Abstract:
  Predicting accurate future trajectories of multiple agents is essential for
autonomous systems, but is challenging due to the complex agent interaction and
the uncertainty in each agent's future behavior. Forecasting multi-agent
trajectories requires modeling two key dimensions: (1) time dimension, where we
model the influence of past agent states over future states; (2) social
dimension, where we model how the state of each agent affects others. Most
prior methods model these two dimensions separately, e.g., first using a
temporal model to summarize features over time for each agent independently and
then modeling the interaction of the summarized features with a social model.
This approach is suboptimal since independent feature encoding over either the
time or social dimension can result in a loss of information. Instead, we would
prefer a method that allows an agent's state at one time to directly affect
another agent's state at a future time. To this end, we propose a new
Transformer, AgentFormer, that jointly models the time and social dimensions.
The model leverages a sequence representation of multi-agent trajectories by
flattening trajectory features across time and agents. Since standard attention
operations disregard the agent identity of each element in the sequence,
AgentFormer uses a novel agent-aware attention mechanism that preserves agent
identities by attending to elements of the same agent differently than elements
of other agents. Based on AgentFormer, we propose a stochastic multi-agent
trajectory prediction model that can attend to features of any agent at any
previous timestep when inferring an agent's future position. The latent intent
of all agents is also jointly modeled, allowing the stochasticity in one
agent's behavior to affect other agents. Our method substantially improves the
state of the art on well-established pedestrian and autonomous driving
datasets.


Abstract:
  I am considering Veltman's "The Infrared - Ultraviolet Connection" addressing
the issue of quadratic divergences and the related huge radiative correction
predicted by the electroweak Standard Model (SM) in the relationship between
the bare and the renormalized theory, commonly called "the hierarchy problem"
which usually is claimed that this has to be cured. After the discovery of the
Higgs particle at CERN, which essentially completed the SM, an amazing
interrelation of the leading interaction strengths of the gauge bosons, the
top-quark and the Higgs boson showed up amounting that the SM allows for a
perturbative extrapolation of the running couplings up to the Planck scale. The
central question concerns the stability of the electroweak vacuum, which
requires that the running Higgs self-coupling stays positive. Although several
evaluations seem to favor the meta-stability within the experimental and
theoretical parameter-uncertainties, one should not exclude the possibility
that other experiments and improved matching conditions will be able to
establish the absolute stability of the SM vacuum in the future. I will discuss
the stable vacuum scenario and its impact on early cosmology, revealing the
Higgs boson as the inflaton. It turns out that the Standard Model's presumed
"hierarchy problem" and similarly the "cosmological constant problem" resolve
themselves when we understand the SM as a low energy effective tail that is
emergent from a cutoff-medium at the Planck scale. "The Infrared - Ultraviolet
Connection" conveyed by the Higgs boson mass renormalization appears in a new
light when the energy dependence of the SM couplings is taken into account. The
bare Higgs boson mass square then changes sign below the Planck scale where it
is activating the Higgs mechanism.


Abstract:
  We present a method to construct "native" type systems for a broad class of
languages, in which types are built from term constructors by predicate logic
and dependent types. Many languages can be modelled as structured
lambda-theories, and the internal language of their presheaf toposes provides
total specification the structure and behavior of programs. The construction is
functorial, thereby providing a shared framework of higher-order reasoning for
most existing programming languages.


Abstract:
  The main object of investigation in this paper is a very general regression
model in optional setting - when an observed process is an optional
semimartingale depending on an unknown parameter. It is well-known that
statistical data may present an information flow/filtration without usual
conditions. The estimation problem is achieved by means of structural least
squares (LS) estimates and their sequential versions. The main results of the
paper are devoted to the strong consistency of such LS-estimates. For
sequential LS-estimates the property of fixed accuracy is proved.


Abstract:
  Using the Purple Mountain Observatory 13.7-m millimeter telescope at Delingha
in China, we have conducted a large-scale simultaneous survey of $^{12}$CO,
$^{13}$CO, and \xxco ($J$ = 1--0) toward the CMa OB1 complex with a sky
coverage of 16.5 deg$^{\rm 2}$ ($221^{\circ}.5\leq l\leq227^{\circ}$,
$-2^{\circ}.5\leq b\leq0^{\circ}.5$). Emission from the CMa OB1 complex is
found in the range 7 km s$^{-1}\ \leq$ V$_\mathrm{LSR}\ \leq$ 25 km s$^{-1}$.
The large-scale structure, physical properties and chemical abundances of the
molecular clouds are presented. A total of 83 C$^{18}$O molecular clumps are
identified with GaussClumps algorithm within the mapped region. We find that
94% of these C$^{18}$O molecular clumps are gravitationally bound. The
relationship between their size and mass indicates that none of the C$^{18}$O
clump has the potential to form high-mass stars. Using semi-automatic IDL
algorithm, we newly discover 85 CO outflow candidates in the mapped area,
including 23 bipolar outflows candidates. Additionally, a comparative study
reveals evidence for significant variety of physical properties, evolutionary
stages, and levels of star formation activity in different sub-regions of the
CMa OB1 complex.


Abstract:
  I consider a spin chain with nearest-neighbor and next nearest-neighbor
interactions. I show that the Hamiltonian obtained by specifying the desired
combination of symmetry blocks is a fast scrambler. This is a smaller model
than the "minimal model" by Belyansky et al..


Abstract:
  In this paper, we recall hypergeometric functions $\mathscr{F}^{\rm
Dw}_{a_1,\cdots,a_s}(t),$ $\mathscr{F}^{(\sigma)}_{a_1,\cdots,a_s}(t)$,
$\widehat{\mathscr{F}}^{(\sigma)}_{a,\cdots,a}(t)$ and their transformation
formulas. Then we prove that one of transformation formula implies another.


Abstract:
  This paper presents a methodology for freight traffic assignment in a
large-scale road-rail intermodal network under uncertainty. Network
uncertainties caused by natural disasters have dramatically increased in recent
years. Several of these disasters (e.g., Hurricane Sandy, Mississippi River
Flooding, Hurricane Harvey) severely disrupted the U.S. freight transport
network, and consequently, the supply chain. To account for these network
uncertainties, a stochastic freight traffic assignment model is formulated. An
algorithmic framework, involving the sample average approximation and gradient
projection algorithm, is proposed to solve this challenging problem. The
developed methodology is tested on the U.S. intermodal network with freight
flow data from the Freight Analysis Framework. The experiments consider four
types of natural disasters that have different risks and impacts on the
transportation network: earthquake, hurricane, tornado, and flood. The results
demonstrate the feasibility of the model and algorithmic framework to obtain
freight flows for a realistic-sized network in reasonable time (between 417 and
716 minutes). It is found that for all disaster scenarios the freight ton-miles
are higher compared to the base case without uncertainty. The increase in
freight ton-miles is the highest under the flooding scenario; this is due to
the fact that there are more states in the flood-risk areas and they are
scattered throughout the U.S.


Abstract:
  It has been shown several times in the literature that telematics data
collected in motor insurance help to better understand an insured's driving
risk. Insurers that use this data reap several benefits, such as a better
estimate of the pure premium, more segmented pricing and less adverse
selection. The flip side of the coin is that collected telematics information
is often sensitive and can therefore compromise policyholders' privacy.
Moreover, due to their large volume, this type of data is costly to store and
hard to manipulate. These factors, combined with the fact that insurance
regulators tend to issue more and more recommendations regarding the collection
and use of telematics data, make it important for an insurer to determine the
right amount of telematics information to collect. In addition to traditional
contract information such as the age and gender of the insured, we have access
to a telematics dataset where information is summarized by trip. We first
derive several features of interest from these trip summaries before building a
claim classification model using both traditional and telematics features. By
comparing a few classification algorithms, we find that logistic regression
with lasso penalty is the most suitable for our problem. Using this model, we
develop a method to determine how much information about policyholders' driving
should be kept by an insurer. Using real data from a North American insurance
company, we find that telematics data become redundant after about 3 months or
4,000 kilometers of observation, at least from a claim classification
perspective.


Abstract:
  Southern Spectroscopic Survey Telescope (SSST) is a wide-field spectroscopic
survey telescope that China plans to build in Chile in the next few years. As
an instrument for astronomical spectroscopic survey, the multi-object and
fiber-fed spectrograph (MOFFS) is one of the most important scientific
instruments for SSST. In this paper, we present a recommended optical design
for the MOFFS system based on the Volume Phase Holographic Gratings (VPHG). The
whole design philosophy and procedure, including the analytic method to
determine the initial structure, optimization procedures of the VPHG and the
camera groups, are demonstrated in detail. The numerical results of the final
obtained spectrograph show a superior imaging quality and a relatively high
transmittance for the whole working waveband and the field of view. The design
method proposed in this paper can provide a reference for the design of MOFFS
accommodated in spectroscopic survey telescopes.


Abstract:
  Traditional supervised learning methods are hitting a bottleneck because of
their dependency on expensive manually labeled data and their weaknesses such
as limited generalization ability and vulnerability to adversarial attacks. A
promising alternative, self-supervised learning, as a type of unsupervised
learning, has gained popularity because of its potential to learn effective
data representations without manual labeling. Among self-supervised learning
algorithms, contrastive learning has achieved state-of-the-art performance in
several fields of research. This literature review aims to provide an
up-to-date analysis of the efforts of researchers to understand the key
components and the limitations of self-supervised learning.


Abstract:
  In this work, we present a modification of explicit Runge-Kutta temporal
integration schemes that guarantees the preservation of any locally-defined
quasiconvex set of bounds for the solution. These schemes operate on the basis
of a bijective mapping between an admissible set of solutions and the real
domain to strictly enforce bounds. Within this framework, we show that it is
possible to recover a wide range of methods independently of the spatial
discretization, including positivity preserving, discrete maximum principle
satisfying, entropy dissipative, and invariant domain preserving schemes.
Furthermore, these schemes are proven to recover the order of accuracy of the
underlying Runge-Kutta method upon which they are built. The additional
computational cost is the evaluation of two nonlinear mappings which generally
have closed-form solutions. We show the utility of this approach in numerical
experiments using a pseudospectral spatial discretization without any explicit
shock capturing schemes for nonlinear hyperbolic problems with discontinuities.


