{7-10}M_{\odot}$) from the TNG50 run of the IllustrisTNG
suite across a vast range of environments. The key factors determining the
dwarfs' SFHs are their status as central or satellite and their stellar mass,
with centrals and more massive dwarfs assembling their stellar mass at later
times on average compared to satellites and lower mass dwarfs. The satellites
(in hosts of total mass $M_{200c,\,host}=10^{12-14.3}M_{\odot}$) assembled 90%
of their z=0 stellar mass ~$7.0_{-5.5}^{+3.3}$ Gyr ago, while the centrals did
so only ~$1.0_{-0.5}^{+4.0}$ Gyr ago. TNG50 predicts a large diversity in SFHs
for both centrals and satellites, so that the stacked cumulative SFHs are
representative of the TNG50 dwarf populations only in an average sense and
individual dwarfs can have significantly different cumulative SFHs. Satellite
dwarfs with the highest stellar mass to host mass ratios have the latest
stellar mass assembly. Satellites at fixed stellar and host halo mass, found
closer to the cluster centre, or accreted at earlier times, show significantly
earlier stellar mass assembly. These trends, as well as the shapes of the SFHs
themselves, are a manifestation of the varying proportions within a given
subsample of quenched vs. star-forming galaxies, which exhibit markedly
distinct SFH shapes. We also find a subtle effect whereby satellite dwarfs in
the most massive hosts at z=0 have higher SFRs at early times, well before
final infall into their z=0 host, compared to a control sample of centrals
mass-matched at the time of accretion. This suggests that the large-scale
environment can have a mild effect even on future satellites by providing the
conditions for enhanced SF at early epochs. Our results are useful theoretical
predictions for comparison to future resolved-stellar-population observations.


Abstract:
  The class of port-Hamiltonian systems incorporates many physical models, such
as mechanical systems in the finite-dimensional case and wave and beam
equations in the infinite-dimensional case. In this paper we study a subclass
of linear first order port-Hamiltonian systems. These systems are exactly
observable when the energy is not dissipated internally and when sufficient
observations are made at the boundary. In this article we study the
observability properties for these systems when internal dissipation of energy
is possible. We cannot show the exact observability, but we do show that the
Hautus test is satisfied. In general, the Hautus test is weaker than exact
observability, but stronger than approximate observability. Hence we conclude
that these systems are approximately observable.


Abstract:
  End-to-end encryption (E2EE) by messaging platforms enable people to securely
and privately communicate with one another. Its widespread adoption however
raised concerns that illegal content might now be shared undetected. Following
the global pushback against key escrow systems, client-side scanning based on
perceptual hashing has been recently proposed by tech companies, governments
and researchers to detect illegal content in E2EE communications. We here
propose the first framework to evaluate the robustness of perceptual
hashing-based client-side scanning to detection avoidance attacks and show
current systems to not be robust. More specifically, we propose three
adversarial attacks--a general black-box attack and two white-box attacks for
discrete cosine transform-based algorithms--against perceptual hashing
algorithms. In a large-scale evaluation, we show perceptual hashing-based
client-side scanning mechanisms to be highly vulnerable to detection avoidance
attacks in a black-box setting, with more than 99.9\% of images successfully
attacked while preserving the content of the image. We furthermore show our
attack to generate diverse perturbations, strongly suggesting that
straightforward mitigation strategies would be ineffective. Finally, we show
that the larger thresholds necessary to make the attack harder would probably
require more than one billion images to be flagged and decrypted daily, raising
strong privacy concerns. Taken together, our results shed serious doubts on the
robustness of perceptual hashing-based client-side scanning mechanisms
currently proposed by governments, organizations, and researchers around the
world.


