Abstract:
  The LDN 1172/1174 cloud complex in the Cepheus Flare region presents a
hub-filament structure with the reflection nebula, NGC 7023, illuminated by a
Herbig Be star, HD 200775, which consists of the hub with a $\sim$5 pc long
narrow filament attached to it. Formation of a sparse cluster of low- and
intermediate-mass stars is presently taking place in the hub. The aim of this
work is to map the magnetic field geometry of LDN 1172/1174 to understand the
role played by the field lines in the formation of the molecular cloud. We made
R-band polarization measurements of 249 stars projected on the entire LDN
1172/1174 cloud complex to map the geometry of the magnetic field of this
region. The magnetic field geometry constructed from our R-band polarization
measurements is found to be parallel to the elongated structure inferred from
the column density distribution of the cloud produced using the Herschel
images. Our R-band polarization measurements are found to be in good agreement
with those obtained from Planck. There is evidence of a possible distortion of
the magnetic fields toward the northwestern part of the cloud by HD 200775. The
magnetic field strength is estimated as $\sim$30 $\mu$G. The estimated star
formation rate (SFR)/mass of 2.0$\pm$1.3 \%Myr$^{-1}$ and 0.4$\pm$0.3
\%Myr$^{-1}$ for LDN 1172/1174 and the neighboring cloud complex, LDN
1147/1158, respectively, are found to be consistent with the mean SFR/mass
found for the clouds with magnetic field orientations parallel and
perpendicular to their elongated structures, respectively. These results
support earlier findings that the clouds with magnetic field lines parallel to
their long axes seem to have higher SFRs compared to those with the magnetic
field orientation perpendicular to the cloud elongation.


Abstract:
  In this paper, we present GEM as a General Evaluation benchmark for
Multimodal tasks. Different from existing datasets such as GLUE, SuperGLUE,
XGLUE and XTREME that mainly focus on natural language tasks, GEM is a
large-scale vision-language benchmark, which consists of GEM-I for
image-language tasks and GEM-V for video-language tasks. Comparing with
existing multimodal datasets such as MSCOCO and Flicker30K for image-language
tasks, YouCook2 and MSR-VTT for video-language tasks, GEM is not only the
largest vision-language dataset covering image-language tasks and
video-language tasks at the same time, but also labeled in multiple languages.
We also provide two baseline models for this benchmark. We will release the
dataset, code and baseline models, aiming to advance the development of
multilingual multimodal research.


Abstract:
  In this paper, we construct a class of non-weight modules over the
affine-Virasoro algebra of type $A_1$ by taking tensor products of irreducibles
defined in [Q. Chen, J. Han, Non-weight modules over the affine-Virasoro
algebra of type $A_1$, J. Math. Phys. 60, 071707 (2019)] with irreducible
highest weight modules. The irreducibility and the isomorphism classes of these
modules are determined. Moreover, we show that these tensor product modules are
different from the known non-weight modules. Finally, we realize some tensor
product modules as induced modules from modules over certain subalgebras of the
affine-Virasoro algebra of type $A_1$, and give sufficient and necessary
conditions for these induced modules to be reducible.


Abstract:
  In this paper, we expand the methodology presented in Mertens et. al (2020,
Biometrical Journal) to the study of life-time (survival) outcome which is
subject to censoring and when imputation is used to account for missing values.
We consider the problem where missing values can occur in both the calibration
data as well as newly - to-be-predicted - observations (validation). We focus
on the Cox model. Methods are described to combine imputation with predictive
calibration in survival modeling subject to censoring. Application to
cross-validation is discussed. We demonstrate how conclusions broadly confirm
the first paper which restricted to the study of binary outcomes only.
Specifically prediction-averaging appears to have superior statistical
properties, especially smaller predictive variation, as opposed to a direct
application of Rubin's rules. Distinct methods for dealing with the baseline
hazards are discussed when using Rubin's rules-based approaches.


Abstract:
  In many practical applications, such as fraud detection, credit risk modeling
or medical decision making, classification models for assigning instances to a
predefined set of classes are required to be both precise as well as
interpretable. Linear modeling methods such as logistic regression are often
adopted, since they offer an acceptable balance between precision and
interpretability. Linear methods, however, are not well equipped to handle
categorical predictors with high-cardinality or to exploit non-linear relations
in the data. As a solution, data preprocessing methods such as
weight-of-evidence are typically used for transforming the predictors. The
binning procedure that underlies the weight-of-evidence approach, however, has
been little researched and typically relies on ad-hoc or expert driven
procedures. The objective in this paper, therefore, is to propose a formalized,
data-driven and powerful method.
  To this end, we explore the discretization of continuous variables through
the binning of spline functions, which allows for capturing non-linear effects
in the predictor variables and yields highly interpretable predictors taking
only a small number of discrete values. Moreover, we extend upon the
weight-of-evidence approach and propose to estimate the proportions using
shrinkage estimators. Together, this offers an improved ability to exploit both
non-linear and categorical predictors for achieving increased classification
precision, while maintaining interpretability of the resulting model and
decreasing the risk of overfitting.
  We present the results of a series of experiments in a fraud detection
setting, which illustrate the effectiveness of the presented approach. We
facilitate reproduction of the presented results and adoption of the proposed
approaches by providing both the dataset and the code for implementing the
experiments and the presented approach.


Abstract:
  Multichannel adaptive signal detection jointly uses the test and training
data to form an adaptive detector, and then make a decision on whether a target
exists or not. Remarkably, the resulting adaptive detectors usually possess the
constant false alarm rate (CFAR) properties, and hence no additional CFAR
processing is needed. Filtering is not needed as a processing procedure either,
since the function of filtering is embedded in the adaptive detector. Moreover,
adaptive detection usually exhibits better detection performance than the
filtering-then-CFAR detection technique. Multichannel adaptive signal detection
has been more than 30 years since the first multichannel adaptive detector was
proposed by Kelly in 1986. However, there are fewer overview articles on this
topic. In this paper we give a tutorial overview of multichannel adaptive
signal detection, with emphasis on Gaussian background. We present the main
deign criteria for adaptive detectors, investigate the relationship between
adaptive detection and filtering-then-CFAR detection, relationship between
adaptive detectors and adaptive filters, summarize typical adaptive detectors,
show numerical examples, give comprehensive literature review, and discuss some
possible further research tracks.


Abstract:
  Multislice ptychography is a high-resolution microscopy technique used to
image multiple separate axial planes using a single illumination direction.
However, multislice ptychography reconstructions are often degraded by
crosstalk, where some features on one plane erroneously contribute to the
reconstructed image of another plane. Here, we demonstrate the use of a
modified "double deep image prior" (DDIP) architecture in mitigating crosstalk
artifacts in multislice ptychography. Utilizing the tendency of generative
neural networks to produce natural images, a modified DDIP method yielded good
results on experimental data. For one of the datasets, we show that using DDIP
could remove the need of using additional experimental data, such as from x-ray
fluorescence, to suppress the crosstalk. Our method may help x-ray multislice
ptychography work for more general experimental scenarios.


Abstract:
  This demo paper presents the technical details and usage scenarios of
$\mu$SE: a mutation-based tool for evaluating security-focused static analysis
tools for Android. Mutation testing is generally used by software practitioners
to assess the robustness of a given test-suite. However, we leverage this
technique to systematically evaluate static analysis tools and uncover and
document soundness issues. $\mu$SE's analysis has found 25 previously
undocumented flaws in static data leak detection tools for Android. $\mu$SE
offers four mutation schemes, namely Reachability, Complex-reachability,
TaintSink, and ScopeSink, which determine the locations of seeded mutants.
Furthermore, the user can extend $\mu$SE by customizing the API calls targeted
by the mutation analysis. $\mu$SE is also practical, as it makes use of
filtering techniques based on compilation and execution criteria that reduces
the number of ineffective mutations.


Abstract:
  Deep neural networks may perform poorly when training datasets are heavily
class-imbalanced. Recently, two-stage methods decouple representation learning
and classifier learning to improve performance. But there is still the vital
issue of miscalibration. To address it, we design two methods to improve
calibration and performance in such scenarios. Motivated by the fact that
predicted probability distributions of classes are highly related to the
numbers of class instances, we propose label-aware smoothing to deal with
different degrees of over-confidence for classes and improve classifier
learning. For dataset bias between these two stages due to different samplers,
we further propose shifted batch normalization in the decoupling framework. Our
proposed methods set new records on multiple popular long-tailed recognition
benchmark datasets, including CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT,
Places-LT, and iNaturalist 2018. Code will be available at
https://github.com/Jia-Research-Lab/MiSLAS.


Abstract:
  We provide a pure state formulation for hydrodynamic dynamics of isolated
quantum many-body systems. A pure state describing quantum systems in local
thermal equilibrium is constructed, which we call a local thermal pure quantum
($\ell$TPQ) state. We show that the thermodynamic functional and the
expectation values of local operators (including a real-time correlation
function) calculated from the $\ell$TPQ state converge to those from a local
Gibbs ensemble in the large fluid-cell limit. As a numerical demonstration, we
investigate a one-dimensional spin chain and observe the hydrodynamic
relaxation obeying the Fourier's law. We further prove the second law of
thermodynamics and the quantum fluctuation theorem, which are also validated
numerically. The $\ell$TPQ formulation gives a useful theoretical basis to
describe the emergent hydrodynamic behavior of quantum many-body systems
furnished with a numerical efficiency, being applicable to both the
non-relativistic and relativistic regimes.


Abstract:
  LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and
Mapping) serves as a basis for indoor cleaning, navigation, and many other
useful applications in both industry and household. From a series of LiDAR
scans, it constructs an accurate, globally consistent model of the environment
and estimates a robot position inside it. SLAM is inherently computationally
intensive; it is a challenging problem to realize a fast and reliable SLAM
system on mobile robots with a limited processing capability. To overcome such
hurdles, in this paper, we propose a universal, low-power, and
resource-efficient accelerator design for 2D LiDAR SLAM targeting
resource-limited FPGAs. As scan matching is at the heart of SLAM, the proposed
accelerator consists of dedicated scan matching cores on the programmable logic
part, and provides software interfaces to facilitate the use. Our accelerator
can be integrated to various SLAM methods including the ROS (Robot Operating
System)-based ones, and users can switch to a different method without
modifying and re-synthesizing the logic part. We integrate the accelerator into
three widely-used methods, i.e., scan matching, particle filter, and
graph-based SLAM. We evaluate the design in terms of resource utilization,
speed, and quality of output results using real-world datasets. Experiment
results on a Pynq-Z2 board demonstrate that our design accelerates scan
matching and loop-closure detection tasks by up to 14.84x and 18.92x, yielding
4.67x, 4.00x, and 4.06x overall performance improvement in the above methods,
respectively. Our design enables the real-time performance while consuming only
2.4W and maintaining accuracy, which is comparable to the software counterparts
and even the state-of-the-art methods.