Abstract:
  A theory of spline quadrature rules for arbitrary continuity class in a
closed interval $[a, b]$ with arbitrary nonuniform subintervals based on
semi-classical orthogonal Jacobi polynomials is proposed. For continuity class
$c \ge 2$ this theory depends on a conjecture.


Abstract:
  The limited memory BFGS (L-BFGS) method is one of the popular methods for
solving large-scale unconstrained optimization. Since the standard L-BFGS
method uses a line search to guarantee its global convergence, it sometimes
requires a large number of function evaluations. To overcome the difficulty, we
propose a new L-BFGS with a certain regularization technique. We show its
global convergence under the usual assumptions. In order to make the method
more robust and efficient, we also extend it with several techniques such as
nonmonotone technique and simultaneous use of the Wolfe line search. Finally,
we present some numerical results for test problems in CUTEst, which show that
the proposed method is robust in terms of solving number of problems.


Abstract:
  Existence of mild solutions for the 3D MHD system in bounded Lipschitz
domains is established in critical spaces with the absolute boundary
conditions.


Abstract:
  New CCD photometric observations and their investigation of the W UMa-type
binary, V870 Ara, are presented. Light curves of the system were taken through
BVI filters from the Congarinni Observatory in Australia. The new ephemeris is
calculated based on seven new determined minimum times, together with the TESS
data and others compiled from the literature. Photometric solutions determined
by the Wilson-Devinney (W-D) code are combined with the Monte Carlo simulation
to determine the adjustable parameters' uncertainties. These solutions suggest
that V870 Ara is a contact binary system with a mass ratio of 0.082, a fillout
factor of 96+-4 percent, and an inclination of 73.60+-0.64 degrees. The
absolute parameters of V870 Ara were determined by combining the Gaia EDR3
parallax and photometric elements.


Abstract:
  We introduce the Colonel Blotto game with favoritism, an extension of the
famous Colonel Blotto game where the winner-determination rule is generalized
to include pre-allocations and asymmetry of the players' resources
effectiveness on each battlefield. Such favoritism is found in many classical
applications of the Colonel Blotto game. We focus on the Nash equilibrium.
First, we consider the closely related model of all-pay auctions with
favoritism and completely characterize its equilibrium. Based on this result,
we prove the existence of a set of optimal univariate distributions -- which
serve as candidate marginals for an equilibrium -- of the Colonel Blotto game
with favoritism and show an explicit construction thereof. In several
particular cases, this directly leads to an equilibrium of the Colonel Blotto
game with favoritism. In other cases, we use these optimal univariate
distributions to derive an approximate equilibrium with well-controlled
approximation error. Finally, we propose an algorithm -- based on the notion of
winding number in parametric curves -- to efficiently compute an approximation
of the proposed optimal univariate distributions with arbitrarily small error.


Abstract:
  There is a vast literature showing the connection between a deformed
relativistic kinematics and a curved momentum space, and, in particular, how
the former can be obtained from the geometrical properties of the latter.
However, there is not any mention about the geometry of a multi-particle system
making manifest a possible modification in the metric of one particle due to
the presence of others. In this work we explore how a curved momentum metric
depending on the particles involved in an interaction arises when considering a
process. We also show that the principle of relative locality obtained in
doubly special relativity from an action is achieved in this geometrical
framework in a direct fashion. Moreover, this formalism allows us to generalize
this principle when a curvature of spacetime is present in a natural way.
Furthermore this geometrical setup allows us to define a new momentum dependent
space-time coordinates for a multi-particle system in which locality of
interactions is recovered.


Abstract:
  We study the dynamics of the quenched Anderson model at finite temperature
using matrix product states. Exploiting a chain mapping for the electron bath,
we investigate the entanglement structure in the MPS for various orderings of
the two chains, which emerge from the thermofield transformation employed to
deal with nonzero temperature. We show that merging both chains can
significantly lower the entanglement at finite temperatures as compared to an
intuitive nearest-neighbor implementation of the Hamiltonian. Analyzing the
population of the free bath modes -- possible when simulating the full dynamics
of impurity plus bath -- we find clear signatures of the Kondo effect in the
quench dynamics.


Abstract:
  A modest extension of the Standard Model by two additional Higgs doublets -
the Higgs Troika Model - can provide a well-motivated scenario for successful
baryogenesis if neutrinos are Dirac fermions. Adapting the "Spontaneous Flavor
Violation" framework, we consider a version of the Troika model where light
quarks have significant couplings to the new multi-TeV Higgs states. Resonant
production of new scalars leading to di-jet or top-pair signals are typical
predictions of this setup. The initial and final state quarks relevant to the
collider phenomenology also play a key role in baryogenesis, potentially
providing direct access to the relevant early Universe physics in high energy
experiments. Viable baryogenesis generally prefers some hierarchy of masses
between the observed and the postulated Higgs states. We show that there is a
complementarity between direct searches at a future 100 TeV $pp$ collider and
indirect searches at flavor experiments, with both sensitive to different
regions of parameter space relevant for baryogenesis. In particular,
measurements of $D-\bar{D}$ mixing at LHCb probe much of the interesting
parameter space. Direct and indirect searches can uncover the new Higgs states
up to masses of $\mathcal{O}(10)$ TeV, thereby providing an impressive reach to
investigate this model.


Abstract:
  Origami has recently emerged as a platform for building functional
engineering systems with versatile characteristics that targeted niche
applications. One widely utilized origami-based structure is known as the
Kresling origami spring (KOS), which inspired, among many other things, the
design of vibration isolators, fluidic muscles, and mechanical bit memory
switches. Previous demonstrations of such concepts were carried out using
paper-based KOSs, which are suitable for conceptual illustrations but not for
implementation in a real environment. In addition to the very low durability
resulting from the high plastic deformations at the paper folds; lack of
repeatability, and high variation of performance among similar samples are also
inevitable. To circumvent these issues, this paper presents a novel approach
for the design of a non-paper based KOS, which mimics the qualitative behavior
of the paper-based KOS without compromising on durability, repeatability, and
functionality. In the new design, each fundamental triangle in the paper-based
KOS is replaced by an inner central rigid core and an outer flexible
rubber-like frame, which are fabricated out of different visco-elastic
materials using advanced 3-D printing technologies. The quasi-static behavior
of the fabricated springs was assessed under both compressive and tensile
loads. It is shown that KOSs with linear, softening, hardening, mono- and
bi-stable restoring force behavior can be fabricated using the proposed design
by simple changes to the geometric design parameters, further pointing to the
vast range of potential applications of such springs.


Abstract:
  We analyze the rainbow tensor model and present the Virasoro constraints,
where the constraint operators obey the Witt algebra and null 3-algebra. We
generalize the method of W-representation in matrix model to the rainbow tensor
model, where the operators preserving and increasing the grading play a crucial
role. It is shown that the rainbow tensor model can be realized by acting on
elementary function with exponent of the operator increasing the grading. We
derive the compact expression of correlators and apply it to several models,
i.e., the red tensor model, Aristotelian tensor model and r=4 rainbow tensor
model. Furthermore, we discuss the case of the non-Gaussian red tensor model
and present a dual expression for partition function through differentiation.


Abstract:
  Data-driven sensor interpretation in autonomous driving can lead to highly
implausible predictions as can most of the time be verified with common-sense
knowledge. However, learning common knowledge only from data is hard and
approaches for knowledge integration are an active research area. We propose to
use a partly human-designed, partly learned set of rules to describe relations
between objects of a traffic scene on a high level of abstraction. In doing so,
we improve and robustify existing deep neural networks consuming low-level
sensor information. We present an initial study adapting the well-established
Probabilistic Soft Logic (PSL) framework to validate and improve on the problem
of semantic segmentation. We describe in detail how we integrate common
knowledge into the segmentation pipeline using PSL and verify our approach in a
set of experiments demonstrating the increase in robustness against several
severe image distortions applied to the A2D2 autonomous driving data set.


Abstract:
  We further investigate the weak topology generated by the irreducible unitary
representations of a group $G$. A deep result due to Ernest \cite{Ernest1971}
and Hughes \cite{Hughes1973} asserts that every weakly compact subset of a
locally compact (LC) group $G$ is compact in the LC-topology, generalizing
thereby a previous result of Glicksberg \cite{glicks1962} for abelian locally
compact (LCA) groups. Here, we first survey some recent findings on the weak
topology and establish some new results about the preservation of several
compact-like properties when going from the weak topology to the original
topology of LC groups. Among others, we deal with the preservation of countably
compactness, pseudocompactness and functional boundedness.


Abstract:
  The accurate prognosis of Glioblastoma Multiforme (GBM) plays an essential
role in planning correlated surgeries and treatments. The conventional models
of survival prediction rely on radiomic features using magnetic resonance
imaging (MRI). In this paper, we propose a radiogenomic overall survival (OS)
prediction approach by incorporating gene expression data with radiomic
features such as shape, geometry, and clinical information. We exploit TCGA
(The Cancer Genomic Atlas) dataset and synthesize the missing MRI modalities
using a fully convolutional network (FCN) in a conditional Generative
Adversarial Network (cGAN). Meanwhile, the same FCN architecture enables the
tumor segmentation from the available and the synthesized MRI modalities. The
proposed FCN architecture comprises octave convolution (OctConv) and a novel
decoder, with skip connections in spatial and channel squeeze & excitation
(skip-scSE) block. The OctConv can process low and high-frequency features
individually and improve model efficiency by reducing channel-wise redundancy.
Skip-scSE applies spatial and channel-wise excitation to signify the essential
features and reduces the sparsity in deeper layers learning parameters using
skip connections. The proposed approaches are evaluated by comparative
experiments with state-of-the-art models in synthesis, segmentation, and
overall survival (OS) prediction. We observe that adding missing MRI modality
improves the segmentation prediction, and expression levels of gene markers
have a high contribution in the GBM prognosis prediction, and fused
radiogenomic features boost the OS estimation.