Abstract:
  Despite pre-trained language models have proven useful for learning
high-quality semantic representations, these models are still vulnerable to
simple perturbations. Recent works aimed to improve the robustness of
pre-trained models mainly focus on adversarial training from perturbed examples
with similar semantics, neglecting the utilization of different or even
opposite semantics. Different from the image processing field, the text is
discrete and few word substitutions can cause significant semantic changes. To
study the impact of semantics caused by small perturbations, we conduct a
series of pilot experiments and surprisingly find that adversarial training is
useless or even harmful for the model to detect these semantic changes. To
address this problem, we propose Contrastive Learning with semantIc Negative
Examples (CLINE), which constructs semantic negative examples unsupervised to
improve the robustness under semantically adversarial attacking. By comparing
with similar and opposite semantic examples, the model can effectively perceive
the semantic changes caused by small perturbations. Empirical results show that
our approach yields substantial improvements on a range of sentiment analysis,
reasoning, and reading comprehension tasks. And CLINE also ensures the
compactness within the same semantics and separability across different
semantics in sentence-level.


Abstract:
  We study deterministic power-law quantum hopping model with an amplitude
$J(r) \propto - r^{-\beta}$ and local Gaussian disorder in low dimensions
$d=1,2$ under the condition $d < \beta < 3d/2$. We demonstrate unusual
combination of exponentially decreasing density of the "tail states" and
localization-delocalization transition (as function of disorder strength $w$)
pertinent to a small (vanishing in thermodynamic limit) fraction of
eigenstates. At sub-critical disorder $w < w_c$ delocalized eigenstates with
energies near the bare band edge co-exist with a strongly localized eigenstates
in the same energy window. At higher disorder $w > w_c$ all eigenstates are
localized. In a broad range of parameters density of states $\nu(E)$ decays
into the tail region $E <0$ as simple exponential, $ \nu(E) = \nu_0 e^{E/E_0}
$, while characteristic energy $E_0$ varies smoothly across edge localization
transition. We develop simple analytic theory which describes $E_0$ dependence
on power-law exponent $\beta$, dimensionality $d$ and disorder strength $W$,
and compare its predictions with exact diagonalization results. At low energies
within the bare "conduction band", all eigenstates are localized due to strong
quantum interference at $d=1,2$; however localization length grows fast with
energy decrease, contrary to the case of usual Schrodinger equation with local
disorder.


Abstract:
  We investigate the existence and non-existence of maximal green sequences for
quivers arising from weighted projective lines. Let $Q$ be the Gabreil quiver
of the endomorphism algebra of a basic cluster-tilting object in the cluster
category $\mathcal{C}_\mathbb{X}$ of a weighted projective line $\mathbb{X}$.
It is proved that there exists a quiver $Q'$ in the mutation equivalence class
$\operatorname{Mut}(Q)$ such that $Q'$ admits a maximal green sequence. On the
other hand, there is a quiver in $\operatorname{Mut}(Q)$ which does not admit a
maximal green sequence if and only if $\mathbb{X}$ is of wild type.


Abstract:
  Much attention has been devoted to water's metastable phase behavior,
including polyamorphism (multiple amorphous solid phases), and the hypothesized
liquid-liquid transition and associated critical point. However, the possible
relationship between these phenomena remains incompletely understood. Using
molecular dynamics simulations of the realistic TIP4P/2005 model, we found a
striking signature of the liquid-liquid critical point in the structure of
water glasses, manifested as a pronounced increase in long-range density
fluctuations at pressures proximate to the critical pressure. By contrast,
these signatures were absent in glasses of two model systems that lack a
critical point. We also characterized the departure from equilibrium upon
vitrification via the non-equilibrium index; water-like systems exhibited a
strong pressure dependence in this metric, whereas simple liquids did not.
These results reflect a surprising relationship between the metastable
equilibrium phenomenon of liquid-liquid criticality and the non-equilibrium
structure of glassy water, with implications for our understanding of water
phase behavior and glass physics. Our calculations suggest a possible
experimental route to probing the existence of the liquid-liquid transition in
water and other fluids.


