Abstract:
  Multi-robot teams can achieve more dexterous, complex and heavier payload
tasks than a single robot, yet effective collaboration is required. Multi-robot
collaboration is extremely challenging due to the different kinematic and
dynamics capabilities of the robots, the limited communication between them,
and the uncertainty of the system parameters. In this paper, a Decentralized
Ability-Aware Adaptive Control is proposed to address these challenges based on
two key features. Firstly, the common manipulation task is represented by the
proposed nominal task ellipsoid, which is used to maximize each robot force
capability online via optimizing its configuration. Secondly, a decentralized
adaptive controller is designed to be Lyapunov stable in spite of heterogeneous
actuation constraints of the robots and uncertain physical parameters of the
object and environment. In the proposed framework, decentralized coordination
and load distribution between the robots is achieved without communication,
while only the control deficiency is broadcast if any of the robots reaches its
force limits. In this case, the object reference trajectory is modified in a
decentralized manner to guarantee stable interaction. Finally, we perform
several numerical and physical simulations to analyse and verify the proposed
method with heterogeneous multi-robot teams in collaborative manipulation
tasks.


Abstract:
  We discuss the generation of field-induced entanglement between two objects
each in a superposition of two trajectories.The objects have currents coupled
to local quantum fields, and the currents are evaluated around each trajectory
of the objects.The fields have only dynamical degrees of freedom and satisfy
the microcausality condition. We find that the superposed state of trajectories
cannot be entangled when the objects are spacelike separated.This means that
the quantum fields do not generate spacelike entanglement in the superposition
of two trajectories of each object.


Abstract:
  We investigate the asymptotic variance of Gaussian nodal excursions in the
Euclidean space, focusing on the case where the spectral measure has
incommensurable atoms. This study requires to establish fine recurrence
properties in 0 for the associated irrational random walk on the torus. We show
in particular that the recurrence magnitude depends strongly on the diophantine
properties of the atoms, and the same goes for the variance asymptotics of
nodal excursions. More specifically, if the spectral measures contains atoms
which ratios are well approximable by rationals, the variance is likely to have
large fluctuations as the observation window grows, whereas the variance is
bounded by the (d -- 1)-dimensional measure of the window boundary if these
ratio are badly approximable. We also show that, given any reasonable function,
there are uncountably many sets of parameters for which the variance is
asymptotically equivalent to this function.


Abstract:
  In this work we present a generalized Kirchhoff-Love shell theory that can
capture anisotropy not only in stretching and out-of-plane bending, but also in
in-plane bending. This setup is particularly suitable for heterogeneous and
fibrous materials such as textiles, biomaterials, composites and pantographic
structures. The presented theory is a direct extension of existing
Kirchhoff-Love shell theory to incorporate the in-plane bending resistance of
fibers. It also extends existing high gradient Kirchhoff-Love shell theory for
initially straight fibers to initially curved fibers. To describe the
additional kinematics of multiple fiber families, a so-called in-plane
curvature tensor -- which is symmetric and of second order -- is proposed. The
effective stress tensor and the in-plane and out-of-plane moment tensors are
then identified from the mechanical power balance. These tensors are all second
order and symmetric for general materials. The constitutive equations for
hyperelastic materials are derived from different expressions of the mechanical
power balance. The weak form is also presented as it is required for
computational shell formulations based on rotation-free finite element
discretizations.


Abstract:
  A detector emulator method has been developed. It allows for testing any
readout electronics with realistic detector-like signals under controlled
conditions, beyond the limits of any experiment based on radiation sources. It
can substitute for expensive test-beam campaigns, or predict their results. The
detector emulator is a powerful tool for the engineering of detector concepts,
with its capability to reproduce any detector signal feature to test the
electronics response. Here, the method is applied to the case of the VMM3a ASIC
tested with sTGC detector emulated signals. Measurements of charge spectra and
muon detection efficiency under intense gamma background are reproduced. The
effect of different attenuation circuits is studied.


Abstract:
  This work addresses the problem of optimizing communications between server
and clients in federated learning (FL). Current sampling approaches in FL are
either biased, or non optimal in terms of server-clients communications and
training stability. To overcome this issue, we introduce \textit{clustered
sampling} for clients selection. We prove that clustered sampling leads to
better clients representatitivity and to reduced variance of the clients
stochastic aggregation weights in FL. Compatibly with our theory, we provide
two different clustering approaches enabling clients aggregation based on 1)
sample size, and 2) models similarity. Through a series of experiments in
non-iid and unbalanced scenarios, we demonstrate that model aggregation through
clustered sampling consistently leads to better training convergence and
variability when compared to standard sampling approaches. Our approach does
not require any additional operation on the clients side, and can be seamlessly
integrated in standard FL implementations. Finally, clustered sampling is
compatible with existing methods and technologies for privacy enhancement, and
for communication reduction through model compression.


Abstract:
  Here we report a developed high performance and simplified version of the
code denominated RIO, which can be easily extended, for the generalized BSSN
formulation. We implement a code which is regular at the center of symmetry,
without use a special procedure for regularization, as usual. We get
exponential convergence for constraints. The numerical algorithm is based on
the Galerkin-Collocation method developed successfully for diverse physical
scenarios by the Numerical Relativity Group at UERJ. For the sake of clarity in
presentation, we consider here the most simple case to display the most salient
features of the procedure. Thus, we focus on the definite tests of the new
numerical framework. The timing and performance of the code show that we can
reach a better accuracy close to the machine precision, for the Hamiltonian and
momentum constraints. RIO will be an open source code; currently is under
continuous development to consider more general and realistic problems.


Abstract:
  We unveil the nature of the structural disorder in bottom-up zigzag graphene
nanoribbons along with its effect on the magnetism and electronic transport on
the basis of scanning probe microscopies and first-principles calculations. We
find that edge-missing m-xylene units emerging during the cyclodehydrogenation
step of the on-surface synthesis are the most common point defects. These
"bite'' defects act as spin-1 paramagnetic centers, severely disrupt the
conductance spectrum around the band extrema, and give rise to spin-polarized
charge transport. We further show that the electronic conductance across
graphene nanoribbons is more sensitive to "bite" defects forming at the zigzag
edges than at the armchair ones. Our work establishes a comprehensive
understanding of the low-energy electronic properties of disordered bottom-up
graphene nanoribbons.


Abstract:
  A power system electromechanical wave propagates from the disturbance
location to the rest of system, influencing various types of protections. In
addition, since more power-electronics-interfaced generation and energy storage
devices are being integrated into power systems, electromechanical wave
propagation speeds in the future power systems are likely to change
accordingly. In this paper, GPS-synchronized measurement data from a wide-area
synchrophasor measurement system FNET/GridEye are used to analyze the
characteristics of electromechanical wave propagation in the U.S. Eastern
Interconnection (EI) system. Afterwards, high levels of photovoltaic (PV)
penetration are modeled in the EI to investigate the influences of a typical
power-electronics--interfaced resource on the electromechanical wave
propagation speed. The result shows a direct correlation between the local
penetration level of inverter-based generation and the electromechanical wave
propagation speed.


Abstract:
  Modern deep unsupervised learning methods have shown great promise for
detecting diseases across a variety of medical imaging modalities. While
previous generative modeling approaches successfully perform anomaly detection
by learning the distribution of healthy 2D image slices, they process such
slices independently and ignore the fact that they are correlated, all being
sampled from a 3D volume. We show that incorporating the 3D context and
processing whole-body MRI volumes is beneficial to distinguishing anomalies
from their benign counterparts. In our work, we introduce a multi-channel
sliding window generative model to perform lesion detection in whole-body MRI
(wbMRI). Our experiments demonstrate that our proposed method significantly
outperforms processing individual images in isolation and our ablations clearly
show the importance of 3D reasoning. Moreover, our work also shows that it is
beneficial to include additional patient-specific features to further improve
anomaly detection in pediatric scans.


Abstract:
  Channel state information (CSI) rapidly becomes outdated in high mobility
scenarios, degrading the performance of wireless communication systems. In
these cases, time series prediction techniques can be applied to combat the
effects of outdated CSI. Recently, it has been shown that recurrent neural
networks (RNNs) exhibit outstanding performance in time series prediction
tasks. In this paper, we investigate the performance of RNN and long short term
memory (LSTM) predictors in a simple Rayleigh flat-fading channel. We conduct
numerical experiments to evaluate whether these machine-learning (ML)-based
predictors can outperform the optimal linear minimum mean square error Wiener
predictor. Our simulation results indicate that the considered neural network
predictors outperform the Wiener predictor for small observation window lengths
and are more robust under weak channel correlation as well as in the presence
of noise. Furthermore, we show that simple shallow RNNs are sufficient to model
Rayleigh channels over a wide range of Doppler shifts.


Abstract:
  Multimedia technologies are now more practical and deployable in real life,
and the algorithms are widely used in various researching areas such as deep
learning, signal processing, haptics, computer vision, robotics, and medical
multimedia processing. This survey provides an overview of multimedia
technologies and robust algorithms in multimedia data processing, medical
multimedia processing, human facial expression tracking and pose recognition,
and multimedia in education and training. This survey will also analyze and
propose a future research direction based on the overview of current robust
algorithms and multimedia technologies. We want to thank the research and
previous work done by the Multimedia Research Centre (MRC), the University of
Alberta, which is the inspiration and starting point for future research.


Abstract:
  The sub-seasonal and synoptic-scale variability of the Indian summer monsoon
rainfall are controlled primarily by monsoon intra-seasonal oscillations (MISO)
and low pressure systems (LPS), respectively. The positive and negative phases
of MISO lead to alternate epochs of above-normal (active) and below-normal
(break) spells of rainfall. LPSs are embedded within the different phases of
MISO and are known to produce heavy precipitation events over central India.
Whether the interaction with the MISO phases modulates the precipitation
response of LPSs, and thereby the characteristics of extreme rainfall events
(EREs) remains unaddressed in the available literature. In this study, we
analyze the LPSs that produce EREs of various spatial extents viz., Small,
Medium, and Large over central India from 1979 to 2012. We also compare them
with the LPSs that pass through central India and do not give any ERE
(LPS-noex). We find that thermodynamic characteristics of LPSs that trigger
different spatial extents of EREs are similar. However, they show differences
in their dynamic characteristics. The ERE producing LPSs are slower, moister
and more intense than LPS-noex. The LPSs that lead to Medium and Large EREs
tend to occur during the positive phase of MISO when an active monsoon trough
is present over central India. On the other hand, LPS-noex and the LPSs that
trigger Small EREs occur mainly during the neutral or negative phases of the
MISO. The large-scale dynamic forcing, intensification of LPSs, and diabatic
generation of low-level potential vorticity due to the presence of active
monsoon trough help in the organization of convection and lead to Medium and
Large EREs. On the other hand, the LPSs that form during the negative or
neutral phases of MISO do not intensify much during their lifetime and trigger
scattered convection, leading to EREs of small size.


