Abstract:
  We consider an inverse source problem in the stationary radiating transport
through a two dimensional absorbing and scattering medium. Of specific
interest, the exiting radiation is measured on an arc. The attenuation and
scattering properties of the medium are assumed known. For scattering kernels
of finite Fourier content in the angular variable, we show how to
quantitatively recover the part of the isotropic sources restricted to the
convex hull of the measurement arc. The approach is based on the Cauchy problem
with partial data for a Beltrami-like equation associated with $A$-analytic
maps in the sense of Bukhgeim, and extends authors' previous work to this
specific partial data case. The robustness of the method is demonstrated by the
results of several numerical experiments.


Abstract:
  Atomtronics is a relatively new subfield of atomic physics that aims to
realize the device behavior of electronic components in ultracold atom-optical
systems. The fact that these systems are coherent makes them particularly
interesting since, in addition to current, one can impart quantum states onto
the current carriers themselves or perhaps perform quantum computational
operations on them. After reviewing the fundamental ideas of this subfield, we
report on the theoretical and experimental progress made towards developing
externally-driven and closed loop devices. The functionality and potential
applications for these atom analogs to electronic and spintronic systems is
also discussed.


Abstract:
  Positron Emission Tomography (PET) is an important tool for studying
Alzheimer's disease (AD). PET scans can be used as diagnostics tools, and to
provide molecular characterization of patients with cognitive disorders.
However, multiple tracers are needed to measure glucose metabolism (18F-FDG),
synaptic vesicle protein (11C-UCB-J), and $\beta$-amyloid (11C-PiB).
Administering multiple tracers to patient will lead to high radiation dose and
cost. In addition, access to PET scans using new or less-available tracers with
sophisticated production methods and short half-life isotopes may be very
limited. Thus, it is desirable to develop an efficient multi-tracer PET
synthesis model that can generate multi-tracer PET from single-tracer PET.
Previous works on medical image synthesis focus on one-to-one fixed domain
translations, and cannot simultaneously learn the feature from multi-tracer
domains. Given 3 or more tracers, relying on previous methods will also create
a heavy burden on the number of models to be trained. To tackle these issues,
we propose a 3D unified anatomy-aware cyclic adversarial network (UCAN) for
translating multi-tracer PET volumes with one unified generative model, where
MR with anatomical information is incorporated. Evaluations on a multi-tracer
PET dataset demonstrate the feasibility that our UCAN can generate high-quality
multi-tracer PET volumes, with NMSE less than 15% for all PET tracers.


Abstract:
  A swarm of cooperating UAVs communicating with a distant multiantenna ground
station can leverage MIMO spatial multiplexing to scale the capacity. Due to
the line-of-sight propagation between the swarm and the ground station, the
MIMO channel is highly correlated, leading to limited multiplexing gains. In
this paper, we optimize the UAV positions to attain the maximum MIMO capacity
given by the single user bound. An infinite set of UAV placements that attains
the capacity bound is first derived. Given an initial swarm placement, we
formulate the problem of minimizing the distance traveled by the UAVs to reach
a placement within the capacity maximizing set of positions. An offline
centralized solution to the problem using block coordinate descent is developed
assuming known initial positions of UAVs. We also propose an online distributed
algorithm, where the UAVs iteratively adjust their positions to maximize the
capacity. Our proposed approaches are shown to significantly increase the
capacity at the expense of a bounded translation from the initial UAV
placements. This capacity increase persists when using a massive MIMO ground
station. Using numerical simulations, we show the robustness of our approaches
in a Rician channel under UAV motion disturbances.


Abstract:
  Recent works have proposed new Byzantine consensus algorithms for blockchains
based on epidemics, a design which enables highly scalable performance at a low
cost. These methods however critically depend on a secure random peer sampling
service: a service that provides a stream of random network nodes where no
attacking entity can become over-represented. To ensure this security property,
current epidemic platforms use a Proof-of-Stake system to select peer samples.
However such a system limits the openness of the system as only nodes with
significant stake can participate in the consensus, leading to an oligopoly
situation. Moreover, this design introduces a complex interdependency between
the consensus algorithm and the cryptocurrency built upon it. In this paper, we
propose a radically different security design for the peer sampling service,
based on the distribution of IP addresses to prevent Sybil attacks. We propose
a new algorithm, $\scriptstyle{BASALT}$, that implements our design using a
stubborn chaotic search to counter attackers' attempts at becoming
over-represented. We show in theory and using Monte Carlo simulations that
$\scriptstyle{BASALT}$ provides samples which are extremely close to the
optimal distribution even in adversarial scenarios such as tentative Eclipse
attacks. Live experiments on a production cryptocurrency platform confirm that
the samples obtained using $\scriptstyle{BASALT}$ are equitably distributed
amongst nodes, allowing for a system which is both open and where no single
entity can gain excessive power.


Abstract:
  The phase transitions of random-field q-state Potts models in d=3 dimensions
are studied by renormalization-group theory by exact solution of a hierarchical
lattice and, equivalently, approximate Migdal-Kadanoff solutions of a cubic
lattice. The recursion, under rescaling, of coupled random-field and
random-bond (induced under rescaling by random fields) coupled probability
distributions is followed to obtain phase diagrams. Unlike the Ising model
(q=2), several types of random fields can be defined for q >= 3 Potts models,
including random-axis favored, random-axis disfavored, random-axis randomly
favored or disfavored cases, all of which are studied. Quantitatively very
similar phase diagrams are obtained, for a given q for the three types of field
randomness, with the low-temperature ordered phase persisting, increasingly as
temperature is lowered, up to random-field threshold in d=3, which is
calculated for all temperatures below the zero-field critical temperature.
Phase diagrams thus obtained are compared as a function of $q$. The ordered
phase in the low-q models reaches higher temperatures, while in the high-q
models it reaches higher random fields. This renormalization-group calculation
result is physically explained.


Abstract:
  We study monopole operators with the lowest possible topological charge
$q=1/2$ at the infrared fixed point of scalar electrodynamics in $2+1$
dimension (scalar QED$_3$) with $N$ complex scalars and Chern-Simons coupling
$|k|=N$. In the large $N$ expansion, monopole operators in this theory with
spins $\ell<O(\sqrt{N})$ and associated flavor representations are expected to
have the same scaling dimension to sub-leading order in $1/N$. We use the
state-operator correspondence to calculate the scaling dimension to sub-leading
order with the result $N-0.2743+O(1/N)$, which improves on existing leading
order results. We also compute the $\ell^2/N$ term that breaks the degeneracy
to sub-leading order for monopoles with spins $\ell=O(\sqrt{N})$.


Abstract:
  Removing the field redefinitions, the Bianchi identities and the total
derivative freedoms from the general form of the gauge invariant couplings at
order $\ell_p^6$ for the bosonic fields of M-theory, we have found that the
minimum number of independent couplings in the structures with even number of
the three-form, is 1062. We find that, except two couplings which have the
Ricci scalar, there are schemes in which there is no coupling involving
$R,\,R_{\mu\nu},\,\nabla_\mu F^{\mu\alpha\beta\gamma}$. In these schemes, there
are sub-schemes in which, except one coupling which has the second derivative
of $F^{(4)}$, the couplings can have no term with more than two derivatives. We
find some of the parameters by dimensionally reducing the couplings on a circle
and comparing them with the known couplings of the one-loop effective action of
type IIA superstring theory. In particular, we find the coupling which has term
with more than two derivatives is zero.


Abstract:
  We present a computation for inclusive charged-current deeply-inelastic
scattering at NNLO (N$^2$LO) in QCD. Mass-dependent quark contributions are
consistently included across a wide range of momentum transfers in the
SACOT-$\chi$ general-mass scheme. When appropriate, we further include N$^3$LO
corrections in the zero-mass scheme. We show theoretical predictions for
several experiments with neutrinos over a wide range of energies and at the
upcoming Electron-Ion Collider. Our prediction reduces perturbative
uncertainties to $\sim$1%, sufficient for the high-precision objectives of
future charged-current DIS measurements.


Abstract:
  End-to-end speech recognition generally uses hand-engineered acoustic
features as input and excludes the feature extraction module from its joint
optimization. To extract learnable and adaptive features and mitigate
information loss, we propose a new encoder that adopts globally attentive
locally recurrent (GALR) networks and directly takes raw waveform as input. We
observe improved ASR performance and robustness by applying GALR on different
window lengths to aggregate fine-grain temporal information into multi-scale
acoustic features. Experiments are conducted on a benchmark dataset AISHELL-2
and two large-scale Mandarin speech corpus of 5,000 hours and 21,000 hours.
With faster speed and comparable model size, our proposed multi-scale GALR
waveform encoder achieved consistent character error rate reductions (CERRs)
from 7.9% to 28.1% relative over strong baselines, including Conformer and
TDNN-Conformer. In particular, our approach demonstrated notable robustness
than the traditional handcrafted features and outperformed the baseline
MFCC-based TDNN-Conformer model by a 15.2% CERR on a music-mixed real-world
speech test set.


Abstract:
  Due to the wide application of average consensus algorithm, its security and
privacy problems have attracted great attention. In this paper, we consider the
system threatened by a set of unknown agents that are both "malicious" and
"curious", who add additional input signals to the system in order to perturb
the final consensus value or prevent consensus, and try to infer the initial
state of other agents. At the same time, we design a privacy-preserving average
consensus algorithm equipped with an attack detector with a time-varying
exponentially decreasing threshold for every benign agent, which can guarantee
the initial state privacy of every benign agent, under mild conditions. The
attack detector will trigger an alarm if it detects the presence of malicious
attackers. An upper bound of false alarm rate in the absence of malicious
attackers and the necessary and sufficient condition for there is no
undetectable input by the attack detector in the system are given.
Specifically, we show that under this condition, the system can achieve
asymptotic consensus almost surely when no alarm is triggered from beginning to
end, and an upper bound of convergence rate and some quantitative estimates
about the error of final consensus value are given. Finally, numerical case is
used to illustrate the effectiveness of some theoretical results.


