Abstract:
  A complete recipe of measure-preserving diffusions in Euclidean space was
recently derived unifying several MCMC algorithms into a single framework. In
this paper, we develop a geometric theory that improves and generalises this
construction to any manifold. We thereby demonstrate that the completeness
result is a direct consequence of the topology of the underlying manifold and
the geometry induced by the target measure $P$; there is no need to introduce
other structures such as a Riemannian metric, local coordinates, or a reference
measure. Instead, our framework relies on the intrinsic geometry of $P$ and in
particular its canonical derivative, the deRham rotationnel, which allows us to
parametrise the Fokker--Planck currents of measure-preserving diffusions using
potentials. The geometric formalism can easily incorporate constraints and
symmetries, and deliver new important insights, for example, a new complete
recipe of Langevin-like diffusions that are suited to the construction of
samplers. We also analyse the reversibility and dissipative properties of the
diffusions, the associated deterministic flow on the space of measures, and the
geometry of Langevin processes. Our article connects ideas from various
literature and frames the theory of measure-preserving diffusions in its
appropriate mathematical context.


Abstract:
  One of the major prerequisites for any deep learning approach is the
availability of large-scale training data. When dealing with scanned document
images in real world scenarios, the principal information of its content is
stored in the layout itself. In this work, we have proposed an automated deep
generative model using Graph Neural Networks (GNNs) to generate synthetic data
with highly variable and plausible document layouts that can be used to train
document interpretation systems, in this case, specially in digital mailroom
applications. It is also the first graph-based approach for document layout
generation task experimented on administrative document images, in this case,
invoices.


Abstract:
  In this second part of a series work, we further develop the theory of higher
fusion categories, including center functors, centralizers and group theoretic
higher fusion categories. Along the way we prove several conjectures on modular
extensions and the representation categories of finite higher groups.


Abstract:
  Post-selection, the power of discarding all runs of a computation in which an
undesirable event occurs, is an influential concept introduced to the field of
quantum complexity theory by Aaronson (Proceedings of the Royal Society A,
2005). In the present paper, we initiate the study of post-selection for
space-bounded quantum complexity classes. Our main result shows the identity
$\sf PostBQL=PL$, i.e., the class of problems that can be solved by a
bounded-error (polynomial-time) logarithmic-space quantum algorithm with
post-selection ($\sf PostBQL$) is equal to the class of problems that can be
solved by unbounded-error logarithmic-space classical algorithms ($\sf PL$).
This result gives a space-bounded version of the well-known result $\sf
PostBQP=PP$ proved by Aaronson for polynomial-time quantum computation. As a
by-product, we also show that $\sf PL$ coincides with the class of problems
that can be solved by bounded-error logarithmic-space quantum algorithms that
have no time bound.


Abstract:
  Artificial intelligence (AI) systems have become increasingly common and the
trend will continue. Examples of AI systems include autonomous vehicles (AV),
computer vision, natural language processing, and AI medical experts. To allow
for safe and effective deployment of AI systems, the reliability of such
systems needs to be assessed. Traditionally, reliability assessment is based on
reliability test data and the subsequent statistical modeling and analysis. The
availability of reliability data for AI systems, however, is limited because
such data are typically sensitive and proprietary. The California Department of
Motor Vehicles (DMV) oversees and regulates an AV testing program, in which
many AV manufacturers are conducting AV road tests. Manufacturers participating
in the program are required to report recurrent disengagement events to
California DMV. This information is being made available to the public. In this
paper, we use recurrent disengagement events as a representation of the
reliability of the AI system in AV, and propose a statistical framework for
modeling and analyzing the recurrent events data from AV driving tests. We use
traditional parametric models in software reliability and propose a new
nonparametric model based on monotonic splines to describe the event process.
We develop inference procedures for selecting the best models, quantifying
uncertainty, and testing heterogeneity in the event process. We then analyze
the recurrent events data from four AV manufacturers, and make inferences on
the reliability of the AI systems in AV. We also describe how the proposed
analysis can be applied to assess the reliability of other AI systems.


Abstract:
  We continue our program of improving the complexity of so-called Boltzmann
sampling algorithms, for the exact sampling of combinatorial structures, and
reach average linear-time complexity, i.e. optimality up to a multiplicative
constant. Here we solve this problem for irreducible context-free structures, a
broad family of structures to which the celebrated Drmota--Lalley--Woods
Theorem applies. Our algorithm is a rejection algorithm. The main idea is to
single out some degrees of freedom, i.e. write $p(x)=p_1(y) p_2(x|y)$, which
allows to introduce a rejection factor at the level of the $y$ object, that is
almost surely of order $1$.


Abstract:
  Monocular 3D object detection is of great significance for autonomous driving
but remains challenging. The core challenge is to predict the distance of
objects in the absence of explicit depth information. Unlike regressing the
distance as a single variable in most existing methods, we propose a novel
geometry-based distance decomposition to recover the distance by its factors.
The decomposition factors the distance of objects into the most representative
and stable variables, i.e. the physical height and the projected visual height
in the image plane. Moreover, the decomposition maintains the self-consistency
between the two heights, leading to robust distance prediction when both
predicted heights are inaccurate. The decomposition also enables us to trace
the causes of the distance uncertainty for different scenarios. Such
decomposition makes the distance prediction interpretable, accurate, and
robust. Our method directly predicts 3D bounding boxes from RGB images with a
compact architecture, making the training and inference simple and efficient.
The experimental results show that our method achieves the state-of-the-art
performance on the monocular 3D Object Detection and Birds Eye View tasks of
the KITTI dataset, and can generalize to images with different camera
intrinsics.


Abstract:
  The translation of an operator is defined by using conjugation with
time-frequency shifts. Thus, one can define $\Lambda$-shift-invariant subspaces
of Hilbert-Schmidt operators, finitely generated, with respect to a lattice
$\Lambda$ in $\mathbb{R}^{2d}$. These spaces can be seen as a generalization of
classical shift-invariant subspaces of square integrable functions. Obtaining
sampling results for these subspaces appears as a natural question that can be
motivated by the problem of channel estimation in wireless communications.
These sampling results are obtained in the light of the frame theory in a
separable Hilbert space.


Abstract:
  A molecular Mott insulator $\kappa$-(ET)$_2$B(CN)$_4$ [ET =
bis(ethylenedithio)tetrathiafulvalene] with a distorted triangular lattice
exhibits a quantum disordered state with gapped spin excitation in the ground
state. $^{13}$C nuclear magnetic resonance, magnetization, and magnetic torque
measurements reveal that magnetic field suppresses valence bond order and
induces long-range magnetic order above a critical field $\sim 8$ T. The
nuclear spin-lattice relaxation rate $1/T_1$ shows persistent evolution of
antiferromagnetic correlation above the transition temperature, highlighting a
quantum spin liquid state with fractional excitations. The field-induced
transition as observed in the spin-Peierls phase suggests that the valence bond
order transition is driven through renormalized one-dimensionality and
spin-lattice coupling.


Abstract:
  We investigate the problem of controller synthesis for hyperproperties
specified in the temporal logic HyperLTL. Hyperproperties are system properties
that relate multiple execution traces. Hyperproperties can elegantly express
information-flow policies like noninterference and observational determinism.
The controller synthesis problem is to automatically design a controller for a
plant that ensures satisfaction of a given specification in the presence of the
environment or adversarial actions. We show that the controller synthesis
problem is decidable for HyperLTL specifications and finite-state plants. We
provide a rigorous complexity analysis for different fragments of HyperLTL and
different system types: tree-shaped, acyclic, and general graphs.


Abstract:
  A major challenge in both neuroscience and machine learning is the
development of useful tools for understanding complex information processing
systems. One such tool is probes, i.e., supervised models that relate features
of interest to activation patterns arising in biological or artificial neural
networks. Neuroscience has paved the way in using such models through numerous
studies conducted in recent decades. In this work, we draw insights from
neuroscience to help guide probing research in machine learning. We highlight
two important design choices for probes $-$ direction and expressivity $-$ and
relate these choices to research goals. We argue that specific research goals
play a paramount role when designing a probe and encourage future probing
studies to be explicit in stating these goals.


Abstract:
  In this paper, we propose a machine learning (ML) method to learn how to
solve a generic constrained continuous optimization problem. To the best of our
knowledge, the generic methods that learn to optimize, focus on unconstrained
optimization problems and those dealing with constrained problems are not
easy-to-generalize. This approach is quite useful in optimization tasks where
the problem's parameters constantly change and require resolving the
optimization task per parameter update. In such problems, the computational
complexity of optimization algorithms such as gradient descent or interior
point method preclude near-optimal designs in real-time applications. In this
paper, we propose an unsupervised deep learning (DL) solution for solving
constrained optimization problems in real-time by relegating the main
computation load to offline training phase. This paper's main contribution is
proposing a method for enforcing the equality and inequality constraints to the
DL-generated solutions for generic optimization tasks.


Abstract:
  Adhesions are an important cause of chronic pain following abdominal surgery.
Recent developments in abdominal cine-MRI have enabled the non-invasive
diagnosis of adhesions. Adhesions are identified on cine-MRI by the absence of
sliding motion during movement. Diagnosis and mapping of adhesions improves the
management of patients with pain. Detection of abdominal adhesions on cine-MRI
is challenging from both a radiological and deep learning perspective. We focus
on classifying presence or absence of adhesions in sagittal abdominal cine-MRI
series. We experimented with spatio-temporal deep learning architectures
centered around a ConvGRU architecture. A hybrid architecture comprising a
ResNet followed by a ConvGRU model allows to classify a whole time-series.
Compared to a stand-alone ResNet with a two time-point (inspiration/expiration)
input, we show an increase in classification performance (AUROC) from 0.74 to
0.83 ($p<0.05$). Our full temporal classification approach adds only a small
amount (5%) of parameters to the entire architecture, which may be useful for
other medical imaging problems with a temporal dimension.


Abstract:
  The search for feebly-interacting new-physics particles in the MeV-GeV mass
range often involves high-intensity beams dumped into thick heavy targets. The
challenge of evaluating the expected backgrounds for these searches from first
principles is limited by the CPU time needed to generate the shower induced by
the primary beam. We present a Monte Carlo biasing method allowing a three
orders of magnitude increase in the efficiency for the simulation of the muon
production in a 400 GeV$/c$ proton beam-dump setup. At the same time, this
biasing method is maintaining nearly every feature of a simulation from first
principles.