Abstract:
  In symmetry protected topological (SPT) phases, the combination of symmetries
and a bulk gap stabilizes protected modes at surfaces or at topological
defects. Understanding the fate of these modes at a quantum critical point,
when the protecting symmetries are on the verge of being broken, is an
outstanding problem. This interplay of topology and criticality must
incorporate both the bulk dynamics of critical points, often described by
nontrivial conformal field theories, and SPT physics. Here, we study the
simplest nontrivial setting - that of a 0+1 dimensional topological mode - a
quantum spin - coupled to a 2+1D critical bulk. Using the large-$N$ technique
we solve a series of models which, as a consequence of topology, demonstrate
intermediate coupling fixed points. We compare our results to previous
numerical simulations and find good agreement. We also point out intriguing
connections to generalized Kondo problems and Sachdev-Ye-Kitaev (SYK) models.
In particular, we show that a Luttinger theorem derived for the complex SYK
models, that relates the charge density to particle-hole asymmetry, also holds
in our setting. These results should help stimulate further analytical study of
the interplay between SPT physics and quantum criticality.


Abstract:
  We study the problem of estimating a rank-$1$ signal in the presence of
rotationally invariant noise-a class of perturbations more general than
Gaussian noise. Principal Component Analysis (PCA) provides a natural
estimator, and sharp results on its performance have been obtained in the
high-dimensional regime. Recently, an Approximate Message Passing (AMP)
algorithm has been proposed as an alternative estimator with the potential to
improve the accuracy of PCA. However, the existing analysis of AMP requires an
initialization that is both correlated with the signal and independent of the
noise, which is often unrealistic in practice. In this work, we combine the two
methods, and propose to initialize AMP with PCA. Our main result is a rigorous
asymptotic characterization of the performance of this estimator. Both the AMP
algorithm and its analysis differ from those previously derived in the Gaussian
setting: at every iteration, our AMP algorithm requires a specific term to
account for PCA initialization, while in the Gaussian case, PCA initialization
affects only the first iteration of AMP. The proof is based on a two-phase
artificial AMP that first approximates the PCA estimator and then mimics the
true AMP. Our numerical simulations show an excellent agreement between AMP
results and theoretical predictions, and suggest an interesting open direction
on achieving Bayes-optimal performance.


Abstract:
  We consider the problem of uncertainty quantification for an unknown low-rank
matrix $\mathbf{X}$, given a partial and noisy observation of its entries. This
quantification of uncertainty is essential for many real-world problems,
including image processing, satellite imaging, and seismology, providing a
principled framework for validating scientific conclusions and guiding
decision-making. However, existing literature has mainly focused on the
completion (i.e., point estimation) of the matrix $\mathbf{X}$, with little
work on investigating its uncertainty. To this end, we propose in this work a
new Bayesian modeling framework, called BayeSMG, which parametrizes the unknown
$\mathbf{X}$ via its underlying row and column subspaces. This Bayesian
subspace parametrization enables efficient posterior inference on matrix
subspaces, which represents interpretable phenomena in many applications. This
can then be leveraged for improved matrix recovery. We demonstrate the
effectiveness of BayeSMG over existing Bayesian matrix recovery methods in
numerical experiments, image inpainting, and a seismic sensor network
application.


Abstract:
  We investigate the IR phases of non-supersymmetric (non-SUSY) $SO(N_c)$ gauge
theories with $N_F$ fermions in the vector representation obtained by
perturbing the SUSY theory with anomaly mediated SUSY breaking (AMSB). We find
that of the wide variety of phases appearing in the SUSY theory only two
survive: for $N_F<\frac{3}{2} (N_c-2)$ the theory confines, breaking the
$SU(N_F)$ global symmetry to $SO(N_F)$, while for $\frac{3}{2}
(N_c-2)<N_F<3(N_c-2)$ the theory flows to a (super)-conformal fixed point. The
abelian Coulomb and free magnetic phases do not survive and collapse to the
confining phase. We also investigate the behavior of loop operators in order to
provide a clear distinction between the confining and screened phases. With the
choice of $Spin(N_c)$ for the global structure of the gauge group, we find that
the electric Wilson loop indeed obeys an area law, providing one of the first
demonstrations of true confinement with chiral symmetry breaking in a non-SUSY
theory. We identify monopole condensation as the dynamics underlying
confinement. These monopoles arise naturally for $N_F=N_c-2$. The case with
smaller number of flavors can be obtained by integrating out flavors, and we
confirm numerically that the monopole condensate persists in the presence of
AMSB and mass perturbations.


Abstract:
  The software inevitably encounters the crash, which will take developers a
large amount of effort to find the fault causing the crash (short for crashing
fault). Developing automatic methods to identify the residence of the crashing
fault is a crucial activity for software quality assurance. Researchers have
proposed methods to predict whether the crashing fault resides in the stack
trace based on the features collected from the stack trace and faulty code,
aiming at saving the debugging effort for developers. However, previous work
usually neglected the feature preprocessing operation towards the crash data
and only used traditional classification models. In this paper, we propose a
novel crashing fault residence prediction framework, called ConDF, which
consists of a consistency based feature subset selection method and a
state-of-the-art deep forest model. More specifically, first, the feature
selection method is used to obtain an optimal feature subset and reduce the
feature dimension by reserving the representative features. Then, a simplified
deep forest model is employed to build the classification model on the reduced
feature set. The experiments on seven open source software projects show that
our ConDF method performs significantly better than 17 baseline methods on
three performance indicators.


Abstract:
  This is the first article in a series of two dealing with a matrix approach
\alex{for} aberration quantification and correction in ultrasound imaging.
Advanced synthetic beamforming relies on a double focusing operation at
transmission and reception on each point of the medium. Ultrasound matrix
imaging (UMI) consists in decoupling the location of these transmitted and
received focal spots. The response between those virtual transducers form the
so-called focused reflection matrix that actually contains much more
information than a raw ultrasound image. In this paper, a time-frequency
analysis of this matrix is performed, which highlights the single and multiple
scattering contributions as well as the impact of aberrations in the
monochromatic and broadband regimes. Interestingly, this analysis enables the
measurement of the incoherent input-output point spread function at any pixel
of this image. A focusing criterion can then be built, and its evolution used
to quantify the amount of aberration throughout the ultrasound image. In
contrast to the standard coherence factor used in the literature, this new
indicator is robust to multiple scattering and electronic noise, thereby
providing a highly contrasted map of the focusing quality. As a
proof-of-concept, UMI is applied here to the in-vivo study of a human calf, but
it can be extended to any kind of ultrasound diagnosis or non-destructive
evaluation.


Abstract:
  We prove sharp reverse H\"older inequalities for minima of multi-phase
variational integrals and apply them to Calder\'on-Zygmund estimates for
nonhomogeneous problems.


Abstract:
  We study a theory of reinforcement learning (RL) in which the learner
receives binary feedback only once at the end of an episode. While this is an
extreme test case for theory, it is also arguably more representative of
real-world applications than the traditional requirement in RL practice that
the learner receive feedback at every time step. Indeed, in many real-world
applications of reinforcement learning, such as self-driving cars and robotics,
it is easier to evaluate whether a learner's complete trajectory was either
"good" or "bad," but harder to provide a reward signal at each step. To show
that learning is possible in this more challenging setting, we study the case
where trajectory labels are generated by an unknown parametric model, and
provide a statistically and computationally efficient algorithm that achieves
sub-linear regret.


Abstract:
  Generative Adversarial Networks (GANs) have significantly advanced image
synthesis, however, the synthesis quality drops significantly given a limited
amount of training data. To improve the data efficiency of GAN training, prior
work typically employs data augmentation to mitigate the overfitting of the
discriminator yet still learn the discriminator with a bi-classification (i.e.,
real vs. fake) task. In this work, we propose a data-efficient Instance
Generation (InsGen) method based on instance discrimination. Concretely,
besides differentiating the real domain from the fake domain, the discriminator
is required to distinguish every individual image, no matter it comes from the
training set or from the generator. In this way, the discriminator can benefit
from the infinite synthesized samples for training, alleviating the overfitting
problem caused by insufficient training data. A noise perturbation strategy is
further introduced to improve its discriminative power. Meanwhile, the learned
instance discrimination capability from the discriminator is in turn exploited
to encourage the generator for diverse generation. Extensive experiments
demonstrate the effectiveness of our method on a variety of datasets and
training settings. Noticeably, on the setting of 2K training images from the
FFHQ dataset, we outperform the state-of-the-art approach with 23.5% FID
improvement.


Abstract:
  The Covid-19 pandemic has radically changed our lives. Under different
circumstances, people react to it in various ways. One way is to work-from-home
since lockdown has been announced in many regions around the world. For some
places, however, we don't know if people really work from home due to the lack
of information. Since there are lots of uncertainties, it would be helpful for
us to understand what really happen in these places if we can detect the
reaction to the Covid-19 pandemic. Working from home indicates that people have
changed the way they interact with the Internet. People used to access the
Internet in the company or at school during the day. Now it is more likely that
they access the Internet at home in the daytime. Therefore, the network usage
changes in one place can be used to indicate if people in this place actually
work from home. In this work, we reuse and analyze Trinocular outages data
(around 5.1M responsive /24 blocks) over 6 months to find network usage changes
by a new designed algorithm. We apply the algorithm to sets of /24 blocks in
several cities and compare the detected network usage changes with real world
covid-19 events to verify if the algorithm can capture the changes reacting to
the Covid-19 pandemic. By applying the algorithm to all measurable /24 blocks
to detect network usages changes, we conclude that network usage can be an
indicator of the reaction to the Covid-19 pandemic.