Abstract:
  Battery energy storage boosts up the response speed of power system frequency
regulation, but must be recharged carefully to minimize the distortion to the
frequency regulation response. This paper proposes a nonlinear feedback
controller to optimize the recharge for storage resources in frequency
regulation. This controller is designed using a data-driven best-hindsight
optimization framework, the resulting nonlinear recharge controller's gain
depends on the storage state of charge as well as its power and energy rating.
The developed controller is compared with two benchmark automatic generation
control designs, one is a proportional-integral-based control from PJM
Interconnection, the other one is based on linear-quadratic regulator.
Simulation results using real area control error data from PJM Interconnection
show the proposed controller achieves smaller deviations in both the area
control error and the storage state of charge compared to the two benchmark
controllers under various storage configurations.


Abstract:
  Monocular (relative or metric) depth estimation is a critical task for
various applications, such as autonomous vehicles, augmented reality and image
editing. In recent years, with the increasing availability of mobile devices,
accurate and mobile-friendly depth models have gained importance. Increasingly
accurate models typically require more computational resources, which inhibits
the use of such models on mobile devices. The mobile use case is arguably the
most unrestricted one, which requires highly accurate yet mobile-friendly
architectures. Therefore, we try to answer the following question: How can we
improve a model without adding further complexity (i.e. parameters)? Towards
this end, we systematically explore the design space of a relative depth
estimation model from various dimensions and we show, with key design choices
and ablation studies, even an existing architecture can reach highly
competitive performance to the state of the art, with a fraction of the
complexity. Our study spans an in-depth backbone model selection process,
knowledge distillation, intermediate predictions, model pruning and loss
rebalancing. We show that our model, using only DIW as the supervisory dataset,
achieves 0.1156 WHDR on DIW with 2.6M parameters and reaches 37 FPS on a mobile
GPU, without pruning or hardware-specific optimization. A pruned version of our
model achieves 0.1208 WHDR on DIW with 1M parameters and reaches 44 FPS on a
mobile GPU.


Abstract:
  In cavity-based axion dark matter detectors, quantum noise remains a primary
barrier to achieving the scan rate necessary for a comprehensive search of
axion parameter space. Here we introduce a method of scan rate enhancement in
which an axion-sensitive cavity is coupled to an auxiliary resonant circuit
through simultaneous two-mode squeezing (entangling) and state swapping
interactions. We show analytically that when combined, these interactions can
amplify an axion signal before it becomes polluted by vacuum noise introduced
by measurement. This internal amplification yields a wider bandwidth of axion
sensitivity, increasing the rate at which the detector can search through
frequency space. With interaction rates predicted by circuit simulations of
this system, we show that this technique can increase the scan rate up to
15-fold relative to the scan rate of a detector limited by vacuum noise.


Abstract:
  This paper presents a new large-scale Japanese speech corpus for training
automatic speech recognition (ASR) systems. This corpus contains over 2,000
hours of speech with transcripts built on Japanese TV recordings and their
subtitles. We develop herein an iterative workflow to extract matching audio
and subtitle segments from TV recordings based on a conventional method for
lightly-supervised audio-to-text alignment. We evaluate a model trained with
our corpus using an evaluation dataset built on Japanese TEDx presentation
videos and confirm that the performance is better than that trained with the
Corpus of Spontaneous Japanese (CSJ). The experiment results show the
usefulness of our corpus for training ASR systems. This corpus is made public
for the research community along with Kaldi scripts for training the models
reported in this paper.


Abstract:
  Guaranteeing the security of transactional systems is a crucial priority of
all institutions that process transactions, in order to protect their
businesses against cyberattacks and fraudulent attempts. Adversarial attacks
are novel techniques that, other than being proven to be effective to fool
image classification models, can also be applied to tabular data. Adversarial
attacks aim at producing adversarial examples, in other words, slightly
modified inputs that induce the Artificial Intelligence (AI) system to return
incorrect outputs that are advantageous for the attacker. In this paper we
illustrate a novel approach to modify and adapt state-of-the-art algorithms to
imbalanced tabular data, in the context of fraud detection. Experimental
results show that the proposed modifications lead to a perfect attack success
rate, obtaining adversarial examples that are also less perceptible when
analyzed by humans. Moreover, when applied to a real-world production system,
the proposed techniques shows the possibility of posing a serious threat to the
robustness of advanced AI-based fraud detection procedures.


Abstract:
  In this paper, we introduce a Grasp Manifold Estimator (GraspME) to detect
grasp affordances for objects directly in 2D camera images. To perform
manipulation tasks autonomously it is crucial for robots to have such
graspability models of the surrounding objects. Grasp manifolds have the
advantage of providing continuously infinitely many grasps, which is not the
case when using other grasp representations such as predefined grasp points.
For instance, this property can be leveraged in motion optimization to define
goal sets as implicit surface constraints in the robot configuration space. In
this work, we restrict ourselves to the case of estimating possible
end-effector positions directly from 2D camera images. To this extend, we
define grasp manifolds via a set of key points and locate them in images using
a Mask R-CNN backbone. Using learned features allows generalizing to different
view angles, with potentially noisy images, and objects that were not part of
the training set. We rely on simulation data only and perform experiments on
simple and complex objects, including unseen ones. Our framework achieves an
inference speed of 11.5 fps on a GPU, an average precision for keypoint
estimation of 94.5% and a mean pixel distance of only 1.29. This shows that we
can estimate the objects very well via bounding boxes and segmentation masks as
well as approximate the correct grasp manifold's keypoint coordinates.


Abstract:
  In LHC Run 3, the upgraded ALICE detector will record Pb-Pb collisions at a
rate of 50 kHz usingcontinuous readout. The resulting stream of raw data at 3.5
TB/s has to be processed with a setof lossy and lossless compression and data
reduction techniques to a storage data rate of 90 GB/swhile preserving relevant
data for physics analysis. This contribution presents a custom losslessdata
compression scheme based on entropy coding as the final component in the data
reductionchain which has to compress the data rate from 300 GB/s to 90 GB/s. A
flexible, multi-processarchitecture for the data compression scheme is proposed
that seamlessly interfaces with the datareduction algorithms of earlier stages
and allows to use parallel processing in order to keep therequired firm
real-time guarantees of the system. The data processed inside the
compressionprocess have a structure that allows the use of an rANS entropy
coder with more resource efficientstatic distribution tables. Extensions to the
rANS entropy coder are introduced to efficientlywork with these static
distribution tables and large but sparse source alphabets consisting of upto 25
Bit per symbol. Preliminary performance results show compliance with the firm
real-timerequirements while offering close-to-optimal data compression.


Abstract:
  Matching and weighting methods for observational studies require the choice
of an estimand, the causal effect with reference to a specific target
population. Commonly used estimands include the average treatment effect in the
treated (ATT), the average treatment effect in the untreated (ATU), the average
treatment effect in the population (ATE), and the average treatment effect in
the overlap (i.e., equipoise population; ATO). Each estimand has its own
assumptions, interpretation, and statistical methods that can be used to
estimate it. This article provides guidance on selecting and interpreting an
estimand to help medical researchers correctly implement statistical methods
used to estimate causal effects in observational studies and to help audiences
correctly interpret the results and limitations of these studies. The
interpretations of the estimands resulting from regression and instrumental
variable analyses are also discussed. Choosing an estimand carefully is
essential for making valid inferences from the analysis of observational data
and ensuring results are replicable and useful for practitioners.


Abstract:
  We present a unified description of spherical discontinuity surfaces in
General Relativity based on two parameters: mass function and surface
permeability. The surfaces considered are: \textit{Impulsive fronts}, massive
permeable layer; \textit{Surface layers}, massive impermeable layer;
\textit{Shock fronts}, massless permeable surface; and \textit{Boundary
surfaces}, massless impermeable surface. We derive the exact jump conditions
for the physical variables across all these surfaces. Finally, we discuss the
quasi-static approximation from studying slow hydrodynamic processes involving
discontinuity surfaces.


Abstract:
  We study electron transport through a multichannel fractional quantum Hall
edge in the presence of both interchannel interaction and random tunneling
between channels, with emphasis on the role of contacts. The prime example in
our discussion is the edge at filling factor 2/3 with two counterpropagating
channels. Having established a general framework to describe contacts to a
multichannel edge as thermal reservoirs, we particularly focus on the
line-junction model for the contacts and investigate incoherent charge
transport for an arbitrary strength of interchannel interaction beneath the
contacts and, possibly different, outside them. We show that the conductance
does not explicitly depend on the interaction strength either in or outside the
contact regions (implicitly, it only depends through renormalization of the
tunneling rates). Rather, a long line-junction contact is characterized by a
single parameter which defines the modes that are at thermal equilibrium with
the contact and is determined by the interplay of various types of scattering
beneath the contact. This parameter -- playing the role of an effective
interaction strength within an idealized model of thermal reservoirs -- is
generically nonzero and affects the conductance. We formulate a framework of
fractionalization-renormalized tunneling to describe the effect of disorder on
transport in the presence of interchannel interaction. Within this framework,
we give a detailed discussion of charge equilibration for arbitrarily strong
interaction in the bulk of the edge and arbitrary effective interaction
characterizing the line-junction contacts.