Abstract:
  We propose a text classification tool based on support vector machines for
the assessment of organizational leadership styles, as appearing to Twitter
users. We collected Twitter data over 51 days, related to the first 30 Italian
organizations in the 2015 ranking of Forbes Global 2000-out of which we
selected the five with the most relevant volumes of tweets. We analyzed the
communication of the company leaders, together with the dialogue among the
stakeholders of each company, to understand the association with perceived
leadership styles and dimensions. To assess leadership profiles, we referred to
the 10-factor model developed by Barchiesi and La Bella in 2007. We maintain
the distinctiveness of the approach we propose, as it allows a rapid assessment
of the perceived leadership capabilities of an enterprise, as they emerge from
its social media interactions. It can also be used to show how companies
respond and manage their communication when specific events take place, and to
assess their stakeholder's reactions.


Abstract:
  The linear and nonlinear theories of electron-acoustic waves (EAWs) are
studied in a partially degenerate quantum plasma with two-temperature electrons
and stationary ions. The initial equilibrium of electrons is assumed to be
given by the Fermi-Dirac distribution at finite temperature. By employing the
multi-scale asymptotic expansion technique to the one-dimensional Wigner-Moyal
and Poisson equations, it is shown that the effects of multi-plasmon resonances
lead to a modified complex Korteweg-de Vries (KdV) equation with a new nonlocal
nonlinearity. Besides giving rise to a nonlocal nonlinear term, the
wave-particle resonance also modifies the local nonlinear coupling coefficient
of the KdV equation. The latter is shown to conserve the number of particles,
however, the wave energy decays with time. A careful analysis shows that the
two-plasmon resonance is the dominant mechanism for nonlinear Landau damping of
EAWs. An approximate soliton solution of the KdV equation is also obtained, and
it is shown that the nonlinear Landau damping causes the wave amplitude to
decay slowly with time compared to the classical theory.


Abstract:
  Gene expression datasets consist of thousand of genes with relatively small
samplesizes (i.e. are large-$p$-small-$n$). Moreover, dependencies of various
orders co-exist in the datasets. In the Undirected probabilistic Graphical
Model (UGM) framework the Glasso algorithm has been proposed to deal with high
dimensional micro-array datasets forcing sparsity. Also, modifications of the
default Glasso algorithm are developed to overcome the problem of complex
interaction structure. In this work we advocate the use of a simple score-based
Hill Climbing algorithm (HC) that learns Gaussian Bayesian Networks (BNs)
leaning on Directed Acyclic Graphs (DAGs). We compare HC with Glasso and its
modifications in the UGM framework on their capability to reconstruct GRNs from
micro-array data belonging to the Escherichia Coli genome. We benefit from the
analytical properties of the Joint Probability Density (JPD) function on which
both directed and undirected PGMs build to convert DAGs to UGMs.
  We conclude that dependencies in complex data are learned best by the HC
algorithm, presenting them most accurately and efficiently, simultaneously
modelling strong local and weaker but significant global connections coexisting
in the gene expression dataset. The HC algorithm adapts intrinsically to the
complex dependency structure of the dataset, without forcing a specific
structure in advance. On the contrary, Glasso and modifications model
unnecessary dependencies at the expense of the probabilistic information in the
network and of a structural bias in the JPD function that can only be relieved
including many parameters.


Abstract:
  We study the anisotropic Kondo line defects in products of chiral $SU(2)$ WZW
models. We propose an ODE/IM correspondence for the anisotropic Kondo problems
by considering the four-dimensional Chern Simons theory in the trigonometric
case. We verify the claim both by explicit perturbative calculations in the
ultraviolet and by exact WKB analysis in the infrared. In doing so, we derived
the infrared dynamics of a large class of anisotropic line defects.


Abstract:
  This work is concerned with the development of an adaptive numerical method
for semilinear heat flow models featuring a general (possibly) nonlinear
reaction term that may cause the solution to blow up in finite time. The fully
discrete scheme consists of a high order discontinuous Galerkin (dG) time
stepping method and a conforming finite element discretisation (cG) in space.
The proposed adaptive procedure is based on rigorously devised conditional a
posteriori error bounds in the $L^{\infty}(L^{\infty})$ norm. Numerical
experiments complement the theoretical results.


Abstract:
  Let $F$ be a local field of characteristic $p>0$. By adapting methods of
Scholze, we give a new proof of the local Langlands correspondence for $\GL_n$
over $F$. More specifically, we construct $\ell$-adic Galois representations
associated with many discrete automorphic representations over global function
fields, which we use to construct a map
$\DeclareMathOperator{\rec}{rec}\pi\mapsto\rec(\pi)$ from isomorphism classes
of irreducible smooth representations of $\GL_n(F)$ to isomorphism classes of
$n$-dimensional semisimple continuous representations of $W_F$. Our map $\rec$
is characterized in terms of a local compatibility condition on traces of a
certain test function $f_{\tau,h}$, and we prove that $\rec$ equals the usual
local Langlands correspondence (after forgetting the monodromy operator).


Abstract:
  The prediction capability of recurrent-type neural networks is investigated
for real-time short-term prediction (nowcasting) of ship motions in high sea
state. Specifically, the performance of recurrent neural networks, long-short
term memory, and gated recurrent units models are assessed and compared using a
data set coming from computational fluid dynamics simulations of a
self-propelled destroyer-type vessel in stern-quartering sea state 7. Time
series of incident wave, ship motions, rudder angle, as well as immersion
probes, are used as variables for a nowcasting problem. The objective is to
obtain about 20 s ahead prediction. Overall, the three methods provide
promising and comparable results.


Abstract:
  We introduce a dispersive regularization of the compressible Euler equations
in Lagrangian coordinates, in the one-dimensional torus. We assume a Van der
Waals pressure law, which presents both hyperbolic and elliptic zones. The
dispersive regularization is of Schroedinger type. In particular, the
regularized system is complex-valued. It has a conservation law, which, for
real unknowns, is identical to the energy of the unregularized physical system.
The regularized system supports high-frequency solutions, with an existence
time or an amplitude which depend strongly on the pressure law.


Abstract:
  Reinforcement learning (RL) has gained increasing interest since the
demonstration it was able to reach human performance on video game benchmarks
using deep Q-learning (DQN). The current consensus for training neural networks
on such complex environments is to rely on gradient-based optimization.
Although alternative Bayesian deep learning methods exist, most of them still
rely on gradient-based optimization, and they typically do not scale on
benchmarks such as the Atari game environment. Moreover none of these
approaches allow performing the analytical inference for the weights and biases
defining the neural network. In this paper, we present how we can adapt the
temporal difference Q-learning framework to make it compatible with the
tractable approximate Gaussian inference (TAGI), which allows learning the
parameters of a neural network using a closed-form analytical method.
Throughout the experiments with on- and off-policy reinforcement learning
approaches, we demonstrate that TAGI can reach a performance comparable to
backpropagation-trained networks while using fewer hyperparameters, and without
relying on gradient-based optimization.


Abstract:
  We present new SOFIA [CII] and ALMA CO(J=1-0) observations of the nearby
asymmetric barred spiral galaxy NGC 7479. The data, which cover the whole bar
of the galaxy and the counter-arms visible in the radio continuum, are analyzed
in conjunction with a wealth of existing visible, infrared, radio, and X-ray
data. As in most normal galaxies, the [CII] emission is generally consistent
with emission from cooling gas excited by photoelectric heating in
photo-dissociation regions. However, anomalously high [CII]/CO ratios are seen
at the two ends of the counter-arms. Both ends show shell-like structures,
possibly bubbles, in H-alpha emission. In addition, the southern end has [CII]
to infrared emission ratios inconsistent with normal star formation. Because
there is little HI emission at this location, the [CII] emission probably
originates in warm shocked molecular gas heated by the interaction of the radio
jet forming the counter-arms with the interstellar medium in the galaxy. At two
other locations, the high [CII]/CO ratios provide evidence for the existence of
patches of CO-dark molecular gas. The [CII] and CO observations also reveal
resolved velocity components along the bar. In particular, the CO emission can
be separated into two components associated to gas along the leading edge of
the bar and gas trailing the bar. The trailing gas component that amounts to
approximately 40% of the gas around the bar region may be related to a minor
merger.


Abstract:
  This paper summarizes the progress in developing a rugged, low-cost,
automated ground cone robot network capable of traffic delineation at
lane-level precision. A holonomic omnidirectional base with a traffic
delineator was developed to allow flexibility in initialization. RTK GPS was
utilized to reduce minimum position error to 2 centimeters. Due to recent
developments, the cost of the platform is now less than $1,600. To minimize the
effects of GPS-denied environments, wheel encoders and an Extended Kalman
Filter were implemented to maintain lane-level accuracy during operation and a
maximum error of 1.97 meters through 50 meters with little to no GPS signal.
Future work includes increasing the operational speed of the platforms,
incorporating lanelet information for path planning, and cross-platform
estimation.


Abstract:
  This paper introduces the Diabetic Foot Ulcers dataset (DFUC2021) for
analysis of pathology, focusing on infection and ischaemia. We describe the
data preparation of DFUC2021 for ground truth annotation, data curation and
data analysis. The final release of DFUC2021 consists of 15,683 DFU patches,
with 5,955 training, 5,734 for testing and 3,994 unlabeled DFU patches. The
ground truth labels are four classes, i.e. control, infection, ischaemia and
both conditions. We curate the dataset using image hashing techniques and
analyse the separability using UMAP projection. We benchmark the performance of
five key backbones of deep learning, i.e. VGG16, ResNet101, InceptionV3,
DenseNet121 and EfficientNet on DFUC2021. We report the optimised results of
these key backbones with different strategies. Based on our observations, we
conclude that EfficientNetB0 with data augmentation and transfer learning
provided the best results for multi-class (4-class) classification with
macro-average Precision, Recall and F1-score of 0.57, 0.62 and 0.55,
respectively. In ischaemia and infection recognition, when trained on
one-versus-all, EfficientNetB0 achieved comparable results with the state of
the art. Finally, we interpret the results with statistical analysis and
Grad-CAM visualisation.