Abstract:
  Spontaneous emergence of correlated states such as flocks and vortices are
prime examples of remarkable collective dynamics and self-organization observed
in active matter. The formation of globally correlated polar states in
geometrically confined systems proceeds through the emergence of a macroscopic
steadily rotating vortex that spontaneously selects a clockwise or
counterclockwise global chiral state. Here, we reveal that a global vortex
formed by colloidal rollers exhibits state memory. The information remains
stored even when the energy injection is ceased and the activity is terminated.
We show that a subsequent formation of the collective states upon re-energizing
the system is not random. We combine experiments and simulations to elucidate
how a combination of hydrodynamic and electrostatic interactions leads to
hidden asymmetries in the local particle positional order encoding the chiral
state of the system. The stored information can be accessed and exploited to
systematically command subsequent polar states of active liquid through
temporal control of the activity. With the chirality of the emergent collective
states controlled on-demand, active liquids offer new possibilities for flow
manipulation, transport, and mixing at the microscale.


Abstract:
  We report the fabrication of small (Ba,A)Fe2As2 (A: Na, K) coils using 10
m-class long round wires, fabricated by powder-in-tube (PIT) method. Coils are
sintered using hot-isostatic-press (HIP) technique after glass-fiber
insulations are installed. Critical current (Ic) of the whole coil using
(Ba,Na)Fe2As2 and (Ba,K)Fe2As2 are 60 A and 66 A under the self-field, and the
generated magnetic fields at the center of the coil reach 2.6 kOe and 2.5 kOe,
respectively. Furthermore, the largest transport critical current density (Jc)
and Ic in (Ba,Na)Fe2As2 wires picked up from the coil reach 54 kAcm-2 and 51.8
A at T = 4.2 K under a magnetic field of 100 kOe, respectively. This value
exceeds transport Jc of all previous iron-based superconducting round wires.
Texturing of grains in the core of the wire due to the improvement of the wire
drawing process plays a key role for the enhancement of Jc.


Abstract:
  The emergence of technologies such as 5G and mobile edge computing has
enabled provisioning of different types of services with different resource and
service requirements to the vehicles in a vehicular network.The growing
complexity of traffic mobility patterns and dynamics in the requests for
different types of services has made service placement a challenging task. A
typical static placement solution is not effective as it does not consider the
traffic mobility and service dynamics. In this paper, we propose a
reinforcement learning-based dynamic (RL-Dynamic) service placement framework
to find the optimal placement of services at the edge servers while considering
the vehicle's mobility and dynamics in the requests for different types of
services. We use SUMO and MATLAB to carry out simulation experiments. In our
learning framework, for the decision module, we consider two alternative
objective functions-minimizing delay and minimizing edge server utilization. We
developed an ILP based problem formulation for the two objective functions. The
experimental results show that 1) compared to static service placement,
RL-based dynamic service placement achieves fair utilization of edge server
resources and low service delay, and 2) compared to delay-optimized placement,
server utilization optimized placement utilizes resources more effectively,
achieving higher fairness with lower edge-server utilization.


Abstract:
  Latent Gaussian models and boosting are widely used techniques in statistics
and machine learning. Tree-boosting shows excellent prediction accuracy on many
data sets, but potential drawbacks are that it assumes conditional independence
of samples, produces discontinuous predictions for, e.g., spatial data, and it
can have difficulty with high-cardinality categorical variables. Latent
Gaussian models, such as Gaussian process and grouped random effects models,
are flexible prior models which explicitly model dependence among samples and
which allow for efficient learning of predictor functions and for making
probabilistic predictions. However, existing latent Gaussian models usually
assume either a zero or a linear prior mean function which can be an
unrealistic assumption. This article introduces a novel approach that combines
boosting and latent Gaussian models to remedy the above-mentioned drawbacks and
to leverage the advantages of both techniques. We obtain increased prediction
accuracy compared to existing approaches in both simulated and real-world data
experiments.