Abstract:
  We consider random-access networks with nodes representing
transmitter-receiver pairs whose signals interfere with each other depending on
their vicinity. Data packets arrive at the nodes over time and form queues. The
nodes can be either active or inactive: a node deactivates at unit rate, while
it activates at a rate that depends on its queue length, provided none of its
neighbors is active. In order to model the effects of user mobility in wireless
networks, we analyze dynamic interference graphs where the edges are allowed to
appear and disappear over time. We focus on bipartite graphs, and study the
transition time between the two states where one part of the network is active
and the other part is inactive, in the limit as the queue lengths become large.
Depending on the speed of the dynamics, we are able to obtain a rough
classification of the effects of the dynamics on the transition time.


Abstract:
  Face recognition has achieved significant progress in deep-learning era due
to the ultra-large-scale and well-labeled datasets.
  However, training on ultra-large-scale datasets is time-consuming and takes
up a lot of hardware resource.
  Therefore, designing an efficient training approach is crucial and
indispensable.
  The heavy computational and memory costs mainly result from the high
dimensionality of the Fully-Connected (FC) layer.
  Specifically, the dimensionality is determined by the number of face
identities, which can be million-level or even more.
  To this end, we propose a novel training approach for ultra-large-scale face
datasets, termed Faster Face Classification (F$^2$C).
  In F$^2$C, we first define a Gallery Net and a Probe Net that are used to
generate identities' centers and extract faces' features for face recognition,
respectively.
  Gallery Net has the same structure as Probe Net and inherits the parameters
from Probe Net with a moving average paradigm.
  After that, to reduce the training time and hardware costs of the FC layer,
we propose a Dynamic Class Pool (DCP) that stores the features from Gallery Net
and calculates the inner product (logits) with positive samples (whose
identities are in the DCP) in each mini-batch.
  DCP can be regarded as a substitute for the FC layer but it is far smaller,
thus greatly reducing the computational and memory costs.
  For negative samples (whose identities are not in DCP), we minimize the
cosine similarities between negative samples and those in DCP.
  Then, to improve the update efficiency of DCP's parameters, we design a dual
data-loader including identity-based and instance-based loaders to generate a
certain of identities and samples in mini-batches.


Abstract:
  In the Wick-Cutkosky model, where two scalar massive constituents interact by
means of the exchange of a scalar massless particle, the Bethe-Salpeter
equation has solutions of two types, called "normal" and "abnormal". In the
non-relativistic limit, the normal solutions correspond to the usual Coulomb
spectrum, whereas the abnormal ones do not have non-relativistic counterparts
-- they are absent in the Schr\"odinger equation framework. We have studied, in
the formalism of the light-front dynamics, the Fock-space content of the
abnormal solutions. It turns out that, in contrast to the normal ones, the
abnormal states are dominated by the massless exchange particles (by 90 \% or
more), what provides a natural explanation of their decoupling from the
two-body Schr\"odinger equation. Assuming that one of the massive constituents
is charged, we have calculated the electromagnetic elastic form factors of the
normal and abnormal states, as well as the transition form factors. The results
on form factors confirm the many-body nature of the abnormal states, as found
from the Fock-space analysis. The abnormal solutions have thus properties
similar to those of hybrid states, made here essentially of two massive
constituents and several or many massless exchange particles. They could also
be interpreted as the Abelian scalar analogs of the QCD hybrid states. The
question of the validity of the ladder approximation of the model is also
examined.


Abstract:
  Strongly correlated electrons in layered perovskite structures have been the
birthplace of high-temperature superconductivity, spin liquid, and quantum
criticality. Specifically, the cuprate materials with layered structures made
of corner sharing square planar CuO$_4$ units have been intensely studied due
to their Mott insulating grounds state which leads to high-temperature
superconductivity upon doping. Identifying new compounds with similar lattice
and electronic structures has become a challenge in solid state chemistry.
Here, we report the hydrothermal crystal growth of a new copper tellurite
sulfate Cu$_3$(TeO$_4$)(SO$_4$)$\cdot$H$_2$O, a promising alternative to
layered perovskites. The orthorhombic phase (space group $Pnma$) is made of
corrugated layers of corner-sharing CuO$_4$ square-planar units that are
edge-shared with TeO$_4$ units. The layers are linked by slabs of
corner-sharing CuO$_4$ and SO$_4$. Using both the bond valence sum analysis and
magnetization data, we find purely Cu$^{2+}$ ions within the layers, but a
mixed valence of Cu$^{2+}$/Cu${^+}$ between the layers.
Cu$_3$(TeO$_4$)(SO$_4$)$\cdot$H$_2$O undergoes an antiferromagnetic transition
at $T_N$=67 K marked by a peak in the magnetic susceptibility. Upon further
cooling, a spin-canting transition occurs at $T^{\star}$=12 K evidenced by a
kink in the heat capacity. The spin-canting transition is explained based on a
$J_1$-$J_2$ model of magnetic interactions, which is consistent with the
slightly different in-plane super-exchange paths. We present
Cu$_3$(TeO$_4$)(SO$_4$)$\cdot$H$_2$O as a promising platform for the future
doping and strain experiments that could tune the Mott insulating ground state
into superconducting or spin liquid states.


Abstract:
  We study the quasi-static limit for the $L^\infty$ entropy weak solution of
scalar one-dimensional hyperbolic equations with strictly concave or convex
flux and time dependent boundary conditions. The quasi-stationary profile
evolves with the quasi-static equation, whose entropy solution is determined by
the stationary profile corresponding to the boundary data at a given time.


Abstract:
  Let $\mathcal{H}$ be a Coxeter hyperplane arrangement on $\mathbb{R}^n$ with
Coxeter group $W$. We assume that the map $-\mathrm{id}$ is in the group $W$
and that $\mathcal{H}$ is not of type $A_1^n$. Let $K$ be a measurable subset
of $\mathbb{R}^n$ with finite volume such that $K$ is stable by $W$ and let
$a\in\mathbb{R}^n$ such that $K$ contains the convex hull of $\{w(a) : w\in
W\}$. The authors proved in a previous article that the alternating sum over
the chambers $T$ of $\mathcal{H}$ of the volumes of the sets $T\cap(K+a)$ is
zero. In this paper we give a dissection proof of this result. In fact, we lift
the identity to an abstract dissection group to obtain a similar identity with
the volume replaced by any valuation that is invariant under affine isometries.
This includes the cases of all intrinsic volumes. Apart from some basic
geometry, the main ingredient is a previous theorem of the authors (the proof
of which uses only properties of Coxeter systems and closed convex polyhedral
cones) where we relate the alternating sum of the values of certain valuations
over the chambers of a Coxeter arrangement to similar alternating sums for
simpler subarrangements called $2$-structures.


Abstract:
  The topic is the average order of a connected induced subgraph of a graph.
This generalizes, to graphs in general, the average order of a subtree of a
tree. In 1984, Jamison proved that the average order, over all trees of order
$n$, is minimized by the path $P_n$. In 2018, Kroeker, Mol, and Oellermann
conjectured that $P_n$ minimizes the average order over all connected graphs.
The main result of this paper confirms this conjecture.


Abstract:
  Variational representations of $f$-divergences are central to many machine
learning algorithms, with Lipschitz constrained variants recently gaining
attention. Inspired by this, we define the Moreau-Yosida approximation of
$f$-divergences with respect to the Wasserstein-$1$ metric. The corresponding
variational formulas provide a generalization of a number of recent results,
novel special cases of interest and a relaxation of the hard Lipschitz
constraint. Additionally, we prove that the so-called tight variational
representation of $f$-divergences can be to be taken over the quotient space of
Lipschitz functions, and give a characterization of functions achieving the
supremum in the variational representation. On the practical side, we propose
an algorithm to calculate the tight convex conjugate of $f$-divergences
compatible with automatic differentiation frameworks. As an application of our
results, we propose the Moreau-Yosida $f$-GAN, providing an implementation of
the variational formulas for the Kullback-Leibler, reverse Kullback-Leibler,
$\chi^2$, reverse $\chi^2$, squared Hellinger, Jensen-Shannon, Jeffreys,
triangular discrimination and total variation divergences as GANs trained on
CIFAR-10, leading to competitive results and a simple solution to the problem
of uniqueness of the optimal critic.


Abstract:
  We prove that small deformations of a projective variety of general type are
also projective varieties of general type, with the same plurigenera. Version
2: small changes in first half. Improved version of the second half is now a
separate preprint (Seshadri's criterion and openness of projectivity).


Abstract:
  We consider the two-dimensional MHD Boundary layer system without
hydrodynamic viscosity, and establish the existence and uniqueness of solutions
in Sobolev spaces under the assumption that the tangential component of
magnetic fields dominates. This gives a complement to the previous works of
Liu-Xie-Yang [Comm. Pure Appl. Math. 72 (2019)] and Liu-Wang-Xie-Yang [J.
Funct. Anal. 279 (2020)], where the well-posedness theory was established for
the MHD boundary layer systems with both viscosity and resistivity and with
viscosity only, respectively. We use the pseudo-differential calculation, to
overcome a new difficulty arising from the treatment of boundary integrals due
to the absence of the diffusion property for the velocity.