Abstract:
  In a world abundant with diverse data arising from complex acquisition
techniques, there is a growing need for new data analysis methods. In this
paper we focus on high-dimensional data that are organized into several
hierarchical datasets. We assume that each dataset consists of complex samples,
and every sample has a distinct irregular structure modeled by a graph. The
main novelty in this work lies in the combination of two complementing powerful
data-analytic approaches: topological data analysis (TDA) and geometric
manifold learning. Geometry primarily contains local information, while
topology inherently provides global descriptors. Based on this combination, we
present a method for building an informative representation of hierarchical
datasets. At the finer (sample) level, we devise a new metric between samples
based on manifold learning that facilitates quantitative structural analysis.
At the coarser (dataset) level, we employ TDA to extract qualitative structural
information from the datasets. We showcase the applicability and advantages of
our method on simulated data and on a corpus of hyper-spectral images. We show
that an ensemble of hyper-spectral images exhibits a hierarchical structure
that fits well the considered setting. In addition, we show that our new method
gives rise to superior classification results compared to state-of-the-art
methods.


Abstract:
  Recently, there are unprecedented data growth originating from different
online platforms which contribute to big data in terms of volume, velocity,
variety and veracity (4Vs). Given this nature of big data which is
unstructured, performing analytics to extract meaningful information is
currently a great challenge to big data analytics. Collecting and analyzing
unstructured textual data allows decision makers to study the escalation of
comments/posts on our social media platforms. Hence, there is need for
automatic big data analysis to overcome the noise and the non-reliability of
these unstructured dataset from the digital media platforms. However, current
machine learning algorithms used are performance driven focusing on the
classification/prediction accuracy based on known properties learned from the
training samples. With the learning task in a large dataset, most machine
learning models are known to require high computational cost which eventually
leads to computational complexity. In this work, two supervised machine
learning algorithms are combined with text mining techniques to produce a
hybrid model which consists of Na\"ive Bayes and support vector machines (SVM).
This is to increase the efficiency and accuracy of the results obtained and
also to reduce the computational cost and complexity. The system also provides
an open platform where a group of persons with a common interest can share
their comments/messages and these comments classified automatically as legal or
illegal. This improves the quality of conversation among users. The hybrid
model was developed using WEKA tools and Java programming language. The result
shows that the hybrid model gave 96.76% accuracy as against the 61.45% and
69.21% of the Na\"ive Bayes and SVM models respectively.


Abstract:
  This paper introduces two decomposition-based methods for two-block
mixed-integer linear programs (MILPs), which break the original problem into a
sequence of smaller MILP subproblems. The first method is based on the
l1-augmented Lagrangian. The second method is based on the alternating
direction method of multipliers. When the original problem has a block-angular
structure, the subproblems of the first block have low dimensions and can be
solved in parallel. We add reverse-norm cuts and augmented Lagrangian cuts to
the subproblems of the second block. For both methods, we show asymptotic
convergence to globally optimal solutions and present iteration upper bounds.
Numerical comparisons with recent decomposition methods demonstrate the
exactness and efficiency of our proposed methods.


Abstract:
  The mean vertical heat transport $\langle wT \rangle$ in convection between
isothermal plates driven by uniform internal heating is investigated by means
of rigorous bounds. These are obtained as a function of the Rayleigh number $R$
by constructing feasible solutions to a convex variational problem, derived
using a formulation of the classical background method in terms of quadratic
auxiliary functions. When the fluid's temperature relative to the boundaries is
allowed to be positive or negative, numerical solution of the variational
problem shows that best previous bound $\langle wT \rangle \leq 1/2$ can only
be improved up to finite $R$. Indeed, we demonstrate analytically that $
\langle wT \rangle \leq 2^{-21/5} R^{1/5}$ and therefore prove that $\langle
wT\rangle< 1/2$ for $R < 65\,536$. However, if the minimum principle for
temperature is invoked, which asserts that internal temperature is at least as
large as the temperature of the isothermal boundaries, then numerically
optimised bounds are strictly smaller than $1/2$ until at least $R =3.4\times
10^{5}$. While the computational results suggest that the best bound on
$\langle wT\rangle$ approaches $1/2$ asymptotically from below as $R\rightarrow
\infty$, we prove that typical analytical constructions cannot be used to prove
this conjecture.


Abstract:
  We present a multi-phase design parameterization to obtain optimized
heterogeneous lattice structures. The 3D domain is discretized into a cubical
grid wherein each cube has eight distinct unit cell types or phases. When all
phases are present, the domain resembles a densely connected ground structure.
The cross-section area of beam segments in lattice units, modelled using
Timoshenko beam theory, are the design variables. All beam segments in a
particular lattice phase have the same area of cross-section to keep the number
of design variables low. The optimization problem is formulated for stiff
structures and is solved using the optimality criteria algorithm. We present a
case study to show the superiority of topology-optimized heterogeneous
structures over uniform lattices of a single phase. In order to interpret the
phase composition, we perform four basic load tests on single phases, namely,
tension or compression, shear, bending, and torsion. The phases are ranked
based on the stiffness corresponding to individual loading conditions. The
results show that in the optimized structure, the local internal load
configuration drives the selection of phases. We also note that micropolar
elasticity captures the bulk behaviour of heterogeneous lattice structures, and
helps not only to interpret the optimality of phases but also to improve the
computational efficiency of the proposed optimization technique.


Abstract:
  This chapter contains an exposition of the sheaf-theoretic framework for
contextuality emphasising resource-theoretic aspects, as well as some original
results on this topic. In particular, we consider functions that transform
empirical models on a scenario S to empirical models on another scenario T, and
characterise those that are induced by classical procedures between S and T
corresponding to 'free' operations in the (non-adaptive) resource theory of
contextuality. We proceed by expressing such functions as empirical models
themselves, on a new scenario built from S and T. Our characterisation then
boils down to the non-contextuality of these models. We also show that this
construction on scenarios provides a closed structure in the category of
measurement scenarios.


Abstract:
  The fast and accessible verification of nonclassical resources is an
indispensable step towards a broad utilization of continuous-variable quantum
technologies. Here, we use machine learning methods for the identification of
nonclassicality of quantum states of light by processing experimental data
obtained via homodyne detection. For this purpose, we train an artificial
neural network to classify classical and nonclassical states from their
quadrature-measurement distributions. We demonstrate that the network is able
to correctly identify classical and nonclassical features from real
experimental quadrature data for different states of light. Furthermore, we
show that nonclassicality of some states that were not used in the training
phase is also recognized. Circumventing the requirement of the large sample
sizes needed to perform homodyne tomography, our approach presents a promising
alternative for the identification of nonclassicality for small sample sizes,
indicating applicability for fast sorting or direct monitoring of experimental
data.


Abstract:
  Language identification of social media text has been an interesting problem
of study in recent years. Social media messages are predominantly in code mixed
in non-English speaking states. Prior knowledge by pre-training contextual
embeddings have shown state of the art results for a range of downstream tasks.
Recently, models such as BERT have shown that using a large amount of unlabeled
data, the pretrained language models are even more beneficial for learning
common language representations. Extensive experiments exploiting transfer
learning and fine-tuning BERT models to identify language on Twitter are
presented in this paper. The work utilizes a data collection of
Hindi-English-Urdu codemixed text for language pre-training and Hindi-English
codemixed for subsequent word-level language classification. The results show
that the representations pre-trained over codemixed data produce better results
by their monolingual counterpart.


Abstract:
  We study the two-dimensional multiphase Muskat problem describing the motion
of three immiscible fluids with equal viscosities in a vertical homogeneous
porous medium identified with $\mathbb{R}^2$ under the effect of gravity. We
first formulate the governing equations as a strongly coupled evolution problem
for the functions that parameterize the sharp interfaces between the fluids.
Afterwards we prove that the problem is of parabolic type and establish its
well-posedness together with two parabolic smoothing properties. For solutions
that are not global we exclude, in a certain regime, that the interfaces come
into contact along a curve segment.


Abstract:
  The void ellipticity distribution today can be well explained by the tidal
field. Going a step further from the overall distribution, we investigate
individuality on the tidal response of void shape in non-linear dynamical
evolution. We perform an N-body simulation and trace individual voids using
particle ID. The voids are defined based on Voronoi tessellation and watershed
algorithm, using public code VIDE. A positive correlation is found between the
time variation of void ellipticity and tidal field around a void if the void
maintains its constituent particles. Such voids tend to have smaller mass
densities. Conversely, not a few voids significantly deform by particle
exchange, rather than the tidal field. Those voids may prevent us from
correctly proving a quadrupole field of gravity out of a void shape.


Abstract:
  Adaptive gradient methods have shown excellent performances for solving many
machine learning problems. Although multiple adaptive gradient methods were
recently studied, they mainly focus on either empirical or theoretical aspects
and also only work for specific problems by using some specific adaptive
learning rates. Thus, it is desired to design a universal framework for
practical algorithms of adaptive gradients with theoretical guarantee to solve
general problems. To fill this gap, we propose a faster and universal framework
of adaptive gradients (i.e., SUPER-ADAM) by introducing a universal adaptive
matrix that includes most existing adaptive gradient forms. Moreover, our
framework can flexibly integrate the momentum and variance reduced techniques.
In particular, our novel framework provides the convergence analysis support
for adaptive gradient methods under the nonconvex setting. In theoretical
analysis, we prove that our SUPER-ADAM algorithm can achieve the best known
gradient (i.e., stochastic first-order oracle (SFO)) complexity of
$\tilde{O}(\epsilon^{-3})$ for finding an $\epsilon$-stationary point of
nonconvex optimization, which matches the lower bound for stochastic smooth
nonconvex optimization. In numerical experiments, we employ various deep
learning tasks to validate that our algorithm consistently outperforms the
existing adaptive algorithms. Code is available at
https://github.com/LIJUNYI95/SuperAdam


Abstract:
  The possibility of shifting sound energy from lower to higher frequency bands
is investigated. The system configuration considered is a segmented structure
having non-linear stiffness characteristics. It is proposed here that such a
frequency-shifting mechanism could complement metamaterial concepts for
mass-efficient sound barriers. The acoustical behavior of the material system
was studied through a representative two-dimensional model consisting of a
segmented plate with a contact interface. Multiple harmonic peaks were observed
in response to a purely single frequency excitation, and the strength of the
response was found to depend on the degree of non-linearity introduced. The
lower and closer an excitation frequency was to the characteristic resonance
frequencies of the base system, the stronger was the predicted higher harmonic
response. The broadband sound transmission loss of these systems has also been
calculated and the low frequency sound transmission loss was found to increase
as the level of the broadband incident sound field increased. The present
findings support the feasibility of designing material systems that transfer
energy from lower frequency bands, where a sound barrier is less efficient, to
higher bands where energy is more readily dissipated.