Abstract:
  We consider a mean-field control problem with linear dynamics and quadratic
control. We apply the vanishing viscosity method: we add a (regularizing) heat
diffusion with a small viscosity coefficient and let such coefficient go to
zero. The main result is that, in this case, the limit optimal control is
exactly the optimal control of the original problem.


Abstract:
  The human somatosensory cortex is intimately linked to other central brain
functions such as vision, audition, mechanoreception, and motor planning and
control. These links are established through brain learning, and display a
considerable functional plasticity. This latter fulfills an important adaptive
role and ensures, for example, that humans are able to reliably manipulate and
control objects in the physical world under constantly changing conditions in
their immediate sensory environment. Variations in human grip force are a
direct reflection of this specific kind of functional plasticity. Data from
preliminary experiments where wearable wireless sensor technology (sensor
gloves) was exploited to measure human grip force variations under varying
sensory input conditions (eyes open or shut, soft music or hard music during
gripping) are discussed here to show the extent to which grip force sensing
permits quantifying somatosensory brain interactions and their functional
plasticity. Experiments to take this preliminary work further are suggested.
Implications for robotics, in particular the development of end-effector robots
for upper limb movement planning and control, are brought forward.


Abstract:
  We investigate the rate-dependent fracture of vitrimers by conducting a tear
test. Based on the relationship between the frac-ture energy and the thickness
of vitrimer films, we, for the first time, obtain the intrinsic fracture energy
and bulk dissipation of vitrimers during crack extension. The intrinsic
fracture energy strongly depends on tear speed, and such dependence can be well
explained by Eyring theory. In contrast, the bulk dissipation only weakly
depends on tear speed, which is drastically different from observations on
traditional viscoelastic polymers. We ascribe such a weak rate-dependence to
the strong force-sensitivity of the exchange reaction of the dynamic covalent
bond in the vitrimer.


Abstract:
  Mathematical modeling of lithium-ion batteries (LiBs) is a central challenge
in advanced battery management. This paper presents a new approach to integrate
a physics-based model with machine learning to achieve high-precision modeling
for LiBs. This approach uniquely proposes to inform the machine learning model
of the dynamic state of the physical model, enabling a deep integration between
physics and machine learning. We propose two hybrid physics-machine learning
models based on the approach, which blend a single particle model with thermal
dynamics (SPMT) with a feedforward neural network (FNN) to perform
physics-informed learning of a LiB's dynamic behavior. The proposed models are
relatively parsimonious in structure and can provide considerable predictive
accuracy even at high C-rates, as shown by extensive simulations.


Abstract:
  In this paper, we introduce UnifiedM2, a general-purpose misinformation model
that jointly models multiple domains of misinformation with a single, unified
setup. The model is trained to handle four tasks: detecting news bias,
clickbait, fake news, and verifying rumors. By grouping these tasks together,
UnifiedM2learns a richer representation of misinformation, which leads to
state-of-the-art or comparable performance across all tasks. Furthermore, we
demonstrate that UnifiedM2's learned representation is helpful for few-shot
learning of unseen misinformation tasks/datasets and model's generalizability
to unseen events.


Abstract:
  A new domain decomposition preconditioner is introduced for efficiently
solving linear systems Ax = b with a symmetric positive definite matrix A. The
particularity of the new preconditioner is that it is not necessary to have
access to the so-called Neumann matrices (i.e.: the matrices that result from
assembling the variational problem underlying A restricted to each subdomain).
All the components in the preconditioner can be computed with the knowledge
only of A (and this is the meaning given here to the word algebraic). The new
preconditioner relies on the GenEO coarse space for a matrix that is a low-rank
modification of A and on the Woodbury matrix identity. The idea underlying the
new preconditioner is introduced here for the first time with a first version
of the preconditioner. Some numerical illustrations are presented. A more
extensive presentation including some improved variants of the new
preconditioner can be found in [7]
(https://hal.archives-ouvertes.fr/hal-03258644).