Abstract:
  At peak, long-duration gamma-ray bursts are the most luminous sources of
electromagnetic radiation known. Since their progenitors are massive stars,
they provide a tracer of star formation and star-forming galaxies over the
whole of cosmic history. Their bright power-law afterglows provide ideal
backlights for absorption studies of the interstellar and intergalactic medium
back to the reionization era. The proposed THESEUS mission is designed to
detect large samples of GRBs at $z>6$ in the 2030s, at a time when supporting
observations with major next generation facilities will be possible, thus
enabling a range of transformative science. THESEUS will allow us to explore
the faint end of the luminosity function of galaxies and the star formation
rate density to high redshifts; constrain the progress of re-ionisation beyond
$z\gtrsim6$; study in detail early chemical enrichment from stellar explosions,
including signatures of Population III stars; and potentially characterize the
dark energy equation of state at the highest redshifts.


Abstract:
  This white paper presents a summary of the discussions regarding critical
considerations to develop an extensive repository of online videos annotated
with labels indicating questionable content. The main discussion points
include: 1) the type of appropriate labels that will result in a valuable
repository for the larger AI community; 2) how to design the collection and
annotation process, as well as the distribution of the corpus to maximize its
potential impact; and, 3) what actions we can take to reduce risk of trauma to
annotators.


Abstract:
  We consider the Cauchy problem for one-dimensional dispersive equations with
a general nonlinearity in the periodic setting. Our main hypotheses are both
that the dispersive operator behaves for high frequencies as a Fourier
multiplier by $ i |\xi|^\alpha \xi $, with $ 1\le \alpha \le 2 $, and that the
nonlinear term is of the form $ \partial_x f(u) $ where $ f $ is the sum of an
entire series with infinite radius of convergence. Under these conditions, we
prove the unconditional local well-posedness of the Cauchy problem in
$H^{s}(\mathbb{T})$ for $ s\ge 1-\frac{\alpha}{2(\alpha+1)}$. This leads to
some global existence results above the energy space $ H^{\alpha/2}(\mathbb{T})
$, for $ \alpha \in [\sqrt{2},2]$.


Abstract:
  The Hubble tension is shown to be solvable, without any free parameter,
conceptually and quantitatively, within the approach of modified weak-field
General Relativity involving the cosmological constant $\Lambda$. That approach
enables one to describe in a unified picture both the dynamics of dark matter
containing galaxies and the accelerated expansion of the Universe, thus
defining a {\it local} Hubble constant of a local flow and the {\it global}
one. The data on the dark matter content of peculiar galaxy samples are shown
to be compatible to that unified picture. Future more refined surveys of galaxy
distribution, hierarchical dynamics and flows within the vicinity of the Local
group and the Virgo supercluster can be decisive in revealing the possible
common nature of the dark sector.


Abstract:
  In this paper, we investigate the optimal output tracking problem for linear
discrete-time systems with unknown dynamics using reinforcement learning and
robust output regulation theory. This output tracking problem only allows to
utilize the outputs of the reference system and the controlled system, rather
than their states, and differs from most existing tracking results that depend
on the state of the system. The optimal tracking problem is formulated into a
linear quadratic regulation problem by proposing a family of dynamic
discrete-time controllers. Then, it is shown that solving the output tracking
problem is equivalent to solving output regulation equations, whose solution,
however, requires the knowledge of the complete and accurate system dynamics.
To remove such a requirement, an off-policy reinforcement learning algorithm is
proposed using only the measured output data along the trajectories of the
system and the reference output. By introducing re-expression error and
analyzing the rank condition of the parameterization matrix, we ensure the
uniqueness of the proposed RL based optimal control via output feedback.


Abstract:
  As an essential component of human cognition, cause-effect relations appear
frequently in text, and curating cause-effect relations from text helps in
building causal networks for predictive tasks. Existing causality extraction
techniques include knowledge-based, statistical machine learning(ML)-based, and
deep learning-based approaches. Each method has its advantages and weaknesses.
For example, knowledge-based methods are understandable but require extensive
manual domain knowledge and have poor cross-domain applicability. Statistical
machine learning methods are more automated because of natural language
processing (NLP) toolkits. However, feature engineering is labor-intensive, and
toolkits may lead to error propagation. In the past few years, deep learning
techniques attract substantial attention from NLP researchers because of its'
powerful representation learning ability and the rapid increase in
computational resources. Their limitations include high computational costs and
a lack of adequate annotated training data. In this paper, we conduct a
comprehensive survey of causality extraction. We initially introduce primary
forms existing in the causality extraction: explicit intra-sentential
causality, implicit causality, and inter-sentential causality. Next, we list
benchmark datasets and modeling assessment methods for causal relation
extraction. Then, we present a structured overview of the three techniques with
their representative systems. Lastly, we highlight existing open challenges
with their potential directions.


Abstract:
  We present a consistent \emph{ab initio} computation of the longitudinal
response function $R_L$ in $^{40}$Ca using the coupled-cluster and Lorentz
integral transform methods starting from chiral nucleon-nucleon and
three-nucleon interactions. We validate our approach by comparing our results
for $R_L$ in $^4$He and the Coulomb sum rule in $^{40}$Ca against experimental
data and other calculations. For $R_L$ in $^{40}$Ca we obtain a very good
agreement with experiment in the quasi-elastic peak up to intermediate momentum
transfers, and we find that final state interactions are essential for an
accurate description of the data. This work presents a milestone towards
\emph{ab initio} computations of neutrino-nucleus cross sections relevant for
experimental long-baseline neutrino programs.


Abstract:
  Scholarly journals are increasingly using social media to share their latest
research publications and communicate with their readers. Having a presence on
social media gives journals a platform to raise their profile and promote their
content. This study compares the number of clicks received when journals
provide two types of links to subscription articles: open access (OA) and paid
content links. We examine the OA effect using unique matched-pair data for the
journal Nature Materials. Our study finds that OA links perform better than
paid content links. In particular, when the journal does not indicate that a
link to an article is an OA link, there is an obvious drop in performance
against clicks on links indicating OA status. OA has a positive effect on the
number of clicks in all countries, but its positive impact is slightly greater
in developed countries. The results suggest that free content is more
attractive to users than paid content. Social media exposure of scholarly
articles promotes the use of research outputs. Combining social media
dissemination with OA appears to enhance the reach of scientific information.
However, extensive further efforts are needed to remove barriers to OA.


Abstract:
  It has long been thought that high-dimensional data encountered in many
practical machine learning tasks have low-dimensional structure, i.e., the
manifold hypothesis holds. A natural question, thus, is to estimate the
intrinsic dimension of a given population distribution from a finite sample. We
introduce a new estimator of the intrinsic dimension and provide finite sample,
non-asymptotic guarantees. We then apply our techniques to get new sample
complexity bounds for Generative Adversarial Networks (GANs) depending only on
the intrinsic dimension of the data.


Abstract:
  We extend the planar Markus-Yamabe Jacobian Conjecture to differential
systems having jacobian matrix with eigenvalues with negative or zero real
parts.


Abstract:
  This editorial presents the quantum field theoretic ambiance in which Elko
and mass dimension one fermions come to exist. This may serve not only to
introduce Elko, and the associated quantum field, but it may also open a door
to a new class of spin one half fermions and bosons. These new particles, as it
will be argued in this volume, are first-principle candidates for dark matter.


Abstract:
  We combine numerical and analytical methods to study two dimensional active
crystals formed by permanently linked swimmers and with two distinct alignment
interactions. The system admits a stationary phase with quasi long range
translational order, as well as a moving phase with quasi-long range velocity
order. The translational order in the moving phase is significantly influenced
by alignment interaction. For Vicsek-like alignment, the translational order is
short-ranged, whereas the bond-orientational order is quasi-long ranged,
implying a moving hexatic phase. For elasticity-based alignment, the
translational order is quasi-long ranged parallel to the motion and
short-ranged in perpendicular direction, whereas the bond orientational order
is long-ranged. We also generalize these results to higher dimensions.


Abstract:
  Quantum memory effects can be related to a bidirectional exchange of
information between an open system and its environment, which in turn modifies
the state and dynamical behavior of the last one. Nevertheless,
non-Markovianity can also be induced by environments whose dynamics is not
affected during the system evolution, implying the absence of any physical
information exchange. An unsolved open problem in the formulation of quantum
memory measures is the apparent impossibility of discerning between both
paradigmatic cases. Here, we present an operational scheme that, based on the
outcomes of successive measurements processes performed over the system of
interest, allows to distinguishing between both kinds of memory effects. The
method accurately detects bidirectional information flows in diverse
dissipative and dephasing non-Markovian open system dynamics.


Abstract:
  Deep convolutional neural networks have shown outstanding performance in
medical image segmentation tasks. The usual problem when training supervised
deep learning methods is the lack of labeled data which is time-consuming and
costly to obtain. In this paper, we propose a novel uncertainty-guided
semi-supervised learning based on a student-teacher approach for training the
segmentation network using limited labeled samples and a large number of
unlabeled images. First, a teacher segmentation model is trained from the
labeled samples using Bayesian deep learning. The trained model is used to
generate soft segmentation labels and uncertainty maps for the unlabeled set.
The student model is then updated using the softly segmented samples and the
corresponding pixel-wise confidence of the segmentation quality estimated from
the uncertainty of the teacher model using a newly designed loss function.
Experimental results on a retinal layer segmentation task show that the
proposed method improves the segmentation performance in comparison to the
fully supervised approach and is on par with the expert annotator. The proposed
semi-supervised segmentation framework is a key contribution and applicable for
biomedical image segmentation across various imaging modalities where access to
annotated medical images is challenging


Abstract:
  The hat guessing number is a graph invariant based on a hat guessing game
introduced by Winkler. Using a new vertex decomposition argument involving an
edge density theorem of Erd\H{o}s for hypergraphs, we show that the hat
guessing number of all outerplanar graphs is less than $2^{125000}$. We also
define the class of layered planar graphs, which contains outerplanar graphs,
and we show that every layered planar graph has bounded hat guessing number.