Abstract:
  Natural image matting separates the foreground from background in fractional
occupancy which can be caused by highly transparent objects, complex foreground
(e.g., net or tree), and/or objects containing very fine details (e.g., hairs).
Although conventional matting formulation can be applied to all of the above
cases, no previous work has attempted to reason the underlying causes of
matting due to various foreground semantics.
  We show how to obtain better alpha mattes by incorporating into our framework
semantic classification of matting regions. Specifically, we consider and learn
20 classes of matting patterns, and propose to extend the conventional trimap
to semantic trimap. The proposed semantic trimap can be obtained automatically
through patch structure analysis within trimap regions. Meanwhile, we learn a
multi-class discriminator to regularize the alpha prediction at semantic level,
and content-sensitive weights to balance different regularization losses.
Experiments on multiple benchmarks show that our method outperforms other
methods and has achieved the most competitive state-of-the-art performance.
Finally, we contribute a large-scale Semantic Image Matting Dataset with
careful consideration of data balancing across different semantic classes.


Abstract:
  The search of an unstructured database amounts to finding one element having
a certain property out of $N$ elements. The classical search with an oracle
checking one element at a time requires on average $N/2$ steps. The Grover
algorithm for the quantum search, and its unitary Hamiltonian evolution
analogue, accomplish the search asymptotically optimally in $\mathcal{O}
(\sqrt{N})$ time steps. We reformulate the search problem as a dissipative
Markov process acting on an $N$-level system weakly coupled to a thermal bath.
Assuming that the energy levels of the system represent the database elements,
we show that, with a proper choice of the spectrum and physically admissible,
long-range transition rates between the energy levels, the system relaxes to
the ground state, corresponding to the sought element, in time $\mathcal{O}
(\ln N)$.


Abstract:
  This demo abstract presents the visualization of deep reinforcement learning
(DRL)-based autonomous aerial mobility simulations. In order to implement the
software, Unity-RL is used and additional buildings are introduced for urban
environment. On top of the implementation, DRL algorithms are used and we
confirm it works well in terms of trajectory and 3D visualization.


Abstract:
  We introduce a non-parametric approach for infinite video texture synthesis
using a representation learned via contrastive learning. We take inspiration
from Video Textures, which showed that plausible new videos could be generated
from a single one by stitching its frames together in a novel yet consistent
order. This classic work, however, was constrained by its use of hand-designed
distance metrics, limiting its use to simple, repetitive videos. We draw on
recent techniques from self-supervised learning to learn this distance metric,
allowing us to compare frames in a manner that scales to more challenging
dynamics, and to condition on other data, such as audio. We learn
representations for video frames and frame-to-frame transition probabilities by
fitting a video-specific model trained using contrastive learning. To
synthesize a texture, we randomly sample frames with high transition
probabilities to generate diverse temporally smooth videos with novel sequences
and transitions. The model naturally extends to an audio-conditioned setting
without requiring any finetuning. Our model outperforms baselines on human
perceptual scores, can handle a diverse range of input videos, and can combine
semantic and audio-visual cues in order to synthesize videos that synchronize
well with an audio signal.


Abstract:
  To meet the ever increasing mobile traffic demand in 5G era, base stations
(BSs) have been densely deployed in radio access networks (RANs) to increase
the network coverage and capacity. However, as the high density of BSs is
designed to accommodate peak traffic, it would consume an unnecessarily large
amount of energy if BSs are on during off-peak time. To save the energy
consumption of cellular networks, an effective way is to deactivate some idle
base stations that do not serve any traffic demand. In this paper, we develop a
traffic-aware dynamic BS sleep control framework, named DeepBSC, which presents
a novel data-driven learning approach to determine the BS active/sleep modes
while meeting lower energy consumption and satisfactory Quality of Service
(QoS) requirements. Specifically, the traffic demands are predicted by the
proposed GS-STN model, which leverages the geographical and semantic
spatial-temporal correlations of mobile traffic. With accurate mobile traffic
forecasting, the BS sleep control problem is cast as a Markov Decision Process
that is solved by Actor-Critic reinforcement learning methods. To reduce the
variance of cost estimation in the dynamic environment, we propose a benchmark
transformation method that provides robust performance indicator for policy
update. To expedite the training process, we adopt a Deep Deterministic Policy
Gradient (DDPG) approach, together with an explorer network, which can
strengthen the exploration further. Extensive experiments with a real-world
dataset corroborate that our proposed framework significantly outperforms the
existing methods.


Abstract:
  In this article we study a class of prescribed curvature problems on complete
noncompact Riemannian manifolds. To be precise, we derive local $C^0$-estimate
under an asymptotic condition which is in effect optimal, and prove the
existence of complete conformal metrics with prescribed curvature functions. A
key ingredient of our strategy is Aviles-McOwen's result or its fully nonlinear
version on the existence of complete conformal metrics with prescribed
curvature functions on manifolds with boundary.


Abstract:
  We prove an abstract critical point theorem based on a cohomological index
theory that produces pairs of nontrivial critical points with nontrivial higher
critical groups. This theorem yields pairs of nontrivial solutions that are
neither local minimizers nor of mountain pass type for problems with combined
nonlinearities. Applications are given to subcritical and critical
$p$-Laplacian problems, Kirchhoff type nonlocal problems, and critical
fractional $p$-Laplacian problems.


Abstract:
  In this paper, a novel method for detecting transformer winding axial
displacement has been presented. In this method, which is based on UWB radar
imaging, a UWB pulse is transmitted to the transformer winding and the
reflection from it is received and recorded. By changing the antenna position
this process is repeated on several points along the axis of the winding. The
measured signals are mapped to a 2-D image of the winding. By analyzing this
image, the axial displacement of the winding can detected and the magnitude of
it is determined with an acceptable precision. Simulation results are provided
to verify the proposed method.


Abstract:
  Visual script languages with a node-based interface have commonly been used
in the video game industry. We examined the bug database obtained in the
development of FINAL FANTASY XV (FFXV), and noticed that several types of bugs
were caused by simple mis-descriptions of visual scripts and could therefore be
mechanically detected. We propose a method for the automatic verification of
visual scripts in order to improve productivity of video game development. Our
method can automatically detect those bugs by using symbolic model checking. We
show a translation algorithm which can automatically convert a visual script to
an input model for NuSMV that is an implementation of symbolic model checking.
For a preliminary evaluation, we applied our method to visual scripts used in
the production for FFXV. The evaluation results demonstrate that our method can
detect bugs of scripts and works well in a reasonable time.


Abstract:
  We consider the problem of training User Verification (UV) models in
federated setting, where each user has access to the data of only one class and
user embeddings cannot be shared with the server or other users. To address
this problem, we propose Federated User Verification (FedUV), a framework in
which users jointly learn a set of vectors and maximize the correlation of
their instance embeddings with a secret linear combination of those vectors. We
show that choosing the linear combinations from the codewords of an
error-correcting code allows users to collaboratively train the model without
revealing their embedding vectors. We present the experimental results for user
verification with voice, face, and handwriting data and show that FedUV is on
par with existing approaches, while not sharing the embeddings with other users
or the server.


Abstract:
  To apply neural sequence models such as the Transformers to music generation
tasks, one has to represent a piece of music by a sequence of tokens drawn from
a finite set of pre-defined vocabulary. Such a vocabulary usually involves
tokens of various types. For example, to describe a musical note, one needs
separate tokens to indicate the note's pitch, duration, velocity (dynamics),
and placement (onset time) along the time grid. While different types of tokens
may possess different properties, existing models usually treat them equally,
in the same way as modeling words in natural languages. In this paper, we
present a conceptually different approach that explicitly takes into account
the type of the tokens, such as note types and metric types. And, we propose a
new Transformer decoder architecture that uses different feed-forward heads to
model tokens of different types. With an expansion-compression trick, we
convert a piece of music to a sequence of compound words by grouping
neighboring tokens, greatly reducing the length of the token sequences. We show
that the resulting model can be viewed as a learner over dynamic directed
hypergraphs. And, we employ it to learn to compose expressive Pop piano music
of full-song length (involving up to 10K individual tokens per song), both
conditionally and unconditionally. Our experiment shows that, compared to
state-of-the-art models, the proposed model converges 5--10 times faster at
training (i.e., within a day on a single GPU with 11 GB memory), and with
comparable quality in the generated music.


Abstract:
  Service manual documents are crucial to the engineering company as they
provide guidelines and knowledge to service engineers. However, it has become
inconvenient and inefficient for service engineers to retrieve specific
knowledge from documents due to the complexity of resources. In this research,
we propose an automated knowledge mining and document classification system
with novel multi-model transfer learning approaches. Particularly, the
classification performance of the system has been improved with three effective
techniques: fine-tuning, pruning, and multi-model method. The fine-tuning
technique optimizes a pre-trained BERT model by adding a feed-forward neural
network layer and the pruning technique is used to retrain the BERT model with
new data. The multi-model method initializes and trains multiple BERT models to
overcome the randomness of data ordering during the fine-tuning process. In the
first iteration of the training process, multiple BERT models are being trained
simultaneously. The best model is then selected for the next phase of the
training process with another two iterations and the training processes for
other BERT models will be terminated. The performance of the proposed system
has been evaluated by comparing with two robust baseline methods, BERT and
BERT-CNN. Experimental results on a widely used Corpus of Linguistic
Acceptability (CoLA) dataset have shown that the proposed techniques perform
better than these baseline methods in terms of accuracy and MCC score.