Abstract:
  Construction equipment is designed to maintain good traction, even when
operating in difficult off-road conditions. To curb wheel slip, the vehicles
are equipped with differential locks. A driver may engage/disengage the locks
to switch between two distinct operating modes: the closed mode is
characterized by greater off-road passability while the open mode allows better
manoeuvrability. However, many drivers lack the education and experience
required to correctly judge the terrain ahead of the vehicle and therefore
engage/disengage the locks in a suboptimal fashion. An automatic traction
control solution for locking and opening the differentials is hence desirable.
This paper compares three on/off differential lock control algorithms, all
derived from the same kinematic vehicle model but each relying on the
availability of output signals from different sensors. The validity of the
kinematic model and the algorithms' sensitivity to the values assumed by a
couple of unobservable states, the wheel slip angles, is investigated by
comparison to a realistic articulated hauler model in the multibody physics
simulator MSC ADAMS.


Abstract:
  We investigate the one-dimensional telegraph random process in the presence
of an elastic boundary at the origin. This process describes a finite-velocity
random motion that alternates between two possible directions of motion
(positive or negative). When the particle hits the origin, it is either
absorbed, with probability $\alpha$, or reflected upwards, with probability
$1-\alpha$. In the case of exponentially distributed random times between
consecutive changes of direction, we obtain the distribution of the renewal
cycles and of the absorption time at the origin. This investigation is
performed both in the case of motion starting from the origin and non-zero
initial state. We also study the probability law of the process within a
renewal cycle.


Abstract:
  The growth of sky surveys and the large amount of stellar spectra in the
current databases, has generated the necessity of developing new methods to
estimate atmospheric parameters, a fundamental task on stellar research. In
this work we present a comparison of different machine learning algorithms,
using for the classification of stellar synthetic spectra and the estimation of
fundamental stellar parameters included T_eff(K), log(L/Lo), log g, M/Mo, and
Vrot. For both tasks, we established a group of supervised learning models, and
propose a database of measures with the same structure to train the algorithms.
This data includes equivalent-width types measurements over noisy synthetic
spectra in order to replicate the natural noise on a real observed spectrum.
Different levels of signal to noise ratio are considered for this analysis.


Abstract:
  Using $p$-adic numbers, we partially categorize the cycles of a sizable class
of polynomial dynamical systems. In turn, we prove a few results related to the
non-trivial cycles of the $\textit{Collatz map}$ $\text{Col} : \mathbb{Z}_+ \to
\mathbb{Z}_+$ defined by $$\text{Col}(n) = \begin{cases} 3n + 1, & n \text{ is
odd;} \\ n/2, & \text{otherwise.}\end{cases}$$ Proving the non-existence of
non-trivial Collatz cycles would reduce the Collatz conjecture to whether the
Collatz map can diverge to infinity.


Abstract:
  Here we consider a non-perturbative description of the non-interacting
resonant model using a scattering approach employed in JCP 152(24) 244126
(2020). We showed that such description coincides with the standard NEGF and
Landauer-Buttiker approaches.


Abstract:
  Deep learning models, such as convolutional neural networks, have long been
applied to image and multi-media tasks, particularly those with structured
data. More recently, there has been more attention to unstructured data that
can be represented via graphs. These types of data are often found in health
and medicine, social networks, and research data repositories. Graph
convolutional neural networks have recently gained attention in the field of
deep learning that takes advantage of graph-based data representation with
automatic feature extraction via convolutions. Given the popularity of these
methods in a wide range of applications, robust uncertainty quantification is
vital. This remains a challenge for large models and unstructured datasets.
Bayesian inference provides a principled approach to uncertainty quantification
of model parameters for deep learning models. Although Bayesian inference has
been used extensively elsewhere, its application to deep learning remains
limited due to the computational requirements of the Markov Chain Monte Carlo
(MCMC) methods. Recent advances in parallel computing and advanced proposal
schemes in MCMC sampling methods has opened the path for Bayesian deep
learning. In this paper, we present Bayesian graph convolutional neural
networks that employ tempered MCMC sampling with Langevin-gradient proposal
distribution implemented via parallel computing. Our results show that the
proposed method can provide accuracy similar to advanced optimisers while
providing uncertainty quantification for key benchmark problems.