Abstract:
  Negation Scope Resolution is an extensively researched problem, which is used
to locate the words affected by a negation cue in a sentence. Recent works have
shown that simply finetuning transformer-based architectures yield
state-of-the-art results on this task. In this work, we look at Negation Scope
Resolution as a Cloze-Style task, with the sentence as the Context and the cue
words as the Query. We also introduce a novel Cloze-Style Attention mechanism
called Orthogonal Attention, which is inspired by Self Attention. First, we
propose a framework for developing Orthogonal Attention variants, and then
propose 4 Orthogonal Attention variants: OA-C, OA-CA, OA-EM, and OA-EMB. Using
these Orthogonal Attention layers on top of an XLNet backbone, we outperform
the finetuned XLNet state-of-the-art for Negation Scope Resolution, achieving
the best results to date on all 4 datasets we experiment with: BioScope
Abstracts, BioScope Full Papers, SFU Review Corpus and the *sem 2012 Dataset
(Sherlock).


Abstract:
  In the rare physics events search, liquid and solid detector plays an
important role. Its analysis is based on the detection of excess events over
the expected background or on the detection of an annual event rate modulation.
Germanium detectors sensitivities have been demonstrated as efficient means to
probe Weakly Interacting Massive Particles (WIMPs). Germanium detectors having
sensitivity less than 100 eV opens new platform for the investigation of
neutrinos and dark matter physics. There are various working configuration
available for germanium detectors such as semi-coaxial, true coaxial and point
contact. The point contact germanium detectors have been widely used for dark
matter and low energy neutrino searches because of their sub KeV energy
threshold and low electronic noise. Pulse shape of P-type point contact (P-PC)
germanium detector has been simulated in the present work and compared to the
pulse shape of Majorana Demonstrator(MJD).


Abstract:
  Multisource image analysis that leverages complementary spectral, spatial,
and structural information benefits fine-grained object recognition that aims
to classify an object into one of many similar subcategories. However, for
multisource tasks that involve relatively small objects, even the smallest
registration errors can introduce high uncertainty in the classification
process. We approach this problem from a weakly supervised learning perspective
in which the input images correspond to larger neighborhoods around the
expected object locations where an object with a given class label is present
in the neighborhood without any knowledge of its exact location. The proposed
method uses a single-source deep instance attention model with parallel
branches for joint localization and classification of objects, and extends this
model into a multisource setting where a reference source that is assumed to
have no location uncertainty is used to aid the fusion of multiple sources in
four different levels: probability level, logit level, feature level, and pixel
level. We show that all levels of fusion provide higher accuracies compared to
the state-of-the-art, with the best performing method of feature-level fusion
resulting in 53% accuracy for the recognition of 40 different types of trees,
corresponding to an improvement of 5.7% over the best performing baseline when
RGB, multispectral, and LiDAR data are used. We also provide an in-depth
comparison by evaluating each model at various parameter complexity settings,
where the increased model capacity results in a further improvement of 6.3%
over the default capacity setting.


Abstract:
  The spectrum of a real and symmetric $N\times N$ matrix determines the matrix
up to unitary equivalence. More spectral data is needed together with some sign
indicators to remove the unitary ambiguities. In the first part of this work we
specify the spectral and sign information required for a unique reconstruction
of general matrices. More specifically, the spectral information consists of
the spectra of the $N$ nested main minors of the original matrix of the sizes
$1,2,\dots,N$. However, due to the complicated nature of the required sign
data, improvements are needed in order to make the reconstruction procedure
feasible. With this in mind, the second part is restricted to banded matrices
where the amount of spectral data exceeds the number of the unknown matrix
entries. It is shown that one can take advantage of this redundancy to
guarantee unique reconstruction of {\it generic} matrices, in other words, this
subset of matrices is open, dense and of full measure in the set of real,
symmetric and banded matrices. It is shown that one can optimize the ratio
between redundancy and genericity by using the freedom of choice of the
spectral information input. We demonstrate our constructions in detail for
pentadiagonal matrices.


Abstract:
  Trust is of paramount concern for tenants to deploy their security-sensitive
services in the cloud. The integrity of VMs in which these services are
deployed needs to be ensured even in the presence of powerful adversaries with
administrative access to the cloud. Traditional approaches for solving this
challenge leverage trusted computing techniques, e.g., vTPM, or hardware CPU
extensions, e.g., AMD SEV. But, they are vulnerable to powerful adversaries, or
they provide only load time (not runtime) integrity measurements of VMs.
  We propose WELES, a protocol allowing tenants to establish and maintain trust
in VM runtime integrity of software and its configuration. WELES is transparent
to the VM configuration and setup. It performs an implicit attestation of VMs
during a secure login and binds the VM integrity state with the secure
connection. Our prototype's evaluation shows that WELES is practical and incurs
low performance overhead.


Abstract:
  We establish the pointwise continuity of bounded weak solutions to of a class
of scalar parabolic equations and strongly coupled parabolic systems. Our
approach to the regularity theory of parabolic scalar equations is quite
elementary and its applications to strongly coupled systems does not require
higher $L^p$ integrability of derivatives.


Abstract:
  Transfer learning has become the dominant paradigm for many natural language
processing tasks. In addition to models being pretrained on large datasets,
they can be further trained on intermediate (supervised) tasks that are similar
to the target task. For small Natural Language Inference (NLI) datasets,
language modelling is typically followed by pretraining on a large (labelled)
NLI dataset before fine-tuning with each NLI subtask. In this work, we explore
Gradient Boosted Decision Trees (GBDTs) as an alternative to the commonly used
Multi-Layer Perceptron (MLP) classification head. GBDTs have desirable
properties such as good performance on dense, numerical features and are
effective where the ratio of the number of samples w.r.t the number of features
is low. We then introduce FreeGBDT, a method of fitting a GBDT head on the
features computed during fine-tuning to increase performance without additional
computation by the neural network. We demonstrate the effectiveness of our
method on several NLI datasets using a strong baseline model (RoBERTa-large
with MNLI pretraining). The FreeGBDT shows a consistent improvement over the
MLP classification head.


Abstract:
  Non-negative Matrix Factorization (NMF) methods offer an appealing
unsupervised learning method for real-time analysis of streaming spectral data
in time-sensitive data collection, such as $\textit{in situ}$ characterization
of materials. However, canonical NMF methods are optimized to reconstruct a
full dataset as closely as possible, with no underlying requirement that the
reconstruction produces components or weights representative of the true
physical processes. In this work, we demonstrate how constraining NMF weights
or components, provided as known or assumed priors, can provide significant
improvement in revealing true underlying phenomena. We present a PyTorch based
method for efficiently applying constrained NMF and demonstrate this on several
synthetic examples. When applied to streaming experimentally measured spectral
data, an expert researcher-in-the-loop can provide and dynamically adjust the
constraints. This set of interactive priors to the NMF model can, for example,
contain known or identified independent components, as well as functional
expectations about the mixing of components. We demonstrate this application on
measured X-ray diffraction and pair distribution function data from $\textit{in
situ}$ beamline experiments. Details of the method are described, and general
guidance provided to employ constrained NMF in extraction of critical
information and insights during $\textit{in situ}$ and high-throughput
experiments.


Abstract:
  Learning visual knowledge from massive weakly-labeled web videos has
attracted growing research interests thanks to the large corpus of easily
accessible video data on the Internet. However, for video action recognition,
the action of interest might only exist in arbitrary clips of untrimmed web
videos, resulting in high label noises in the temporal space. To address this
issue, we introduce a new method for pre-training video action recognition
models using queried web videos. Instead of trying to filter out, we propose to
convert the potential noises in these queried videos to useful supervision
signals by defining the concept of Sub-Pseudo Label (SPL). Specifically, SPL
spans out a new set of meaningful "middle ground" label space constructed by
extrapolating the original weak labels during video querying and the prior
knowledge distilled from a teacher model. Consequently, SPL provides enriched
supervision for video models to learn better representations. SPL is fairly
simple and orthogonal to popular teacher-student self-training frameworks
without extra training cost. We validate the effectiveness of our method on
four video action recognition datasets and a weakly-labeled image dataset to
study the generalization ability. Experiments show that SPL outperforms several
existing pre-training strategies using pseudo-labels and the learned
representations lead to competitive results when fine-tuning on HMDB-51 and
UCF-101 compared with recent pre-training methods.


Abstract:
  It is known that the contribution of torsion to the equation for the chiral
Weyl fermions can be equivalently considered in terms of the axial $U(1)$ gauge
field. In this scenario the gravitational field transforms to the $U(1)$ gauge
field. Here we show that in chiral superconductors the opposite scenario takes
place: the electromagnetic $U(1)$ field serves as the spin connection for the
Bogoliubov fermionic quasiparticles. As a result the electromagnetic field
gives rise to the gravitational anomaly, which contains the extra factor $1/3$
in the corresponding Adler-Bell-Jackiw equation as compared with the
conventional chiral anomaly. We also consider the gravitational anomaly
produced in neutral Weyl superfluids by the analog of the gravitational
instanton, the process of creation and annihilation of the 3D topological
objects -- hopfions. The gravitational instanton leads to creation of the
chiral charge.


Abstract:
  Recently there has been increased interest in semi-supervised classification
in the presence of graphical information. A new class of learning models has
emerged that relies, at its most basic level, on classifying the data after
first applying a graph convolution. To understand the merits of this approach,
we study the classification of a mixture of Gaussians, where the data
corresponds to the node attributes of a stochastic block model. We show that
graph convolution extends the regime in which the data is linearly separable by
a factor of roughly $1/\sqrt{D}$, where $D$ is the expected degree of a node,
as compared to the mixture model data on its own. Furthermore, we find that the
linear classifier obtained by minimizing the cross-entropy loss after the graph
convolution generalizes to out-of-distribution data where the unseen data can
have different intra- and inter-class edge probabilities from the training
data.