Abstract:
  In this work, we investigate the influence of planetary tidal interactions on
the transit-timing variations of short-period low-mass rocky exoplanets. For
such purpose, we employ the recently-developed creep tide theory to compute
tidally-induced TTVs. We implement the creep tide in the recently-developed
Posidonius N-body code, thus allowing for a high-precision evolution of the
coupled spin-orbit dynamics of planetary systems. As a working example for the
analyses of tidally-induced TTVs, we apply our version of the code to the
K2-265 b planet. We analyse the dependence of tidally-induced TTVs with the
planetary rotation rate, uniform viscosity coefficient and eccentricity. Our
results show that the tidally-induced TTVs are more significant in the case
where the planet is trapped in non-synchronous spin-orbit resonances, in
particular the 3/2 and 2/1 spin-orbit resonant states. An analysis of the TTVs
induced separately by apsidal precession and tidally-induced orbital decay has
allowed for the conclusion that the latter effect is much more efficient at
causing high-amplitude TTVs than the former effect by 2 - 3 orders of
magnitude. We compare our findings for the tidally-induced TTVs obtained with
Posidonius with analytical formulations for the transit timings used in
previous works, and verified that the results for the TTVs coming from
Posidonius are in excellent agreement with the analytical formulations. These
results show that the new version of Posidonius containing the creep tide
theory implementation can be used to study more complex cases in the future.
For instance, the code can be used to study multiplanetary systems, in which
case planet-planet gravitational perturbations must be taken into account
additionally to tidal interactions to obtain the TTVs.


Abstract:
  While deep learning reshaped the classical motion capture pipeline with
feed-forward networks, generative models are required to recover fine alignment
via iterative refinement. Unfortunately, the existing models are usually
hand-crafted or learned in controlled conditions, only applicable to limited
domains. We propose a method to learn a generative neural body model from
unlabelled monocular videos by extending Neural Radiance Fields (NeRFs). We
equip them with a skeleton to apply to time-varying and articulated motion. A
key insight is that implicit models require the inverse of the forward
kinematics used in explicit surface models. Our reparameterization defines
spatial latent variables relative to the pose of body parts and thereby
overcomes ill-posed inverse operations with an overparameterization. This
enables learning volumetric body shape and appearance from scratch while
jointly refining the articulated pose; all without ground truth labels for
appearance, pose, or 3D shape on the input videos. When used for
novel-view-synthesis and motion capture, our neural model improves accuracy on
diverse datasets. Project website: https://lemonatsu.github.io/anerf/ .


Abstract:
  The exact factorization of the time-dependent electron-nuclear wavefunction
has been employed successfully in the field of quantum molecular dynamics
simulations for interpreting and simulating light-induced ultrafast processes.
In this work, we summarize the major developments leading to the formulation of
a trajectory-based approach, derived from the exact factorization equations,
capable of dealing with nonadiabatic electronic processes, and including
spin-orbit coupling and the non-perturbative effect of an external
time-dependent field. This trajectory-based quantum-classical approach has been
dubbed coupled-trajectory mixed quantum-classical (CT-MQC) algorithm, whose
performance is tested here to study the photo-dissociation dynamics of IBr.


Abstract:
  Core-collapse supernovae are fascinating astrophysical objects for
multimessenger studies. Gravitational waves (GWs) are expected to play a role
in the supernova explosion mechanism, but their modelling is also challenging
due to the stochastic nature of the dynamics and the vast possible progenitors,
and moreover, the GW detection from these objects is still elusive with the
already advanced detectors. Low-energy neutrinos will be emitted enormously
during the core-collapse explosion and can help for the gravitational wave
counterpart search. In this work we develop a multi-messenger strategy to
search for such astrophysical objects by exploiting a global network of both
low-energy neutrino and gravitational wave detectors. First, we discuss how to
improve the detection potential of the neutrino sub-network by exploiting the
temporal behaviour of a neutrino burst from a core-collapse supernova. We show
that with the proposed approach neutrino detectors can gain at least $10\%$ of
detection efficiency at the distance where their efficiency drops. Then, we
combine the information provided by GW and neutrino in a multimessenger
strategy. In particular, we obtain an increase of the probability to detect the
GW signal from a CCSN at $60$ kpc from zero when using only GW analysis to
$\sim 33\%$ with our combined GW-$\nu$ approach.
  Keywords: multimessenger, supernova, core-collapse, low-energy neutrino,
gravitational wave.


Abstract:
  In this note, we prove the existence of an equilibrium concept, dubbed
conditional strategy equilibrium, for non-cooperative games in which a strategy
of a player is a function from the other players' actions to her own actions.
We also study the properties of conditional strategy equilibrium.


Abstract:
  Experimental research has shown that the brain's fast electrochemical
dynamics, or neurodynamics (ND), is strongly stochastic, chaotic, and instanton
(neuroavalanche)-dominated. It is also partly scale-invariant which has been
loosely associated with critical phenomena. It has been recently demonstrated
that the supersymmetric theory of stochastics (STS) offers a theoretical
framework that can explain all of the above ND features. In the STS, all
stochastic models possess a topological supersymmetry (TS), and the
"criticality" of ND and similar stochastic processes is associated with
noise-induced, spontaneous breakdown of this TS (due to instanton condensation
near the border with ordinary chaos in which TS is broken by
non-integrability). Here, we propose a new approach that may be useful for the
construction of low-energy effective theories of ND. Its centerpiece is a
coarse-graining procedure of neural networks based on simplicial complexes and
the concept of the "enveloping lattice." It represents a neural network as a
continuous, high-dimensional base space whose rich topology reflects that of
the original network. The reduced one-instanton state space is determined by
the de Rham cohomology classes of this base space, and the effective ND
dynamics can be recognized as interactions of the instantons in the spirit of
the Segal-Atiyah formalism.


Abstract:
  Recent theoretical studies inspired by experiments on the Kitaev magnet
$\alpha$-RuCl$_3$ highlight the nontrivial impact of phonons on the thermal
Hall conductivity of chiral topological phases. Here we introduce mixed
mesoscopic-macroscopic devices that allow refined thermal-transport probes of
non-Abelian spin liquids with Ising topological order. These devices feature a
quantum-coherent mesoscopic region with negligible phonon conductance, flanked
by macroscopic lobes that facilitate efficient thermalization between chiral
Majorana edge modes and bulk phonons. We show that our devices enable $(i)$
accurate determination of the quantized thermal Hall conductivity, $(ii)$
identification of non-Abelian Ising anyons via the temperature dependence of
the thermal conductance, and most interestingly $(iii)$ single-anyon detection
through heat-based anyon interferometry. Analogous results apply broadly to
phonon-coupled chiral topological orders.


Abstract:
  There are six different mathematical formulations of the symmetry group in
quantum mechanics, among them the set of pure states $\mathbf{P}$ -- i.e., the
set of one-dimensional projections on a complex Hilbert space $H$ -- and the
orthomodular lattice $\mathbf{L}$ of closed subspaces of $H$. These six groups
are isomorphic when the dimension of $H$ is $\geq 3$. Despite of the
difficulties caused by $M_2(\mathbb{C})$, rank two algebras are used for
quantum mechanics description of the spin state of spin-$\frac12$ particles,
there is a counterexample for Uhlhorn's version of Wigner's theorem for such
state space.
  In this note we prove that in order that the description of the spin will be
relativistic, it is not enough to preserve the projection lattice equipped with
its natural partial order and orthogonality, but we also need to preserve the
partial order set of all tripotents and orthogonality among them (a set which
strictly enlarges the lattice of projections). Concretely, let $M$ and $N$ be
two atomic JBW$^*$-triples not containing rank-one Cartan factors, and let
$\mathcal{U} (M)$ and $\mathcal{U} (N)$ denote the set of all tripotents in $M$
and $N$, respectively. We show that each bijection $\Phi: \mathcal{U} (M)\to
\mathcal{U} (N)$, preserving the partial ordering in both directions,
orthogonality in one direction and satisfying some mild continuity hypothesis
can be extended to a real linear triple automorphism. This, in particular,
extends a result of Moln{\'a}r to the wider setting of atomic JBW$^*$-triples
not containing rank-one Cartan factors, and provides new models to present
quantum behavior.


Abstract:
  Speech recognition systems for the Spanish language, such as Google's,
produce errors quite frequently when used in applications of a specific domain.
These errors mostly occur when recognizing words new to the recognizer's
language model or ad hoc to the domain. This article presents an algorithm that
uses Levenshtein distance on phonemes to reduce the speech recognizer's errors.
The preliminary results show that it is possible to correct the recognizer's
errors significantly by using this metric and using a dictionary of specific
phrases from the domain of the application. Despite being designed for
particular domains, the algorithm proposed here is of general application. The
phrases that must be recognized can be explicitly defined for each application,
without the algorithm having to be modified. It is enough to indicate to the
algorithm the set of sentences on which it must work. The algorithm's
complexity is $O(tn)$, where $t$ is the number of words in the transcript to be
corrected, and $n$ is the number of phrases specific to the domain.


Abstract:
  We investigated coherent betatron oscillations of a deuteron beam in the
storage ring COSY, excited by a detuned radio-frequency Wien filter. The beam
oscillations were detected by conventional beam position monitors. With the
currently available apparatus, we show that oscillation amplitudes down to
\SI{1}{\micro \meter} can be detected. The interpretation of the response of
the stored beam to the detuned radio-frequency Wien filter is based on
simulations of the beam evolution in the lattice of the ring and realistic
time-dependent 3D field maps of the Wien filter. Future measurements of the
electric dipole moment of protons will, however, require control of the
relative position of counter-propagating beams in the sub-picometer range.
Since here the stored beam can be considered as a rarefied gas of uncorrelated
particles, we moreover demonstrate that the amplitudes of the zero-point
(ground state) betatron oscillations of individual particles are only a factor
of about 10 larger than the Heisenberg uncertainty limit. As a consequence of
this, we conclude that quantum mechanics does not preclude the control of the
beam centroids to sub-picometer accuracy. The smallest Lorentz force exerted on
a single particle that we have been able to determine is \SI{10}{aN}.