Abstract:
  We study Lorentzian supersymmetric configurations in $D=4$ and $D=5$ gauged
$\mathcal{N}=2$ supergravity. We show that there are smooth $1/2$ BPS solutions
which are asymptotically AdS$_{4}$ and AdS$_{5}$ with a planar boundary, a
compact spacelike direction and with a Wilson line on that circle. There are
solitons where the $S^{1}$ shrinks smoothly to zero in the interior, with a
magnetic flux through the circle determined by the Wilson line, which are AdS
analogues of the Melvin fluxtube. There is also a solution with a constant
gauge field, which is pure AdS. Both solutions preserve half of the
supersymmetries at a special value of the Wilson line. There is a phase
transition between these two saddle-points as a function of the Wilson line
precisely at the supersymmetric point. Thus, the supersymmetric solutions are
degenerate, at least at the supergravity level. We extend this discussion to
one of the Romans solutions in four dimensions when the Euclidean boundary is
$S^{1}\times\Sigma_{g}$ where $\Sigma_{g}$ is a Riemann surface with genus $g >
0$. We speculate that the supersymmetric state of the CFT on the boundary is
dual to a superposition of the two degenerate geometries.


Abstract:
  In this paper, we focus on the use of Low-Eart Orbit (LEO) satellites
providing the Narrowband Internet of Things (NB-IoT) connectivity to the
on-ground user equipment (UEs). Conventional resource allocation algorithms for
the NBIoT systems are particularly designed for terrestrial infrastructures,
where devices are under the coverage of a specific base station and the whole
system varies very slowly in time. The existing methods in the literature
cannot be applied over LEO satellite-based NB-IoT systems for several reasons.
First, with the movement of the LEO satellite, the corresponding channel
parameters for each user will quickly change over time. Delaying the scheduling
of a certain user would result in a resource allocation based on outdated
parameters. Second, the differential Doppler shift, which is a typical
impairment in communications over LEO, directly depends on the relative
distance among users. Scheduling at the same radio frame users that overcome a
certain distance would violate the differential Doppler limit supported by the
NB-IoT standard. Third, the propagation delay over a LEO satellite channel is
around 4-16 times higher compared to a terrestrial system, imposing the need
for message exchange minimization between the users and the base station. In
this work, we propose a novel uplink resource allocation strategy that jointly
incorporates the new design considerations previously mentioned together with
the distinct channel conditions, satellite coverage times and data demands of
various users on Earth. The novel methodology proposed in this paper can act as
a framework for future works in the field.


Abstract:
  UK Biobank (UKB) conducts large-scale examinations of more than half a
million volunteers, collecting health-related information on genetics,
lifestyle, blood biochemistry, and more. Medical imaging of 100,000 subjects,
with 70,000 follow-up sessions, enables measurements of organs, muscle, and
body composition. With up to 170,000 mounting MR images, various methodologies
are accordingly engaged in large-scale image analysis. This work presents an
experimental inference engine that can automatically predict a comprehensive
profile of subject metadata from UKB neck-to-knee body MRI. It was evaluated in
cross-validation for baseline characteristics such as age, height, weight, and
sex, but also measurements of body composition, organ volumes, and abstract
properties like grip strength, pulse rate, and type 2 diabetic status. It
predicted subsequently released test data covering twelve body composition
metrics with a 3% median error. The proposed system can automatically analyze
one thousand subjects within ten minutes, providing individual confidence
intervals. The underlying methodology utilizes convolutional neural networks
for image-based mean-variance regression on two-dimensional representations of
the MRI data. This work aims to make the proposed system available for free to
researchers, who can use it to obtain fast and fully-automated estimates of 72
different measurements immediately upon release of new UKB image data.