Abstract:
  This paper considers the uplink of user-centric cell-free massive MIMO
(multiple-input multiple-output) systems. We utilize the user-centric dynamic
cooperation clustering (DCC) framework and derive the achievable spectral
efficiency with two-layer decoding that is divided between the access points
and the central processing unit (CPU). This decoding method is also known as
large-scale fading decoding (LSFD). The fronthaul signaling load is analyzed
and a nearly optimal second-layer decoding scheme at the CPU is proposed to
reduce the fronthaul requirements compared to the optimal scheme. We also
revisit the joint optimization of LSFD weights and uplink power control and
show that the corresponding max-min fair optimization problem can be solved
optimally via an efficient fixed-point algorithm. We provide simulations that
bring new insights into the cell-free massive MIMO implementation.


Abstract:
  Most modern multiple object tracking (MOT) systems follow the
tracking-by-detection paradigm, consisting of a detector followed by a method
for associating detections into tracks. There is a long history in tracking of
combining motion and appearance features to provide robustness to occlusions
and other challenges, but typically this comes with the trade-off of a more
complex and slower implementation. Recent successes on popular 2D tracking
benchmarks indicate that top-scores can be achieved using a state-of-the-art
detector and relatively simple associations relying on single-frame spatial
offsets -- notably outperforming contemporary methods that leverage learned
appearance features to help re-identify lost tracks. In this paper, we propose
an efficient joint detection and tracking model named DEFT, or "Detection
Embeddings for Tracking." Our approach relies on an appearance-based object
matching network jointly-learned with an underlying object detection network.
An LSTM is also added to capture motion constraints. DEFT has comparable
accuracy and speed to the top methods on 2D online tracking leaderboards while
having significant advantages in robustness when applied to more challenging
tracking data. DEFT raises the bar on the nuScenes monocular 3D tracking
challenge, more than doubling the performance of the previous top method. Code
is publicly available.


Abstract:
  Deep convolutional neural networks (DCNNs) have revolutionized computer
vision and are often advocated as good models of the human visual system.
However, there are currently many shortcomings of DCNNs, which preclude them as
a model of human vision. For example, in the case of adversarial attacks, where
adding small amounts of noise to an image, including an object, can lead to
strong misclassification of that object. But for humans, the noise is often
invisible. If vulnerability to adversarial noise cannot be fixed, DCNNs cannot
be taken as serious models of human vision. Many studies have tried to add
features of the human visual system to DCNNs to make them robust against
adversarial attacks. However, it is not fully clear whether human vision
inspired components increase robustness because performance evaluations of
these novel components in DCNNs are often inconclusive. We propose a set of
criteria for proper evaluation and analyze different models according to these
criteria. We finally sketch future efforts to make DCCNs one step closer to the
model of human vision.


Abstract:
  Euclids algorithm is widely used in calculating of GCD (Greatest Common
Divisor) of two positive numbers. There are various fields where this division
is used such as channel coding, cryptography, and error correction codes. This
makes the GCD a fundamental algorithm in number theory, so a number of methods
have been discovered to efficiently compute it. The main contribution of this
paper is to investigate a method that computes the GCD of two 32-bit numbers
based on Euclidean algorithm which targets six different Xilinx chips. The
complexity of this method that we call Optimized_GCDSAD is achieved by
utilizing Sum of Absolute Difference (SAD) block which is based on a fast
carry-out generation function. The efficiency of the proposed architecture is
evaluated based on criteria such as time (latency), area delay product (ADP)
and space (slice number) complexity. The VHDL codes of these architectures have
been implemented and synthesized through ISE 14.7. A detailed comparative
analysis indicates that the proposed Optimized_GCDSAD method based on SAD block
outperforms previously known results.


Abstract:
  We present a formalization in Isabelle/HOL of quantum projective
measurements, a class of measurements involving orthogonal projectors that is
frequently used in quantum computing. We also formalize the CHSH inequality, a
result that holds on arbitrary probability spaces, which can used to disprove
the existence of a local hidden-variable theory for quantum mechanics.


Abstract:
  Let $E$ be a CM number field and $F$ its maximal real subfield. We prove a
level-raising result for regular algebraic conjugate self-dual automorphic
representations of $GL_n(\mathbb{A}_E)$. This generalizes previously known
results of Thorne by removing certain hypotheses occurring in that work. In
particular, the level-raising prime $p$ is allowed to be unramified as opposed
to inert in $F$, the field $E/F$ is not assumed to be everywhere unramified,
and the field $E$ is allowed to be a general CM field.


Abstract:
  Stealthy false data injection attacks on cyber-physical systems (CPSs)
introduce erroneous measurement information to on-board sensors with the
purpose to degrade system performance. An intelligent attacker is able to
leverage knowledge of the system model and noise characteristics to alter
sensor measurements while remaining undetected. To achieve this objective, the
stealthy attack sequence is designed such that the detector performs similarly
in the attacked and attack-free cases. Consequently, an attacker that wants to
remain hidden will leave behind traces of inconsistent behavior, contradicting
the system model. To deal with this problem, we propose a runtime monitor to
find these inconsistencies in sensor measurements by monitoring for serial
inconsistencies of the detection test measure. Specifically, we employ the
chi-square fault detection procedure to monitor the magnitude and signed
sequence of its chi-square test measure. We validate our approach with
simulations on an unmanned ground vehicle (UGV) under stealthy attacks and
compare the detection performance with various state-of-the-art anomaly
detectors.


Abstract:
  Field observations and laboratory experiments have shown that ash
sedimentation can be significantly affected by collective settling mechanisms
that promote premature ash deposition, with important implications for
associated impacts. Among these mechanisms, settling-driven gravitational
instabilities result from the formation of a gravitationally-unstable particle
boundary layer (PBL) that grows between volcanic ash clouds and the underlying
atmosphere. The PBL destabilises once it reaches a critical thickness,
triggering the formation of rapid, downward-moving ash fingers that remain
poorly characterised. We simulate this process by coupling a Lattice Boltzmann
model, which solves the Navier-Stokes equations for the fluid phase, with a
Weighted Essentially Non Oscillatory (WENO) finite difference scheme which
solves the advection-diffusion-settling equation describing particle transport.
Since the physical problem is advection dominated, the use of the WENO scheme
reduces numerical diffusivity and ensures accurate tracking of the temporal
evolution of the interface between the layers. We have validated the new model
by showing that the simulated early-time growth rate of the instability is in
very good agreement with that predicted by linear stability analysis, whilst
the modelled late-stage behaviour also successfully reproduces quantitative
results from published laboratory experiments.


Abstract:
  We present a practical approach to treat static and dynamical correlation
accurately in large multi-configurational systems. The static correlation is
accounted for using the spin-flip approach which is well known for capturing
static correlation accurately at low-computational expense. Unlike previous
approaches to add dynamical correlation to spin-flip models which use
perturbation theory or coupled-cluster theory, we explore the ability to use
the on-top pair-density functional theory approaches recently developed by
Gagliardi and co-workers (JCTC, 10, 3669, 2014). External relaxations are
carried out in the spin-flip calculations though a restricted active space
framework for which a truncation scheme for the orbitals used in the external
excitation is presented. The performance of the approach is demonstrated by
computing energy gaps between ground and excited states for diradicals,
triradicals and linear polyacene chains ranging from naphthalene to dodecacene.
Accurate results are obtained using the new approach for these challenging
open-shell molecular systems.


Abstract:
  For a subgroup of $PGL(2,q)$ we show how some irreducible polynomials over
$\mathbb{F}_q$ arise from the field of invariant rational functions. The proofs
rely on two actions of $PGL(2,F)$, one on the projective line over a field $F$
and the other on the rational function field $F(x)$. The invariant functions in
$F(x)$ are used to show that regular patterns exist in the factorization of
certain polynomials into irreducible polynomials. We use some results about
group actions and the orbit polynomial, whose proofs are included. An unusual
connection to the conjugacy classes of $PGL(2,q)$ is shown. At the end of the
paper we present an alternative approach, using Lang's theorem on algebraic
groups.


Abstract:
  We present algorithmic perturbative solution of $\mathcal{N}=4$ SYM quantum
spectral curve in the case of twist 2 operators, valid to in principle
arbitrary order in coupling constant. The latter treats operator spins as
arbitrary integer values and is written in terms of special class of functions
-- products of rational functions in spectral parameter with sums of Baxter
polynomials and Hurwitz functions. It is shown that this class of functions is
closed under elementary operations, such as shifts, partial fractions,
multiplication by spectral parameter and differentiation. Also, it is fully
sufficient to solve arising non-homogeneous multiloop Baxter and first order
difference equations. As an application of the proposed method we present the
computation of anomalous dimensions of twist 2 operators up to four loop order.


Abstract:
  The results of a search for gluino and squark pair production with the pairs
decaying via the lightest charginos into a final state consisting of two $W$
bosons, the lightest neutralinos ($\tilde\chi^0_1$), and quarks, are presented.
The signal is characterised by the presence of a single charged lepton
($e^{\pm}$ or $\mu^{\pm}$) from a $W$ boson decay, jets, and missing transverse
momentum. The analysis is performed using 139 fb$^{-1}$ of proton-proton
collision data taken at a centre-of-mass energy $\sqrt{s}=13$ TeV delivered by
the Large Hadron Collider and recorded by the ATLAS experiment. No
statistically significant excess of events above the Standard Model expectation
is found. Limits are set on the direct production of squarks and gluinos in
simplified models. Masses of gluino (squark) up to 2.2 TeV (1.4 TeV) are
excluded at 95% confidence level for a light $\tilde\chi^0_1$.