Abstract:
  Regardless of their different types of progenitors and central engines,
gamma-ray bursts (GRBs) were always assumed to be standalone systems after they
formed. Little attention has been paid to the possibility that a stellar
companion can still accompany a GRB itself. This paper investigates such a
GRB-involved binary system and studies the effects of the stellar companion on
the observed GRB emission when it is located inside the jet opening angle.
Assuming a typical emission radius of $\sim10^{15}\,$cm, we show that the
blockage by a companion star with a radius of
$R_\mathrm{c}\sim67\,\mathrm{R_\odot}$ becomes non-negligible when it is
located within a typical GRB jet opening angle (e.g., $\sim10$ degrees) and
beyond the GRB emission site. In such a case, an on-axis observer will see a
GRB with a similar temporal behavior but 25% dimmer. On the other hand, an
off-axis observer outside the jet opening angle (hence missed the original GRB)
can see a delayed "reflected" GRB, which is much fainter in brightness, much
wider in the temporal profile and slightly softer in energy. Our study can
naturally explain the origin of some low-luminosity GRBs. Moreover, we also
point out that the companion star may be shocked if it is located inside the
GRB emission site, which can give rise to an X-ray transient or a GRB followed
by a delayed X-ray bump on top of X-ray afterglows.


Abstract:
  We present a far-UV (FUV) study of the star-forming complexes (SFCs) in three
nearby galaxies using the Ultraviolet Imaging Telescope (UVIT). The galaxies
are close to face-on and show significant outer disk star formation. Two of
them are isolated (NGC 628, NGC 6946), and one is interacting with distant
companions (NGC 5457). We compared the properties of the SFCs inside and
outside the optical radius (R$_{25}$). We estimated the sizes, star formation
rates (SFRs), metallicities, and the Toomre Q parameter of the SFCs. We find
that the outer disk SFCs are at least ten times smaller in area than those in
the inner disk. The SFR per unit area ($\Sigma_{SFR}$) in both regions have
similar mean values, but the outer SFCs have a much smaller range of
$\Sigma_{SFR}$. They are also metal-poor compared to the inner disk SFCs. The
FUV emission is well correlated with the neutral hydrogen gas (\HI)
distribution and is detected within and near several \HI~holes. Our estimation
of the Q parameter in the outer disks of the two isolated galaxies suggests
that their outer disks are stable (Q$>$1). However, their FUV images indicate
that there is ongoing star formation in these regions. This suggests that there
may be some non-luminous mass or dark matter in their outer disks, which
increases the disk surface density and supports the formation of local
gravitational instabilities. In the interacting galaxy, NGC 5457, the baryonic
surface density is sufficient (Q$<$1) to trigger local disk instabilities in
the outer disk.


Abstract:
  We study a new application for text generation -- idiomatic sentence
generation -- which aims to transfer literal phrases in sentences into their
idiomatic counterparts. Inspired by psycholinguistic theories of idiom use in
one's native language, we propose a novel approach for this task, which
retrieves the appropriate idiom for a given literal sentence, extracts the span
of the sentence to be replaced by the idiom, and generates the idiomatic
sentence by using a neural model to combine the retrieved idiom and the
remainder of the sentence. Experiments on a novel dataset created for this task
show that our model is able to effectively transfer literal sentences into
idiomatic ones. Furthermore, automatic and human evaluations show that for this
task, the proposed model outperforms a series of competitive baseline models
for text generation.


Abstract:
  Extracting temporal relationships over a range of scales is a hallmark of
human perception and cognition -- and thus it is a critical feature of machine
learning applied to real-world problems. Neural networks are either plagued by
the exploding/vanishing gradient problem in recurrent neural networks (RNNs) or
must adjust their parameters to learn the relevant time scales (e.g., in
LSTMs). This paper introduces DeepSITH, a network comprising
biologically-inspired Scale-Invariant Temporal History (SITH) modules in series
with dense connections between layers. SITH modules respond to their inputs
with a geometrically-spaced set of time constants, enabling the DeepSITH
network to learn problems along a continuum of time-scales. We compare DeepSITH
to LSTMs and other recent RNNs on several time series prediction and decoding
tasks. DeepSITH achieves state-of-the-art performance on these problems.


Abstract:
  The core problem of Magnetic Resonance Imaging (MRI) is the trade off between
acceleration and image quality. Image reconstruction and super-resolution are
two crucial techniques in Magnetic Resonance Imaging (MRI). Current methods are
designed to perform these tasks separately, ignoring the correlations between
them. In this work, we propose an end-to-end task transformer network
(T$^2$Net) for joint MRI reconstruction and super-resolution, which allows
representations and feature transmission to be shared between multiple task to
achieve higher-quality, super-resolved and motion-artifacts-free images from
highly undersampled and degenerated MRI data. Our framework combines both
reconstruction and super-resolution, divided into two sub-branches, whose
features are expressed as queries and keys. Specifically, we encourage joint
feature learning between the two tasks, thereby transferring accurate task
information. We first use two separate CNN branches to extract task-specific
features. Then, a task transformer module is designed to embed and synthesize
the relevance between the two tasks. Experimental results show that our
multi-task model significantly outperforms advanced sequential methods, both
quantitatively and qualitatively.


Abstract:
  In recent years, weak lensing of the cosmic microwave background (CMB) has
emerged as a powerful tool to probe fundamental physics, such as neutrino
masses, primordial non-Gaussianity, dark energy, and modified gravity. The
prime target of CMB lensing surveys is the lensing potential, which is
reconstructed from observed CMB temperature $T$ and polarization $E$ and $B$
fields. Until very recently, this reconstruction has been performed with
quadratic estimators (QEs), which, although known to be suboptimal for
high-sensitivity experiments, are numerically efficient, and useful to make
forecasts and cross-check the results of more sophisticated likelihood-based
methods. It is expected that ongoing and near-future CMB experiments such as
AdvACT, SPT-3G and the Simons Observatory (SO), will also rely on QEs. Here, we
review different QEs, and clarify their differences. In particular, we show
that the Hu-Okamoto (HO02) estimator is not the absolute optimal lensing
estimator that can be constructed out of quadratic combinations of $T, E$ and
$B$ fields. Instead, we derive the global-minimum-variance (GMV) lensing
quadratic estimator. Although this estimator can be found elsewhere in the
literature, it was erroneously described as equivalent to the HO02 estimator,
and has never been used in real data analyses. Here, we show explicitly that
the HO02 estimator is suboptimal to the GMV estimator, with a reconstruction
noise larger by up to $\sim 9\%$ for a SO-like experiment. We further show that
the QE used in the Planck, and recent SPT lensing analysis are suboptimal to
both the HO02 and GMV estimator, and would have a reconstruction noise up to
$\sim 11\%$ larger than that of the GMV estimator for a SO-like experiment. In
addition to clarifying differences between different QEs, this work should thus
provide motivation to implement the GMV estimator in future lensing analyses
relying on QEs.


Abstract:
  We propose a neural network approach for solving high-dimensional optimal
control problems arising in real-time applications. Our approach yields
controls in a feedback form, where the policy function is given by a neural
network (NN). Specifically, we fuse the Hamilton-Jacobi-Bellman (HJB) and
Pontryagin Maximum Principle (PMP) approaches by parameterizing the value
function with an NN. We can therefore synthesize controls in real-time without
having to solve an optimization problem. Once the policy function is trained,
generating a control at a given space-time location takes milliseconds; in
contrast, efficient nonlinear programming methods typically perform the same
task in seconds. We train the NN offline using the objective function of the
control problem and penalty terms that enforce the HJB equations. Therefore,
our training algorithm does not involve data generated by another algorithm. By
training on a distribution of initial states, we ensure the controls'
optimality on a large portion of the state-space. Our grid-free approach scales
efficiently to dimensions where grids become impractical or infeasible. We
demonstrate the effectiveness of our approach on several multi-agent
collision-avoidance problems in up to 150 dimensions. Furthermore, we
empirically observe that the number of parameters in our approach scales
linearly with the dimension of the control problem, thereby mitigating the
curse of dimensionality.


Abstract:
  We report measurements of branching fractions ($\mathcal B$) and direct ${\it
CP}$-violating asymmetries ($\mathcal A_{\it CP}$) for the decays $B^+\to
K^+\pi^0$ and $B^+ \to \pi^+\pi^0$ reconstructed with the Belle II detector in
a sample of asymmetric-energy electron-positron collisions at the
$\Upsilon(4S)$ resonance corresponding to 62.8 $\text{fb}^{-1}$ of integrated
luminosity. The results are $\mathcal{B}(B^+ \to K^+\pi^0) = [11.9
^{+1.1}_{-1.0} (\rm stat)\pm 1.6(\rm syst)]\times 10^{-6}$, $\mathcal{B}(B^+
\to \pi^+\pi^0) = [5.5 ^{+1.0}_{-0.9} (\rm stat)\pm 0.7(\rm syst)]\times
10^{-6}$, $\mathcal A_{\it CP}(B^+ \to K^+\pi^0) = -0.09 \pm 0.09 (\rm stat)\pm
0.03(\rm syst)$, and $\mathcal A_{\it CP}(B^+ \to \pi^+\pi^0) = -0.04 \pm 0.17
(\rm stat)\pm 0.06(\rm syst)$. The results are consistent with previous
measurements and show a detector performance comparable with early Belle
performance.


Abstract:
  Myers shows that every compact, connected, orientable $3$--manifold with no
$2$--sphere boundary components contains a hyperbolic knot. We use work of
Ikeda with an observation of Adams-Reid to show that every $3$--manifold
subject to the above conditions contains a hyperbolic knot which admits a
non-trivial non-hyperbolic surgery, a toroidal surgery in particular. We
conclude with a question and a conjecture about reducible surgeries.