Abstract:
  The free energy of glasses cannot be estimated using thermodynamic
integration, as glasses are intrinsically not in equilibrium. We present
numerical simulations showing that, in contrast, plausible free-energy
estimates of a Kob-Andersen glass can be obtained using the Jarzynski relation.
Using the Jarzynski relation, we also compute the chemical potential difference
of the two components of this system, and find that, in the glassy regime, the
Jarzynski estimate matches well with the extrapolated value of the supercooled
liquid. Our findings are of broader interest as they show that the Jarzynski
method can be used under conditions where the thermodynamic integration
approach, which is normally more accurate, breaks down completely. Systems
where such an approach might be useful are gels and jammed, glassy structures
formed by compression.


Abstract:
  Nonlinear dynamical systems such as Lorenz63 equations are known to be
chaotic in nature and sensitive to initial conditions. As a result, a small
perturbation in the initial conditions results in deviation in state trajectory
after a few time steps. The algorithms and computational resources needed to
accurately identify the system states vary depending on whether the solution is
in transition region or not. We refer to the transition and non-transition
regions as unstable and stable regions respectively. We label a system state to
be stable if it's immediate past and future states reside in the same regime.
However, at a given time step we don't have the prior knowledge about whether
system is in stable or unstable region. In this paper, we develop and train a
feed forward (multi-layer perceptron) Neural Network to classify the system
states of a Lorenz system as stable and unstable. We pose this task as a
supervised learning problem where we train the neural network on Lorenz system
which have states labeled as stable or unstable. We then test the ability of
the neural network models to identify the stable and unstable states on a
different Lorenz system that is generated using different initial conditions.
We also evaluate the classification performance in the mismatched case i.e.,
when the initial conditions for training and validation data are sampled from
different intervals. We show that certain normalization schemes can greatly
improve the performance of neural networks in especially these mismatched
scenarios. The classification framework developed in the paper can be a
preprocessor for a larger context of sequential decision making framework where
the decision making is performed based on observed stable or unstable states.


Abstract:
  The advent of long-range magnetic order in non-centrosymmetric compounds has
stimulated interest in the possibility of exotic spin transport phenomena and
topologically protected spin textures for applications in next-generation
spintronics. This work reports a novel wurtzite-structure polar magnetic metal,
identified as AA'-stacked (Fe0.5Co0.5)5-xGeTe2, which exhibits a Neel-type
skyrmion lattice as well as a Rashba-Edelstein effect at room temperature.
Atomic resolution imaging of the structure reveals a structural transition as a
function of Co-substitution, leading to the polar phase at 50% Co. This
discovery reveals an unprecedented layered polar magnetic system for
investigating intriguing spin topologies and ushers in a promising new
framework for spintronics.


Abstract:
  The most direct approach for characterizing the quantum dynamics of a
strongly-interacting system is to measure the time-evolution of its full
many-body state. Despite the conceptual simplicity of this approach, it quickly
becomes intractable as the system size grows. An alternate framework is to
think of the many-body dynamics as generating noise, which can be measured by
the decoherence of a probe qubit. Our work centers on the following question:
What can the decoherence dynamics of such a probe tell us about the many-body
system? In particular, we utilize optically addressable probe spins to
experimentally characterize both static and dynamical properties of
strongly-interacting magnetic dipoles. Our experimental platform consists of
two types of spin defects in diamond: nitrogen-vacancy (NV) color centers
(probe spins) and substitutional nitrogen impurities (many-body system). We
demonstrate that signatures of the many-body system's dimensionality, dynamics,
and disorder are naturally encoded in the functional form of the NV's
decoherence profile. Leveraging these insights, we directly characterize the
two-dimensional nature of a nitrogen delta-doped diamond sample. In addition,
we explore two distinct facets of the many-body dynamics: First, we address a
persistent debate about the microscopic nature of spin dynamics in
strongly-interacting dipolar systems. Second, we demonstrate direct control
over the spectral properties of the many-body system, including its correlation
time. Our work opens the door to new directions in both quantum sensing and
simulation.


Abstract:
  Joint access point (AP) association and physical carrier sensing (PCS)
threshold selection has the potential to improve the performance in high
density wireless LANs (WLANs) under high contention, interference and
self-interference (SI) limited transmissions. Using tools from stochastic
geometry, user and AP locations are independent realizations of spatial point
processes. Considering the inherent effects of the channel access protocol, the
spatial density of throughput (SDT), which depends on channel access
probability and coverage rate, is derived as the performance objective.
Leveraging spatial statistics of the network, a throughput-utility maximization
problem is formulated to seek AP association and PCS threshold selection
policies that jointly maximize SDT. The AP association and the PCS threshold
selection policies are derived analytically while an algorithm is proposed for
numerical solution. Under simulated scenarios involving full-duplex (FD) nodes,
optimizing AP association yields performance gains for low to high node density
in large-scale wireless networks. Considering PCS threshold selection
optimization jointly with AP association is shown to improve performance by
effectively separating concurrent transmissions in space. It is shown that AP
association in FD WLANs groups users into minimal contention domains and PCS
threshold optimization reduces the interference domain of user groups for
additional performance gains.


Abstract:
  Ablowitz and Ladik discovered a discretization which preserves the
integrability of the nonlinear Schroedinger equation in one dimension. We
compute the generalized free energy of this model and determine the GGE
averaged fields and currents. They are linked to the mean-field circular
unitary matrix ensemble (CUE). The resulting hydrodynamic equations follow the
pattern already known from other integrable many-body systems. Studied is also
the discretized modified Korteweg-de-Vries equation which turns out to be
related to the beta Jacobi log gas.


Abstract:
  In analogy to the theory of nilpotent orbit in finite-dimensional semisimple
Lie algebras, it is known that the principal $\mathfrak{sl}_2$ subalgebras can
be constructed in hyperbolic Kac-Moody Lie algebras. We obtained a series of
$\mathfrak{sl}_2$ subalgebras in rank 2 symmetric hyperbolic Kac-Moody Lie
algebras by extending the aforementioned construction. We present this result
and also discuss $\mathfrak{sl}_2$ modules obtained by the action of the
$\mathfrak{sl}_2$ subalgebras on the original Lie algebras.


Abstract:
  Compared with cheap addition operation, multiplication operation is of much
higher computation complexity. The widely-used convolutions in deep neural
networks are exactly cross-correlation to measure the similarity between input
feature and convolution filters, which involves massive multiplications between
float values. In this paper, we present adder networks (AdderNets) to trade
these massive multiplications in deep neural networks, especially convolutional
neural networks (CNNs), for much cheaper additions to reduce computation costs.
In AdderNets, we take the $\ell_1$-norm distance between filters and input
feature as the output response. We first develop a theoretical foundation for
AdderNets, by showing that both the single hidden layer AdderNet and the
width-bounded deep AdderNet with ReLU activation functions are universal
function approximators. An approximation bound for AdderNets with a single
hidden layer is also presented. We further analyze the influence of this new
similarity measure on the optimization of neural network and develop a special
training scheme for AdderNets. Based on the gradient magnitude, an adaptive
learning rate strategy is proposed to enhance the training procedure of
AdderNets. AdderNets can achieve a 75.7% Top-1 accuracy and a 92.3% Top-5
accuracy using ResNet-50 on the ImageNet dataset without any multiplication in
the convolutional layer.


Abstract:
  We present a new finite-sample analysis of M-estimators of locations in
$\mathbb{R}^d$ using the tool of the influence function. In particular, we show
that the deviations of an M-estimator can be controlled thanks to its influence
function (or its score function) and then, we use concentration inequality on
M-estimators to investigate the robust estimation of the mean in high dimension
in a corrupted setting (adversarial corruption setting) for bounded and
unbounded score functions. For a sample of size $n$ and covariance matrix
$\Sigma$, we attain the minimax speed
$\sqrt{Tr(\Sigma)/n}+\sqrt{\|\Sigma\|_{op}\log(1/\delta)/n}$ with probability
larger than $1-\delta$ in a heavy-tailed setting. One of the major advantages
of our approach compared to others recently proposed is that our estimator is
tractable and fast to compute even in very high dimension with a complexity of
$O(nd\log(Tr(\Sigma)))$ where $n$ is the sample size and $\Sigma$ is the
covariance matrix of the inliers. In practice, the code that we make available
for this article proves to be very fast.


Abstract:
  We experimentally generate cylindrically polarized wavepackets with
transverse orbital angular momentum, demonstrating the coexistence of
spatiotemporal optical vortex with spatial polarization singularity. The
results in this paper extend the study of spatiotemporal wavepackets to a
broader scope, paving the way for its applications in various areas such as
light-matter interaction, optical tweezers, spatiotemporal spin-orbit angular
momentum coupling, etc.


Abstract:
  We have previously reported ferromagnetism evinced by a large hysteretic
anomalous Hall effect in twisted bilayer graphene (tBLG). Subsequent
measurements of a quantized Hall resistance and small longitudinal resistance
confirmed that this magnetic state is a Chern insulator. Here we report that,
when tilting the sample in an external magnetic field, the ferromagnetism is
highly anisotropic. Because spin-orbit coupling is negligible in graphene such
anisotropy is unlikely to come from spin, but rather favors theories in which
the ferromagnetism is orbital. We know of no other case in which ferromagnetism
has a purely orbital origin. For an applied in-plane field larger than $5\
\mathrm{T}$, the out-of-plane magnetization is destroyed, suggesting a
transition to a new phase.