Abstract:
  Weakly Interacting Massive Particles (WIMPs) are considered to be one of the
favoured dark matter candidates. Searching for any detectable signal due to the
annihilation and decay of WIMPs over the entire electromagnetic spectrum has
become a matter of interest for the last few decades. WIMP annihilation to
Standard Model particles gives rise to a possibility of detection of this
signal at low radio frequencies via synchrotron radiation. Dwarf Spheroidal
Galaxies (dSphs) are expected to contain a huge amount of dark matter which
makes them promising targets to search for such large scale diffuse radio
emission. In this work, we present a stacking analysis of 23 dSph galaxies
observed at low frequency (147.5MHz) as part of the TIFR-GMRT Sky Survey
(TGSS). The non-detection of any signal from these stacking exercises put very
tight constraints on the dark matter parameters. The best limit comes from the
novel method of stacking after scaling the radio images of the individual dSph
galaxy fields after scaling them by the respective half-light radius. The
constraint on the thermally averaged cross-section is below the thermal relic
cross-section value over a range of WIMP mass for reasonable choices of
relevant astrophysical parameters. Such analysis using future deeper
observation of individual targets as well as stacking can potentially reveal
more about the WIMP dark matter properties.


Abstract:
  We prove the Lieb-Schultz-Mattis (LSM) theorem on the energy spectrum of a
general two or three-dimensional quantum many-body system with the U(1)
particle number conservation and translation symmetry. Especially, it is
demonstrated that the theorem holds in a system with long-range interactions.
To this end, we introduce approximate magnetic translation symmetry under the
total magnetic flux $\Phi=2\pi$ instead of the exact translation symmetry, and
explicitly construct low energy variational states. The energy spectrum at
$\Phi=2\pi$ is shown to agree with that at $\Phi=0$ in the thermodynamic limit,
which concludes the LSM theorem.


Abstract:
  Context. As the importance of Gravitational Wave (GW) Astrophysics increases
rapidly, astronomers in different fields and with different backgrounds can
have the need to get a quick idea of which GW source populations can be
detected by which detectors and with what measurement uncertainties.
  Aims. The GW-Toolbox is an easy-to-use, flexible tool to simulate
observations on the GW universe with different detectors, including
ground-based interferometers (advanced LIGO, advanced VIRGO, KAGRA, Einstein
Telescope, and also customised designs), space-borne interferometers (LISA and
a customised design), pulsar timing arrays mimicking the current working ones
(EPTA, PPTA, NANOGrav, IPTA) and future ones. We include a broad range of
sources such as mergers of stellar mass compact objects, namely black holes,
neutron stars and black hole-neutron stars; and supermassive black hole
binaries mergers and inspirals, Galactic double white dwarfs in ultra-compact
orbit, extreme mass ratio inspirals and Stochastic GW backgrounds.
  Methods. We collect methods to simulate source populations and determine
their detectability with the various detectors. The paper aims at giving a
comprehensive description on the algorithm and functionality of the GW-Toolbox.
  Results. The GW-Toolbox produces results that are consistent with more
detailed calculations of the different source classes and can be accessed with
a website interface (gw-universe.org) or as a python package
(https://bitbucket.org/radboudradiolab/gwtoolbox). In the future, it will be
upgraded with more functionality.


Abstract:
  With the recent advances in machine learning, problems that traditionally
would require accurate modeling to be solved analytically can now be
successfully approached with data-driven strategies. Among these, computing the
inverse kinematics of a redundant robot arm poses a significant challenge due
to the non-linear structure of the robot, the hard joint constraints and the
non-invertible kinematics map. Moreover, most learning algorithms consider a
completely data-driven approach, while often useful information on the
structure of the robot is available and should be positively exploited. In this
work, we present a simple, yet effective, approach for learning the inverse
kinematics. We introduce a structured prediction algorithm that combines a
data-driven strategy with the model provided by a forward kinematics function
-- even when this function is misspecified -- to accurately solve the problem.
The proposed approach ensures that predicted joint configurations are well
within the robot's constraints. We also provide statistical guarantees on the
generalization properties of our estimator as well as an empirical evaluation
of its performance on trajectory reconstruction tasks.


Abstract:
  We put forward a novel, twofold scheme that enables at the same time
all-optical enantioselection and sorting of single multipolar chiral
microspheres based on optical pulling forces exerted by two non-collinear,
nonstructured, circularly-polarized light sources. Our chiral resolution method
can be externally controlled by varying the angle between their incident
wavevectors, allowing for a fine-tuning of the range of chiral indices for
enantioselection. Enantioselectivity is achieved by choosing angles such that
only particles with the same handedness of the light sources are pulled. This
proposal allows one to achieve all-optical sorting of chiral microspheres with
arbitrarily small chiral parameters, thus outperforming current optical
methods.


Abstract:
  A widely held assumption on network dynamics is that similar components are
more likely to exhibit similar behavior than dissimilar ones and that generic
differences among them are necessarily detrimental to synchronization. Here, we
show that this assumption does not generally hold in oscillator networks when
communication delays are present. We demonstrate, in particular, that random
parameter heterogeneity among oscillators can consistently rescue the system
from losing synchrony. This finding is supported by electrochemical-oscillator
experiments performed on a multi-electrode array network. Remarkably, at
intermediate levels of heterogeneity, random mismatches are more effective in
promoting synchronization than parameter assignments specifically designed to
facilitate identical synchronization. Our results suggest that, rather than
being eliminated or ignored, intrinsic disorder in technological and biological
systems can be harnessed to help maintain coherence required for function.


Abstract:
  X-ray scattering experiments using Free Electron Lasers (XFELs) are a
powerful tool to determine the molecular structure and function of unknown
samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to
computing in two ways: i) due to the high cost of running XFELs, a fast
turnaround time from data acquisition to data analysis is essential to make
informed decisions on experimental protocols; ii) data collection rates are
growing exponentially, requiring new scalable algorithms. Here we report our
experiences analyzing data from two experiments at the Linac Coherent Light
Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori
XC40 system, using the Superfacility paradigm: our workflow automatically moves
raw data between LCLS and NERSC, where it is analyzed using the software
package CCTBX. We achieved real time data analysis with a turnaround time from
data acquisition to full molecular reconstruction in as little as 10 min --
sufficient time for the experiment's operators to make informed decisions. By
hosting the data analysis on Cori, and by automating LCLS-NERSC
interoperability, we achieved a data analysis rate which matches the data
acquisition rate. Completing data analysis with 10 mins is a first for XFEL
experiments and an important milestone if we are to keep up with data
collection trends.


Abstract:
  A fully implicit phase field model has been implemented in Fortran for 1-3D
simulations. It is intended for simulations of diffusion and diffusion
controlled transformations. Gibbs energy contributions from both gradients in
concentration and gradients in phase field variables are included. Orientation
dependent interfacial energy is also supported. The implementation supports the
use of Calphad type databases as well as analytical Gibbs energy expressions,
though in the latter case phases may not have internal degrees of freedom.


Abstract:
  Low frequency ionization oscillations involving plasma and neutral density
(breathing modes) are the most violent perturbations in Hall thrusters for
electric propulsion. Because of its simplicity, the zero-dimensional (0-D)
predator-prey model of two nonlinearly coupled ordinary differential equations
for plasma and neutral density has been often used for the characterization of
such oscillations and scaling estimates. We investigate the properties of its
continuum analog, the one-dimensional (1-D) system of two nonlinearly coupled
equations in partial derivatives (PDE) for plasma and neutral density. This is
a more general model, of which the standard 0-D predator-prey model is a
special limit case. We show that the 1-D model is stable and does not show any
oscillations for the boundary conditions relevant to Hall thruster and the
uniform ion velocity. We then propose a reduced 1-D model based on two coupled
PDE for plasma and neutral densities that is unstable and exhibit oscillations
if the ion velocity profile with the near the anode back-flow (toward the
anode) region is used. Comparisons of the reduced model with the predictions of
the full model that takes into account the self-consistent plasma response show
that the main properties of the breathing mode are well captured. In
particular, it is shown that the frequency of the breathing mode oscillations
is weakly dependent on the final ion velocity but shows a strong correlation
with the width of the ion back-flow region.


Abstract:
  This work deals with turbo coded single user massive multiple input multiple
output (SU-MMIMO) systems, with and without precoding. SU-MMIMO has a much
higher spectral efficiency compared to multi-user massive MIMO (MU-MMIMO) since
independent signals are transmitted from each of the antenna elements (spatial
multiplexing). MU-MMIMO that uses beamforming has a much lower spectral
efficiency, since the same signal (with a delay) is transmitted from each of
the antenna elements. In this work, expressions for the upper bound on the
average signal-to-noise ratio (SNR) per bit and spectral efficiency are derived
for SU-MMIMO with and without precoding. We propose a performance index
$f(N_t)$, which is a function of the number of transmit antennas $N_t$. Here
$f(N_t)$ is the sum of the upper bound on the average SNR per bit and the
spectral efficiency. We demonstrate that when the total number of antennas
($N_{\mathrm{tot}}$) in the transmitter and receiver is fixed, there exists a
minimum value of $f(N_t)$, which has to be avoided. Computer simulations show
that the bit-error-rate (BER) is nearly insensitive to a wide range of the
number of transmit antennas and re-transmissions, when $N_{\mathrm{tot}}$ is
large and kept constant. Thus, the spectral efficiency can be made as large as
possible, for a given BER and $N_{\mathrm{tot}}$.