Abstract:
  In this paper, we have constructed a bottom-up holographic model for the
color superconductivity (CSC) of the Yang-Mills theory with including the
higher derivative corrections which allow to study the CSC phase with the color
number $N_c\geq2$. First, we consider the CSC phase transition in the context
of Einstein-Gauss-Bonnet (EGB) gravity. We analyze the Cooper pair condensate
in the deconfinement and confinement phases which are dual to the planar
GB-RN-AdS black hole and GB-AdS soliton, respectively, where the backreaction
of the matter part is taken into account. By examining the breakdown of the
Breitenlohner-Freedman bound in the background of the planar GB-RN-AdS black
hole, we find that the positive GB coupling parameter $\alpha>0$ leads to a
lower upper bound of the color number in comparison to Einstein gravity where
the CSC phase for $N_c\geq2$ is not realized. But, with the $\alpha<0$ case it
is possible to observe the Cooper pair condensate for $N_c\geq2$ with the
reasonable magnitude of $\alpha$. This is confirmed and the corresponding phase
diagram is found by solving numerically the equations of motion for the
gravitational system. In addition, we show that the CSC phase disappears in the
confinement phase for the magnitude of $\alpha$ below a certain value which
means that beyond that value it might lead to the breakdown region of the EGB
gravity in investigating the CSC phase. However, the CSC phase transition
occurring with $N_c\geq2$ requires the magnitude of the GB coupling parameter
to be rather large. As a result, the GB term would no longer be considered as
the correction and it also violates the boundary causality bound. We resolve
this problem by including additionally the higher derivative correction for the
Maxwell electrodynamics and the non-minimal coupled Maxwell field.


Abstract:
  Deep learning-based image super-resolution (DL-SR) has shown great promise in
medical imaging applications. To date, most of the proposed methods for DL-SR
have only been assessed by use of traditional measures of image quality (IQ)
that are commonly employed in the field of computer vision. However, the impact
of these methods on objective measures of image quality that are relevant to
medical imaging tasks remains largely unexplored. In this study, we investigate
the impact of DL-SR methods on binary signal detection performance. Two popular
DL-SR methods, the super-resolution convolutional neural network (SRCNN) and
the super-resolution generative adversarial network (SRGAN), were trained by
use of simulated medical image data. Binary signal-known-exactly with
background-known-statistically (SKE/BKS) and signal-known-statistically with
background-known-statistically (SKS/BKS) detection tasks were formulated.
Numerical observers, which included a neural network-approximated ideal
observer and common linear numerical observers, were employed to assess the
impact of DL-SR on task performance. The impact of the complexity of the DL-SR
network architectures on task-performance was quantified. In addition, the
utility of DL-SR for improving the task-performance of sub-optimal observers
was investigated. Our numerical experiments confirmed that, as expected, DL-SR
could improve traditional measures of IQ. However, for many of the study
designs considered, the DL-SR methods provided little or no improvement in task
performance and could even degrade it. It was observed that DL-SR could improve
the task-performance of sub-optimal observers under certain conditions. The
presented study highlights the urgent need for the objective assessment of
DL-SR methods and suggests avenues for improving their efficacy in medical
imaging applications.


Abstract:
  General solutions to the quantum Rabi model involve subspaces with unbounded
number of photons. However, for the multiqubit multimode case, we find special
solutions with at most one photon for arbitrary number of qubits and photon
modes. Unlike the Juddian solution, ours exists for arbitrary single
qubit-photon coupling strength with constant eigenenergy. This corresponds to a
horizontal line in the spectrum, while still being a qubit-photon entangled
state. As a possible application, we propose an adiabatic scheme for the fast
generation of arbitrary single-photon multimode W states with nonadiabatic
error less than 1%. Finally, we propose a superconducting circuit design,
showing the experimental feasibility of the multimode multiqubit Rabi model.


Abstract:
  As first shown by H. S. Green in 1952, the entropy of a classical fluid of
identical particles can be written as a sum of many-particle contributions,
each of them being a distinctive functional of all spatial distribution
functions up to a given order. By revisiting the combinatorial derivation of
the entropy formula, we argue that a similar correlation expansion holds for
the entropy of a crystalline system. We discuss how one- and two-body entropies
scale with the size of the crystal, and provide fresh numerical data to check
the expectation, grounded on theoretical arguments, that both entropies are
extensive quantities.


Abstract:
  Studying the changed water dynamics in the hydration layers of biomolecules
is an important step towards fuller understanding of their function and
mechanisms, but has shown to be quite difficult. The measurement of the
time-dependent Stokes shift of a chromophore attached to the biomolecule is a
promising method to achieve this goal, as published in J. Phys. Chem. Lett. 5
(2014), 1845, where trehalose was used as biomolecule,
1-methyl-6-oxyquinolinium betaine as chromophore and water as solvent. An
overall retardation of solvent molecules is then obtained by comparison of the
linked system to the same system without trehalose, but contributions from
different subgroups of solvent molecules, for example molecules close to or far
from trehalose, are unknown. The difficulty arising from these unknown
contributions of retarded and possibly unretarded solvent molecules is overcome
in this work by conducting computer simulations on this system and decomposing
the overall signal into contributions from various molecules at different
locations. We performed non-equilibrium molecular dynamics simulation using a
polarizable water model and a non-polarizable solute model and could reproduce
the experimental time-dependent Stokes shift accurately for the linked
trehalose-oxyquinolinium and the pure oxyquinolinium over a wide temperature
range, indicating the correctness of our employed models. Decomposition of the
shift into contributions from different solvent subgroups showed that the
amplitude of the measured shift is made up only half by the desired retarded
solvent molecules in the hydration layer, but to another half by unretarded
bulk water, so that measured relaxation times of the overall Stokes shift are
only a lower boundary for the true relaxation times in the hydration layer of
trehalose.


Abstract:
  Exciting contemporary machine learning problems have recently been phrased in
the classic formalism of tree search -- most famously, the game of Go.
Interestingly, the state-space underlying these sequential decision-making
problems often posses a more general latent structure than can be captured by a
tree. In this work, we develop a probabilistic framework to exploit a search
space's latent structure and thereby share information across the search tree.
The method is based on a combination of approximate inference in jointly
Gaussian models for the explored part of the problem, and an abstraction for
the unexplored part that imposes a reduction of complexity ad hoc. We
empirically find our algorithm to compare favorably to existing
non-probabilistic alternatives in Tic-Tac-Toe and a feature selection
application.


Abstract:
  We present in this document some essential properties of solutions to the
homogeneous Landau-Fermi-Dirac equation for moderately soft potentials. Uniform
in time estimates for statistical moments, $L^{p}$-norm generation and Sobolev
regularity are shown using a combination of techniques that include recent
developments concerning level set analysis in the spirit of De Giorgi and
refined entropy-entropy dissipation functional inequalities for the Landau
collision operator which are extended to the case in question here. As a
consequence of the analysis, we prove algebraic relaxation of non degenerate
distributions towards the Fermi-Dirac statistics under a weak non saturation
condition for the initial datum. All quantitative estimates are uniform with
respect to the quantum parameter. They therefore also hold for the classical
limit, that is the Landau equation.


Abstract:
  Within the de Broglie-Bohm (dBB) theory, the measurement process and the
determination of its outcome are usually discussed in terms of the effect of
the Bohmian positions of the measured system S. %} This article shows that the
Bohmian positions associated with the measurement apparatus M can actually play
a crucial role in the determination of the result of measurement. Indeed, in
many cases, the result is practically independent of the initial value of a
Bohmian position associated with S, and determined only by those of M. The
measurement then does not reveal the value of any pre-existing variable
attached to S, but just the initial state of the measurement apparatus. Quantum
contextuality then appears with particular clarity as a consequence of the dBB
dynamics for entangled systems.


Abstract:
  In this paper, we de\"One a new type curve as V-Mannheim curve, V Mannheim
partner curve and generating curve of Mannheim curve. We give characterization
of these curve. In addition, we study a relation between Mannheim curve and
spherical curve. Eventually, with Salkowski method, we give an example of the
Mannheim curve.


Abstract:
  In recent years, researchers have proposed many deep learning (DL) methods
for various tasks, and particularly face recognition (FR) made an enormous leap
using these techniques. Deep FR systems benefit from the hierarchical
architecture of the DL methods to learn discriminative face representation.
Therefore, DL techniques significantly improve state-of-the-art performance on
FR systems and encourage diverse and efficient real-world applications. In this
paper, we present a comprehensive analysis of various FR systems that leverage
the different types of DL techniques, and for the study, we summarize 168
recent contributions from this area. We discuss the papers related to different
algorithms, architectures, loss functions, activation functions, datasets,
challenges, improvement ideas, current and future trends of DL-based FR
systems. We provide a detailed discussion of various DL methods to understand
the current state-of-the-art, and then we discuss various activation and loss
functions for the methods. Additionally, we summarize different datasets used
widely for FR tasks and discuss challenges related to illumination, expression,
pose variations, and occlusion. Finally, we discuss improvement ideas, current
and future trends of FR tasks.


Abstract:
  We present a comprehensive study of the low-energy band structure and Fermi
surface (FS) topology of $A$Co$_2$As$_2$ ($A=$ Ca, Sr, Ba, Eu) using
high-resolution angle-resolved photoemission spectroscopy. The experimental FS
topology and band dispersion data are compared with theoretical full-potential
linearized augmented-plane-wave (FP-LAPW) calculations, which yielded
reasonably good agreement. We demonstrate that the FS maps of $A$Co$_2$As$_2$
are significantly different from those of the parent compounds of Fe-based
high-temperature superconductors. Further, the FSs of CaCo$_2$As$_2$ do not
show significant changes across its antiferromagnetic transition temperature.
The band dispersions extracted in different momentum $(k_{\it x}, k_{\it y})$
directions show a small electron pocket at the center and a large electron
pocket at the corner of the Brillouin zone (BZ). The absence of the hole FS in
these compounds does not allow nesting between pockets at the Fermi energy
({\it E}$_{\rm F}$), which is in contrast to $A$Fe$_2$As$_2$-type parent
compounds of the iron-based superconductors. Interestingly, we find that the
hole bands are moved 300--400~meV below $E_{\rm F}$ depending on the $A$
element. Moreover, the existence of nearly flat bands in the vicinity of
$E_{\rm F}$ are consistent with the large density of states at $E_{\rm F}$.
These results are important to understand the physical properties as well as
the possibility of the emergence of superconductivity in related materials.