Abstract:
  Glioblastoma (GBM) is a highly morbid and lethal disease with poor prognosis.
Their emergent properties such as cellular heterogeneity, therapy resistance,
and self-renewal are largely attributed to the interactions between a subset of
their population known as glioblastoma-derived stem cells (GSCs) and their
microenvironment. Identifying causal patterns in the developmental trajectories
between GSCs and the mature, well-differentiated GBM phenotypes remains a
challenging problem in oncology. The paper presents a blueprint of complex
systems approaches to infer attractor dynamics from the single-cell gene
expression datasets of pediatric GBM and adult GSCs. These algorithms include
Waddington landscape reconstruction, Generative Adversarial Networks, and
fractal dimension analysis. Here I show, a Rossler-like strange attractor with
a fractal dimension of roughly 1.7 emerged in the GAN-reconstructed patterns of
all twelve patients. The findings suggest a strange attractor may be driving
the complex dynamics and adaptive behaviors of GBM in signaling state-space.


Abstract:
  The jet-medium interaction in high energy heavy ion collisions is an
important phenomena to characterize the hot and dense medium produced in such
collisions. The study of medium-induced modifications to the substructure of
inclusive charged jets indicates a redistribution of energy inside the jet cone
and provides insight into the energy loss mechanisms of jets in the medium. We
investigate the in-medium modification to two jet shape observables i.e., the
differential jet shape ($\rho$(r)) and the angularity (g) in the most central
$Pb-Pb$ collisions at $\sqrt{s_{NN}} ~=~ 2.76 $ TeV using two commonly used
event generators i.e., JEWEL (recoil OFF) and EPOS-3 in the jet-p$_T$ range of
20-40 GeV/c. JEWEL with recoil OFF has been used primarily as a reference
system as that has been found to explain the global jet observables
satisfactorily but lacks in jet-shape variables at higher jet-radii. EPOS-3
that explains the bulk properties in such collisions quite well takes into
account a hydrodynamically evolving bulk matter, jets and hard-soft
interactions. A comparison between the results from these models shows that
while JEWEL (recoil OFF) does not explain the distribution of lost energy at
higher radii with respect to the jet-axis, EPOS-3 explains the effect quite
well. However, in EPOS-3, the partonic energy loss mechanism and secondary
hard-soft interactions during hadronization and hadronic cascade phase are
different from the conventional jet energy loss models. The current study can,
therefore, provide important new insights on mechanisms regarding the modeling
of the medium and hard-soft interactions in heavy ion collisions.


Abstract:
  We consider the first Dirichlet eigenvalue problem for a mixed local/nonlocal
elliptic operator and we establish a quantitative Faber-Krahn inequality. More
precisely, we show that balls minimize the first eigenvalue among sets of given
volume and we provide a stability result for sets that almost attain the
minimum.


Abstract:
  We investigated progestin and corticosteroid activation of the progesterone
receptor (PR) from elephant shark (Callorhinchus milii), a cartilaginous fish
belonging to the oldest group of jawed vertebrates. Comparison with human PR
experiments provides insights into the evolution of steroid activation of human
PR. At 1 nM steroid, elephant shark PR is activated by progesterone,
17-hydroxy-progesterone, 20beta-hydroxy-progesterone, 11-deoxycorticosterone
(21-hydroxyprogesterone) and 11-deoxycortisol. At 1 nM steroid, human PR is
activated only by progesterone and11-deoxycorticosterone indicating increased
specificity for progestins and corticosteroids during the evolution of human
PR. RU486, an important clinical antagonist of human PR, did not inhibit
progesterone activation of elephant shark PR. Cys-528 in elephant shark PR
corresponds to Gly-722 in human PR, which is essential for RU486 inhibition of
human PR. Confirming the importance of this site on elephant shark PR, RU486
inhibited progesterone activation of the Cys528Gly mutant PR. There also was a
decline in activation of elephant shark Cys528Gly PR by 11-deoxycortisol,
17-hydroxy-progesterone and 20beta-hydroxy-progesterone and an increase in
activation of human Gly722Cys PR by 11-deoxycortisol and decreased activation
by corticosterone. One or more of these changes may have selected for the
mutation corresponding to human glycine-722 PR that first evolved in platypus
PR, a basal mammal.


Abstract:
  Bayesian optimization (BO) conventionally relies on handcrafted acquisition
functions (AFs) to sequentially determine the sample points. However, it has
been widely observed in practice that the best-performing AF in terms of regret
can vary significantly under different types of black-box functions. It has
remained a challenge to design one AF that can attain the best performance over
a wide variety of black-box functions. This paper aims to attack this challenge
through the perspective of reinforced few-shot AF learning (FSAF).
Specifically, we first connect the notion of AFs with Q-functions and view a
deep Q-network (DQN) as a surrogate differentiable AF. While it serves as a
natural idea to combine DQN and an existing few-shot learning method, we
identify that such a direct combination does not perform well due to severe
overfitting, which is particularly critical in BO due to the need of a
versatile sampling policy. To address this, we present a Bayesian variant of
DQN with the following three features: (i) It learns a distribution of
Q-networks as AFs based on the Kullback-Leibler regularization framework. This
inherently provides the uncertainty required in sampling for BO and mitigates
overfitting. (ii) For the prior of the Bayesian DQN, we propose to use a demo
policy induced by an off-the-shelf AF for better training stability. (iii) On
the meta-level, we leverage the meta-loss of Bayesian model-agnostic
meta-learning, which serves as a natural companion to the proposed FSAF.
Moreover, with the proper design of the Q-networks, FSAF is general-purpose in
that it is agnostic to the dimension and the cardinality of the input domain.
Through extensive experiments, we demonstrate that the FSAF achieves comparable
or better regrets than the state-of-the-art benchmarks on a wide variety of
synthetic and real-world test functions.


Abstract:
  In the context of the quest for a holographic formulation of quantum gravity,
we investigate the basic boundary theory structure for loop quantum gravity. In
3+1 space-time dimensions, the boundary theory lives on the 2+1-dimensional
time-like boundary and is supposed to describe the time evolution of the edge
modes living on the 2-dimensional boundary of space, i.e. the space-time
corner. Focusing on "electric" excitations -- quanta of area -- living on the
corner, we formulate their dynamics in terms of classical spinor variables and
we show that the coupling constants of a polynomial Hamiltonian can be
understood as the components of a background boundary 2+1-metric. This leads to
a deeper conjecture of a correspondence between boundary Hamiltonian and
boundary metric states. We further show that one can reformulate the quanta of
area data in terms of a SL(2,C) connection, transporting the spinors on the
boundary surface and whose SU(2) component would define "magnetic" excitations
(tangential Ashtekar-Barbero connection), thereby opening the door to writing
the loop quantum gravity boundary dynamics as a 2+1-dimensional SL(2,C) gauge
theory.


Abstract:
  Federated learning (FL), invented by Google in 2016, has become a hot
research trend. However, enabling FL in wireless networks has to overcome the
limited battery challenge of mobile users. In this regard, we propose to apply
unmanned aerial vehicle (UAV)-empowered wireless power transfer to enable
sustainable FL-based wireless networks. The objective is to maximize the UAV
transmit power efficiency, via a joint optimization of transmission time and
bandwidth allocation, power control, and the UAV placement. Directly solving
the formulated problem is challenging, due to the coupling of variables. Hence,
we leverage the decomposition technique and a successive convex approximation
approach to develop an efficient algorithm, namely UAV for sustainable FL
(UAV-SFL). Finally, simulations illustrate the potential of our proposed
UAV-SFL approach in providing a sustainable solution for FL-based wireless
networks, and in reducing the UAV transmit power by 32.95%, 63.18%, and 78.81%
compared with the benchmarks.


Abstract:
  Galaxy mergers are central to our understanding of galaxy formation,
especially within the context of hierarchical models. Besides having a large
impact on the star formation history, mergers are also able to influence gas
motions at the centre of galaxies and trigger an Active Galactic Nucleus (AGN).
In this paper, we present a case study of the Seyfert galaxy NGC 2992, which
together with NGC 2993 forms the early-stage merger system Arp 245. Using
Gemini Multi-Object Spectrograph (GMOS) integral field unit (IFU) data from the
inner 1.1 kpc of the galaxy we were able to spatially resolve the stellar
populations, the ionisation mechanism and kinematics of ionised gas. From full
spectral synthesis, we found that the stellar population is primarily composed
by old metal-rich stars (t $\geq$ 1.4 Gyr, $Z \geq 2.0$\zsun), with a
contribution of at most 30 per cent of the light from a young and metal-poor
population (t $\leq$ 100 Myr, $Z \leq 1.0$\zsun). We detect \halpha and \hbeta
emission from the Broad Line Region (BLR) with a Full Width at Half Maximum
(FWHM) of $\sim$ 2000\kms. The Narrow Line Region (NLR) kinematics presents two
main components: one from gas orbiting the galaxy disk and a blueshifted
(velocity $\approx$ -200\kms) outflow, possibly correlated with the radio
emission, with mass outflow rate of $\sim$ 2 M$_{\odot}$ yr$^{-1}$ and a
kinematic power of $\sim$ 2 $\times 10^{40}$ erg s$^{-1}$ (\Eout/\Lbol
$\approx$ 0.2 per cent). We also show even though the main ionisation mechanism
is the AGN radiation, ionisation by young stars and shocks may also contribute
to the emission line ratios presented in the innermost region of the galaxy.


Abstract:
  The accurate computation of the covariance matrix of fitted model parameters
is a somewhat neglected task in Statistics. Algorithms are given for computing
accurate covariance matrices derived from computing the Hessian matrix by
numerical differentiation, and also for the covariance matrix of the posterior
distribution of model parameters. Evaluations on two datasets where the Hessian
could be computed analytically show that the numerical differentiation
algorithm is very accurate.


Abstract:
  We present a general technique for constructing Graph Neural Networks (GNNs)
capable of using multi-relational domain knowledge. The technique is based on
mode-directed inverse entailment (MDIE) developed in Inductive Logic
Programming (ILP). Given a data instance $e$ and background knowledge $B$, MDIE
identifies a most-specific logical formula $\bot_B(e)$ that contains all the
relational information in $B$ that is related to $e$. We represent $\bot_B(e)$
by a "bottom-graph" that can be converted into a form suitable for GNN
implementations. This transformation allows a principled way of incorporating
generic background knowledge into GNNs: we use the term `BotGNN' for this form
of graph neural networks. For several GNN variants, using real-world datasets
with substantial background knowledge, we show that BotGNNs perform
significantly better than both GNNs without background knowledge and a recently
proposed simplified technique for including domain knowledge into GNNs. We also
provide experimental evidence comparing BotGNNs favourably to multi-layer
perceptrons (MLPs) that use features representing a "propositionalised" form of
the background knowledge; and BotGNNs to a standard ILP based on the use of
most-specific clauses. Taken together, these results point to BotGNNs as
capable of combining the computational efficacy of GNNs with the
representational versatility of ILP.