Abstract:
  The electrohydrodynamic (EHD) force produced by ions ejected from a corona
plasma is a solid state, silent mechanism for accelerating air, useful for
applications ranging from electronics cooling to flying microrobots. This paper
presents the theoretical motivation and the first implementation of a
multi-stage, highly miniaturized EHD device, which can provide both improved
absolute power output and power density as compared to single-stage devices. A
laser microfabricated, folded electrode design reduces component count and
assembly time. Data from one, two, and three-stage devices demonstrates a near
linear scaling of output force with stage count, indicating inter-stage ducting
successfully reduces losses. Device lifetime is assessed to validate the use of
stainless-steel emission electrodes. Areal thrust, force density, and
volumetric power density for the three-stage device are among the highest ever
measured from an EHD actuator.


Abstract:
  We propose a general procedure for iterative inclusion of Stueckelberg fields
to convert the theory into gauge system being equivalent to the original one.
In so doing, we admit reducibility of the Stueckelberg gauge symmetry. In this
case, no pairing exists between Stueckelberg fields and gauge parameters,
unlike the irreducible Stueckelberg symmetry. The general procedure is
exemplified by the case of Proca model, with the third order involutive closure
chosen as the starting point. In this case, the set of Stueckelberg fields
includes, besides the scalar, also the second rank antisymmetric tensor. The
reducible Stueckelberg gauge symmetry is shown to admit different gauge fixing
conditions. One of the gauges reproduces the original Proca theory, while
another one excludes the original vector and the Stueckelberg scalar. In this
gauge, the irreducible massive spin one is represented by antisymmetric second
rank tensor obeying the third order field equations. Similar dual formulations
are expected to exist for the fields of various spins.


Abstract:
  The accuracy and effectiveness of Hermite spectral methods for the numerical
discretization of partial differential equations on unbounded domains, are
strongly affected by the amplitude of the Gaussian weight function employed to
describe the approximation space. This is particularly true if the problem is
under-resolved, i.e., there are no enough degrees of freedom. The issue becomes
even more crucial when the equation under study is time-dependent, forcing in
this way the choice of Hermite functions where the corresponding weight depends
on time. In order to adapt dynamically the approximation space, it is here
proposed an automatic decision-making process that relies on machine learning
techniques, such as deep neural networks and support vector machines. The
algorithm is numerically tested with success on a simple 1D problem, but the
main goal is its exportability in the context of more serious applications.


Abstract:
  In this paper, we propose a physical layer security scheme that exploits a
novel index modulation (IM) technique for coordinate interleaved orthogonal
designs (CIOD). Utilizing the diversity gain of CIOD transmission, the proposed
scheme, named CIOD-IM, provides an improved spectral efficiency by means of IM.
In order to provide a satisfactory secrecy rate, we design a particular
artificial noise matrix, which does not affect the performance of the
legitimate receiver, while deteriorating the performance of the eavesdropper.
We derive expressions of the ergodic secrecy rate and the theoretical bit error
rate upper bound. In addition, we analyze the case of imperfect channel
estimation by taking practical concerns into consideration. It is shown via
computer simulations that the proposed scheme outperforms the existing IM-based
schemes and might be a candidate for future secure communication systems.


Abstract:
  This paper proposes an additive phoneme-aware margin softmax (APM-Softmax)
loss to train the multi-task learning network with phonetic information for
language recognition. In additive margin softmax (AM-Softmax) loss, the margin
is set as a constant during the entire training for all training samples, and
that is a suboptimal method since the recognition difficulty varies in training
samples. In additive angular margin softmax (AAM-Softmax) loss, the additional
angular margin is set as a costant as well. In this paper, we propose an
APM-Softmax loss for language recognition with phoneitc multi-task learning, in
which the additive phoneme-aware margin is automatically tuned for different
training samples. More specifically, the margin of language recognition is
adjusted according to the results of phoneme recognition. Experiments are
reported on Oriental Language Recognition (OLR) datasets, and the proposed
method improves AM-Softmax loss and AAM-Softmax loss in different language
recognition testing conditions.


Abstract:
  The first computation of the compressible energy transfer rate from $\sim$
0.2 AU up to $\sim$ 1.7 AU is obtained using PSP, THEMIS and MAVEN
observations. The compressible energy cascade rate $\varepsilon_C$ is computed
for hundred of events at different heliocentric distances, for time intervals
when the spacecraft were in the pristine solar wind. The observational results
show moderate increases of $\varepsilon_C$ with respect to the incompressible
cascade rate $\varepsilon_I$. Depending on the level of compressibility in the
plasma, which reach up to 25 $\%$ in the PSP perihelion, the different terms in
the compressible exact relation are shown to have different impact in the total
cascade rate $\varepsilon_C$. Finally, the observational results are connected
with the local ion temperature and the solar wind heating problem.


Abstract:
  We consider $U(N)$ $\mathcal N=4$ super Yang-Mills theory and discuss how to
extract the strong coupling limit of non-planar corrections to observables
involving the $\frac{1}{2}$-BPS Wilson loop. Our approach is based on a
suitable saddle point treatment of the Eynard-Orantin topological recursion in
the Gaussian matrix model. Working directly at strong coupling we avoid the
usual procedure of first computing observables at finite planar coupling
$\lambda$, order by order in $1/N$, and then taking the $\lambda\gg 1$ limit.
In the proposed approach, matrix model multi-point resolvents take a simplified
form and some structures of the genus expansion, hardly visible at low order,
may be identified and rigorously proved. As a sample application, we consider
the expectation value of multiple coincident circular supersymmetric Wilson
loops as well as their correlator with single trace chiral operators. For these
quantities we provide novel results about the structure of their genus
expansion at large tension, generalising recent results in arXiv:2011.02885.


Abstract:
  We introduce trace definability, a weak notion of interpretability, and trace
equivalence, a weak notion of equivalence for first order structures and
theories. In particular we get an interesting weak equivalence notion for
$\mathrm{NIP}$ theories. We describe a close connection to indiscernible
collapse. We also show that if $Q$ is a divisible subgroup of $(\mathbb{R};+)$
and $\mathcal{Q}$ is a dp-rank one expansion of $(Q;+,<)$ then exactly one of
the following holds: $\mathrm{Th}(\mathcal{Q})$ trace defines $\mathrm{RCF}$ or
$\mathcal{Q}$ is trace equivalent to a reduct of an ordered vector space.


Abstract:
  We show that in type-II superconductors a magnetic field applied transversely
to correlated columnar disorder, drives a phase transition to a distinct
"smectic" vortex glass (SmVG) state. SmVG is characterized by an infinitely
anisotropic electrical transport, resistive (dissipationless) for current
perpendicular to (along) columnar defects. Its positional order is also quite
unusual, long-ranged with true Bragg peaks along columnar defects and
logarithmically rough vortex lattice distortions with quasi-Bragg peaks
transverse to columnar defects. For low temperatures and sufficiently weak
columnar-only disorder, SmVG is a true topologically-ordered "Bragg glass",
characterized by a vanishing dislocation density. At sufficiently long scales
the residual ever-present point disorder converts this state to a more
standard, but highly anisotropic vortex glass.


Abstract:
  This is an introductory textbook of the history of economics of inequality
for undergraduates and genreral readers. It begins with Adam Smith's critique
of Rousseau. The first and second chapters focus on Smith and Karl Marx, in the
broad classical tradition of economics, where it is believed that there is an
inseparable relationship between production and distribution, economic growth
and inequality. Chapters 3 and 4 argue that despite the fact that the founders
of the neoclassical school had shown an active interest in social issues,
namely worker poverty, the issues of production and distribution became
discussed separately among neoclassicals. Toward the end of the 20th century,
however, there was a renewed awareness within economics of the problem of the
relationship between production and distribution. The young Piketty's
beginnings as an economist are set against this backdrop. Chapters 5 to 8
explain the circumstances of the restoration of classical concerns within the
neoclassical framework. Then, in chapters 9 and 10, I discuss the fact that
Thomas Piketty's seminal work is a new development in this "inequality
renaissance," and try to gain a perspective on future trends in the debate.
Mathematical appendix presents simple models of growth and distribution.


Abstract:
  Twin neural network regression (TNNR) is a semi-supervised regression
algorithm, it can be trained on unlabelled data points as long as other,
labelled anchor data points, are present. TNNR is trained to predict
differences between the target values of two different data points rather than
the targets themselves. By ensembling predicted differences between the targets
of an unseen data point and all training data points, it is possible to obtain
a very accurate prediction for the original regression problem. Since any loop
of predicted differences should sum to zero, loops can be supplied to the
training data, even if the data points themselves within loops are unlabelled.
Semi-supervised training improves TNNR performance, which is already state of
the art, significantly.


Abstract:
  Motivated by an imaging proteomics study for Alzheimer's disease (AD), in
this article, we propose a mediation analysis approach with high-dimensional
exposures and high-dimensional mediators to integrate data collected from
multiple platforms. The proposed method combines principal component analysis
with penalized least squares estimation for a set of linear structural equation
models. The former reduces the dimensionality and produces uncorrelated linear
combinations of the exposure variables, whereas the latter achieves
simultaneous path selection and effect estimation while allowing the mediators
to be correlated. Applying the method to the AD data identifies numerous
interesting protein peptides, brain regions, and protein-structure-memory
paths, which are in accordance with and also supplement existing findings of AD
research. Additional simulations further demonstrate the effective empirical
performance of the method.