Abstract:
  Inspired by the way human brain works, the emerging hyperdimensional
computing (HDC) is getting more and more attention. HDC is an emerging
computing scheme based on the working mechanism of brain that computes with
deep and abstract patterns of neural activity instead of actual numbers.
Compared with traditional ML algorithms such as DNN, HDC is more
memory-centric, granting it advantages such as relatively smaller model size,
less computation cost, and one-shot learning, making it a promising candidate
in low-cost computing platforms. However, the robustness of HDC models have not
been systematically studied. In this paper, we systematically expose the
unexpected or incorrect behaviors of HDC models by developing HDXplore, a
blackbox differential testing-based framework. We leverage multiple HDC models
with similar functionality as cross-referencing oracles to avoid manual
checking or labeling the original input. We also propose different perturbation
mechanisms in HDXplore. HDXplore automatically finds thousands of incorrect
corner case behaviors of the HDC model. We propose two retraining mechanisms
and using the corner cases generated by HDXplore to retrain the HDC model, we
can improve the model accuracy by up to 9%.


Abstract:
  The Ladder of Causation describes three qualitatively different types of
activities an agent may be interested in engaging in, namely, seeing
(observational), doing (interventional), and imagining (counterfactual) (Pearl
and Mackenzie, 2018). The inferential challenge imposed by the causal hierarchy
is that data is collected by an agent observing or intervening in a system
(layers 1 and 2), while its goal may be to understand what would have happened
had it taken a different course of action, contrary to what factually ended up
happening (layer 3). While there exists a solid understanding of the conditions
under which cross-layer inferences are allowed from observations to
interventions, the results are somewhat scarcer when targeting counterfactual
quantities. In this paper, we study the identification of nested
counterfactuals from an arbitrary combination of observations and experiments.
Specifically, building on a more explicit definition of nested counterfactuals,
we prove the counterfactual unnesting theorem (CUT), which allows one to map
arbitrary nested counterfactuals to unnested ones. For instance, applications
in mediation and fairness analysis usually evoke notions of direct, indirect,
and spurious effects, which naturally require nesting. Second, we introduce a
sufficient and necessary graphical condition for counterfactual identification
from an arbitrary combination of observational and experimental distributions.
Lastly, we develop an efficient and complete algorithm for identifying nested
counterfactuals; failure of the algorithm returning an expression for a query
implies it is not identifiable.


Abstract:
  The thermal properties of light mesons, including the temperature dependence
of their masses (both screening and pole masses) and thermal widths, are
studied in a two-flavor ($N_f=2$) soft-wall AdS/QCD model. By solving the
spatial correlation functions, we extract the screening masses ($m_{\rm{scr}}$)
from their poles. The screening masses of pseudo-scalar ($\pi$) and
axial-vector ($a_1$) mesons increase almost monotonously with the increase of
temperature. The screening masses of scalar ($\sigma$) and vector ($\rho$)
mesons decrease at low temperature and increase at high temperature. The pole
masses ($m_{\rm{pole}}$) and the thermal widths ($\Gamma$) are extracted from
the temporal correlation functions and the corresponding spectral functions.
The results indicate that the pole masses have local minima at low temperature
and increase at high temperature. The thermal widths increase rapidly above the
chiral crossover temperature $T_{cp}$, indicating the dissociations of mesons
at high temperature. Furthermore, the degeneration of the chiral partners
($\pi$ and $\sigma$, $\rho$ and $a_1$) above $T_{cp}$ is observed from the
screening and pole masses, revealing the chiral symmetry restoration at the
hadronic spectrum level. Finally, we numerically verify that the spectral
functions in the temporal regime are strongly related to the quasi-normal modes
with complex frequencies $\omega_0=m_{\rm{pole}}-i \Gamma/2$.


Abstract:
  In this work, we explore the phenomenon of cosmic evolution using curved FLRW
space-time bounded by apparent horizon with a specific holographic cut-off. To
this end, we use the framework of Rastall gravity (RG) and universe is assumed
to be consists of interacting/ non-interacting dark energy (DE) and dark matter
(DM). In both scenarios, we evaluate exact solutions of the dynamical equations
and constraint the holographic parameter $c^{2}(z)$ assuming a slowly varying
function of red-shift during non-interacting model. For interacting model, we
consider $c^{2}$ as a constant function of red-shift. Moreover, we analyze
nature of the obtained results via deceleration parameter $(q)$, statefinder
pair $(j,s)$ and $Om(z)$-diagnostic by constraining the involved model
parameters using latest observational data. The graphical analysis showed that
interacting model is very close to $\Lambda$ cold DM ($\Lambda$CDM) model as
compared to non-interacting case. We conclude that this holographic proposal is
enough to describe the cosmic evolution at an accelerating rate.


Abstract:
  End-to-end production of object tracklets from high resolution video in
real-time and with high accuracy remains a challenging problem due to the cost
of object detection on each frame. In this work we present Localization-based
Tracking (LBT), an extension to any tracker that follows the tracking by
detection or joint detection and tracking paradigms. Localization-based
Tracking focuses only on regions likely to contain objects to boost detection
speed and avoid matching errors. We evaluate LBT as an extension to two example
trackers (KIOU and SORT) on the UA-DETRAC and MOT20 datasets. LBT-extended
trackers outperform all other reported algorithms in terms of PR-MOTA, PR-MOTP,
and mostly tracked objects on the UA-DETRAC benchmark, establishing a new
state-of-the art. relative to tracking by detection with KIOU, LBT-extended
KIOU achieves a 25% higher frame-rate and is 1.1% more accurate in terms of
PR-MOTA on the UA-DETRAC dataset. LBT-extended SORT achieves a 62% speedup and
a 3.2% increase in PR-MOTA on the UA-DETRAC dataset. On MOT20, LBT-extended
KIOU has a 50% higher frame-rate than tracking by detection and is 0.4% more
accurate in terms of MOTA. As of submission time, our LBT-extended KIOU tracker
places 10th overall on the MOT20 benchmark.


Abstract:
  In this report, we present a theoretical support of the continual learning
method \textbf{Elastic Weight Consolidation}, introduced in paper titled
`Overcoming catastrophic forgetting in neural networks'. Being one of the most
cited paper in regularized methods for continual learning, this report
disentangles the underlying concept of the proposed objective function. We
assume that the reader is aware of the basic terminologies of continual
learning.


Abstract:
  Domain specific neural network accelerators have garnered attention because
of their improved energy efficiency and inference performance compared to CPUs
and GPUs. Such accelerators are thus well suited for resource-constrained
embedded systems. However, mapping sophisticated neural network models on these
accelerators still entails significant energy and memory consumption, along
with high inference time overhead. Binarized neural networks (BNNs), which
utilize single-bit weights, represent an efficient way to implement and deploy
neural network models on accelerators. In this paper, we present a novel
optical-domain BNN accelerator, named ROBIN, which intelligently integrates
heterogeneous microring resonator optical devices with complementary
capabilities to efficiently implement the key functionalities in BNNs. We
perform detailed fabrication-process variation analyses at the optical device
level, explore efficient corrective tuning for these devices, and integrate
circuit-level optimization to counter thermal variations. As a result, our
proposed ROBIN architecture possesses the desirable traits of being robust,
energy-efficient, low latency, and high throughput, when executing BNN models.
Our analysis shows that ROBIN can outperform the best-known optical BNN
accelerators and also many electronic accelerators. Specifically, our
energy-efficient ROBIN design exhibits energy-per-bit values that are ~4x lower
than electronic BNN accelerators and ~933x lower than a recently proposed
photonic BNN accelerator, while a performance-efficient ROBIN design shows ~3x
and ~25x better performance than electronic and photonic BNN accelerators,
respectively.


Abstract:
  For the 2D compressible isentropic Euler equations of polytropic gases with
an initial perturbation of size $\ve$ of a rest state, it has been known that
if the initial data are rotationnally invariant or irrotational, then the
lifespan $T_{\ve}$ of the classical solutions is of order $O(\f{1}{\ve^2})$; if
the initial vorticity is of size $\ve^{1+\al}$ ($0\le\al\le 1$), then $T_{\ve}$
is of $O(\f{1}{\ve^{1+\al}})$. In the present paper, for the 2D compressible
isentropic Euler equations of Chaplygin gases, if the initial data are a
perturbation of size $\ve$, and the initial vorticity is of any size $\dl$ with
$0<\dl\le \ve$, we will establish the lifespan $T_{\dl}=O(\f{1}{\dl})$. For
examples, if $\dl=e^{-\f{1}{\ve^2}}$ or $\dl=e^{-e^{\f{1}{\ve^2}}}$ are chosen,
then $T_{\dl}=O(e^{\f{1}{\ve^2}})$ or $T_{\dl}=O(e^{e^{\f{1}{\ve^2}}})$
although the perturbations of the initial density and the divergence of the
initial velocity are only of order $O(\ve)$. Our main ingredients are: finding
the null condition structures in 2D compressible Euler equations of Chaplygin
gases and looking for the good unknown; establishing a new class of weighted
space-time $L^\infty$-$L^\infty$ estimates for the solution itself and its
gradients of 2D linear wave equations; introducing some suitably weighted
energies and taking the $L^p$ $(1<p<\infty)$ estimates on the vorticity.


Abstract:
  We survey determinantal singularities, their deformations, and their
topology. This class of singularities generalizes the well studied case of
complete intersections in several different aspects, but exhibits a plethora of
new phenomena such as for instance non-isolated singularities which are
finitely determined, or smoothings with low connectivity; already the union of
the coordinate axes in $(\mathbb{C}^3,0)$ is determinantal, but not a complete
intersection. We start with the algebraic background and then continue by
discussing the subtle interplay of unfoldings and deformations in this setting,
including a survey of the case of determinantal hypersurfaces, Cohen-Macaulay
codimension $2$ and Gorenstein codimension $3$ singularities, and determinantal
rational surface singularities. We conclude with a discussion of essential
smoothings and provide an appendix listing known classifications of simple
determinantal singularities.