Abstract:
  We show that the degrees of rational endomorphisms of very general complex
Fano and Calabi-Yau hypersurfaces satisfy certain congruence conditions by
specializing to characteristic p. As a corollary we show that very general
n-dimensional hypersurfaces of degree $d\ge 5\lceil (n+3)/6\rceil$ are not
birational to elliptic fibrations. A key part of the argument is to resolve
singularities of general p-cyclic covers in mixed characteristic p.


Abstract:
  Successor-style representations have many advantages for reinforcement
learning: for example, they can help an agent generalize from past experience
to new goals, and they have been proposed as explanations of behavioral and
neural data from human and animal learners. They also form a natural bridge
between model-based and model-free RL methods: like the former they make
predictions about future experiences, and like the latter they allow efficient
prediction of total discounted rewards. However, successor-style
representations are not optimized to generalize across policies: typically, we
maintain a limited-length list of policies, and share information among them by
representation learning or GPI. Successor-style representations also typically
make no provision for gathering information or reasoning about latent
variables. To address these limitations, we bring together ideas from
predictive state representations, belief space value iteration, successor
features, and convex analysis: we develop a new, general successor-style
representation, together with a Bellman equation that connects multiple sources
of information within this representation, including different latent states,
policies, and reward functions. The new representation is highly expressive:
for example, it lets us efficiently read off an optimal policy for a new reward
function, or a policy that imitates a new demonstration. For this paper, we
focus on exact computation of the new representation in small, known
environments, since even this restricted setting offers plenty of interesting
questions. Our implementation does not scale to large, unknown environments --
nor would we expect it to, since it generalizes POMDP value iteration, which is
difficult to scale. However, we believe that future work will allow us to
extend our ideas to approximate reasoning in large, unknown environments.


Abstract:
  Heterogeneous computing systems provide high performance and energy
efficiency. However, to optimally utilize such systems, solutions that
distribute the work across host CPUs and accelerating devices are needed. In
this paper, we present a performance and energy aware approach that combines AI
planning heuristics for parameter space exploration with a machine learning
model for performance and energy evaluation to determine a near-optimal system
configuration. For data-parallel applications our approach determines a
near-optimal host-device distribution of work, number of processing units
required and the corresponding scheduling strategy. We evaluate our approach
for various heterogeneous systems accelerated with GPU or the Intel Xeon Phi.
The experimental results demonstrate that our approach finds a near-optimal
system configuration by evaluating only about 7% of reasonable configurations.
Furthermore, the performance per Joule estimation of system configurations
using our machine learning model is more than 1000x faster compared to the
system evaluation by program execution.


Abstract:
  Passionate employees are essential for organisational success as they foster
higher performance and exhibit lower turnover or absenteeism. While a large
body of research has investigated the consequences of passion, we know only
little about its antecedents. Integrating trait interaction theory with trait
activation theory, this paper examines how personality traits, i.e.
conscientiousness, agreeableness, and neuroticism impact passion at work across
different job situations. Passion has been conceptualized as a two-dimensional
construct, consisting of harmonious work passion (HWP) and obsessive work
passion (OWP). Our study is based on a sample of N = 824 participants from the
myPersonality project. We find a positive relationship between neuroticism and
OWP in enterprising environments. Further, we find a three-way interaction
between conscientiousness, agreeableness, and enterprising environment in
predicting OWP. Our findings imply that the impact of personality
configurations on different forms of passion is contingent on the job
environment. Moreover, in line with self-regulation theory, the results reveal
agreeableness as a "cool influencer" and neuroticism as a "hot influencer" of
the relationship between conscientiousness and work passion. We derive
practical implications for organisations on how to foster work passion,
particularly HWP, in organisations.


Abstract:
  A long-standing open problem in the semantics of programming languages
supporting probabilistic choice is to find a commutative monad for probability
on the category DCPO. In this paper we present three such monads and a general
construction for finding even more. We show how to use these monads to provide
a sound and adequate denotational semantics for the Probabilistic FixPoint
Calculus (PFPC) -- a call-by-value simply-typed lambda calculus with
mixed-variance recursive types, term recursion and probabilistic choice. We
also show that in the special case where we consider continuous dcpo's, then
all three monads coincide with the valuations monad of Jones and we fully
characterise the induced Eilenberg-Moore categories by showing that they are
all isomorphic to the category of continuous Kegelspitzen of Keimel and
Plotkin.


Abstract:
  We propose a new approach to text semantic analysis and general corpus
analysis using, as termed in this article, a "bi-gram graph" representation of
a corpus. The different attributes derived from graph theory are measured and
analyzed as unique insights or against other corpus graphs. We observe a vast
domain of tools and algorithms that can be developed on top of the graph
representation; creating such a graph proves to be computationally cheap, and
much of the heavy lifting is achieved via basic graph calculations.
Furthermore, we showcase the different use-cases for the bi-gram graphs and how
scalable it proves to be when dealing with large datasets.


Abstract:
  Amino acids are the essential keys in chemistry that contribute to the study
of the formation of life. The complex organic molecule glycine
(NH$_{2}$CH$_{2}$COOH) is the simplest amino acid that has been investigated in
the interstellar medium for a long period to search for a potential connection
between the Universe and the origin of life. In the last forty years, several
attempts have failed to detect the interstellar glycine in the hot molecular
cores and star-forming regions. We report the possible detection of the
rotational emission lines of interstellar glycine with conformer I and II in
the hot molecular core G10.47+0.03 between the frequency range of $\nu$ =
158.6$-$160.4 GHz with Atacama Large Millimeter/Submillimeter Array (ALMA)
observation. Under the Local Thermodynamic Equilibrium (LTE) condition, we
apply the rotational diagram method to estimate the column density ($N$) and
rotational temperature ($T_{rot}$) of the detected amino acid glycine. Using
rotational diagram, we find the column density of glycine
$N$(NH$_{2}$CH$_{2}$COOH) = 2.8$\times$10$^{18}$ cm$^{-2}$ with rotational
temperature $T_{rot}$ = 115.9 K. We also apply the Levenberg$-$Marquardt
algorithm to extract the line parameters of detected emission lines of glycine.


Abstract:
  Reinforcement learning is generally difficult for partially observable Markov
decision processes (POMDPs), which occurs when the agent's observation is
partial or noisy. To seek good performance in POMDPs, one strategy is to endow
the agent with a finite memory, whose update is governed by the policy.
However, policy optimization is non-convex in that case and can lead to poor
training performance for random initialization. The performance can be
empirically improved by constraining the memory architecture, then sacrificing
optimality to facilitate training. Here we study this trade-off in a
two-hypothesis testing problem, akin to the two-arm bandit problem. We compare
two extreme cases: (i) the random access memory where any transitions between
$M$ memory states are allowed and (ii) a fixed memory where the agent can
access its last $m$ actions and rewards. For (i), the probability $q$ to play
the worst arm is known to be exponentially small in $M$ for the optimal policy.
Our main result is to show that similar performance can be reached for (ii) as
well, despite the simplicity of the memory architecture: using a conjecture on
Gray-ordered binary necklaces, we find policies for which $q$ is exponentially
small in $2^m$, i.e. $q\sim\alpha^{2^m}$ with $\alpha < 1$. In addition, we
observe empirically that training from random initialization leads to very poor
results for (i), and significantly better results for (ii) thanks to the
constraints on the memory architecture.


Abstract:
  The paper investigates the effects of the credit market development on the
labor mobility between the informal and formal labor sectors. In the case of
Russia, due to the absence of a credit score system, a formal lender may set a
credit limit based on the verified amount of income. To get a loan, an informal
worker must first formalize his or her income (switch to a formal job), and
then apply for a loan. To show this mechanism, the RLMS data was utilized, and
the empirical method is the dynamic multinomial logit model of employment. The
empirical results show that a relaxation of credit constraints increases the
probability of transition from an informal to a formal job, and improved CMA
(by one standard deviation) increases the chances of informal sector workers to
formalize by 5.4 ppt. These results are robust in different specifications of
the model. Policy simulations show strong support for a reduction in informal
employment in response to better CMA in credit-constrained communities.


Abstract:
  We prove the existence of ballistic transport for a Schr\"odinger operator
with a generic quasi-periodic potential in any dimension $d>1$.


Abstract:
  Two novel decoupling and matching networks (DMNs) in microstrip technology
for three-element uniform circular arrays (UCAs) are investigated and compared
to a more conventional DMN approach with simple neutralization lines. The array
elements are coaxially-fed quarter-wavelength monopole antennas over a finite
groundplane. Three-element arrays are considered since UCAs with an odd number
of elements are able to provide an almost constant maximum array factor over
the whole azimuthal angular range. The new designs are explained from a
theoretical point of view and their implementations are compared to four- and
three-elements UCAs without DMN in terms of decoupling and matching bandwidth
as well as beamforming capabilities. In addition to excellent decoupling and
matching below -16 dB, a broader bandwidth is obtained by the two DMNs. The
reasons for the enhanced bandwidth are similar in both cases: By introducing
several circuit elements offering additional degrees of freedom, matching of
the monopole input impedances at different frequencies becomes feasible. One of
the presented designs offers a larger bandwidth, while the other design is able
to provide a better total efficiency. Scattering parameters, radiation
patterns, beamforming capabilities, and enhanced gain are all verified by
measurements over the operating bandwidth.


Abstract:
  Deep residual network architectures have been shown to achieve superior
accuracy over classical feed-forward networks, yet their success is still not
fully understood. Focusing on massively over-parameterized, fully connected
residual networks with ReLU activation through their respective neural tangent
kernels (ResNTK), we provide here a spectral analysis of these kernels.
Specifically, we show that, much like NTK for fully connected networks
(FC-NTK), for input distributed uniformly on the hypersphere
$\mathbb{S}^{d-1}$, the eigenfunctions of ResNTK are the spherical harmonics
and the eigenvalues decay polynomially with frequency $k$ as $k^{-d}$. These in
turn imply that the set of functions in their Reproducing Kernel Hilbert Space
are identical to those of FC-NTK, and consequently also to those of the Laplace
kernel. We further show, by drawing on the analogy to the Laplace kernel, that
depending on the choice of a hyper-parameter that balances between the skip and
residual connections ResNTK can either become spiky with depth, as with FC-NTK,
or maintain a stable shape.