Abstract:
  We consider a gauge theory of vector fields in $3d$ Minkowski space. At the
free level, the dynamical variables are subjected to the extended Chern-Simons
(ECS) equations with higher derivatives. If the color index takes $n$ values,
the third-order model admits a $2n$-parameter series of second-rank conserved
tensors, which includes the canonical energy-momentum. Even though the
canonical energy is unbounded, the other representatives in the series can have
bounded from below $00$-component. The theory admits consistent
self-interactions with the Yang-Mills gauge symmetry. The Lagrangian couplings
preserve the unbounded from below energy-momentum tensor, and they do not lead
to a stable non-linear theory. The non-Lagrangian couplings are consistent with
the existence of conserved tensor with a bounded from below $00$-component.
These models are stable at the non-linear level. The dynamics of interacting
theory admits a constraint Hamiltonian form. The Hamiltonian density is given
by the $00$-component of the conserved tensor. In the case of stable
interactions, the Poisson bracket and Hamiltonian do not follow from the
canonical Ostrogradski construction. The particular attention is paid to the
"triply massless" ECS theory, which demonstrates instability already at the
free level. It is shown that the introduction of extra scalar field, serving as
Higgs, can stabilize dynamics in the vicinity of the local minimum of energy.
The equations of motion of stable model are non-Lagrangian, but they admit the
Hamiltonian form of dynamics with a bounded from below Hamiltonian.


Abstract:
  An important goal in human-robot-interaction (HRI) is for machines to achieve
a close to human level of face perception. One of the important differences
between machine learning and human intelligence is the lack of
compositionality. This paper introduces a new scheme to enable generative
adversarial networks to learn the distribution of face images composed of
smaller parts. This results in a more flexible machine face perception and
easier generalization to outside training examples. We demonstrate that this
model is able to produce realistic high-quality face images by generating and
piecing together the parts. Additionally, we demonstrate that this model learns
the relations between the facial parts and their distributions. Therefore, the
specific facial parts are interchangeable between generated face images.


Abstract:
  In this paper, we prove that it is always possible to define a realization of
the Laplacian $\Delta_{\kappa,\theta}$ on $L^2(\Omega)$ subject to nonlocal
Robin boundary conditions with general jump measures on arbitrary open subsets
of $\mathbb R^N$. This is made possible by using a capacity approach to define
admissible pair of measures $(\kappa,\theta)$ that allows the associated form
$\mathcal E_{\kappa,\theta}$ to be closable. The nonlocal Robin Laplacian
$\Delta_{\kappa,\theta}$ generates a sub-Markovian $C_0-$semigroup on
$L^2(\Omega)$ which is not dominated by Neumann Laplacian semigroup unless the
jump measure $\theta$ vanishes. Finally, the convergence of semigroups
sequences $e^{-t\Delta_{\kappa_n,\theta_n}}$ is investigated in the case of
vague convergence and $\gamma-$convergence of admissible pair of measures
$(\kappa_n,\theta_n)$.


Abstract:
  In this paper, we study the framework of two-player Stackelberg games played
on graphs in which Player 0 announces a strategy and Player 1 responds
rationally with a strategy that is an optimal response. While it is usually
assumed that Player 1 has a single objective, we consider here the new setting
where he has several. In this context, after responding with his strategy,
Player 1 gets a payoff in the form of a vector of Booleans corresponding to his
satisfied objectives. Rationality of Player 1 is encoded by the fact that his
response must produce a Pareto-optimal payoff given the strategy of Player 0.
We study the Stackelberg-Pareto Synthesis problem which asks whether Player 0
can announce a strategy which satisfies his objective, whatever the rational
response of Player 1. For games in which objectives are either all parity or
all reachability objectives, we show that this problem is fixed-parameter
tractable and NEXPTIME-complete. This problem is already NP-complete in the
simple case of reachability objectives and graphs that are trees.


Abstract:
  The strong suppression of high-$p_T$ jets in heavy ion collisions is a result
of elastic and inelastic energy loss suffered by the jet multi-prong collection
of color charges that are resolved by medium interactions. Hence, quenching
effects depend on the fluctuations of the jet substructure that are probed by
the cone size dependence of the spectrum. In this letter, we present the first
complete, analytic calculation of the inclusive $R$-dependent jet spectrum in
PbPb collisions at LHC energies, including resummation of energy loss effects
from hard, vacuum-like emissions occurring in the medium and modeling of soft
energy flow and recovery at the jet cone. Both the geometry of the collision
and the local medium properties, such as the temperature and fluid velocity,
are given by a hydrodynamic evolution of the medium, leaving only the coupling
constant in the medium as a free parameter. The calculation yields a good
description of the centrality and $p_T$ dependence of jet suppression for
$R=0.4$ together with a mild cone size dependence, which is in agreement with
recent experimental results. Gauging the theoretical uncertainties, we find
that the largest sensitivity resides in the leading logarithmic approximation
of the phase space of resolved splittings, which can be improved
systematically, while non-perturbative modeling of the soft-gluon sector is of
relatively minor importance up to large cone sizes.


Abstract:
  In this paper, we show that the permeability of porous media and that of a
bubbly fluid are limiting cases of the complexified version of the two-fluid
models posed in the 1990 paper by Lipton and Avellaneda. We assume the
viscosity of the inclusion fluid is $z\mu_1$ and the viscosity of the hosting
fluid is $\mu_1$, $z\in\mathbb{C}$. The proof is carried out by construction of
solutions for large $|z|$ and small $|z|$ by an iteration process similar with
the one used in the 1993 paper by Bruno and Leo. Moreover, we also show that
for a fixed microstructure, the permeabilities of these three cases share the
same integral representation formula (IRF) with different values of $s'$, as
long as the 'contrast parameter' $s':=\frac{1}{z-1}$ is not in the interval
$[-\frac{2E_2^2}{1+2E_2^2},-\frac{1}{1+2E_1^2}]$, where the constants $E_1$ and
$E_2$ are the extension constants that depend on the geometry of $Q_1$, $Q_2$
and $Q$.


Abstract:
  We study stochastic Nash equilibrium problems with expected valued cost
functions whose pseudogradient satisfies restricted monotonicity properties
which hold only with respect to the solution. We propose a forward-backward
algorithm and prove its convergence under restricted strong monotonicity,
restricted strict monotonicity and restricted cocoercivity of the
pseudogradient mapping. To approximate the expected value, we use either a
finite number of samples and a vanishing step size or an increasing number of
samples with a constant step. Numerical simulations show that our proposed
algorithm might be faster than the available algorithms.


Abstract:
  We investigated the ground state of monolayer 1T-XCl$_{2}$ (X: Fe, Co, and
Ni) using the generalized Bloch theorem, which can generate ferromagnetic,
spiral, and antiferromagnetic states. Each state was represented by a unique
spiral vector that arranges the magnetic moment of magnetic atom in the
primitive unit cell. We found the ferromagnetic ground state for the FeCl$_{2}$
and NiCl$_{2}$ while the spiral ground state appears for the CoCl$_{2}$. We
also showed that the ground state depends sensitively on the lattice constant.
When the hole-electron doping was taken into account, we found the phase
transition, which involves the ferromagnetic, spiral, and antiferromagnetic
states, for all the systems. Since the spin-spin interaction in the monolayer
metal dichlorides is influenced by the competition between the direct exchange
and the superexchange, we justify that the carrier concentration determines
which interaction should dominate.


Abstract:
  The paper considers subspaces of the strictly upper triangular matrices,
which are stable under Lie bracket with any upper triangular matrix. These
subspaces are called ad-nilpotent ideals and there are Catalan number of such
subspaces. Each ad-nilpotent ideal meets a unique largest nilpotent orbit in
the Lie algebra of all matrices. The main result of the paper is that under an
equivalence relation on ad-nilpotent ideals studied by Mizuno and others, the
equivalence classes are the ad-nilpotent ideals with the same largest nilpotent
orbit. We include two applications of the result, one to the higher vanishing
of cohomology groups of vector bundles on the flag variety and another to the
Kazhdan-Lusztig cells in the affine Weyl group of the symmetric group. Finally,
some combinatorial results are discussed.


Abstract:
  It is well-known that standard neural networks, even with a high
classification accuracy, are vulnerable to small $\ell_\infty$-norm bounded
adversarial perturbations. Although many attempts have been made, most previous
works either can only provide empirical verification of the defense to a
particular attack method, or can only develop a certified guarantee of the
model robustness in limited scenarios. In this paper, we seek for a new
approach to develop a theoretically principled neural network that inherently
resists $\ell_\infty$ perturbations. In particular, we design a novel neuron
that uses $\ell_\infty$-distance as its basic operation (which we call
$\ell_\infty$-dist neuron), and show that any neural network constructed with
$\ell_\infty$-dist neurons (called $\ell_{\infty}$-dist net) is naturally a
1-Lipschitz function with respect to $\ell_\infty$-norm. This directly provides
a rigorous guarantee of the certified robustness based on the margin of
prediction outputs. We then prove that such networks have enough expressive
power to approximate any 1-Lipschitz function with robust generalization
guarantee. We further provide a holistic training strategy that can greatly
alleviate optimization difficulties. Experimental results show that using
$\ell_{\infty}$-dist nets as basic building blocks, we consistently achieve
state-of-the-art performance on commonly used datasets: 93.09% certified
accuracy on MNIST ($\epsilon=0.3$), 35.42% on CIFAR-10 ($\epsilon=8/255$) and
16.31% on TinyImageNet ($\epsilon=1/255$).


Abstract:
  How information is encoded in bio-molecular sequences is difficult to
quantify since such an analysis usually requires sampling an exponentially
large genetic space. Here we show how information theory reveals both robust
and compressed encodings in the largest complete genotype-phenotype map (over 5
trillion sequences) obtained to date.