Abstract:
  Outflows and feedback are key ingredients of galaxy evolution. Evidence for
an outflow arising from the Galactic center (GC) has recently been discovered
at different wavelength. We show that the X-ray, radio, and infrared emissions
are deeply interconnected, affecting one another and forming coherent features
on scales of hundreds of parsecs, therefore indicating a common physical link
associated with the GC outflow. We debate the location of the northern chimney
and suggest that it might be located on the front side of the GC because of a
significant tilt of the chimneys toward us. We report the presence of strong
shocks at the interface between the chimneys and the interstellar medium, which
are traced by radio and warm dust emission. We observe entrained molecular gas
outflowing within the chimneys, revealing the multiphase nature of the outflow.
In particular, the molecular outflow produces a long, strong, and structured
shock along the northwestern wall of the chimney. Because of the different
dynamical times of the various components of the outflow, the chimneys appear
to be shaped by directed large-scale winds launched at different epochs. The
data support the idea that the chimneys are embedded in an (often dominant)
vertical magnetic field, which likely diverges with increasing latitude. We
observe that the thermal pressure associated with the hot plasma appears to be
smaller than the ram pressure of the molecular outflow and the magnetic
pressure. This leaves open the possibility that either the main driver of the
outflow is more powerful than the observed hot plasma, or the chimneys
represent a "relic" of past and more powerful activity. These multiwavelength
observations corroborate the idea that the chimneys represent the channel
connecting the quasi-continuous, but intermittent, activity at the GC with the
base of the Fermi bubbles.


Abstract:
  The computing continuum extends the high-performance cloud data centers with
energy-efficient and low-latency devices close to the data sources located at
the edge of the network. However, the heterogeneity of the computing continuum
raises multiple challenges related to application management. These include
where to offload an application - from the cloud to the edge - to meet its
computation and communication requirements. To support these decisions, we
provide in this article a detailed performance and carbon footprint analysis of
a selection of use case applications with complementary resource requirements
across the computing continuum over a real-life evaluation testbed.


Abstract:
  Writing code is hard; proving it correct is even harder. As the scale of
verified software projects reaches new heights, the problem of efficiently
verifying large amounts of software becomes more and more salient. Nowhere is
this issue more evident than in the context of verified cryptographic
libraries. To achieve feature-parity and be competitive with unverified
cryptographic libraries, a very large number of algorithms and APIs need to be
verified. However, the task is oftentimes repetitive, and factoring out
commonality between algorithms is fraught with difficulties, requiring until
now a significant amount of manual effort.
  This paper shows how a judicious combination of known functional programming
techniques leads to an order-of-magnitude improvement in the amount of verified
code produced by the popular HACL* cryptographic library, without compromising
performance. We review three techniques that build upon each other, in order of
increasing sophistication. First, we use dependent types to crisply capture the
specification and state machine of a block algorithm, a cryptographic notion
that was until now only informally and imprecisely specified. Next, we rely on
partial evaluation to author a higher-order, stateful functor that transforms
any unsafe block API into a safe counterpart. Finally, we rely on elaborator
reflection to automate the very process of authoring a functor, using a
code-rewriting tactic. This culminates in a style akin to templatized C++ code,
but relying on a userland tactic and partial evaluation, rather than built-in
compiler support.


Abstract:
  We present experimental evidence of incoherent Kondo scattering as the source
of resistivity minima in bulk polycrystalline and nanocrystalline
Pr$_2$Ir$_2$O$_7$. The temperature dependence of thermopower shows a positive
maximum at high temperature followed by a negative minimum at low temperature,
with the sign inversion occurring at a much higher temperature than $T_K$.
Moreover, we observe little correlation between TK and intersite coupling
strength given by $\theta_{CW}$. We describe the temperature dependence of
thermopower and resistivity within the framework of crystal field excitation in
a Kondo lattice.


Abstract:
  Mapping a single exposure low dynamic range (LDR) image into a high dynamic
range (HDR) is considered among the most strenuous image to image translation
tasks due to exposure-related missing information. This study tackles the
challenges of single-shot LDR to HDR mapping by proposing a novel two-stage
deep network. Notably, our proposed method aims to reconstruct an HDR image
without knowing hardware information, including camera response function (CRF)
and exposure settings. Therefore, we aim to perform image enhancement task like
denoising, exposure correction, etc., in the first stage. Additionally, the
second stage of our deep network learns tone mapping and bit-expansion from a
convex set of data samples. The qualitative and quantitative comparisons
demonstrate that the proposed method can outperform the existing LDR to HDR
works with a marginal difference. Apart from that, we collected an LDR image
dataset incorporating different camera systems. The evaluation with our
collected real-world LDR images illustrates that the proposed method can
reconstruct plausible HDR images without presenting any visual artefacts. Code
available: https://github. com/sharif-apu/twostageHDR_NTIRE21.


Abstract:
  We consider the optimal control problem of a general nonlinear
spatio-temporal system described by Partial Differential Equations (PDEs).
Theory and algorithms for control of spatio-temporal systems are of rising
interest among the automatic control community and exhibit numerous challenging
characteristic from a control standpoint. Recent methods focus on
finite-dimensional optimization techniques of a discretized finite dimensional
ODE approximation of the infinite dimensional PDE system. In this paper, we
derive a differential dynamic programming (DDP) framework for distributed and
boundary control of spatio-temporal systems in infinite dimensions that is
shown to generalize both the spatio-temporal LQR solution, and modern finite
dimensional DDP frameworks. We analyze the convergence behavior and provide a
proof of global convergence for the resulting system of continuous-time
forward-backward equations. We explore and develop numerical approaches to
handle sensitivities that arise during implementation, and apply the resulting
STDDP algorithm to a linear and nonlinear spatio-temporal PDE system. Our
framework is derived in infinite dimensional Hilbert spaces, and represents a
discretization-agnostic framework for control of nonlinear spatio-temporal PDE
systems.


Abstract:
  Visual engagement in social media platforms comprises interactions with photo
posts including comments, shares, and likes. In this paper, we leverage such
visual engagement clues as supervisory signals for representation learning.
However, learning from engagement signals is non-trivial as it is not clear how
to bridge the gap between low-level visual information and high-level social
interactions. We present VisE, a weakly supervised learning approach, which
maps social images to pseudo labels derived by clustered engagement signals. We
then study how models trained in this way benefit subjective downstream
computer vision tasks such as emotion recognition or political bias detection.
Through extensive studies, we empirically demonstrate the effectiveness of VisE
across a diverse set of classification tasks beyond the scope of conventional
recognition.


Abstract:
  Nonlinear optical properties, such as bulk photovoltaic effects, possess
great potential in energy harvesting, photodetection, rectification, etc. To
enable efficient light-current conversion, materials with strong
photo-responsivity are highly desirable. In this work, we predict that
monolayer Janus transition metal dichalcogenides (JTMDs) in the 1T' phase
possess colossal nonlinear photoconductivity owing to their topological band
mixing, strong inversion symmetry breaking, and small electronic bandgap. 1T'
JTMDs have inverted bandgaps on the order of 10 meV and are exceptionally
responsive to light in the terahertz (THz) range. By first-principles
calculations, we reveal that 1T' JTMDs possess shift current (SC) conductivity
as large as $2300 ~\rm nm \cdot \mu A / V^2$, equivalent to a
photo-responsivity of $2800 ~\rm mA/W$. The circular current (CC) conductivity
of 1T' JTMDs is as large as $10^4~ \rm nm \cdot \mu A / V^2$. These remarkable
photo-responsivities indicate that the 1T' JTMDs can serve as efficient
photodetectors in the THz range. We also find that external stimuli such as the
in-plane strain and out-of-plane electric field can induce topological phase
transitions in 1T' JTMDs and that the SC can abruptly flip their directions.
The abrupt change of the nonlinear photocurrent can be used to characterize the
topological transition and has potential applications in 2D optomechanics and
nonlinear optoelectronics.


Abstract:
  Ultrafast imaging is essential in physics and chemistry to investigate the
femtosecond dynamics of nonuniform samples or of phenomena with strong spatial
variations. It relies on observing the phenomena induced by an ultrashort laser
pump pulse using an ultrashort probe pulse at a later time. Recent years have
seen the emergence of very successful ultrafast imaging techniques of single
non-reproducible events with extremely high frame rate, based on wavelength or
spatial frequency encoding. However, further progress in ultrafast imaging
towards high spatial resolution is hampered by the lack of characterization of
weak probe beams. Because of the difference in group velocities between pump
and probe in the bulk of the material, the determination of the absolute
pump-probe delay depends on the sample position. In addition, pulse-front tilt
is a widespread issue, unacceptable for ultrafast imaging, but which is
conventionally very difficult to evaluate for the low-intensity probe pulses.
Here we show that a pump-induced micro-grating generated from the electronic
Kerr effect provides a detailed in-situ characterization of a weak probe pulse.
It allows solving the two issues. Our approach is valid whatever the
transparent medium, whatever the probe pulse polarization and wavelength.
Because it is nondestructive and fast to implement, this in-situ probe
diagnostic can be repeated to calibrate experimental conditions, particularly
in the case where complex wavelength, spatial frequency or polarization
encoding is used. We anticipate that this technique will enable previously
inaccessible spatiotemporal imaging in all fields of ultrafast science and high
field physics at the micro- and nanoscale.


Abstract:
  A deterministic finite automaton is directable if it has a directing word
which takes the automaton from every state to the same state. These notions
have been extended also to other kinds of automata. Thus, B.~Imreh and
M.~Steinby (1999) identified three natural types of directing words, called
D1-, D2- and D3-directing words, for nondeterministic finite automata (NFAs).
Here we adapt these notions for fuzzy finite automata (FFAs). The D3-directing
words obtained this way are precisely the directing words introduced by
V.~Karthikeyan and M. Rajasekar (2015). With any FFA F we associate an NFA Fnd
which has the same Di-directing words as F. Thus, if these definitions are
used, the theory of directable FFAs reduces to that of NFAs.
  We also introduce three new kinds of directing words of fuzzy automata that
we call DD1-, DD2- and DD3-directing words, respectively which depend more on
the fuzzy transition degrees between states. We establish some basic properties
of the sets $DDi(F)$ of DDi-directing words of any given FFA F. In particular,
it is shown that these languages are regular, and that DDi-directability is
decidable. For so-called normal FFAs the languages DDi(F) are shown to have
some special properties. Several relationships between the families of the
corresponding sets DDi(F) of DDi-directing words are presented. We also
determine the complete meet-semilattice of the various classes of
DDi-directable FFAs and normal FFAs and their intersections.