Abstract:
  This work examines the discrete-time networked SIR
(susceptible-infected-recovered) epidemic model, where the infection and
recovery parameters may be time-varying. We provide a sufficient condition for
the SIR model to converge to the set of healthy states exponentially. We
propose a stochastic framework to estimate the system states from observed
testing data and provide an analytic expression for the error of the estimation
algorithm. Employing the estimated and the true system states, we provide two
novel eradication strategies that guarantee at least exponential convergence to
the set of healthy states. We illustrate the results via simulations over
northern Indiana, USA.


Abstract:
  Photonic brain-inspired platforms are emerging as novel analog computing
devices, enabling fast and energy-efficient operations for machine learning.
These artificial neural networks generally require tailored optical elements,
such as integrated photonic circuits, engineered diffractive layers,
nanophotonic materials, or time-delay schemes, which are challenging to train
or stabilize. Here we present a neuromorphic photonic scheme - photonic extreme
learning machines - that can be implemented simply by using an optical encoder
and coherent wave propagation in free space. We realize the concept through
spatial light modulation of a laser beam, with the far field that acts as
feature mapping space. We experimentally demonstrated learning from data on
various classification and regression tasks, achieving accuracies comparable to
digital extreme learning machines. Our findings point out an optical machine
learning device that is easy-to-train, energetically efficient, scalable and
fabrication-constraint free. The scheme can be generalized to a plethora of
photonic systems, opening the route to real-time neuromorphic processing of
optical data.


Abstract:
  We study Bridgeland moduli spaces of semistable objects of $(-1)$-classes and
$(-4)$-classes in the Kuznetsov components on index one prime Fano threefold
$X_{4d+2}$ of degree $4d+2$ and index two prime Fano threefold $Y_d$ of degree
$d$ for $d=3,4,5$. For every Serre-invariant stability condition on the
Kuznetsov components, we show that the moduli spaces of stable objects of
$(-1)$-classes on $X_{4d+2}$ and $Y_d$ are isomorphic. We show that moduli
spaces of stable objects of $(-1)$-classes on $X_{14}$ are realized by Fano
surface $\mathcal{C}(X)$ of conics, moduli spaces of semistable sheaves
$M_X(2,1,6)$ and $M_X(2,-1,6)$ and the correspondent moduli spaces on cubic
threefold $Y_3$ are realized by moduli spaces of stable vector bundles
$M^b_Y(2,1,2)$ and $M^b_Y(2,-1,2)$. We show that moduli spaces of semistable
objects of $(-4)$-classes on $Y_{d}$ are isomorphic to the moduli spaces of
instanton sheaves $M^{inst}_Y$ when $d\neq 1,2$, and show that there're open
immersions of $M^{inst}_Y$ into moduli spaces of semistable objects of
$(-4)$-classes when $d=1,2$. Finally, when $d=3,4,5$ we show that these moduli
spaces are all isomorphic to $M^{ss}_X(2,0,4)$.


Abstract:
  We compute curvatures of a family of tractable metrics on Stiefel manifolds,
introduced recently by H{\"u}per, Markina and Silva Leite, which includes the
well-known embedded and canonical metrics on Stiefel manifolds as special
cases. The metrics could be identified with the Cheeger deformation metrics. We
identify parameter values in the family to make a Stiefel manifold an Einstein
manifold and show Stiefel manifolds always carry an Einstein metric. We analyze
the sectional curvature range and identify the parameter range where the
manifold has non-negative sectional curvature. We provide the exact sectional
curvature range when the number of columns in a Stiefel matrix is $2$, and a
conjectural range for other cases. We derive the formulas from two approaches,
one from a global curvature formula derived in our recent work, another using
curvature formulas for left-invariant metrics. The second approach leads to
curvature formulas for Cheeger deformation metrics on normal homogeneous
spaces.


Abstract:
  Deep learning techniques have achieved remarkable performance in wide-ranging
tasks. However, when trained on privacy-sensitive datasets, the model
parameters may expose private information in training data. Prior attempts for
differentially private training, although offering rigorous privacy guarantees,
lead to much lower model performance than the non-private ones. Besides,
different runs of the same training algorithm produce models with large
performance variance. To address these issues, we propose DPlis--Differentially
Private Learning wIth Smoothing. The core idea of DPlis is to construct a
smooth loss function that favors noise-resilient models lying in large flat
regions of the loss landscape. We provide theoretical justification for the
utility improvements of DPlis. Extensive experiments also demonstrate that
DPlis can effectively boost model quality and training stability under a given
privacy budget.


Abstract:
  Deep-Neural-Network (DNN) based speaker verification sys-tems use the angular
softmax loss with margin penalties toenhance the intra-class compactness of
speaker embeddings,which achieved remarkable performance. In this paper, we
pro-pose a novel angular loss function called adaptive margin cir-cle loss for
speaker verification. The stage-based margin andchunk-based margin are applied
to improve the angular discrim-ination of circle loss on the training set. The
analysis on gradi-ents shows that, compared with the previous angular loss
likeAdditive Margin Softmax(Am-Softmax), circle loss has flexi-ble optimization
and definite convergence status. Experimentsare carried out on the Voxceleb and
SITW. By applying adap-tive margin circle loss, our best system achieves
1.31%EER onVoxceleb1 and 2.13% on SITW core-core.


Abstract:
  Levitated systems are desirable due to reduced clamping losses and reduced
thermal contact. These advantageous properties have been exploited in
optomechanics to achieve ultra-strong coupling between the mechanical mode and
the electromagnetic mode. Such schemes provide an opportunity for the quantum
manipulation of a macroscopic system. In this letter, we report the first
successful experiments with a levitated millimeter-scale neodymium magnet
within a centimeter-scale superconducting aluminum coaxial quarter-wave stub
cavity. The magnet levitated near the top of the stub, where the electric field
is concentrated, perturbs the electric field distribution allowing for small
perturbations in the magnet's position to be detected through shifts in the
resonance frequency. Resonance spectra are collected via a vector network
analyzer (VNA) between temperatures of 5 K and 50 mK revealing movement of the
magnet inside of the cavity. Room temperature measurements and finite element
calculations are done to calculate the shift in frequency for various positions
of the magnet, and an experimentally measured 100 MHz upshift when
transitioning into a superconducting state confirms levitation with remanences
up to 140 times stronger than the critical field of the aluminum. We achieve
levitation heights of 1 - 1.8 mm. We investigate the dependence of levitation
height and levitation temperature on the strength of the magnet and,
surprisingly, we observe that the levitation temperature and height both
increase with permanent magnet strength. Our work describes a novel macroscopic
mechanical system capable of sensing and transducing forces, thus allowing for
the coupling of disparate classical and quantum systems.


Abstract:
  Performing inference in graphs is a common task within several machine
learning problems, e.g., image segmentation, community detection, among others.
For a given undirected connected graph, we tackle the statistical problem of
exactly recovering an unknown ground-truth binary labeling of the nodes from a
single corrupted observation of each edge. Such problem can be formulated as a
quadratic combinatorial optimization problem over the boolean hypercube, where
it has been shown before that one can (with high probability and in polynomial
time) exactly recover the ground-truth labeling of graphs that have an
isoperimetric number that grows with respect to the number of nodes (e.g.,
complete graphs, regular expanders). In this work, we apply a powerful
hierarchy of relaxations, known as the sum-of-squares (SoS) hierarchy, to the
combinatorial problem. Motivated by empirical evidence on the improvement in
exact recoverability, we center our attention on the degree-4 SoS relaxation
and set out to understand the origin of such improvement from a graph
theoretical perspective. We show that the solution of the dual of the relaxed
problem is related to finding edge weights of the Johnson and Kneser graphs,
where the weights fulfill the SoS constraints and intuitively allow the input
graph to increase its algebraic connectivity. Finally, as byproduct of our
analysis, we derive a novel Cheeger-type lower bound for the algebraic
connectivity of graphs with signed edge weights.


Abstract:
  We develop a stochastic mean-field theory to describe active frequency
measurements of pulsed superradiant emission, studied in recent experiments
with strontium-87 atoms trapped in an optical lattice inside an optical cavity
[M. Norcia, et al., Phys. Rev. X 8, 21036 (2018)]. Our theory reveals the
intriguing dynamics of atomic ensembles with multiple transition frequencies,
and it reproduces the superradiant beats signal, noisy power spectra, and
frequency uncertainty in remarkable agreement with the experiments. Moreover,
by reducing the number of atoms, elongating the superradiant pulses and
shortening the experimental duty cycle, we predict a short-term frequency
uncertainty $9\times10^{-16} \sqrt{\tau/s}$, which makes active frequency
measurements with superradiant transitions comparable with the record
performance of current frequency standards [M. Schioppo, et al., Nat.
Photonics, 11, 48 (2017)]. Our theory combines cavity-quantum electrodynamics
and quantum measurement theory, and it can be readily applied to explore
conditional quantum dynamics and describe frequency measurements for other
processes such as steady-state superradiance and superradiant Raman lasing.


Abstract:
  In real-world scenarios, many factors may harm face recognition performance,
e.g., large pose, bad illumination,low resolution, blur and noise. To address
these challenges, previous efforts usually first restore the low-quality faces
to high-quality ones and then perform face recognition. However, most of these
methods are stage-wise, which is sub-optimal and deviates from the reality. In
this paper, we address all these challenges jointly for unconstrained face
recognition. We propose an Multi-Degradation Face Restoration (MDFR) model to
restore frontalized high-quality faces from the given low-quality ones under
arbitrary facial poses, with three distinct novelties. First, MDFR is a
well-designed encoder-decoder architecture which extracts feature
representation from an input face image with arbitrary low-quality factors and
restores it to a high-quality counterpart. Second, MDFR introduces a pose
residual learning strategy along with a 3D-based Pose Normalization Module
(PNM), which can perceive the pose gap between the input initial pose and its
real-frontal pose to guide the face frontalization. Finally, MDFR can generate
frontalized high-quality face images by a single unified network, showing a
strong capability of preserving face identity. Qualitative and quantitative
experiments on both controlled and in-the-wild benchmarks demonstrate the
superiority of MDFR over state-of-the-art methods on both face frontalization
and face restoration.