Abstract:
  The solid-on-solid model is a model of height functions, introduced to study
the interface separating the $+$ and $-$ phase in the Ising model. The planar
solid-on-solid model thus corresponds to the three-dimensional Ising model.
Delocalisation of this model at high temperature and at zero slope was first
derived by Fr\"ohlich and Spencer, in parallel to proving the
Kosterlitz-Thouless phase transition. The first main result of this article
consists of a simple, alternative proof for delocalisation of the
solid-on-solid model. In fact, the argument is more general: it works on any
planar graph -- not just the square lattice -- and implies that the interface
delocalises at any slope rather than exclusively at the zero slope. The second
main result is that the absolute value of the height function in this model
satisfies the FKG lattice condition. This property is believed to be intimately
linked to the (quantitative) understanding of delocalisation, given the recent
successes in the context of the square ice and (more generally) the six-vertex
model. The new FKG inequality is shown to hold true for both the solid-on-solid
model as well as for the discrete Gaussian model, which implies immediately
that the two notions of delocalisation, namely delocalisation in finite volume
and delocalisation of shift-invariant Gibbs measures, coincide.


Abstract:
  The dark (or hidden) photon is a massive U(1) gauge boson theorized as a dark
force mediator and as a dark matter candidate. Dark photons can be detected
with axion cavity haloscopes by probing for a power excess caused by the dark
photon's kinetic mixing with Standard Model photons. Haloscope axion exclusion
limits may therefore be converted into competitive dark photon parameter limits
via the calculation of a corresponding dark photon to photon coupling factor.
This calculation allows for an improvement in sensitivity of around four orders
of magnitude relative to other dark photon exclusions and may be attained using
existing data. We present how one converts haloscope axion search limits and a
summary of relevant experimental parameters from published searches. In
addition, we have included the code that can be used to generate our dark
photon exclusion limits for the cases described in this paper. Finally, we
present limits on the kinetic mixing coefficient between dark photons and the
Standard Model photons based on existing haloscope axion searches.


Abstract:
  It is widely assumed that a star and its protoplanetary disk are initially
aligned, with the stellar equator parallel to the disk plane. When observations
reveal a misalignment between stellar rotation and the orbital motion of a
planet, the usual interpretation is that the initial alignment was upset by
gravitational perturbations that took place after planet formation. Most of the
previously known misalignments involve isolated hot Jupiters, for which
planet-planet scattering or secular effects from a wider-orbiting planet are
the leading explanations. In theory, star/disk misalignments can result from
turbulence during star formation or the gravitational torque of a wide-orbiting
companion star, but no definite examples of this scenario are known. An ideal
example would combine a coplanar system of multiple planets -- ruling out
planet-planet scattering or other disruptive post-formation events -- with a
backward-rotating star, a condition that is easier to obtain from a primordial
misalignment than from post-formation perturbations. There are two previously
known examples of a misaligned star in a coplanar multi-planet system, but in
neither case has a suitable companion star been identified, nor is the stellar
rotation known to be retrograde. Here, we show that the star K2-290 A is tilted
by $124\pm 6$ degrees compared to the orbits of both of its known planets, and
has a wide-orbiting stellar companion that is capable of having tilted the
protoplanetary disk. The system provides the clearest demonstration that stars
and protoplanetary disks can become grossly misaligned due to the gravitational
torque from a neighbouring star.


Abstract:
  Humans can synchronize with musical events whilst coordinating their
movements with others. Interpersonal entrainment phenomena, such as dance,
involve multiple body parts and movement directions. Along with being
multidimensional, dance movement interaction is plurifrequential, since it can
occur at different frequencies simultaneously. Moreover, it is prone to
nonstationarity, due to, for instance, displacements around the dance floor.
Various methodological approaches have been adopted to study entrainment, but
only spectrogram-based techniques allow for an integral analysis thereof. This
article proposes an alternative approach based upon the cross-wavelet
transform, a technique for nonstationary and plurifrequential analysis of
univariate interaction. The presented approach generalizes the cross-wavelet
transform to multidimensional signals. It allows to identify, for different
frequencies of movement, interaction estimates of interaction and
leader-follower dynamics across body parts and movement directions. Further,
the generalized cross-wavelet transform can be used to quantify the
frequency-wise contribution of individual body parts and movement directions to
overall synchrony. The article provides a thorough mathematical description of
the method and includes proofs of its invariance under translation, rotation,
and reflection. Finally, its properties and performance are illustrated via
examples using simulated data and behavioral data collected through a mirror
game task and a free dance movement task.


Abstract:
  This report presents the application of object detection on a database of
underwater images of different species of crabs, as well as aerial images of
sea lions and finally the Pascal VOC dataset. The model is an end-to-end object
detection neural network based on a convolutional network base and a Long
Short-Term Memory detector.


Abstract:
  Several classes of near-MDS codes of ${\rm PG}(3,q)$ are described. They are
obtained either by considering the intersection of an elliptic quadric ovoid
and a Suzuki-Tits ovoid of a symplectic polar space ${\cal W}(3, q)$ or
starting from the $q+1$ points of a twisted cubic of ${\rm PG}(3, q)$. As a
by-product two classes of complete caps of ${\rm PG}(4,q)$ of size $2q^2-q \pm
\sqrt{2q} + 2$ are exhibited.


Abstract:
  We showcase the advantages of orbital-free density-potential functional
theory (DPFT), a more flexible variant of Hohenberg-Kohn density functional
theory. DPFT resolves the usual trouble with the gradient-expanded kinetic
energy functional by facilitating systematic semiclassical approximations in
terms of an effective potential energy that incorporates all interactions. With
the aid of two systematic approximation schemes we demonstrate that DPFT is not
only scalable, universally applicable in both position and momentum space, and
allows kinetic and interaction energy to be approximated consistently, but can
also compete with highly accurate, yet restricted, methods. As two- and
three-dimensional geometries are extensively covered elsewhere, our focus here
is on one-dimensional settings, with semiclassical observables systematically
derived from both the Wigner function formalism and a split-operator approach.
The high quality of our results for Fermi gases in Morse potentials invites the
use of DPFT for describing more exotic systems, such as trapped large-spin
fermion mixtures with contact or dipole-dipole interactions.


Abstract:
  We aim to characterize a diverse selection of dense, potentially star-forming
cores, clumps, and clouds within the Milky Way in terms of their dust emission
and SF activity. We studied 53 fields that have been observed in the JCMT
SCUBA-2 continuum survey SCOPE and have been mapped with Herschel. We estimated
dust properties by fitting Herschel observations with modified blackbody
functions, studied the relationship between dust temperature and dust opacity
spectral index $\beta$, and estimated column densities. We extracted clumps
from the SCUBA-2 850 $\mu$m maps with the FellWalker algorithm and examined
their masses and sizes. Clumps are associated with young stellar objects found
in several catalogs. We estimated the gravitational stability of the clumps
with virial analysis. The clumps are categorized as unbound starless,
prestellar, or protostellar. We find 529 dense clumps, typically with high
column densities from (0.3-4.8)$\times 10^{22}$ cm$^{-2}$, with a mean of
(1.5$\pm$0.04)$\times10^{22}$ cm$^{-2}$, low temperatures ($T\sim $10-20 K),
and estimated submillimeter $\beta$ =1.7$\pm$0.1. We detect a slight increase
in opacity spectral index toward millimeter wavelengths. Masses of the sources
range from 0.04 $M_\odot$ to 4259 $M_\odot$. Mass, linear size, and temperature
are correlated with distance. Furthermore, the estimated gravitational
stability is dependent on distance, and more distant clumps appear more
virially bound. Finally, we present a catalog of properties of the clumps.Our
sources present a large array of SF regions, from high-latitude, nearby diffuse
clouds to large SF complexes near the Galactic center. Analysis of these
regions will continue with the addition of molecular line data, which will
allow us to study the densest regions of the clumps in more detail.


Abstract:
  Progress in deep reinforcement learning (RL) research is largely enabled by
benchmark task environments. However, analyzing the nature of those
environments is often overlooked. In particular, we still do not have agreeable
ways to measure the difficulty or solvability of a task, given that each has
fundamentally different actions, observations, dynamics, rewards, and can be
tackled with diverse RL algorithms. In this work, we propose policy information
capacity (PIC) -- the mutual information between policy parameters and episodic
return -- and policy-optimal information capacity (POIC) -- between policy
parameters and episodic optimality -- as two environment-agnostic,
algorithm-agnostic quantitative metrics for task difficulty. Evaluating our
metrics across toy environments as well as continuous control benchmark tasks
from OpenAI Gym and DeepMind Control Suite, we empirically demonstrate that
these information-theoretic metrics have higher correlations with normalized
task solvability scores than a variety of alternatives. Lastly, we show that
these metrics can also be used for fast and compute-efficient optimizations of
key design parameters such as reward shaping, policy architectures, and MDP
properties for better solvability by RL algorithms without ever running full RL
experiments.


Abstract:
  Recently, topological phases in non-Hermitian systems have attracted much
attention because non-Hermiticity sometimes gives rise to unique phases with no
Hermitian counterparts. Non-Hermitian Bloch Hamiltonians can always be mapped
to doubled Hermitianized Hamiltonians with chiral symmetry, which enables us to
utilize the existing framework for Hermitian systems into the classification of
non-Hermitian topological phases. While this strategy succeeded in the
topological classification of non-Hermitian Bloch Hamiltonians in the presence
of internal symmetries, the generalization of symmetry indicators -- a way to
efficiently diagnose topological phases -- to non-Hermitian systems is still
elusive. In this work, we study a theory of symmetry indicators for
non-Hermitian systems. We define space group symmetries of non-Hermitian Bloch
Hamiltonians as ones of the doubled Hermitianized Hamiltonians. Consequently,
symmetry indicator groups for chiral symmetric Hermitian systems are equivalent
to those for non-Hermitian systems. Based on this equivalence, we list symmetry
indicator groups for non-Hermitian systems in the presence of space group
symmetries. We also discuss the physical implications of symmetry indicators
for some symmetry classes. Furthermore, explicit formulas of symmetry
indicators for spinful electronic systems are included in appendices.


Abstract:
  A channel from a process p to a process q satisfies the ADD property if there
are constants K and D, unknown to the processes, such that in any sequence of K
consecutive messages sent by p to q, at least one of them is delivered to q at
most D time units after it has been sent. This paper studies implementations of
an eventual leader, namely, an {\Omega} failure detector, in an arbitrarily
connected network of eventual ADD channels, where processes may fail by
crashing. It first presents an algorithm that assumes that processes initially
know n, the total number of processes, sending messages of size O( log n).
Then, it presents a second algorithm that does not assume the processes know n.
Eventually the size of the messages sent by this algorithm is also O( log n).
These are the first implementations of leader election in the ADD model. In
this model, only eventually perfect failure detectors were considered, sending
messages of size O(n log n).