Abstract:
  Model parallelism has become a necessity for training modern large-scale deep
language models. In this work, we identify a new and orthogonal dimension from
existing model parallel approaches: it is possible to perform pipeline
parallelism within a single training sequence for Transformer-based language
models thanks to its autoregressive property. This enables a more fine-grained
pipeline compared with previous work. With this key idea, we design TeraPipe, a
high-performance token-level pipeline parallel algorithm for synchronous
model-parallel training of Transformer-based language models. We develop a
novel dynamic programming-based algorithm to calculate the optimal pipelining
execution scheme given a specific model and cluster configuration. We show that
TeraPipe can speed up the training by 5.0x for the largest GPT-3 model with 175
billion parameters on an AWS cluster with 48 p3.16xlarge instances compared
with state-of-the-art model-parallel methods. The code for reproduction can be
found at https://github.com/zhuohan123/terapipe


Abstract:
  Growing interest in quantum computing for practical applications has led to a
surge in the availability of programmable machines for executing quantum
algorithms. Present day photonic quantum computers have been limited either to
non-deterministic operation, low photon numbers and rates, or fixed random gate
sequences. Here we introduce a full-stack hardware-software system for
executing many-photon quantum circuits using integrated nanophotonics: a
programmable chip, operating at room temperature and interfaced with a fully
automated control system. It enables remote users to execute quantum algorithms
requiring up to eight modes of strongly squeezed vacuum initialized as two-mode
squeezed states in single temporal modes, a fully general and programmable
four-mode interferometer, and genuine photon number-resolving readout on all
outputs. Multi-photon detection events with photon numbers and rates exceeding
any previous quantum optical demonstration on a programmable device are made
possible by strong squeezing and high sampling rates. We verify the
non-classicality of the device output, and use the platform to carry out
proof-of-principle demonstrations of three quantum algorithms: Gaussian boson
sampling, molecular vibronic spectra, and graph similarity.


Abstract:
  Elastic cross sections are calculated and compared with the data on
$\pi^{\pm}$ scattering on $^{28}$Si, $^{40}$Ca, $^{58}$Ni, $^{208}$Pb nuclei in
the energy range from 130 to 290 MeV. To this end, both the folding optical
potential (OP) and the local modified Kisslinger-type OP were calculated, and
then the $\pi A$ cross sections were obtained by solving the Klein-Gordon
equation to account for the relativization and distortion wave effects. In the
folding OPs, the parameters of the elementary $\pi N$ amplitude were fitted
when describing the $\pi A$ scattering data, and thus the essential in-medium
effect on the parameters of the $\pi N$ amplitude was established since the
pion is scattered not on a free but on a bound nuclear nucleon. Fairly good
agreement with experimental data was obtained for both models of optical
potentials and their forms turn out to be in coincidence in the region of their
surfaces.


Abstract:
  In this paper, we present a novel approach of automated evaluation of
programming assignments~(AEPA) the highlight of which is that it automatically
identifies multiple solution approaches to the programming question from the
set of submitted solutions. Our approach does not require the instructor to
foresee all the possible solution approaches and accomplishes this task with
little or no human intervention. This paves the way to multiple fundamental
improvements in the way automated evaluation of programming assignments is done
today. We have applied our method on multiple data sets of practical scale. In
all cases, our method was able to detect the solution approaches employed by
the students.


Abstract:
  The main result of this article regards a small time approximation for the
Girsanov's exponential. We prove that the latter is well described over short
time intervals by the solution of a deterministic partial differential
equation.The rate of convergence of the approximation is of order one in the
length of the interval. As a possible application we show that our
approximation can be used to obtain an estimation of the short-time transition
density of a Langevin equation representing the dynamics of a Brownian particle
moving under the influence of an external, non-linear force. Using this
approach is equivalent to consider a random ordinary differential equation,
where the dynamics of the particle is deterministic and given by the
aforementioned force and the stochasticity enters through the initial
condition. The result is in accordance with those obtained by discretization
methods such as Euler-Maruyama, implicit Euler and by approximating the
associated Fokker-Planck-Smoluchowski equation.


Abstract:
  Two of the main challenges for cropland classification by satellite
time-series images are insufficient ground-truth data and inaccessibility of
high-quality hyperspectral images for under-developed areas. Unlabeled
medium-resolution satellite images are abundant, but how to benefit from them
is an open question. We will show how to leverage their potential for cropland
classification using self-supervised tasks. Self-supervision is an approach
where we provide simple training signals for the samples, which are apparent
from the data's structure. Hence, they are cheap to acquire and explain a
simple concept about the data. We introduce three self-supervised tasks for
cropland classification. They reduce epistemic uncertainty, and the resulting
model shows superior accuracy in a wide range of settings compared to SVM and
Random Forest. Subsequently, we use the self-supervised tasks to perform
unsupervised domain adaptation and benefit from the labeled samples in other
regions. It is crucial to know what information to transfer to avoid degrading
the performance. We show how to automate the information selection and transfer
process in cropland classification even when the source and target areas have a
very different feature distribution. We improved the model by about 24%
compared to a baseline architecture without any labeled sample in the target
domain. Our method is amenable to gradual improvement, works with
medium-resolution satellite images, and does not require complicated models.
Code and data are available.


Abstract:
  A high level of physical detail in a molecular model improves its ability to
perform high accuracy simulations, but can also significantly affect its
complexity and computational cost. In some situations, it is worthwhile to add
additional complexity to a model to capture properties of interest; in others,
additional complexity is unnecessary and can make simulations computationally
infeasible. In this work we demonstrate the use of Bayes factors for molecular
model selection, using Monte Carlo sampling techniques to evaluate the evidence
for different levels of complexity in the two-centered Lennard-Jones +
quadrupole (2CLJQ) fluid model. Examining three levels of nested model
complexity, we demonstrate that the use of variable quadrupole and bond length
parameters in this model framework is justified only sometimes. We also explore
the effect of the Bayesian prior distribution on the Bayes factors, as well as
ways to propose meaningful prior distributions. This Bayesian Markov Chain
Monte Carlo (MCMC) process is enabled by the use of analytical surrogate models
that accurately approximate the physical properties of interest. This work
paves the way for further atomistic model selection work via Bayesian inference
and surrogate modeling


Abstract:
  Accurate prediction of the stability of molecular crystals is a longstanding
challenge, as often minuscule free energy differences between polymorphs are
sensitively affected by the description of electronic structure, the
statistical mechanics of the nuclei and the cell, and thermal expansion. The
importance of these effects has been individually established, but rigorous
free energy calculations, which simultaneously account for all terms, have been
prevented by prohibitive computational costs. Here we reproduce the
experimental stabilities of polymorphs of three prototypical compounds --
benzene, glycine, and succinic acid -- by computing rigorous ab initio Gibbs
free energies, at a fraction of the cost of conventional harmonic
approximations. This is achieved by a bottom-up approach, which involves
generating machine-learning potentials to calculate surrogate free energies and
subsequently calculating true ab initio free energies using inexpensive free
energy perturbations. Accounting for all relevant physical effects is no longer
a daunting task and provides the foundation for reliable structure predictions
for more complex systems of industrial importance.


Abstract:
  In this article, we introduce a numerical framework for quantum tomography
and entanglement quantification of three-qubit generalized Werner states. The
scheme involves the single-qubit SIC-POVM, which is then generalized to perform
three-qubit measurements. We introduce random errors into the scheme by
imposing the Poisson noise on the measured photon counts. The precision of
state estimation is quantified and presented on graphs. As a special case, we
compare the efficiency of the framework for the pure three-qubit generalized
Werner state and the W state. The reconstructed states are presented
graphically and discussed.


Abstract:
  The last experimental campaign of Tore Supra was also the last one with a set
of plasma facing components mainly dating from the assembly of the machine in
1988. This campaign was interrupted a few days ahead of schedule because of the
rupture of one coolant tube of the Inner First Wall (IFW). The rupture was
observed by an abrupt increase of the pressure in the vessel. It was caused by
a water leak which occurred two seconds after the end of the lower hybrid
heating pulse during the current ramp-down. Investigations show that the
accident is not related to the operation of the machine or a weaker element but
rather to a localised heat flux exceeding the heat removal capability of the
IFW.


Abstract:
  Against the background of what has been termed a reproducibility crisis in
science, the NLP field is becoming increasingly interested in, and
conscientious about, the reproducibility of its results. The past few years
have seen an impressive range of new initiatives, events and active research in
the area. However, the field is far from reaching a consensus about how
reproducibility should be defined, measured and addressed, with diversity of
views currently increasing rather than converging. With this focused
contribution, we aim to provide a wide-angle, and as near as possible complete,
snapshot of current work on reproducibility in NLP, delineating differences and
similarities, and providing pointers to common denominators.


Abstract:
  Modeling and managing portfolio risk is perhaps the most important step to
achieve growing and preserving investment performance. Within the modern
portfolio construction framework that built on Markowitz's theory, the
covariance matrix of stock returns is a required input to calculate portfolio
risk. Traditional approaches to estimate the covariance matrix are based on
human-designed risk factors, which often require tremendous time and effort to
design better risk factors to improve the covariance estimation. In this work,
we formulate the quest of mining risk factors as a learning problem and propose
a deep learning solution to effectively ``design'' risk factors with neural
networks. The learning objective is also carefully set to ensure the learned
risk factors are effective in explaining the variance of stock returns as well
as having desired orthogonality and stability. Our experiments on the stock
market data demonstrate the effectiveness of the proposed solution: our method
can obtain $1.9\%$ higher explained variance measured by $R^2$ and also reduce
the risk of a global minimum variance portfolio. The incremental analysis
further supports our design of both the architecture and the learning
objective.