Abstract:
  Infant motility assessment using intelligent wearables is a promising new
approach for assessment of infant neurophysiological development, and where
efficient signal analysis plays a central role. This study investigates the use
of different end-to-end neural network architectures for processing infant
motility data from wearable sensors. We focus on the performance and
computational burden of alternative sensor encoder and time-series modelling
modules and their combinations. In addition, we explore the benefits of data
augmentation methods in ideal and non-ideal recording conditions. The
experiments are conducted using a data-set of multi-sensor movement recordings
from 7-month-old infants, as captured by a recently proposed smart jumpsuit for
infant motility assessment. Our results indicate that the choice of the encoder
module has a major impact on classifier performance. For sensor encoders, the
best performance was obtained with parallel 2-dimensional convolutions for
intra-sensor channel fusion with shared weights for all sensors. The results
also indicate that a relatively compact feature representation is obtainable
for within-sensor feature extraction without a drastic loss to classifier
performance. Comparison of time-series models revealed that feed-forward
dilated convolutions with residual and skip connections outperformed all
RNN-based models in performance, training time, and training stability. The
experiments also indicate that data augmentation improves model robustness in
simulated packet loss or sensor dropout scenarios. In particular, signal- and
sensor-dropout-based augmentation strategies provided considerable boosts to
performance without negatively affecting the baseline performance. Overall the
results provide tangible suggestions on how to optimize end-to-end neural
network training for multi-channel movement sensor data.


Abstract:
  The recent results from the Fermilab muon $g-2$ experiment, as well as the
persisting hints of lepton flavor universality violation in $B$-meson decays,
present a very strong case for some flavor-nonuniversal beyond the Standard
Model physics. We reinforce our previous claim that a minimal $R$-parity
violating supersymmetric framework with relatively light third-generation
sfermions (dubbed as `RPV3') provides a natural, well-motivated framework for
the simultaneous explanation of all flavor anomalies, while being consistent
with current low- and high-energy experimental constraints. We further propose
complementary tests and distinct signatures of this scenario in the high-$p_T$
searches at current and future colliders. In particular, we emphasize that the
dominant resolution to muon $g-2$ in RPV3 comes from a sub-TeV scale sneutrino
with a relatively large coupling to muons, which leads to a spectacular
four-muon signal at the LHC.


Abstract:
  Deep learning based models are vulnerable to adversarial attacks. These
attacks can be much more harmful in case of targeted attacks, where an attacker
tries not only to fool the deep learning model, but also to misguide the model
to predict a specific class. Such targeted and untargeted attacks are
specifically tailored for an individual sample and require addition of an
imperceptible noise to the sample. In contrast, universal adversarial attack
calculates a special imperceptible noise which can be added to any sample of
the given dataset so that, the deep learning model is forced to predict a wrong
class. To the best of our knowledge these targeted and universal attacks on
time series data have not been studied in any of the previous works. In this
work, we have performed untargeted, targeted and universal adversarial attacks
on UCR time series datasets. Our results show that deep learning based time
series classification models are vulnerable to these attacks. We also show that
universal adversarial attacks have good generalization property as it need only
a fraction of the training data. We have also performed adversarial training
based adversarial defense. Our results show that models trained adversarially
using Fast gradient sign method (FGSM), a single step attack, are able to
defend against FGSM as well as Basic iterative method (BIM), a popular
iterative attack.


Abstract:
  Accurate modeling of boundary conditions is crucial in computational physics.
The ever increasing use of neural networks as surrogates for physics-related
problems calls for an improved understanding of boundary condition treatment,
and its influence on the network accuracy. In this paper, several strategies to
impose boundary conditions (namely padding, improved spatial context, and
explicit encoding of physical boundaries) are investigated in the context of
fully convolutional networks applied to recurrent tasks. These strategies are
evaluated on two spatio-temporal evolving problems modeled by partial
differential equations: the 2D propagation of acoustic waves (hyperbolic PDE)
and the heat equation (parabolic PDE). Results reveal a high sensitivity of
both accuracy and stability on the boundary implementation in such recurrent
tasks. It is then demonstrated that the choice of the optimal padding strategy
is directly linked to the data semantics. Furthermore, the inclusion of
additional input spatial context or explicit physics-based rules allows a
better handling of boundaries in particular for large number of recurrences,
resulting in more robust and stable neural networks, while facilitating the
design and versatility of such networks.


Abstract:
  If gamma-ray bursts are at cosmological distances, they must be
gravitationally lensed occasionally. The detection of lensed images with
millisecond-to-second time delays provides evidence for intermediate-mass black
holes, a population that has been difficult to observe. Several studies have
searched for these delays in gamma-ray burst light curves, which would indicate
an intervening gravitational lens. Among the $\sim 10^4$ gamma-ray bursts
observed, there have been a handful of claimed lensing detections, but none
have been statistically robust. Here we present a Bayesian analysis identifying
gravitational lensing in the light curve of GRB950830. The inferred lens mass
depends on the unknown lens redshift $z_l$, and is given by $(1+z_l)M_l =
5.5^{+1.7}_{-0.9}\times 10^4 $ M$_\odot$ (90% credibility), which we interpret
as evidence for an intermediate-mass black hole. The most probable
configuration, with a lens redshift $z_l\sim 1$ and a gamma-ray burst redshift
$z_s\sim 2$, yields a present day number density of $n_\text{imbh}\approx
2.3^{+4.9}_{-1.6}\times10^{3} \text{ Mpc}^{-3}$ (90% credibility) with a
dimensionless energy density $\Omega_\text{imbh} \approx
4.6^{+9.8}_{-3.3}\times10^{-4}$. The false alarm probability for this detection
is $\sim0.6\%$ with trial factors. While it is possible that GRB950830 was
lensed by a globular cluster, it is unlikely since we infer a cosmic density
inconsistent with predictions for globular clusters $\Omega_\text{gc} \approx 8
\times 10^{-6}$ at 99.8% credibility. If a significant intermediate-mass black
hole population exists, it could provide the seeds for the growth of
supermassive black holes in the early Universe.


Abstract:
  Recent studies have shown convolutional neural networks (CNNs) can be trained
to perform modal decomposition using intensity images of optical fields. A
fundamental limitation of these techniques is that the modal phases can not be
uniquely calculated using a single intensity image. The knowledge of modal
phases is crucial for wavefront sensing, alignment and mode matching
applications. Heterodyne imaging techniques can provide images of the
transverse complex amplitude & phase profile of laser beams at high resolutions
and frame rates. In this work we train a CNN to perform modal decomposition
using simulated heterodyne images, allowing the complete modal phases to be
predicted. This is to our knowledge the first machine learning decomposition
scheme to utilize complex phase information to perform modal decomposition. We
compare our network with a traditional overlap integral & center-of-mass
centering algorithm and show that it is both less sensitive to beam centering
and on average more accurate.


Abstract:
  The problem of natural language processing over structured data has become a
growing research field, both within the relational database and the Semantic
Web community, with significant efforts involved in question answering over
knowledge graphs (KGQA). However, many of these approaches are either
specifically targeted at open-domain question answering using DBpedia, or
require large training datasets to translate a natural language question to
SPARQL in order to query the knowledge graph. Hence, these approaches often
cannot be applied directly to complex scientific datasets where no prior
training data is available.
  In this paper, we focus on the challenges of natural language processing over
knowledge graphs of scientific datasets. In particular, we introduce Bio-SODA,
a natural language processing engine that does not require training data in the
form of question-answer pairs for generating SPARQL queries. Bio-SODA uses a
generic graph-based approach for translating user questions to a ranked list of
SPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm
that includes node centrality as a measure of relevance for selecting the best
SPARQL candidate query. Our experiments with real-world datasets across several
scientific domains, including the official bioinformatics Question Answering
over Linked Data (QALD) challenge, show that Bio-SODA outperforms publicly
available KGQA systems by an F1-score of least 20% and by an even higher factor
on more complex bioinformatics datasets.


Abstract:
  We examine the problem of time delay estimation, or temporal calibration, in
the context of multisensor data fusion. Differences in processing intervals and
other factors typically lead to a relative delay between measurement updates
from disparate sensors. Correct (optimal) data fusion demands that the relative
delay must either be known in advance or identified online. There have been
several recent proposals in the literature to determine the delay using
recursive, causal filters such as the extended Kalman filter (EKF). We
carefully review this formulation and show that there are fundamental issues
with the structure of the EKF (and related algorithms) when the delay is
included in the filter state vector as a parameter to be estimated. These
structural issues, in turn, leave recursive filters prone to bias and
inconsistency. Our theoretical analysis is supported by simulation studies that
demonstrate the implications in terms of filter performance; although tuning of
the filter noise variances may reduce the chance of inconsistency or
divergence, the underlying structural concerns remain. We offer brief
suggestions for ways to maintain the computational efficiency of recursive
filtering for temporal calibration while avoiding the drawbacks of the standard
filtering algorithms.


Abstract:
  Topological data analysis (TDA) studies the shape patterns of data.
Persistent homology (PH) is a widely used method in TDA that summarizes
homological features of data at multiple scales and stores them in persistence
diagrams (PDs). In this paper, we propose a random persistence diagram
generation (RPDG) method that generates a sequence of random PDs from the ones
produced by the data. RPDG is underpinned by (i) a model based on pairwise
interacting point processes for inference of persistence diagrams, and (ii) by
a reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm for generating
samples of PDs. A first example, which is based on a synthetic dataset,
demonstrates the efficacy of RPDG and provides a detailed comparison with other
existing methods for sampling PDs. A second example demonstrates the utility of
RPDG to solve a materials science problem given a real dataset of small sample
size.


Abstract:
  The motive of this paper is to discuss the local convergence of a two-step
Newton type method of convergence rate three for solving nonlinear equations in
Banach spaces. It is assumed that the first order derivative of nonlinear
operator satisfies the generalized Lipschitz i.e. $L$-average condition. Also,
some results on convergence of the same method in Banach spaces are established
under the assumption that the derivative of the operators satisfies the radius
or center Lipschitz condition with a weak $L$-average particularly it is
assumed that $L$ is positive integrable function but not necessarily
non-decreasing.


Abstract:
  Although zero-shot learning (ZSL) has an inferential capability of
recognizing new classes that have never been seen before, it always faces two
fundamental challenges of the cross modality and crossdomain challenges. In
order to alleviate these problems, we develop a generative network-based ZSL
approach equipped with the proposed Cross Knowledge Learning (CKL) scheme and
Taxonomy Regularization (TR). In our approach, the semantic features are taken
as inputs, and the output is the synthesized visual features generated from the
corresponding semantic features. CKL enables more relevant semantic features to
be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy
Regularization (TR) significantly improves the intersections with unseen images
with more generalized visual features generated from generative network.
Extensive experiments on several benchmark datasets (i.e., AwA1, AwA2, CUB, NAB
and aPY) show that our approach is superior to these state-of-the-art methods
in terms of ZSL image classification and retrieval.