Abstract:
  In this paper we present the dataset of Himachali low resource endangered
language, Kangri (ISO 639-3xnr) listed in the United Nations Educational,
Scientific and Cultural Organization (UNESCO). The compilation of kangri corpus
has been a challenging task due to the non-availability of the digitalized
resources. The corpus contains 1,81,552 Monolingual and 27,362 Hindi-Kangri
Parallel corpora. We shared pre-trained kangri word embeddings. We also
reported the Bilingual Evaluation Understudy (BLEU) score and Metric for
Evaluation of Translation with Explicit ORdering (METEOR) score of Statistical
Machine Translation (SMT) and Neural Machine Translation (NMT) results for the
corpus. The corpus is freely available for non-commercial usages and research.
To the best of our knowledge, this is the first Himachali low resource
endangered language corpus. The resources are available at
(https://github.com/chauhanshweta/Kangri_corpus)


Abstract:
  We consider the yielding behavior of amorphous solids under cyclic shear
deformation and show that it can be mapped into a random walk in a confining
potential with an absorbing boundary. The resulting dynamics is governed by the
first passage time into the absorbing state and suffices to capture the
essential qualitative features recently observed in atomistic simulations of
amorphous solids. Our results provide insight into the mechanism underlying
yielding and its robustness. When the possibility of activated escape from
absorbing states is added, it leads to a unique determination of a threshold
energy and yield strain, suggesting thereby an appealing approach to
understanding fatigue failure.


Abstract:
  Following the work of Gao and Xie in [2], we state some properties of the
inverse Kazhdan-Lusztig polynomial of a matroid. We also give partial answers
to a conjecture that states that regular connected matroids are non-degenerate.
We link the degeneracy of a matroid to the inverse Kazhdan-Lusztig polynomial
and we show that the Conjecture holds for modular matroids, by proving that
degenerate modular matroids are not regular.


Abstract:
  In this article it is reported a formulation of the solenoidal nature of
quantum electronic currents at the nanoscale whose divergence is expressed as
the coupling of a magnetic field, interacting with a quantum body, and a
weighted Cern invariant vector making then a direct topological interpretation
of this quantum magnetic phenomenon. Also a Fourier analysis of the signaling
of electronic waves is reported in an ab initio formalism from first
pinciples\cite{Martin}.


Abstract:
  Problem definition: Mining for heterogeneous responses to an intervention is
a crucial step for data-driven operations, for instance to personalize
treatment or pricing. We investigate how to estimate price sensitivity from
transaction-level data. In causal inference terms, we estimate heterogeneous
treatment effects when (a) the response to treatment (here, whether a customer
buys a product) is binary, and (b) treatment assignments are partially observed
(here, full information is only available for purchased items).
Methodology/Results: We propose a recursive partitioning procedure to estimate
heterogeneous odds ratio, a widely used measure of treatment effect in medicine
and social sciences. We integrate an adversarial imputation step to allow for
robust inference even in presence of partially observed treatment assignments.
We validate our methodology on synthetic data and apply it to three case
studies from political science, medicine, and revenue management. Managerial
Implications: Our robust heterogeneous odds ratio estimation method is a simple
and intuitive tool to quantify heterogeneity in patients or customers and
personalize interventions, while lifting a central limitation in many revenue
management data.


Abstract:
  In this paper, we investigate the recent studies on multimedia edge
computing, from sensing not only traditional visual/audio data but also
individuals' geographical preference and mobility behaviors, to performing
distributed machine learning over such data using the joint edge and cloud
infrastructure and using evolutional strategies like reinforcement learning and
online learning at edge devices to optimize the quality of experience for
multimedia services at the last mile proactively. We provide both a
retrospective view of recent rapid migration (resp. merge) of cloud multimedia
to (resp. and) edge-aware multimedia and insights on the fundamental guidelines
for designing multimedia edge computing strategies that target satisfying the
changing demand of quality of experience. By showing the recent research
studies and industrial solutions, we also provide future directions towards
high-quality multimedia services over edge computing.


Abstract:
  Hydrodynamics is shown to induce non-Hermitian topological phenomena in
ordinary, passive soft matter. This is demonstrated for the first time by
subjecting a 2D elastic lattice to a low-Reynolds viscous flow. The interplay
of hydrodynamics and elasticity splits Dirac cones into bulk Fermi arcs,
pairing exceptional points with opposite half-integer topological charges. The
bulk Fermi arc is a generic hallmark of the system exhibited in all lattice and
flow symmetries. Analytic model and simulations explain how the emergent
singularities shape the spectral bands and give rise to a web of van Hove
singularity lines in the density of states. The present findings suggest that
non-Hermitian physics can be explored in a broad class of ordinary soft matter,
living and artificial alike, opening avenues for topology-based technology in
this regime.


Abstract:
  This paper provides a robust epistemic foundation for predicting and
implementing collective actions when only the proportions that take specific
actions in the population matter. We apply $\Delta$-rationalizability to
analyze strategic sophistication entailed in (structural) quantal response
equilibrium (QRE); the former is called $\Delta(p)$-rationalization to
emphasize the only requirement on first-order beliefs is that they should be
consistent with the transparent knowledge of the distributions of errors in the
population. We show that each QRE is a $\Delta(p)$-rationalizable outcome. We
also give conditions under which the converse also holds, and prove that the
condition is almost never satisfied in generic games. It implies that QRE may
be too demanding as a predictor in general, and $\Delta(p)$-rationalizable
outcomes can be a robust benchmark to start from.


Abstract:
  Reinforcement learning is about learning agent models that make the best
sequential decisions in unknown environments. In an unknown environment, the
agent needs to explore the environment while exploiting the collected
information, which usually forms a sophisticated problem to solve.
Derivative-free optimization, meanwhile, is capable of solving sophisticated
problems. It commonly uses a sampling-and-updating framework to iteratively
improve the solution, where exploration and exploitation are also needed to be
well balanced. Therefore, derivative-free optimization deals with a similar
core issue as reinforcement learning, and has been introduced in reinforcement
learning approaches, under the names of learning classifier systems and
neuroevolution/evolutionary reinforcement learning. Although such methods have
been developed for decades, recently, derivative-free reinforcement learning
exhibits attracting increasing attention. However, recent survey on this topic
is still lacking. In this article, we summarize methods of derivative-free
reinforcement learning to date, and organize the methods in aspects including
parameter updating, model selection, exploration, and parallel/distributed
methods. Moreover, we discuss some current limitations and possible future
directions, hoping that this article could bring more attentions to this topic
and serve as a catalyst for developing novel and efficient approaches.


Abstract:
  Distributional collision resistance is a relaxation of collision resistance
that only requires that it is hard to sample a collision $(x,y)$ where $x$ is
uniformly random and $y$ is uniformly random conditioned on colliding with $x$.
The notion lies between one-wayness and collision resistance, but its exact
power is still not well-understood. On one hand, distributional collision
resistant hash functions cannot be built from one-way functions in a black-box
way, which may suggest that they are stronger. On the other hand, so far, they
have not yielded any applications beyond one-way functions.
  Assuming distributional collision resistant hash functions, we construct
\emph{constant-round} statistically hiding commitment scheme. Such commitments
are not known based on one-way functions and are impossible to obtain from
one-way functions in a black-box way. Our construction relies on the reduction
from inaccessible entropy generators to statistically hiding commitments by
Haitner et al.\ (STOC '09). In the converse direction, we show that two-message
statistically hiding commitments imply distributional collision resistance,
thereby establishing a loose equivalence between the two notions.
  A corollary of the first result is that constant-round statistically hiding
commitments are implied by average-case hardness in the class $SZK$ (which is
known to imply distributional collision resistance). This implication seems to
be folklore, but to the best of our knowledge has not been proven explicitly.
We provide yet another proof of this implication, which is arguably more direct
than the one going through distributional collision resistance.


Abstract:
  Deep neural network-based classifiers trained with the categorical
cross-entropy (CCE) loss are sensitive to label noise in the training data. One
common type of method that can mitigate the impact of label noise can be viewed
as supervised robust methods; one can simply replace the CCE loss with a loss
that is robust to label noise, or re-weight training samples and down-weight
those with higher loss values. Recently, another type of method using
semi-supervised learning (SSL) has been proposed, which augments these
supervised robust methods to exploit (possibly) noisy samples more effectively.
Although supervised robust methods perform well across different data types,
they have been shown to be inferior to the SSL methods on image classification
tasks under label noise. Therefore, it remains to be seen that whether these
supervised robust methods can also perform well if they can utilize the
unlabeled samples more effectively. In this paper, we show that by initializing
supervised robust methods using representations learned through contrastive
learning leads to significantly improved performance under label noise.
Surprisingly, even the simplest method (training a classifier with the CCE
loss) can outperform the state-of-the-art SSL method by more than 50\% under
high label noise when initialized with contrastive learning. Our implementation
will be publicly available at
{\url{https://github.com/arghosh/noisy_label_pretrain}}.


Abstract:
  Recently, several authors have adopted new alternative approaches in the
study of some classical notions of modules. Among them, we find the notion of
subprojectivity which was introduced to measure in a way the degree of
projectivity of modules. The study of subprojectivity has recently been
extended to the context of abelian categories, which has brought to light some
interesting new aspects. For instance, in the category of complexes, it gives a
new way to measure, among other things, the exactness of complexes. In this
paper, we prove that the subprojectivity notion provides a new sight of
null-homotopic morphisms in the category of complexes. This will be proven
through two main results. Moreover, various results which emphasize the
importance of subprojectivity in the category of complexes are also given.
Namely, we give some applications by characterizing some classical rings and
establish various examples that allow us to reflect the scope and limits of our
results.


Abstract:
  In this paper we investigate the possibility of modeling a single antenna
alone and in close proximity to a physical object by means of discrete point
source scatterers. The scatter point model allows joint modeling of a physical
antenna and the human body as a single extended object with direction dependent
scattering coefficients for the scatter points. We introduce the term extended
antenna describing antenna and human body together. To investigate the
identifiability of the model parameters we make use of ultrawideband channel
measurements and accurate ground truth position and orientation measurements
obtained with an optical tracking system. By comparing measurements of the
antenna attached directly to the user with measurements for the antenna without
the user nearby, we show the shadowing and scattering effects of the human body
and the antenna.