Abstract:
  Two-dimensional (2D) surface of the topological materials is an attractive
channel for the electrical conduction reflecting the linearly-dispersive
electronic bands. By applying a reliable systematic thickness t dependent
measurement of sheet conductance, here we elucidate the dimensionality of the
electrical conduction paths of a Weyl semimetal Co3Sn2S2. Under the
ferromagnetic phase, the 2D conduction path clearly emerges in Co3Sn2S2 thin
films, indicating a formation of the Fermi arcs projected from Weyl nodes.
Comparison between 3D conductivity and 2D conductance provides the effective
thickness of the surface conducting region being estimated to be approximately
20 nm, which is rather thicker than 5 nm in topological insulator Bi2Se3. This
large value may come from the narrow gap at Weyl point and relatively weak
spin-orbit interaction of the Co3Sn2S2. The emergent surface conduction will
provide a pathway to activate quantum and spintronic transport features
stemming from a Weyl node in thin-film-based devices.


Abstract:
  The triple-based knowledge in large-scale knowledge bases is most likely
lacking in structural logic and problematic of conducting knowledge hierarchy.
In this paper, we introduce the concept of metaknowledge to knowledge
engineering research for the purpose of structural knowledge construction.
Therefore, the Metaknowledge Extraction Framework and Document Structure Tree
model are presented to extract and organize metaknowledge elements (titles,
authors, abstracts, sections, paragraphs, etc.), so that it is feasible to
extract the structural knowledge from multi-modal documents. Experiment results
have proved the effectiveness of metaknowledge elements extraction by our
framework. Meanwhile, detailed examples are given to demonstrate what exactly
metaknowledge is and how to generate it. At the end of this paper, we propose
and analyze the task flow of metaknowledge applications and the associations
between knowledge and metaknowledge.


Abstract:
  The competing effect of heterogeneity and symmetry breaking coupling on the
emerging dynamics in a system of N globally coupled Stuart-Landau oscillators
is investigated. Increasing the heterogeneity, using the standard deviation of
the Hopf bifurcation parameter, favors the macroscopic oscillatory state for
low values of the symmetry breaking coupling and inhomogeneous steady state for
larger values of the coupling. There is also a transition, tipping, to
homogeneous steady state (aging state) from the macroscopic oscillatory state.
The limiting factor in the diffusive coupling favors the macroscopic
oscillatory state even in the presence of a large fraction of inactive
oscillators in the network thereby increasing the robustness of the network.
The globally coupled oscillators are reduced to a system of two evolution
equations for the macroscopic order parameters, corresponding to the mean-field
and the shape parameter, using the self-consistent field approach. The
bifurcation diagrams obtained from the mean-field variables elucidate various
bifurcation scenarios responsible for the dynamical transitions observed in N
globally coupled Stuart-Landau oscillators. In particular, tipping to the aging
state is found to occur via the Hopf and pitchfork bifurcations illustrating
the phenomenon of bifurcation induced tipping. Analytical stability (critical)
curves of these bifurcations, deduced from the mean-field variables, are found
to fairly well agree with the simulation results.


Abstract:
  Stock price prediction can be made more efficient by considering the price
fluctuations and understanding the sentiments of people. A limited number of
models understand financial jargon or have labelled datasets concerning stock
price change. To overcome this challenge, we introduced FinALBERT, an ALBERT
based model trained to handle financial domain text classification tasks by
labelling Stocktwits text data based on stock price change. We collected
Stocktwits data for over ten years for 25 different companies, including the
major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets
were labelled with three labelling techniques based on stock price changes. Our
proposed model FinALBERT is fine-tuned with these labels to achieve optimal
results. We experimented with the labelled dataset by training it on
traditional machine learning, BERT, and FinBERT models, which helped us
understand how these labels behaved with different model architectures. Our
labelling method competitive advantage is that it can help analyse the
historical data effectively, and the mathematical function can be easily
customised to predict stock movement.