Abstract:
  We propose an algorithm inspired by optical coherent Ising machines to solve
the problem of polynomial unconstrained binary optimisation (PUBO). We
benchmark the proposed algorithm against existing PUBO algorithms on the
extended Sherrington-Kirkpatrick model and random third-degree polynomial
pseudo-Boolean functions, and observe its superior performance. We also address
instances of practically relevant computational problems such as protein
folding and electronic structure calculations with problem sizes not accessible
to existing quantum annealing devices. In particular, we successfully find the
lowest-energy conformation of lattice protein molecules containing up to eleven
amino-acids. The application of our algorithm to quantum chemistry sheds light
on the shortcomings of approximating the electronic structure problem by a PUBO
problem, which, in turn, puts into question the applicability of quantum
annealers in this context.


Abstract:
  The known three-component reaction-diffusion system modeling competition and
co-existence of different language speakers is under study. A modification of
this system is proposed, which is examined by Lie symmetry method; furthermore
exact solutions in the form of traveling fronts are constructed and their
properties are identified. Plots of the traveling fronts are presented and the
relevant interpretation describing the language shift occurred in Ukraine
during the Soviet times is suggested.


Abstract:
  Joint radar and communication (RadCom) systems have been proposed to achieve
the spectrum sharing between radar and communication in recent years. However,
the joint RadCom systems cause the clutter modulation and the performance
degradation of both radar and communication. As a consequence, it's very
critical to improve the performance of both radar and communication when
designing joint RadCom systems. Firstly, this paper designs the constant
modulus dual function waveforms for spatial modulation based joint RadCom
systems. In order to solve the nonsmooth and nonconvex optimization problem, we
propose a method based on alternating direction method of multipliers (ADMM) to
obtain a suboptimal solution. Secondly, this paper analyses the effect of
beampattern variation on clutter modulation for spatial modulation based joint
RadCom systems. Besides, this paper considers the design of robust doppler
filter for joint RadCom systems when the receive temporal steering vector is
mismatched to the desired temporal steering vector. This paper aims to maximize
the worst signal-to-interference-plus-noise-ratio (SINR) of the doppler filter
in the specific doppler interval under the similarity constraint and constant
white noise gain constraint. In order to solve the nonconvex optimization
problem, we relax the original optimization problem to a semidefinite
programming (SDP), which is convex and can be solved using off-the-shelf
optimization solvers. What's more, the rank-one decomposition method is
utilized to synthesize the weight vector of the proposed robust doppler filter.
Finally, lots of simulation results are presented to show the robustness of the
designed doppler filter.


Abstract:
  We study in this paper the possible existence of Roskilde-simple liquids and
their isomorphs in a rough-wall nanoconfinement. Isomorphs are curves in the
thermodynamic phase diagram along which structure and dynamics are invariant in
suitable nondimensionalized units. Two model liquids using molecular dynamics
computer simulations are considered: the single-component Lennard-Jones (LJ)
liquid and the Kob-Andersen binary LJ mixture, both of which in the bulk phases
are known to have isomorphs. Nanoconfinement is implemented by adopting a
slit-pore geometry with fcc crystalline walls; this implies inhomogenous
density profiles both parallel and perpendicular to the confining walls.
Despite this fact and consistent with an earlier study [Ingebrigtsen et. al,
Phys. Rev. Lett. 111, 235901 (2013)] we find that these nanoconfined liquids
have isomorphs to a good approximation. More specifically, we show good scaling
of inhomogenous density profiles, mean-square displacements, and higher-order
structures probed using the topological cluster classification algorithm along
the isomorphs. From this study, we conjecture that in experiments,
Roskilde-simple liquids may exhibit isomorphs if confined in a suitable manner,
for example with carbon nanotubes. Our study thus provides an alternative
framework for understanding nanoconfined liquids.


Abstract:
  Multi-speaker spoken datasets enable the creation of text-to-speech synthesis
(TTS) systems which can output several voice identities. The multi-speaker
(MSPK) scenario also enables the use of fewer training samples per speaker.
However, in the resulting acoustic model, not all speakers exhibit the same
synthetic quality, and some of the voice identities cannot be used at all.
  In this paper we evaluate the influence of the recording conditions, speaker
gender, and speaker particularities over the quality of the synthesised output
of a deep neural TTS architecture, namely Tacotron2. The evaluation is possible
due to the use of a large Romanian parallel spoken corpus containing over 81
hours of data. Within this setup, we also evaluate the influence of different
types of text representations: orthographic, phonetic, and phonetic extended
with syllable boundaries and lexical stress markings.
  We evaluate the results of the MSPK system using the objective measures of
equal error rate (EER) and word error rate (WER), and also look into the
distances between natural and synthesised t-SNE projections of the embeddings
computed by an accurate speaker verification network. The results show that
there is indeed a large correlation between the recording conditions and the
speaker's synthetic voice quality. The speaker gender does not influence the
output, and that extending the input text representation with syllable
boundaries and lexical stress information does not equally enhance the
generated audio across all speaker identities. The visualisation of the t-SNE
projections of the natural and synthesised speaker embeddings show that the
acoustic model shifts some of the speakers' neural representation, but not all
of them. As a result, these speakers have lower performances of the output
speech.


Abstract:
  An increasing body of research focuses on using neural networks to model time
series. A common assumption in training neural networks via maximum likelihood
estimation on time series is that the errors across time steps are
uncorrelated. However, errors are actually autocorrelated in many cases due to
the temporality of the data, which makes such maximum likelihood estimations
inaccurate. In this paper, in order to adjust for autocorrelated errors, we
propose to learn the autocorrelation coefficient jointly with the model
parameters. In our experiments, we verify the effectiveness of our approach on
time series forecasting. Results across a wide range of real-world datasets
with various state-of-the-art models show that our method enhances performance
in almost all cases. Based on these results, we suggest empirical critical
values to determine the severity of autocorrelated errors. We also analyze
several aspects of our method to demonstrate its advantages. Finally, other
time series tasks are also considered to validate that our method is not
restricted to only forecasting.


Abstract:
  A prospective measurement of the differential cross section of $tWZ$
production with respect to the transverse momentum of the $Z$ boson using a
general-purpose detector at the High-Luminosity Large Hadron Collider (HL-LHC)
is described. Constraints on the Standard Model Effective Field Theory (SMEFT)
enabled by the measurement are estimated. Uncertainties and inter-bin
correlations are used to determine the measurement's expected covariance
matrix. A parametric model of the differential cross section in the SMEFT is
constructed and, together with the expected covariance matrix, is used to
determine the expected posterior probability function of six SMEFT Wilson
coefficients. Expected 95% Bayesian credible intervals for each coefficient and
pair of coefficients are derived by marginalising this posterior function. The
intervals suggest that the measurement will result in improved constraints on
the $c_{tZ}$ coefficient. For the other coefficients, the measurement will
likely provide weaker constraints than those derived from other HL-LHC
measurements involving top quarks and $Z$ bosons. However, as the measurement
is simultaneously sensitive to many coefficients, it will provide a useful
input to a SMEFT analysis that simultaneously utilises measurements of multiple
processes.


Abstract:
  Scotogenic models are among the most popular possibilities to link dark
matter and neutrino masses. In this work we discuss a variant of the Scotogenic
model that includes charged fermions and a doublet with hypercharge $3/2$.
Neutrino masses are induced at the one-loop level thanks to the states
belonging to the dark sector. However, in contrast to the standard Scotogenic
model, only the scalar dark matter candidate is viable in this version. After
presenting the model and explaining some particularities about neutrino mass
generation, we concentrate on its dark matter phenomenology. We show that the
observed dark matter relic density can be correctly reproduced in the usual
parameter space regions found for the standard Scotogenic model or the Inert
Doublet model. In addition, the presence of the charged fermions may open up
new regions, provided some tuning of the parameters is allowed.


Abstract:
  Twenty years have passed since Kechris' seminal survey paper [A. S. Kechris,
New directions in descriptive set theory. Bull. Symbolic Logic, 5(2):161-174,
1999]. As a follow-up of that work, we review some ot the (anti-)classification
results that have been obtained in the last decade using Borel reducibility and
its generalizations to uncountable cardinals.


Abstract:
  We theoretically study the antiferromagnetic domain wall motion actuated by
an inhomogeneous external magnetic field. The Lagrangian and the equations of
motion of antiferromagnetic spins under an inhomogeneous magnetic field are
derived, first in terms of the N\'eel vector, and then using collective
coordinates of the domain wall. A solution is found that describes the
actuation of a domain wall by an inhomogeneous field, in which the motion is
initiated by a paramagnetic response of wall magnetization, which is then
driven by a Stern-Gerlach like force. The effects of pinning potential are also
investigated. These results are in good agreement with atomistic simulations.
While the present formulation contains the so-called intrinsic magnetization
associated with N\'eel texture, a supplementary discussion is given to
reformulate the theory in terms of physical magnetization without the intrinsic
magnetization.


Abstract:
  The Raychaudhuri equations for the expansion, shear and vorticity are
generalized in a spacetime with torsion for timelike as well as null
congruences. These equations are purely geometrical like the original
Raychaudhuri equations and could be reduced to them when there is no torsion.
Using the Einstein-Cartan-Sciama-Kibble field equations the effective
stress-energy tensor is derived. We also consider an Oppenheimer-Snyder model
for the gravitational collapse of dust. It is shown that the null energy
condition (NEC) is violated before the density of the collapsing dust reaches
the Planck density, hinting that the spacetime singularity may be avoided if
there is a non-zero torsion,i.e. if the collapsing dust particles possess
intrinsic spin.