Abstract:
  In this article, we investigate some standard geometric properties of the
integral operator $$
C_{\alpha,\beta}[f,g](z)=\int_{0}^{z}\bigg(\frac{f(w)}{w}\bigg)^\alpha
(g'(w))^\beta dw, \,\,\, \alpha,\beta \in \mathbb{R} \text{ and } |z|<1, $$
where $f$ and $g$ are elements of certain classical families of normalized
analytic functions defined on the unit disk. Here, the exponents are chosen in
such a way that the respective functions are analytic in suitable branches.


Abstract:
  Medical systems in general, and patient treatment decisions and outcomes in
particular, are affected by bias based on gender and other demographic
elements. As language models are increasingly applied to medicine, there is a
growing interest in building algorithmic fairness into processes impacting
patient care. Much of the work addressing this question has focused on biases
encoded in language models -- statistical estimates of the relationships
between concepts derived from distant reading of corpora. Building on this
work, we investigate how word choices made by healthcare practitioners and
language models interact with regards to bias. We identify and remove gendered
language from two clinical-note datasets and describe a new debiasing procedure
using BERT-based gender classifiers. We show minimal degradation in health
condition classification tasks for low- to medium-levels of bias removal via
data augmentation. Finally, we compare the bias semantically encoded in the
language models with the bias empirically observed in health records. This work
outlines an interpretable approach for using data augmentation to identify and
reduce the potential for bias in natural language processing pipelines.


Abstract:
  This paper details an accessible geometric derivation of the forward and
inverse kinematics of a parallel robotic linkage known as the Canfield joint,
which can be used for pointing applications. The original purpose of the
Canfield joint was to serve as a human wrist replacement, and it can be
utilized for other purposes such as the precision pointing and tracking of
antennas, telescopes, and thrusters. We build upon previous analyses, and
generalize them to include the situation where one of the three legs freezes;
the kinematics are also substantially generalized beyond failure modes,
detailed within. The core of this work states and clarifies the assumptions
necessary to analyze this type of parallel robotic linkage. Specific guidance
is included for engineering use cases.


Abstract:
  Let g be a G-invariant Einstein metric on a compact homogeneous space M=G/K.
We use a formula for the Lichnerowicz Laplacian of g at G-invariant TT-tensors
to study the stability type of g as a critical point of the scalar curvature
function. The case when g is naturally reductive is studied in special detail.


Abstract:
  We present the B-BOP instrument, a polarimetric camera on board the future
ESA-JAXA SPICA far-infrared space observatory. B-BOP will allow the study of
the magnetic field in various astrophysical environments thanks to its
unprecedented ability to measure the linear polarization of the submillimeter
light. The maps produced by B-BOP will contain not only information on total
power, but also on the degree and the angle of polarization, simultaneously in
three spectral bands (70, 200 and 350 microns). The B-BOP detectors are
ultra-sensitive silicon bolometers that are intrinsically sensitive to
polarization. Their NEP is close to 10E-18 W/sqrt(Hz). We will present the
optical and thermal architectures of the instrument, we will detail the
bolometer design and we will show the expected performances of the instrument
based on preliminary lab work.


Abstract:
  While showing great promise, circuit synthesis techniques that combine
numerical optimization with search over circuit structures face scalability
challenges due to a large number of parameters, exponential search spaces, and
complex objective functions. The LEAP algorithm improves scaling across these
dimensions using iterative circuit synthesis, incremental re-optimization,
dimensionality reduction, and improved numerical optimization. LEAP draws on
the design of the optimal synthesis algorithm QSearch by extending it with an
incremental approach to determine constant prefix solutions for a circuit. By
narrowing the search space, LEAP improves scalability from four to six qubit
circuits. LEAP was evaluated with known quantum circuits such as QFT and
physical simulation circuits like the VQE, TFIM, and QITE. LEAP can compile
four qubit unitaries up to $59\times$ faster than QSearch and five and six
qubit unitaries with up to $1.2\times$ fewer CNOTs compared to the QFAST
package. LEAP can reduce the CNOT count by up to $36\times$, or $7\times$ on
average, compared to the CQC Tket compiler. Despite its heuristics, LEAP has
generated optimal circuits for many test cases with a priori known solutions.
The techniques introduced by LEAP are applicable to other
numerical-optimization-based synthesis approaches.


Abstract:
  For nonlinear hyperbolic systems of conservation laws in one space variable,
we establish the existence of nonclassical entropy solutions exhibiting
nonlinear interactions between shock waves with strong strength. The proposed
theory is relevant in the theory of phase transition dynamics, and the
solutions under consideration enjoy a splitting--merging pattern, consisting of
(compressive) classical and (undercompressive) nonclassical waves, interacting
together as well as with classical waves of smaller strength. Our analysis is
based on three novel ideas. First, a generalization of Hayes--LeFloch's
nonclassical Riemann solver is introduced for systems and is based on
prescribing, on one hand, a kinetic relation for the propagation of
nonclassical undercompressive shocks and, on the other hand, a nucleation
criterion that selects between classical and nonclassical behavior. Second, we
extend LeFloch-Shearer's theorem to systems and we prove that the presence of a
nucleation condition implies that only a finite number of splitting and merging
cycles can occur. Third, our arguments of nonlinear stability build upon recent
work by the last two authors who identified a natural total variation
functional for scalar conservation laws and, specifically, for systems of
conservation laws we introduce here novel functionals which measure the total
variation and wave interaction of nonclassical and classical waves.


Abstract:
  Many trajectory forecasting methods, implementing deterministic and
stochastic models, have been presented in the last decade for automotive
applications. In this work, a deep-learning framework is proposed to model and
predict the evolution of the coupled driver-vehicle system dynamics.
Particularly, we aim to describe how the road geometry affects the actions
performed by the driver. Differently from other works, the problem is
formulated in such a way that the user may specify the features of interest.
Nonetheless, we propose a set of features that is commonly used for automotive
control applications to practically show the functioning of the algorithm. To
solve the prediction problem, a deep recurrent neural network based on Long
Short-Term Memory autoencoders is designed. It fuses the information on the
road geometry and the past driver-vehicle system dynamics to produce
context-aware predictions. Also, the complexity of the neural network is
constrained to favour its use in online control tasks. The efficacy of the
proposed approach was verified in a case study centered on motion cueing
algorithms, using a dataset collected during test sessions of a
non-professional driver on a dynamic driving simulator. A 3D track with complex
geometry was employed as driving environment to render the prediction task
challenging. Finally, the robustness of the neural network to changes in the
driver and track was investigated to set guidelines for future works.


Abstract:
  The discrete fracture model (DFM) has been widely used in the simulation of
fluid flow in fractured porous media. Traditional DFM uses the so-called
hybrid-dimensional approach to treat fractures explicitly as low-dimensional
entries (e.g. line entries in 2D media and face entries in 3D media) on the
interfaces of matrix cells and then couple the matrix and fracture flow systems
together based on the principle of superposition with the fracture thickness
used as the dimensional homogeneity factor. Because of this methodology, DFM is
considered to be limited on conforming meshes and thus may raise difficulties
in generating high quality unstructured meshes due to the complexity of
fracture's geometrical morphology. In this paper, we clarify that the DFM
actually can be extended to non-conforming meshes without any essential
changes. To show it clearly, we provide another perspective for DFM based on
hybrid-dimensional representation of permeability tensor to describe fractures
as one-dimensional line Dirac delta functions contained in permeability tensor.
A finite element DFM scheme for single-phase flow on non-conforming meshes is
then derived by applying Galerkin finite element method to it. Analytical
analysis and numerical experiments show that our DFM automatically degenerates
to the classical finite element DFM when the mesh is conforming with fractures.
Moreover, the accuracy and efficiency of the model on non-conforming meshes are
demonstrated by testing several benchmark problems. This model is also
applicable to curved fracture with variable thickness.


Abstract:
  By learning Variable Impedance Control policy, robot assistants can
intelligently adapt their manipulation compliance to ensure both safe
interaction and proper task completion when operating in human-robot
interaction environments. In this paper, we propose a DMP-based framework that
learns and generalizes variable impedance manipulation skills from human
demonstrations. This framework improves robots$'$ adaptability to environment
changes(i.e. the weight and shape changes of grasping object at the robot
end-effector) and inherits the efficiency of demonstration-variance-based
stiffness estimation methods. Besides, with our stiffness estimation method, we
generate not only translational stiffness profiles but also rotational
stiffness profiles that are ignored or incomplete in most learning Variable
Impedance Control papers. Real-world experiments on a 7 DoF redundant robot
manipulator have been conducted to validate the effectiveness of our framework.


Abstract:
  Overlay measurements are a critical part of modern semiconductor fabrication,
but overlay targets have not scaled down in the way devices have. In this work,
we produce overlay targets with very small footprint, consisting of just a few
scattering nanoparticles in two separate device layers. Using moir\'e patterns
to deterministically generate many overlay errors on a single chip, we
demonstrate successful readout of the relative displacement between the two
layers and show that calibration on one realization of the targets can be used
for overlay measurements on subsequent instances. Our results suggest using
greater quantities of smaller overlay targets may benefit performance both
directly and through finer sampling of deformation.


Abstract:
  A function on an algebra is congruence preserving if, for any congruence, it
maps pairs of congruent elements onto pairs of congruent elements. An algebra
  is said to be affine complete if every congruence preserving function is a
polynomial function. We show that the algebra of (possibly empty) binary trees
whose leaves are labeled by letters of an alphabet containing at least one
letter, and the free monoid on an alphabet containing at least two letters are
affine complete.