Abstract:
  Flexibility design problems are a class of problems that appear in strategic
decision-making across industries, where the objective is to design a ($e.g.$,
manufacturing) network that affords flexibility and adaptivity. The underlying
combinatorial nature and stochastic objectives make flexibility design problems
challenging for standard optimization methods. In this paper, we develop a
reinforcement learning (RL) framework for flexibility design problems.
Specifically, we carefully design mechanisms with noisy exploration and
variance reduction to ensure empirical success and show the unique advantage of
RL in terms of fast-adaptation. Empirical results show that the RL-based method
consistently finds better solutions compared to classical heuristics.


Abstract:
  We consider the order parameter $u=\left<{\rm Tr}\phi^2\right>$ as function
of the running coupling constant $\tau \in \mathbb{H}$ of asymptotically free
$\mathcal{N}=2$ QCD with gauge group $SU(2)$ and $N_f\leq 3$ massive
hypermultiplets. If the domain for $\tau$ is restricted to an appropriate
fundamental domain $\mathcal{F}_{N_f}$, the function $u$ is one-to-one. We
demonstrate that these domains consist of six or less images of an ${\rm
SL}(2,\mathbb{Z})$ keyhole fundamental domain, with appropriate identifications
of the boundaries. For special choices of the masses, $u$ does not give rise to
branch points and cuts, such that $u$ is a modular function for a congruence
subgroup $\Gamma$ of ${\rm SL}(2,\mathbb{Z})$ and the fundamental domain is
$\Gamma\backslash\mathbb{H}$. For generic masses, however, branch points and
cuts are present, and subsets of $\mathcal{F}_{N_f}$ are being cut and glued
upon varying the mass. We study this mechanism for various phenomena, such as
decoupling of hypermultiplets, merging of local singularities, as well as
merging of non-local singularities which give rise to superconformal
Argyres-Douglas theories.


Abstract:
  Stratifying commercial product portfolios into multiple classes of decreasing
priority, ABCD analysis, is a common supply chain tool. Key planning parameters
that drive strategic and execution priorities are tied to the resulting
segmentation. These priorities in turn drive supply chain performance. For
large product assortments, manual segmentation is infeasible so an automated
algorithm is needed. We therefore advocate that careful attention be paid to
the design of such an ABCD algorithm and present three key features that can be
incorporated into such a calculation to improve its quality and commercial
utility.


Abstract:
  There are relatively few conventions followed in reinforcement learning (RL)
environments to structure the action spaces. As a consequence the application
of RL algorithms to tasks with large action spaces with multiple components
require additional effort to adjust to different formats. In this paper we
introduce {\em Conditional Action Trees} with two main objectives: (1) as a
method of structuring action spaces in RL to generalise across several action
space specifications, and (2) to formalise a process to significantly reduce
the action space by decomposing it into multiple sub-spaces, favoring a
multi-staged decision making approach. We show several proof-of-concept
experiments validating our scheme, ranging from environments with basic
discrete action spaces to those with large combinatorial action spaces commonly
found in RTS-style games.


Abstract:
  Let $\mathcal{B}$ be a collection of rectangular parallelepipeds in
$\mathbb{R}^3$ whose sides are parallel to the coordinate axes and such that
$\mathcal{B}$ contains parallelepipeds with side lengths of the form $s,
\frac{2^N}{s} , t $, where $s, t > 0$ and $N$ lies in a nonempty subset $S$ of
the natural numbers. We show that if $S$ is an infinite set, then the
associated geometric maximal operator $M_\mathcal{B}$ satisfies the weak type
estimate
  $$\left|\left\{x \in \mathbb{R}^3 : M_{\mathcal{B}}f(x) >
\alpha\right\}\right| \leq C \int_{\mathbb{R}^3} \frac{|f|}{\alpha} \left(1 +
\log^+ \frac{|f|}{\alpha}\right)^{2}$$ but does not satisfy an estimate of the
form
  $$\left|\left\{x \in \mathbb{R}^3 : M_{\mathcal{B}}f(x) >
\alpha\right\}\right| \leq C \int_{\mathbb{R}^3}
\phi\left(\frac{|f|}{\alpha}\right)$$ for any convex increasing function $\phi:
\mathbb[0, \infty) \rightarrow [0, \infty)$ satisfying the condition $$\lim_{x
\rightarrow \infty}\frac{\phi(x)}{x (\log(1 + x))^2} = 0\;.$$


Abstract:
  We study the evolution of the low-temperature field-induced magnetic defects
observed under an applied magnetic field in a series of frustrated amorphous
ferromagnets (Fe$_{1-x}$Mn$_{x}$)$_{75}$P$_{16}$B$_{3}$Al$_{3}$ (a-FeMn).
Combining small-angle neutron scattering and Monte Carlo simulations, we show
that the morphology of these defects resemble that of quasi-bidimensional spin
vortices. They are observed in the reentrant spin-glass (RSG) phase, up to the
critical concentration $x_{\rm C} \approx 0.36$ which separates the RSG and
"true" spin glass (SG) within the low temperature part of the magnetic phase
diagram of a-FeMn. These vortices systematically decrease in size with
increasing magnetic field or decreasing the average exchange interaction, and
they finally disappear in the SG sample ($x = 0.41$), being replaced by
field-induced correlations over finite length scales. We argue that the study
of these nanoscopic defects could be used to probe the nature of the critical
line between the RSG and SG phases.


Abstract:
  In this letter, we discuss a new method to probe the redshift evolution of
the gas depletion factor, i.e. the ratio by which the gas mass fraction of
galaxy clusters is depleted with respect to the universal mean of baryon
fraction. The dataset we use for this purpose consists of 40 gas mass fraction
measurements measured at $r_{2500}$ using Chandra X-ray observations, strong
gravitational lensing sub-samples obtained from SLOAN Lens ACS + BOSS
Emission-line Lens Survey (BELLS) + Strong Legacy Survey SL2S + SLACS. For our
analysis, the validity of cosmic distance duality relation is assumed. We find
a mildly decreasing trend for the gas depletion factor as a function of
redshift at about 2.7$\sigma$. This is the first result in literature which
does not find a constant gas depletion factor as a function of redshift using
gas mass fraction measurements at $r_{2500}$.


Abstract:
  In a 1976 landmark paper, Gordon James defined the regularisation maps on
integer partition, yielding certain decomposition numbers for modular
representations of $\mathfrak{S}_n$. We describe a generalisation of James's
regularisation map and give with proof an algorithm for such maps in the
abacus.


Abstract:
  Interactions between the winds of stars and the magnetospheres and
atmospheres of planets involve many processes, including the acceleration of
particles, heating of upper atmospheres, and a diverse range of atmospheric
loss processes. Winds remove angular momentum from their host stars causing
rotational spin-down and a decay in magnetic activity, which protects
atmospheres from erosion. While wind interactions are strongly influenced by
the X-ray and ultraviolet activity of the star and the chemical composition of
the atmosphere, the role of planetary magnetic fields is unclear. In this
chapter, I review our knowledge of the properties and evolution of stellar
activity and winds and discuss the influences of these processes on the long
term evolution of planetary atmospheres. I do not consider the large number of
important processes taking place at the surfaces of planets that cause
exchanges between the atmosphere and the planet's interior.


Abstract:
  Dimension reduction of data sets is a standard problem in the realm of
machine learning and knowledge reasoning. They affect patterns in and
dependencies on data dimensions and ultimately influence any decision-making
processes. Therefore, a wide variety of reduction procedures are in use, each
pursuing different objectives. A so far not considered criterion is the
conceptual continuity of the reduction mapping, i.e., the preservation of the
conceptual structure with respect to the original data set. Based on the notion
scale-measure from formal concept analysis we present in this work a) the
theoretical foundations to detect and quantify conceptual errors in data
scalings; b) an experimental investigation of our approach on eleven data sets
that were respectively treated with a variant of non-negative matrix
factorization.


Abstract:
  Traditional opinion dynamics models are simple and yet, enough to explore the
consequences in basic scenarios. But, to better describe problems such as
polarization and extremism, we might need to include details about human biases
and other cognitive characteristics. In this paper, I explain how we can
describe and use mental models and assumptions of the agents using
Bayesian-inspired model building. The relationship between human rationality
and Bayesian methods will be explored, and we will see that Bayesian ideas can
indeed be used to explain how humans reason. We will see how to use
Bayesian-inspired rules using the simplest version of the Continuous Opinions
and Discrete Actions (CODA) model. From that, we will explore how we can obtain
update rules that include human behavioral characteristics such as confirmation
bias, motivated reasoning, or our tendency to change opinions much less than we
should.
  Keywords: Opinion dynamics, Bayesian methods, Cognition, CODA, Agent-based
models


Abstract:
  Joint network topology inference represents a canonical problem of jointly
learning multiple graph Laplacian matrices from heterogeneous graph signals. In
such a problem, a widely employed assumption is that of a simple common
component shared among multiple networks. However, in practice, a more
intricate topological pattern, comprising simultaneously of sparse, homogeneity
and heterogeneity components, would exhibit in multiple networks. In this
paper, we propose a general graph estimator based on a novel structured fusion
regularization that enables us to jointly learn multiple graph Laplacian
matrices with such complex topological patterns, and enjoys both high
computational efficiency and rigorous theoretical guarantee. Moreover, in the
proposed regularization term, the topological pattern among networks is
characterized by a Gram matrix, endowing our graph estimator with the ability
of flexible modelling different types of topological patterns by different
choices of the Gram matrix. Computationally, the regularization term, coupling
the parameters together, makes the formulated optimization problem intractable
and thus, we develop a computationally-scalable algorithm based on the
alternating direction method of multipliers (ADMM) to solve it efficiently.
Theoretically, we provide a theoretical analysis of the proposed graph
estimator, which establishes a non-asymptotic bound of the estimation error
under the high-dimensional setting and reflects the effect of several key
factors on the convergence rate of our algorithm. Finally, the superior
performance of the proposed method is illustrated through simulated and real
data examples.