Abstract:
  We investigate the formation of the satellite galaxy population of a Milky
Way-mass halo in a very highly resolved magneto-hydrodynamic cosmological
zoom-in simulation (baryonic mass resolution $m_b =$ 800 $\rm M_{\odot}$). We
show that the properties of the central star-forming galaxy, such as the radial
stellar surface density profile and star formation history, are: i) robust to
stochastic variations associated with the so-called ``Butterfly Effect''; and
ii) well converged over 3.5 orders of magnitude in mass resolution. We find
that there are approximately five times as many satellite galaxies at this high
resolution compared to a standard ($m_b\sim 10^{4-5}\, \rm M_{\odot}$)
resolution simulation of the same system. This is primarily because 2/3rds of
the high resolution satellites do not form at standard resolution. A smaller
fraction (1/6th) of the satellites present at high resolution form and disrupt
at standard resolution; these objects are preferentially low-mass satellites on
intermediate- to low-eccentricity orbits with impact parameters $\lesssim 30$
kpc. As a result, the radial distribution of satellites becomes substantially
more centrally concentrated at higher resolution, in better agreement with
recent observations of satellites around Milky Way-mass haloes. Finally, we
show that our galaxy formation model successfully forms ultra-faint galaxies
and reproduces the stellar velocity dispersion, half-light radii, and $V$-band
luminosities of observed Milky Way and Local Group dwarf galaxies across 6
orders of magnitude in luminosity ($10^3$-$10^{9}$ $\rm L_{\odot}$).


Abstract:
  A Hayward black hole and magnetically charged black hole based on rational
nonlinear electrodynamics with the Lagrangian ${\cal L} = -{\cal
F}/(1+2\beta{\cal F})$ (${\cal F}$ is a field invariant) are considered. It was
shown that the metric function in both models possesses a de Sitter core
without singularities as $r\rightarrow 0$. The behavior of the Hawking
temperature and the heat capacity in these models are similar. The phase
transitions take place when the Hawking temperature has a maximum, and black
holes are thermodynamically stable at some event horizon radii when the heat
capacity is positive. We show that the source of gravity in the Hayward model
is questionable.


Abstract:
  3D shape editing is widely used in a range of applications such as movie
production, computer games and computer aided design. It is also a popular
research topic in computer graphics and computer vision. In past decades,
researchers have developed a series of editing methods to make the editing
process faster, more robust, and more reliable. Traditionally, the deformed
shape is determined by the optimal transformation and weights for an energy
term. With increasing availability of 3D shapes on the Internet, data-driven
methods were proposed to improve the editing results. More recently as the deep
neural networks became popular, many deep learning based editing methods have
been developed in this field, which is naturally data-driven. We mainly survey
recent research works from the geometric viewpoint to those emerging neural
deformation techniques and categorize them into organic shape editing methods
and man-made model editing methods. Both traditional methods and recent neural
network based methods are reviewed.


Abstract:
  With the rapid growth of traffic sensors deployed, a massive amount of
traffic flow data are collected, revealing the long-term evolution of traffic
flows and the gradual expansion of traffic networks. How to accurately
forecasting these traffic flow attracts the attention of researchers as it is
of great significance for improving the efficiency of transportation systems.
However, existing methods mainly focus on the spatial-temporal correlation of
static networks, leaving the problem of efficiently learning models on networks
with expansion and evolving patterns less studied. To tackle this problem, we
propose a Streaming Traffic Flow Forecasting Framework, TrafficStream, based on
Graph Neural Networks (GNNs) and Continual Learning (CL), achieving accurate
predictions and high efficiency. Firstly, we design a traffic pattern fusion
method, cleverly integrating the new patterns that emerged during the long-term
period into the model. A JS-divergence-based algorithm is proposed to mine new
traffic patterns. Secondly, we introduce CL to consolidate the knowledge
learned previously and transfer them to the current model. Specifically, we
adopt two strategies: historical data replay and parameter smoothing. We
construct a streaming traffic dataset to verify the efficiency and
effectiveness of our model. Extensive experiments demonstrate its excellent
potential to extract traffic patterns with high efficiency on long-term
streaming network scene. The source code is available at
https://github.com/AprLie/TrafficStream.


Abstract:
  J. Stix proved that a curve of positive genus over $\mathbb{Q}$ which maps to
a non-trivial Brauer-Severi variety satisfies the section conjecture. We prove
that, if $X$ is a curve of positive genus over a number field $k$ and the Weil
restriction $R_{k/\mathbb{Q}}X$ admits a rational map to a non-trivial
Brauer-Severi variety, then $X$ satisfies the section conjecture. As a
consequence, if $X$ maps to a Brauer-Severi variety $P$ such that the
corestriction
$\operatorname{cor}_{k/\mathbb{Q}}([P])\in\operatorname{Br}(\mathbb{Q})$ is
non-trivial, then $X$ satisfies the section conjecture.


Abstract:
  We present a 0.15$^{\prime\prime}$ resolution (21 au) ALMA 870 $\mu$m
continuum survey of 25 pointings containing 31 young stellar objects in the
Ophiuchus molecular clouds. Using the dust continuum as a proxy for dust mass
and circumstellar disk radius in our sample, we report a mean mass of
2.8$^{+2.1}_{-1.3}$ and 2.5$^{+9.2}_{-1.1}$ M$_{\oplus}$ and a mean radii of
23.5$^{+1.8}_{-1.2}$ and 16.5$^{+2.8}_{-0.9}$ au, for Class I and Flat spectrum
protostars, respectively. In addition, we calculate the multiplicity statistics
of the dust surrounding young stellar objects in Ophiuchus. The multiplicity
fraction (MF) and companion star fraction (CSF) of the combined Class I and
Flats based solely on this work is 0.25 $\pm$ 0.09 and 0.33 $\pm$ 0.10,
respectively, which are consistent with the values for Perseus and Orion. While
we see clear differences in mass and radius between the Ophiuchus and
Perseus/Orion protostellar surveys, we do not see any significant differences
in the multiplicities of the various regions. We posit there are some
differences in the conditions for star formation in Ophiuchus that strongly
affects disk size (and consequently disk mass), but does not affect system
multiplicity, which could imply important variation in planet formation
processes.


Abstract:
  Millisecond pulsars are very likely the main source of gamma-ray emission
from globular clusters. However, the relative contributions of two separate
emission processes--curvature radiation from millisecond pulsar magnetospheres
vs. inverse Compton emission from relativistic pairs launched into the globular
cluster environment by millisecond pulsars--have long been unclear. To address
this, we search for evidence of inverse Compton emission in 8-year
$\textit{Fermi}$-LAT data from the directions of 157 Milky Way globular
clusters. We find a mildly statistically significant (3.8$\sigma$) correlation
between the measured globular cluster gamma-ray luminosities and their photon
field energy densities. However, this may also be explained by a hidden
correlation between the photon field densities and the stellar encounter rates
of globular clusters. Analysed $\textit{in toto}$, we demonstrate that the
gamma-ray emission of globular clusters can be resolved spectrally into two
components: i) an exponentially cut-off power law and ii) a pure power law. The
latter component--which we uncover at a significance of 8.2$\sigma$--has a
power index of 2.79 $\pm$ 0.25. It is most naturally interpreted as inverse
Compton emission by cosmic-ray electrons and positrons injected by millisecond
pulsars. We find the luminosity of this power-law component is comparable to,
or slightly smaller than, the luminosity of the curved component, suggesting
the fraction of millisecond pulsar spin-down luminosity into relativistic
leptons is similar to the fraction of the spin-down luminosity into prompt
magnetospheric radiation.


Abstract:
  The notion of retro Banach frame with the help of b-linear functional in
n-Banach spaces is being presented. Some properties related to the construction
of new retro Banach frame in n-Banach space have been studied. In n-Banach
spaces, some perturbation results of retro Banach frame have been discussed.
Finally, we give a condition in which finite sum of retro Banach frames is a
retro Banach frame in n-Banach space.


Abstract:
  Conceptually, partial information decomposition (PID) is concerned with
separating the information contributions several sources hold about a certain
target by decomposing the corresponding joint mutual information into
contributions such as synergistic, redundant, or unique information. Despite
PID conceptually being defined for any type of random variables, so far, PID
could only be quantified for the joint mutual information of discrete systems.
Recently, a quantification for PID in continuous settings for two or three
source variables was introduced. Nonetheless, no ansatz has managed to both
quantify PID for more than three variables and cover general measure-theoretic
random variables, such as mixed discrete-continuous, or continuous random
variables yet. In this work we will propose an information quantity, defining
the terms of a PID, which is well-defined for any number or type of source or
target random variable. This proposed quantity is tightly related to a recently
developed local shared information quantity for discrete random variables based
on the idea of shared exclusions. Further, we prove that this newly proposed
information-measure fulfills various desirable properties, such as satisfying a
set of local PID axioms, invariance under invertible transformations,
differentiability with respect to the underlying probability density, and
admitting a target chain rule.


Abstract:
  Modern modeling languages for general physical systems, such as Modelica,
Amesim, or Simscape, rely on Differential Algebraic Equations (DAEs), i.e.,
constraints of the form f(\dot{x},x,u)=0. This drastically facilitates modeling
from first principles of the physics, as well as model reuse. In recent works
[RR-9334], we presented the mathematical theory needed to establish the
development of compilers and tools for DAE-based physical modeling languages on
solid mathematical grounds.At the core of this analysis sits the so-called
*structural analysis*, whose purpose, at compile time, is to either identify
under- and over-specified subsystems (if any), or to rewrite the model in a
form amenable of existing DAE solvers, including the handling of mode change
events. The notion of "structure" collects, for each mode and mode change
event, the variables and equations involved, as well as the *latent equations*
(additional equations redundant with the system), needed to prepare the code
submitted to the solver. The notion of DAE *index* (the minimal number of times
any equation has to be possibly differentiated) is part of this structural
analysis. This report complements [RR-9334] by collecting all the needed
background on structural analysis. The body of knowledge on structural analysis
is large and scattered, which also motivated us to collect it in a single
report.We first explain the primary meaning of structural analysis of systems
of equations, namely the study of their regularity or singularity in some
generic sense. We then briefly review the body of graph theory used in this
context. We develop some extensions, for which we are not aware of any
reference, namely the structural analysis of systems of equations with
existential quantifiers. For the structural analysis of DAE systems, we focus
on John Pryce's Sigma-method, that we both summarize and extend to non-square
systems. The uses of these tools and methods in [RR9334] are highlighted in
this report.