Abstract:
  From a diabatic bound state approach to $J^{PC}=1^{--}$ and $(0,1,2)^{++}$
charmoniumlike resonances below $4.1$ GeV, formulated in terms of
${c\overline{c}}$ and closed meson-meson channels, we calculate mass shifts and
widths due to open meson-meson channels. This calculation does not involve any
new free parameter, so comparison of our predictions with existing data
provides a direct test of our approach. Further mass corrections are also
estimated and good agreement with the measured masses comes out. As for the
calculated widths, overall reasonable, they point out to the need of some
refinement of our current bound state approximation for an accurate description
of data. These results give additional support to the diabatic approach in QCD
as an adequate framework for a complete unified description of conventional and
unconventional charmoniumlike resonances. In this respect, the experimental
discovery of a predicted $2^{++}$ resonance with a mass around $4$ GeV would be
of special relevance.


Abstract:
  Classical distributed estimation scenarios typically assume timely and
reliable exchanges of information over the sensor network. This paper, in
contrast, considers single time-scale distributed estimation via a sensor
network subject to transmission time-delays. The proposed discrete-time
networked estimator consists of two steps: (i) consensus on (delayed) a-priori
estimates, and (ii) measurement update. The sensors only share their a-priori
estimates with their out-neighbors over (possibly) time-delayed transmission
links. The delays are assumed to be fixed over time, heterogeneous, and known.
We assume distributed observability instead of local observability, which
significantly reduces the communication/sensing loads on sensors. Using the
notions of augmented matrices and Kronecker product, the convergence of the
proposed estimator over strongly-connected networks is proved for a specific
upper-bound on the time-delay.


Abstract:
  Colorectal cancer is a leading cause of death worldwide. However, early
diagnosis dramatically increases the chances of survival, for which it is
crucial to identify the tumor in the body. Since its imaging uses
high-resolution techniques, annotating the tumor is time-consuming and requires
particular expertise. Lately, methods built upon Convolutional Neural
Networks(CNNs) have proven to be at par, if not better in many biomedical
segmentation tasks. For the task at hand, we propose another CNN-based
approach, which uses atrous convolutions and residual connections besides the
conventional filters. The training and inference were made using an efficient
patch-based approach, which significantly reduced unnecessary computations. The
proposed AtResUNet was trained on the DigestPath 2019 Challenge dataset for
colorectal cancer segmentation with results having a Dice Coefficient of 0.748.


Abstract:
  We develop a distribution-free, unsupervised anomaly detection method called
ECAD, which wraps around any regression algorithm and sequentially detects
anomalies. Rooted in conformal prediction, ECAD does not require data
exchangeability but approximately controls the Type-I error when data are
normal. Computationally, it involves no data-splitting and efficiently trains
ensemble predictors to increase statistical power. We demonstrate the superior
performance of ECAD on detecting anomalous spatio-temporal traffic flow.


Abstract:
  We show that the weak disorder phase for the directed polymer model in a
bounded random environment is characterized by the integrability of the running
supremum $\sup_{n\in \mathbb N}W_n^\beta$ of the associated martingale
$(W_n^\beta)_{n\in \mathbb N}$. Using this characterization, we prove that
$(W_n^\beta)_{n\in \mathbb N}$ is $L^p$-bounded in the whole weak disorder
phase, for some $p>1$. The argument generalizes to non-negative martingales
with a certain product structure.


Abstract:
  Bronstein's lazy Hermite reduction is a symbolic integration technique that
reduces algebraic functions to integrands with only simple poles without the
prior computation of an integral basis. We sharpen the lazy Hermite reduction
by combining it with the polynomial reduction to solve the decomposition
problem of algebraic functions. The sharpened reduction is then used to design
a reduction-based telescoping algorithm for algebraic functions in two
variables.


Abstract:
  In this paper, we study the connection between the Ericksen-Leslie equations
and the Beris-Edwards equations in dimension two. It is shown that the weak
solutions to the Beris-Edwards equations converge to the one to the
Ericksen-Leslie equations as the elastic coefficient tends to zero. Moreover,
the limiting weak solutions to the Ericksen-Leslie equations may have singular
points.


Abstract:
  One-dimensional diffusion of Co ad-atoms on graphene nanoribbons has been
induced and investigated by means of scanning tunnelling microscopy (STM). To
this end, the nanoribbons and the Co ad-atoms have been imaged before and after
injecting current pulses into the nanoribbons, with the STM tip in direct
contact with the ribbon. We observe current-induced motion of the Co atoms
along the nanoribbons, which is approximately described by a distribution
expected for a thermally activated one-dimensional random walk. This indicates
that the nanoribbons reach temperatures far beyond 100 K, which is well above
the temperature of the underlying Au substrate. This model system can be
developed further for the study of electromigration at the single-atom level.


Abstract:
  It is very important which the Hamiltonian of the quantum system is time
changing, especially the potential well that its width can change, the
schrodinger equation and klein Gordon equation of this kind of circumstance are
solved by some studies, but the Dirac equation haven't be solved, so this
article discussed the solution of the Dirac equation in this kind of
circumstance.


Abstract:
  This paper introduced the space mission DikpolaSat Mission, how this research
fits into the mission, and the importance of having a trained DNN model instead
of the usual GN&C functionality. This paper shows how the controller
demonstration is carried out by having the spacecraft follow a desired path,
specified in the referenced model. Increases can be made by examining the route
used to construct a DNN and understanding the effects of various activating
functions on system efficiency. The obstacle avoidance algorithm is built into
the control features to respond spontaneously using inputs from the neural
network for collision avoidance while optimizing the modified trajectory. The
action of a neural network to control the adaptive nature of the nonlinear
mechanisms in the controller will make the control system capable of handling
multiple nonlinear events and also uncertainties that have not been induced in
the control algorithm. Multiple algorithms for optimizing flight controls and
fuel consumption can be implemented using knowledge of flight dynamics in
trajectory and also in the event of obstacle avoidance. This paper also
explains how a DNN can learn to control the flight path and make the system
more reliable with each launch, thereby improving the chances of predicting
collisions of space objects. The data released from this research is used to
design more advanced DNN model capable of predicting other orbital events as
well.


Abstract:
  The collective attention on online items such as web pages, search terms, and
videos reflects trends that are of social, cultural, and economic interest.
Moreover, attention trends of different items exhibit mutual influence via
mechanisms such as hyperlinks or recommendations. Many visualisation tools
exist for time series, network evolution, or network influence; however, few
systems connect all three. In this work, we present AttentionFlow, a new system
to visualise networks of time series and the dynamic influence they have on one
another. Centred around an ego node, our system simultaneously presents the
time series on each node using two visual encodings: a tree ring for an
overview and a line chart for details. AttentionFlow supports interactions such
as overlaying time series of influence and filtering neighbours by time or
flux. We demonstrate AttentionFlow using two real-world datasets, VevoMusic and
WikiTraffic. We show that attention spikes in songs can be explained by
external events such as major awards, or changes in the network such as the
release of a new song. Separate case studies also demonstrate how an artist's
influence changes over their career, and that correlated Wikipedia traffic is
driven by cultural interests. More broadly, AttentionFlow can be generalised to
visualise networks of time series on physical infrastructures such as road
networks, or natural phenomena such as weather and geological measurements.


Abstract:
  In recent years there has been a special interest in word embeddings as a new
approach to convert words to vectors. It has been a focal point to understand
how much of the semantics of the the words has been transferred into embedding
vectors. This is important as the embedding is going to be used as the basis
for downstream NLP applications and it will be costly to evaluate the
application end-to-end in order to identify quality of the used embedding
model. Generally the word embeddings are evaluated through a number of tests,
including analogy test. In this paper we propose a test framework for Persian
embedding models. Persian is a low resource language and there is no rich
semantic benchmark to evaluate word embedding models for this language. In this
paper we introduce an evaluation framework including a hand crafted Persian SAT
based analogy dataset, a colliquial test set (specific to Persian) and a
benchmark to study the impact of various parameters on the semantic evaluation
task.


Abstract:
  The Shuffle Test is the most common task to evaluate whether NLP models can
measure coherence in text. Most recent work uses direct supervision on the
task; we show that by simply finetuning a RoBERTa model, we can achieve a near
perfect accuracy of 97.8%, a state-of-the-art. We argue that this outstanding
performance is unlikely to lead to a good model of text coherence, and suggest
that the Shuffle Test should be approached in a Zero-Shot setting: models
should be evaluated without being trained on the task itself. We evaluate
common models in this setting, such as Generative and Bi-directional
Transformers, and find that larger architectures achieve high-performance
out-of-the-box. Finally, we suggest the k-Block Shuffle Test, a modification of
the original by increasing the size of blocks shuffled. Even though human
reader performance remains high (around 95% accuracy), model performance drops
from 94% to 78% as block size increases, creating a conceptually simple
challenge to benchmark NLP models. Code available:
https://github.com/tingofurro/shuffle_test/


Abstract:
  We study massive scalar field perturbation on Kerr black holes in dynamical
Chern-Simons gravity by performing a $(2+1)$-dimensional simulation. Object
pictures of the wave dynamics in time domain are obtained. The tachyonic
instability is found to always occur for any nonzero black hole spin and any
scalar field mass as long as the coupling constant exceeds a critical value.
The presence of the mass term suppresses or even quenches the instability. The
quantitative dependence of the onset of the tachyonic instability on the
coupling constant, the scalar field mass and the black hole spin is given
numerically.


Abstract:
  We revisit the conserved quantities of the Mathisson-Papapetrou-Tulczyjew
equations describing the motion of spinning particles on a fixed background.
Assuming Ricci-flatness and the existence of a Killing-Yano tensor, we
demonstrate that besides the two non-trivial quasi-conserved quantities, i.e.
conserved at linear order in the spin, found by R\"udiger, non-trivial
quasi-conserved quantities are in one-to-one correspondence with non-trivial
mixed-symmetry Killing tensors. We prove that no such stationary and
axisymmetric mixed-symmetry Killing tensor exists on the Kerr geometry. We
discuss the implications for the motion of spinning particles on Kerr spacetime
where the quasi-constants of motion are shown not to be in complete involution.