Abstract:
  At present, the Synthetic Aperture Radar (SAR) image classification method
based on convolution neural network (CNN) has faced some problems such as poor
noise resistance and generalization ability. Spiking neural network (SNN) is
one of the core components of brain-like intelligence and has good application
prospects. This article constructs a complete SAR image classifier based on
unsupervised and supervised learning of SNN by using spike sequences with
complex spatio-temporal information. We firstly expound the spiking neuron
model, the receptive field of SNN, and the construction of spike sequence. Then
we put forward an unsupervised learning algorithm based on STDP and a
supervised learning algorithm based on gradient descent. The average
classification accuracy of single layer and bilayer unsupervised learning SNN
in three categories images on MSTAR dataset is 80.8\% and 85.1\%, respectively.
Furthermore, the convergent output spike sequences of unsupervised learning can
be used as teaching signals. Based on the TensorFlow framework, a single layer
supervised learning SNN is built from the bottom, and the classification
accuracy reaches 90.05\%. By comparing noise resistance and model parameters
between SNNs and CNNs, the effectiveness and outstanding advantages of SNN are
verified. Code to reproduce our experiments is available at
\url{https://github.com/Jiankun-chen/Supervised-SNN-with-GD}.


Abstract:
  We extend the Levi-Civita (L-C) and Kustaanheimo-Stiefel (K-S) regularization
methods that maps the classical system where a particle moves under the
combined influence of $\frac{1}{r}$ and $r^2$ potentials to a harmonic
oscillator with inverted sextic potential and interactions to corresponding
quantum mechanical counterparts, both in 2 and 3 dimensions. Using the
perturbative solutions of the Schr\"odinger equation of the later systems, we
derive the eigen spectrum of the Hydrogen atom in presence of an additional
harmonic potential. We have also obtained the mapping of a particle moving in
the shifted harmonic potential to H-atom using Bohlin-Sundman transformation,
for quantum regime. Exploiting this equivalence, the solution to the
Schr\"odinger equation of the former is obtained from the solutions of the
later.


Abstract:
  We study compatibility of the Standard Model of particle physics and General
Relativity by means of gravitational positivity bounds, which provide a
necessary condition for a low-energy gravitational theory to be UV completable
within the weakly coupled regime of gravity. In particular, we identify the
cutoff scale of the Standard Model coupled to gravity by studying consistency
of light-by-light scattering. While the precise value depends on details of the
Pomeron effects in QCD, the cutoff scale reads $10^{16}$GeV if the
single-Pomeron exchange picture works well up to this scale. We also
demonstrate that the cutoff scale is lowered to $10^{13}$GeV if we consider the
electroweak theory without the QCD sector.


Abstract:
  The relations between the specific angular momenta ($j$) and masses ($M$) of
galaxies are often used as a benchmark in analytic models and hydrodynamical
simulations as they are considered to be amongst the most fundamental scaling
relations. Using accurate measurements of the stellar ($j_\ast$), gas ($j_{\rm
gas}$), and baryonic ($j_{\rm bar}$) specific angular momenta for a large
sample of disc galaxies, we report the discovery of tight correlations between
$j$, $M$, and the cold gas fraction of the interstellar medium ($f_{\rm gas}$).
At fixed $f_{\rm gas}$, galaxies follow parallel power laws in 2D $(j,M)$
spaces, with gas-rich galaxies having a larger $j_\ast$ and $j_{\rm bar}$ (but
a lower $j_{\rm gas}$) than gas-poor ones. The slopes of the relations have a
value around 0.7. These new relations are amongst the tightest known scaling
laws for galaxies. In particular, the baryonic relation ($j_{\rm bar}-M_{\rm
bar}-f_{\rm gas}$), arguably the most fundamental of the three, is followed not
only by typical discs but also by galaxies with extreme properties, such as
size and gas content, and by galaxies previously claimed to be outliers of the
standard 2D $j-M$ relations. The stellar relation ($j_{\ast}-M_{\ast}-f_{\rm
gas}$) may be connected to the known $j_\ast-M_\ast-$bulge fraction relation;
however, we argue that the $j_{\rm bar}-M_{\rm bar}-f_{\rm gas}$ relation can
originate from the radial variation in the star formation efficiency in
galaxies, although it is not explained by current disc instability models.


Abstract:
  Photoacoustic (PA) computed tomography (PACT) shows great potentials in
various preclinical and clinical applications. A great number of measurements
are the premise that obtains a high-quality image, which implies a low imaging
rate or a high system cost. The artifacts or sidelobes could pollute the image
if we decrease the number of measured channels or limit the detected view. In
this paper, a novel compressed sensing method for PACT using an untrained
neural network is proposed, which decreases half number of the measured
channels and recoveries enough details. This method uses a neural network to
reconstruct without the requirement for any additional learning based on the
deep image prior. The model can reconstruct the image only using a few
detections with gradient descent. Our method can cooperate with other existing
regularization, and further improve the quality. In addition, we introduce a
shape prior to easily converge the model to the image. We verify the
feasibility of untrained network based compressed sensing in PA image
reconstruction, and compare this method with a conventional method using total
variation minimization. The experimental results show that our proposed method
outperforms 32.72% (SSIM) with the traditional compressed sensing method in the
same regularization. It could dramatically reduce the requirement for the
number of transducers, by sparsely sampling the raw PA data, and improve the
quality of PA image significantly.


Abstract:
  Sarcasm detection is the task of identifying irony containing utterances in
sentiment-bearing text. However, the figurative and creative nature of sarcasm
poses a great challenge for affective computing systems performing sentiment
analysis. This article compiles and reviews the salient work in the literature
of automatic sarcasm detection. Thus far, three main paradigm shifts have
occurred in the way researchers have approached this task: 1) semi-supervised
pattern extraction to identify implicit sentiment, 2) use of hashtag-based
supervision, and 3) incorporation of context beyond target text. In this
article, we provide a comprehensive review of the datasets, approaches, trends,
and issues in sarcasm and irony detection.


Abstract:
  In this talk, we present a way to improve the accuracy of theoretical
predictions for Higgs boson production cross sections at the LHC using the
measurements of lepton angular distributions. In this regards, we exploit the
sensitivity of the lepton angular coefficient associated with the longitudinal
Z-boson polarization to the parton density function (PDF) for gluons resolved
from the incoming protons, in order to constrain the Higgs boson cross section
from gluon fusion processes. We find that high-statistics determinations of the
longitudinally polarized angular coefficient at the LHC Run 3 and
high-luminosity HL-LHC improve the PDF systematics of the Higgs boson cross
section predictions by 50% over a broad range of Higgs boson rapidities. This
study has been conducted using the open-source fitting framework xFitter. This
talk refers to the following paper: arXiv:2012.10298[hep-ph]


Abstract:
  Moving mirrors have been known as tractable setups modeling Hawking radiation
from black holes. In this paper, motivated by recent developments regarding the
black hole information problem, we present extensive studies of moving mirrors
in conformal field theories by employing both field theoretic as well as
holographic methods. Reviewing first the usual field theoretic formulation of
moving mirrors, we construct their gravity dual by resorting to the AdS/BCFT
construction. Based on our holographic formulation, we then calculate the time
evolution of entanglement entropy in various moving mirror models. In doing so,
we mainly focus on three different setups: escaping mirror, which models
constant Hawking radiation emanating from an eternal black hole; kink mirror,
which models an evaporating black hole formed from collapse; and the double
escaping mirror, which models two constantly radiating eternal black holes. In
particular, by computing the holographic entanglement entropy, we show that the
kink mirror gives rise to an ideal Page curve. We also find that an interesting
phase transition arises in the case of the double escaping mirror. Furthermore,
we argue and provide evidence for an interpretation of moving mirrors in terms
of two dimensional Liouville gravity. We also discuss the connection between
quantum energy conditions and the time evolution of holographic entanglement
entropy in moving mirror models.


Abstract:
  Ensuring group fairness among groups of individuals in our society is
desirable and crucial for many application domains. A social planner's typical
medium of achieving group fair outcomes is through solving an optimization
problem under a given objective for a particular domain. When the input is
provided by strategic agents, the planner is facing a difficult situation of
achieving fair outcomes while ensuring agent truthfulness without using
incentive payment. To address this challenge, we consider the approximate
mechanism design without money paradigm with group-fair objectives. We first
consider the group-fair facility location problems where agents are divided
into groups. The agents are located on a real line, modeling agents' private
ideal preferences/points for the facility's location. Our aim is to locate a
facility to approximately minimize the costs of groups of agents to the
facility fairly while eliciting the agents' private locations truthfully. We
consider various group-fair objectives and show that many objectives have an
unbounded approximation ratio. We then consider the objectives of minimizing
the maximum total group cost and the average group cost. For the first
objective, we show that the approximation ratio of the median mechanism depends
on the number of groups and provide a new group-based mechanism with an
approximation ratio of 3. For the second objective, the median mechanism
obtains a ratio of 3, and we propose a randomized mechanism that obtains a
better approximation ratio. We also provide lower bounds for both objectives.
We then study the notion of intergroup and intragroup fairness that measures
fairness between groups and within each group. We consider various objectives
and provide mechanisms with tight approximation ratios.


Abstract:
  Robot navigation in crowded public spaces is a complex task that requires
addressing a variety of engineering and human factors challenges. These
challenges have motivated a great amount of research resulting in important
developments for the fields of robotics and human-robot interaction over the
past three decades. Despite the significant progress and the massive recent
interest, we observe a number of significant remaining challenges that prohibit
the seamless deployment of autonomous robots in public pedestrian environments.
In this survey article, we organize existing challenges into a set of
categories related to broader open problems in motion planning, behavior
design, and evaluation methodologies. Within these categories, we review past
work, and offer directions for future research. Our work builds upon and
extends earlier survey efforts by a) taking a critical perspective and
diagnosing fundamental limitations of adopted practices in the field and b)
offering constructive feedback and ideas that we aspire will drive research in
the field over the coming decade.