Abstract:
  Imitation learning has been widely used to learn control policies for
autonomous driving based on pre-recorded data. However, imitation learning
based policies have been shown to be susceptible to compounding errors when
encountering states outside of the training distribution. Further, these agents
have been demonstrated to be easily exploitable by adversarial road users
aiming to create collisions. To overcome these shortcomings, we introduce
Adversarial Mixture Density Networks (AMDN), which learns two distributions
from separate datasets. The first is a distribution of safe actions learned
from a dataset of naturalistic human driving. The second is a distribution
representing unsafe actions likely to lead to collision, learned from a dataset
of collisions. During training, we leverage these two distributions to provide
an additional loss based on the similarity of the two distributions. By
penalising the safe action distribution based on its similarity to the unsafe
action distribution when training on the collision dataset, a more robust and
safe control policy is obtained. We demonstrate the proposed AMDN approach in a
vehicle following use-case, and evaluate under naturalistic and adversarial
testing environments. We show that despite its simplicity, AMDN provides
significant benefits for the safety of the learned control policy, when
compared to pure imitation learning or standard mixture density network
approaches.


Abstract:
  COVID-19 is having a dramatic impact on research and researchers. The
pandemic has underlined the severity of known challenges in research and
surfaced new ones, but also accelerated the adoption of innovations and
manifested new opportunities. This review considers early trends emerging from
meta-research on COVID-19. In particular, it focuses on the following topics:
i) mapping COVID-19 research; ii) data and machine learning; iii) research
practices including open access and open data, reviewing, publishing and
funding; iv) communicating research to the public; v) the impact of COVID-19 on
researchers, in particular with respect to gender and career trajectories. This
overview finds that most early meta-research on COVID-19 has been reactive and
focused on short-term questions, while more recently a shift to consider the
long-term consequences of COVID-19 is taking place. Based on these findings,
the author speculates that some aspects of doing research during COVID-19 are
more likely to persist than others. These include: the shift to virtual for
academic events such as conferences; the use of openly accessible pre-prints;
the `datafication' of scholarly literature and consequent broader adoption of
machine learning in science communication; the public visibility of research
and researchers on social and online media.


Abstract:
  We consider variational integrals of linear growth satisfying the condition
of $\mu$-ellipticity for some exponent $\mu >1$ and prove that stationary
points $u$: $\mathbb{R}^2 \to \mathbb{R}^N$ with the property \[
\limsup_{|x|\to \infty} \frac{|u(x)|}{|x|} < \infty \] must be affine
functions. The latter condition can be dropped in the scalar case together with
appropriate assumptions on the energy density providing an extension of
Bernstein's theorem.


Abstract:
  A distinct signature of compact extra dimensions would be a Kaluza-Klein
tower of gravitational waves. Motivated by this prospect, we compute the
corresponding spectrum on a warped toroidal background. We evaluate in
particular the impact of the warp factor on the spectrum. To that end, we use
the complete warp factor H of standard string compactifications, generated by
D-branes and orientifolds, thus connecting to recent works on stringy de Sitter
constructions. The problematic region close to an orientifold where H < 0 leads
to unphysical tachyonic modes in the spectrum. We develop tools that overcome
this difficulty and lead to a tachyon-free spectrum. We show, in particular,
that the warp factor can lower the first Kaluza-Klein mass by at least 69%.


Abstract:
  Evolution algebras with one dimensional square are classified using the
theory of inner product spaces. More precisely, for $A$ an evolution algebra
with $\dim(A^2) = 1$ and $a$ a generator of $A^2$, the product of $A$ is given
by $xy = \langle x,y\rangle a$. Three broad classes of algebras are obtained:
  (1) $a\in\hbox{Ann}(A)$;
  (2) $a\notin\text{Ann}(A)$ and $a$ is isotropic relative to $\langle\cdot,
\cdot\rangle$;
  (3) $a\notin\text{Ann}(A)$ and $a$ is anisotropic relative to $\langle\cdot,
\cdot\rangle$.


Abstract:
  Risk contributions of portfolios form an indispensable part of risk adjusted
performance measurement. The risk contribution of a portfolio, e.g., in the
Euler or Aumann-Shapley framework, is given by the partial derivatives of a
risk measure applied to the portfolio return in direction of the asset weights.
For risk measures that are not positively homogeneous of degree 1, however,
known capital allocation principles do not apply. We study the class of lambda
quantile risk measures, that includes the well-known Value-at-Risk as a special
case, but for which no known allocation rule is applicable. We prove
differentiability and derive explicit formulae of the derivatives of lambda
quantiles with respect to their portfolio composition, that is their risk
contribution. For this purpose, we define lambda quantiles on the space of
portfolio compositions and consider generic (also non-linear) portfolio
operators.
  We further derive the Euler decomposition of lambda quantiles for generic
portfolios and show that lambda quantiles are homogeneous in the space of
portfolio compositions, with a homogeneity degree that depends on the portfolio
composition and the lambda function. This result is in stark contrast to the
positive homogeneity properties of risk measures defined on the space of random
variables which admit a constant homogeneity degree. We introduce a generalised
version of Euler contributions and Euler allocation rule, which are compatible
with risk measures of any homogeneity degree and non-linear portfolios. We
further provide financial interpretations of the homogeneity degree of lambda
quantiles and introduce the notion of event-specific homogeneity of portfolio
operators.


Abstract:
  Given a finite set E and an operator sigma:2^{E}-->2^{E}, two subsets X,Y of
the ground set E are cospanning if sigma(X)=sigma(Y) (Korte, Lovasz, Schrader;
1991). We investigate cospanning relations on violator spaces. A notion of a
violator space was introduced in (Gartner, Matousek, Rust, Skovrovn; 2008) as a
combinatorial framework that encompasses linear programming and other geometric
optimization problems. Violator spaces are defined by violator operators. We
introduce co-violator spaces based on contracting operators known also as
choice functions. Let alpha,beta:2^{E}-->2^{E} be a violator operator and a
co-violator operator, respectively. Cospanning characterizations of violator
spaces allow us to obtain some new properties of violator operators,
co-violator operators, and their interconnections. In particular, we show that
uniquely generated violator spaces enjoy so-called Krein-Milman properties,
i.e., alpha(beta(X))=alpha(X) and beta(alpha}(X))=beta(X) for every subset X of
the ground set E.


Abstract:
  We present a novel double-copy prescription for gauge fields at the
Lagrangian level and apply it both to the original double copy and the soft
theorem. The Yang-Mills Lagrangian in light-cone gauge is mapped directly to
the $\mathcal{N}=0$ supergravity Lagrangian in light-cone gauge to trilinear
order, and we show that the obtained result is manifestly equivalent to
Einstein gravity at tree level up to this order. The application of the
double-copy prescription to the soft-collinear effective QCD Lagrangian yields
an effective description of an energetic Dirac fermion coupled to the graviton,
Kalb-Ramond, and dilaton fields, from which the fermionic gravitational soft
and next-to-soft theorems follow.


Abstract:
  Within the framework of likelihood-based statistical tests for high energy
physics measurements, we derive generalized expressions for estimating the
statistical significance of discovery using the asymptotic approximations of
Wilks and Wald for a variety of measurement models. These models include
arbitrary numbers of signal regions, control regions, and Gaussian constraints.
We extend our expressions to use the representative or "Asimov" dataset
proposed by Cowan et al. such that they are made data-free. While many of the
generalized expressions are complicated and often involve solving systems of
coupled, multivariate equations, we show these expressions reduce to
closed-form results under simplifying assumptions. We also validate the
predicted significance using toy-based data in select cases.


Abstract:
  Many robot applications call for autonomous exploration and mapping of
unknown and unstructured environments. Information-based exploration
techniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) and
fast Shannon mutual information (FSMI), have successfully achieved active
binary occupancy mapping with range measurements. However, as we envision
robots performing complex tasks specified with semantically meaningful objects,
it is necessary to capture semantic categories in the measurements, map
representation, and exploration objective. This work develops a Bayesian
multi-class mapping algorithm utilizing range-category measurements. We derive
a closed-form efficiently computable lower bound for the Shannon mutual
information between the multi-class map and the measurements. The bound allows
rapid evaluation of many potential robot trajectories for autonomous
exploration and mapping. We compare our method against frontier-based and FSMI
exploration and apply it in a 3-D photo-realistic simulation environment.


Abstract:
  Topological structures of electromagnetic fields could give access to
nontrivial light-matter interactions and additional degrees of freedom for
information and energy transfer. A characteristic example of such
electromagnetic excitations are space-time non-separable single-cycle pulses,
the exact solutions of Maxwell equation of toroidal topology predicted by
Hellwarth and Nouchi in 1996 and recently observed experimentally. Here we
introduce a new family of electromagnetic excitation of toroidal topology with
increasing complexity in which the Hellwarth-Nouchi pulse is just the simplest
member. The electromagnetic excitations of the new family can be parametrised
by a single real number and exhibit skyrmionic structures of various orders.
They feature multiple singularities in the electromagnetic and Poynting vector
fields are accompanied by the fractal-like distributions of energy backflow.
The generalized family of toroidal electromagnetic excitation with salient
topologies are of interest for transient light-matter interactions, ultrafast
optics, spectroscopy, and toroidal electrodynamics.


Abstract:
  GCIRS 7, the brightest star in the Galactic central parsec, formed $6\pm2$
Myr ago together with dozens of massive stars in a disk orbiting the central
black-hole. It has been argued that GCIRS 7 is a pulsating body, on the basis
of photometric variability. We present the first medium-resolution ($R=500$),
K-band spectro-interferometric observations of GCIRS 7, using the GRAVITY
instrument with the four auxiliary telescopes of the ESO VLTI. We looked for
variations using two epochs, namely 2017 and 2019. We find GCIRS 7 to be
moderately resolved with a uniform-disk photospheric diameter of
$\theta^*_\text{UD}=1.55 \pm 0.03$ mas ($R^*_\text{UD}=1368 \pm 26$ $R_\odot$)
in the K-band continuum. The narrow-band uniform-disk diameter increases above
2.3 $\mu$m, with a clear correlation with the CO band heads in the spectrum.
This correlation is aptly modeled by a hot ($T_\text{L}=2368\pm37$ K),
geometrically thin molecular shell with a diameter of
$\theta_\text{L}=1.74\pm0.03$ mas, as measured in 2017. The shell diameter
increased ($\theta_\text{L}=1.89\pm0.03$ mas), while its temperature decreased
($T_\text{L}=2140\pm42$ K) in 2019. In contrast, the photospheric diameter
$\theta^*_\text{UD}$ and the extinction up to the photosphere of GCIRS 7
($A_{\mathrm{K}_\mathrm{S}}=3.18 \pm 0.16$) have the same value within
uncertainties at the two epochs. In the context of previous interferometric and
photo-spectrometric measurements, the GRAVITY data allow for an interpretation
in terms of photospheric pulsations. The photospheric diameter measured in 2017
and 2019 is significantly larger than previously reported using the PIONIER
instrument ($\theta_*=1.076 \pm 0.093$ mas in 2013 in the H band). The
parameters of the photosphere and molecular shell of GCIRS 7 are comparable to
those of other red supergiants that have previously been studied using
interferometry.


Abstract:
  In this article, we investigate the problem of entanglement characterization
with polarization measurements combined with maximum likelihood estimation
(MLE). A realistic scenario is considered with measurement results distorted by
random experimental errors. In particular, by imposing unitary rotations acting
on the measurement operators, we can test the performance of the tomographic
technique versus the amount of noise. Then, dark counts are introduced to
explore the efficiency of the framework in a multi-dimensional noise scenario.
The concurrence is used as a figure of merit to quantify how well entanglement
is preserved through noisy measurements. Quantum fidelity is computed to
quantify the accuracy of state reconstruction. The results of numerical
simulations are depicted on graphs and discussed.


Abstract:
  Parareal is a well-studied algorithm for numerically integrating systems of
time-dependent differential equations by parallelising the temporal domain.
Given approximate initial values at each temporal sub-interval, the algorithm
locates a solution in a fixed number of iterations using a predictor-corrector,
stopping once a tolerance is met. This iterative process combines solutions
located by inexpensive (coarse resolution) and expensive (fine resolution)
numerical integrators. In this paper, we introduce a stochastic parareal
algorithm with the aim of accelerating the convergence of the deterministic
parareal algorithm. Instead of providing the predictor-corrector with a
deterministically located set of initial values, the stochastic algorithm
samples initial values from dynamically varying probability distributions in
each temporal sub-interval. All samples are then propagated by the numerical
method in parallel. The initial values yielding the most continuous (smoothest)
trajectory across consecutive sub-intervals are chosen as the new, more
accurate, set of initial values. These values are fed into the
predictor-corrector, converging in fewer iterations than the deterministic
algorithm with a given probability. The performance of the stochastic
algorithm, implemented using various probability distributions, is illustrated
on systems of ordinary differential equations. When the number of sampled
initial values is large enough, we show that stochastic parareal converges
almost certainly in fewer iterations than the deterministic algorithm while
maintaining solution accuracy. Additionally, it is shown that the expected
value of the convergence rate decreases with increasing numbers of samples.


Abstract:
  Optical flow is inherently a 2D search problem, and thus the computational
complexity grows quadratically with respect to the search window, making large
displacements matching infeasible for high-resolution images. In this paper, we
take inspiration from Transformers and propose a new method for high-resolution
optical flow estimation with significantly less computation. Specifically, a 1D
attention operation is first applied in the vertical direction of the target
image, and then a simple 1D correlation in the horizontal direction of the
attended image is able to achieve 2D correspondence modeling effect. The
directions of attention and correlation can also be exchanged, resulting in two
3D cost volumes that are concatenated for optical flow estimation. The novel 1D
formulation empowers our method to scale to very high-resolution input images
while maintaining competitive performance. Extensive experiments on Sintel,
KITTI and real-world 4K ($2160 \times 3840$) resolution images demonstrated the
effectiveness and superiority of our proposed method. Code and models are
available at \url{https://github.com/haofeixu/flow1d}.


Abstract:
  The multidisciplinary nature of response robotics has brought about a
diversified research community with extended expertise. Motivated by the recent
accelerated rate of publications in the field, this paper analyzes the
technical content, statistics, and implications of the literature from
bibliometric standpoints. The aim is to study the global progress of response
robotics research and identify the contemporary trends. To that end, we
investigated the collaboration mapping together with the citation network to
formally recognize impactful and contributing authors, publications, sources,
institutions, funding agencies, and countries. We found how natural and
human-made disasters contributed to forming productive regional research
communities, while there are communities that only view response robotics as an
application of their research. Furthermore, through an extensive discussion on
the bibliometric results, we elucidated the philosophy behind research priority
shifts in response robotics and presented our deliberations on future research
directions.


Abstract:
  We solve the Dirichlet Problem of Monge-Amp\`ere equation near an isolate Klt
singularity, which generalizes the result of Eyssidieux-Guedj-Zeriahi
\cite{EGZ}, where the Monge-Amp\`ere equation is solved on singular varieties
without boundary. As a corollary, we construct solutions to Monge-Amp\`ere
equation with isolated singularity on strongly pseudoconvex domain $\Omega$
contained in $\mathbb{C}^n$.


Abstract:
  Homomorphic secret sharing (HSS) allows multiple input clients to
secret-share their data among multiple servers such that each server is able to
locally compute a function on its shares to obtain a partial result and all
partial results enable the reconstruction of the function's value on the
outsourced data by an output client. The existing HSS schemes for {\em
high-degree} polynomials either {\em require a large number of servers} or {\em
lack verifiability}, which is essential for ensuring the correctness of the
outsourced computations. In this paper, we propose a two-server verifiable HSS
(VHSS) model and construct a scheme that supports the computation of
high-degree polynomials. The degree of the outsourced polynomials can be as
high as a polynomial in the system's security parameter. Despite of using only
2 servers, our VHSS ensures that each single server learns no information about
the outsourced data and no single server is able to persuade the client to
output a wrong function value. Our VHSS is significantly more efficient. When
computing degree-7 polynomials, our scheme could be 3-10 times faster than the
previously best construction.


Abstract:
  One of the biggest problems in itemset mining is the requirement of
developing a data structure or algorithm, every time a user wants to extract a
different type of itemsets. To overcome this, we propose a method, called
Generic Itemset Mining based on Reinforcement Learning (GIM-RL), that offers a
unified framework to train an agent for extracting any type of itemsets. In
GIM-RL, the environment formulates iterative steps of extracting a target type
of itemsets from a dataset. At each step, an agent performs an action to add or
remove an item to or from the current itemset, and then obtains from the
environment a reward that represents how relevant the itemset resulting from
the action is to the target type. Through numerous trial-and-error steps where
various rewards are obtained by diverse actions, the agent is trained to
maximise cumulative rewards so that it acquires the optimal action policy for
forming as many itemsets of the target type as possible. In this framework, an
agent for extracting any type of itemsets can be trained as long as a reward
suitable for the type can be defined. The extensive experiments on mining high
utility itemsets, frequent itemsets and association rules show the general
effectiveness and one remarkable potential (agent transfer) of GIM-RL. We hope
that GIM-RL opens a new research direction towards learning-based itemset
mining.


Abstract:
  An essential need for many model-based robot control algorithms is the
ability to quickly and accurately compute partial derivatives of the equations
of motion. State of the art approaches to this problem often use analytical
methods based on the chain rule applied to existing dynamics algorithms.
Although these methods are an improvement over finite differences in terms of
accuracy, they are not always the most efficient. In this paper, we contribute
new closed-form expressions for the first-order partial derivatives of inverse
dynamics, leading to a recursive algorithm. The algorithm is benchmarked
against chain-rule approaches in Fortran and against an existing algorithm from
the Pinocchio library in C++. Tests consider computing the partial derivatives
of inverse and forward dynamics for robots ranging from kinematic chains to
humanoids and quadrupeds. Compared to the previous open-source Pinocchio
implementation, our new analytical results uncover a key computational
restructuring that enables efficiency gains. Speedups of up to 1.4x are
reported for calculating the partial derivatives of inverse dynamics for the
50-dof Talos humanoid.


Abstract:
  Video summarization aims at generating a compact yet representative visual
summary that conveys the essence of the original video. The advantage of
unsupervised approaches is that they do not require human annotations to learn
the summarization capability and generalize to a wider range of domains.
Previous work relies on the same type of deep features, typically based on a
model pre-trained on ImageNet data. Therefore, we propose the incorporation of
multiple feature sources with chunk and stride fusion to provide more
information about the visual content. For a comprehensive evaluation on the two
benchmarks TVSum and SumMe, we compare our method with four state-of-the-art
approaches. Two of these approaches were implemented by ourselves to reproduce
the reported results. Our evaluation shows that we obtain state-of-the-art
results on both datasets, while also highlighting the shortcomings of previous
work with regard to the evaluation methodology. Finally, we perform error
analysis on videos for the two benchmark datasets to summarize and spot the
factors that lead to misclassifications.


Abstract:
  Many versions of Thermodynamic Uncertainty Relations (TUR) have recently been
discovered, which impose lower bounds on relative fluctuations of integrated
currents in irreversible dissipative processes, and suggest that there may be
fundamental limitations on the precision of small scale machines and heat
engines. In this work we rigorously demonstrate that TUR can be evaded by using
dynamic protocols that are asymmetric under time-reversal. We illustrate our
results using a model heat engine using two-level systems, and also discuss
heuristically the fundamental connections between TUR and time-reversal
symmetry.


Abstract:
  It is of extreme importance to monitor and manage the battery health to
enhance the performance and decrease the maintenance cost of operating electric
vehicles. This paper concerns the machine-learning-enabled state-of-health
(SoH) prognosis for Li-ion batteries in electric trucks, where they are used as
energy sources. The paper proposes methods to calculate SoH and cycle life for
the battery packs. We propose autoregressive integrated modeling average
(ARIMA) and supervised learning (bagging with decision tree as the base
estimator; BAG) for forecasting the battery SoH in order to maximize the
battery availability for forklift operations. As the use of data-driven methods
for battery prognostics is increasing, we demonstrate the capabilities of ARIMA
and under circumstances when there is little prior information available about
the batteries. For this work, we had a unique data set of 31 lithium-ion
battery packs from forklifts in commercial operations. On the one hand, results
indicate that the developed ARIMA model provided relevant tools to analyze the
data from several batteries. On the other hand, BAG model results suggest that
the developed supervised learning model using decision trees as base estimator
yields better forecast accuracy in the presence of large variation in data for
one battery.