Abstract:
  Consider the collection of hyperplanes in $\mathbb{R}^n$ whose defining
equations are given by $\{x_i + x_j = 0\mid 1\leq i<j\leq n\}$. This
arrangement is called the threshold arrangement since its regions are in
bijection with labeled threshold graphs on $n$ vertices. Zaslavsky's theorem
implies that the number of regions of this arrangement is the sum of
coefficients of the characteristic polynomial of the arrangement. In the
present article we give a combinatorial meaning to these coefficients as the
number of labeled threshold graphs with a certain property, thus answering a
question posed by Stanley.


Abstract:
  We describe the phase space structures related to the semi-major axis of
Molniya-like satellites subject to tesseral and lunisolar resonances. In
particular, the questions answered in this contribution are: (i) we study the
indirect interplay of the critical inclination resonance on the
semi-geosynchronous resonance using a hierarchy of more realistic dynamical
systems, thus discussing the dynamics beyond the integrable approximation. By
introducing ad hoc tractable models averaged over fast angles, (ii) we
numerically demarcate the hyperbolic structures organising the long-term
dynamics via Fast Lyapunov Indicators cartography. Based on the publicly
available two-line elements space orbital data, (iii) we identify two
satellites, namely Molniya 1-69 and Molniya 1-87, displaying fingerprints
consistent with the dynamics associated to the hyperbolic set. Finally, (iv)
the computations of their associated dynamical maps highlight that the
spacecraft are trapped within the hyperbolic tangle. This research therefore
reports evidence of actual artificial satellites in the near-Earth environment
whose dynamics are ruled by manifolds and resonant mechanisms. The tools,
formalism and methodologies we present are exportable to other region of space
subject to similar commensurabilities as the geosynchronous region.


Abstract:
  Error mitigation is likely to be key in obtaining near term quantum
advantage. In this work we present one of the first implementations of several
Clifford data regression based methods which are used to mitigate the effect of
noise in real quantum data. We explore the dynamics of the 1-D Ising model with
transverse and longitudinal magnetic fields, highlighting signatures of
confinement. We find in general Clifford data regression based techniques are
advantageous in comparison with zero-noise extrapolation and obtain
quantitative agreement with exact results for systems of 9 qubits with circuit
depths of up to 176, involving hundreds of CNOT gates. This is the largest
systems investigated so far in a study of this type. We also investigate the
two-point correlation function and find the effect of noise on this more
complicated observable can be mitigated using Clifford quantum circuit data
highlighting the utility of these methods.


Abstract:
  The rise of deep learning technologies has quickly advanced many fields,
including that of generative music systems. There exist a number of systems
that allow for the generation of good sounding short snippets, yet, these
generated snippets often lack an overarching, longer-term structure. In this
work, we propose CM-HRNN: a conditional melody generation model based on a
hierarchical recurrent neural network. This model allows us to generate
melodies with long-term structures based on given chord accompaniments. We also
propose a novel, concise event-based representation to encode musical lead
sheets while retaining the notes' relative position within the bar with respect
to the musical meter. With this new data representation, the proposed
architecture can simultaneously model the rhythmic, as well as the pitch
structures in an effective way. Melodies generated by the proposed model were
extensively evaluated in quantitative experiments as well as a user study to
ensure the musical quality of the output as well as to evaluate if they contain
repeating patterns. We also compared the system with the state-of-the-art
AttentionRNN. This comparison shows that melodies generated by CM-HRNN contain
more repeated patterns (i.e., higher compression ratio) and a lower tonal
tension (i.e., more tonally concise). Results from our listening test indicate
that CM-HRNN outperforms AttentionRNN in terms of long-term structure and
overall rating.


Abstract:
  We study the Gorenstein weak global dimension of associative rings and its
relation to the Gorenstein global dimension. In particular, we prove the
conjecture that the Gorenstein weak global dimension is a left-right symmetric
invariant -- just like the (absolute) weak global dimension.


Abstract:
  We establish $L^2$ boundedness of all "nice" parabolic singular integrals on
"Good Parabolic Graphs", aka {\em regular} Lip(1,1/2) graphs. The novelty here
is that we include non-homogeneous kernels, which are relevant to the theory of
parabolic uniform rectifiability. Previously, the third named author had
treated the case of homogeneous kernels. The present proof combines the methods
of that work (which in turn was based on methods described in Christ's CBMS
lecture notes), with the techniques of Coifman-David-Meyer.
  This is a very preliminary draft. Eventually, these results will be part of a
more extensive work on parabolic uniform rectifiability and singular integrals.


Abstract:
  The present contribution proposes a thermodynamically consistent model for
the simulation of the ductile damage. The model couples the phase field method
of fracture to the Armstrong-Frederick plasticity model with kinematic
hardening. The latter is particularly suitable for simulating the material
behavior under a cyclic load. The model relies on the minimum principle of the
dissipation potential. However, the application of this approach is challenging
since potentials of coupled methods are defined in different spaces: The
dissipation potential of the phase field model is expressed in terms of rates
of internal variables, whereas the Armstrong-Frederick model proposes a
formulation depending on thermodynamic forces. For this reason, a unique
formulation requires the Legendre transformation of one of the potentials. The
present work performs the transformation of the Armstrong-Frederick potential,
such that final formulation is only expressed in the space of rates of internal
variables. With the assumption for the free energy and the joint dissipation
potential at hand, the derivation of evolution equations is straightforward.
The application of the model is illustrated by selected numerical examples
studying the material response for different load constellations and sample
geometries. The paper provides a comparison with the experimental results as
well.


Abstract:
  Epidemic spreading is well understood when a disease propagates around a
contact graph.
  In a stochastic susceptible-infected-susceptible setting, spectral conditions
characterise whether the disease vanishes. However, modelling human
interactions using a graph is a simplification which only considers pairwise
relationships.
  This does not fully represent the more realistic case where people meet in
groups. Hyperedges can be used to record such group interactions, yielding more
faithful and flexible models, allowing for the rate of infection of a node to
vary as a nonlinear function of the number of infectious neighbors. We discuss
different types of contagion models in this hypergraph setting, and derive
spectral conditions that characterize whether the disease vanishes.
  We study both the exact individual-level stochastic model and a deterministic
mean field ODE approximation.
  Numerical simulations are provided to illustrate the analysis. We also
interpret our results and show how the hypergraph model allows us to
distinguish between contributions to infectiousness that (a) are inherent in
the nature of the pathogen and (b) arise from behavioural choices (such as
social distancing, increased hygiene and use of masks).
  This raises the possibility of more accurately quantifying the effect of
interventions that are designed to contain the spread of a virus.


Abstract:
  Stress accumulation-relaxation meta-models of pulsar glitches make precise,
microphysics-agnostic predictions of long-term glitch statistics, which can be
falsified by existing and future timing data. Previous meta-models assume that
glitches are triggered by an avalanche process, e.g. involving superfluid
vortices, and that the probability density function (PDF) of the avalanche
sizes is history-independent and specified exogenously. Here a recipe is
proposed to generate the avalanche sizes endogenously in a history-dependent
manner, by tracking the thresholds of occupied vortex pinning sites as a
function of time. Vortices unpin spasmodically from sites with thresholds below
a global, time-dependent stress and repin at sites with thresholds above the
global stress, imbuing the system with long-term memory. The meta-model
predicts PDFs, auto- and cross-correlations for glitch sizes and waiting times,
which are provisionally inconsistent with current observations, unlike some
previous meta-models (e.g. state dependent Poisson process), whose predictions
are consistent. The theoretical implications are intriguing, albeit uncertain,
because history-dependent avalanches embody faithfully the popular, idealized
understanding in the literature of how vortex unpinning operates as a driven,
stochastic process. The meta-model predicts aftershocks, which occur with
larger than average sizes and longer than average waiting times after the
largest, system-resetting glitches. This prediction will be tested, once more
data are generated by the next generation of pulsar timing campaigns.


Abstract:
  In this work, we extended our statistical model with charmed and bottomed
hadrons, and fit the quark creational probabilities for the heavy quarks, using
low energy inclusive charmonium and bottomonium data. With the finalized fit
for all the relevant types of quarks (up, down, strange, charm, bottom) at the
energy range from a few GeV up to a few tens of GeV's, the model is now
considered complete. Some examples are also given for proton-proton,
pion-proton, and proton-antiproton collisions with charmonium, bottomonium, and
open charm hadrons in the final state.


Abstract:
  We state conditions for which a definable local homomorphism between two
locally definable groups $\mathcal{G}$, $\mathcal{G^{\prime}}$ can be uniquely
extended when $\mathcal{G}$ is simply connected (Theorem 2.1). As an
application of this result we obtain an easy proof of [3, Thm. 9.1] (see
Corollary 2.2). We also prove that Theorem 10.2 in [3] also holds for any
definably connected definably compact semialgebraic group $G$ not necessarily
abelian over a sufficiently saturated real closed field $R$; namely, that the
o-minimal universal covering group $\widetilde{G}$ of $G$ is an open locally
definable subgroup of $\widetilde{H\left(R\right)^{0}}$ for some $R$-algebraic
group $H$ (Thm. 3.3). Finally, for an abelian definably connected semialgebraic
group $G$ over $R$, we describe $\widetilde{G}$ as a locally definable
extension of subgroups of the o-minimal universal covering groups of
commutative $R$-algebraic groups (Theorem 3.4)


Abstract:
  Driving energy consumption plays a major role in the navigation of mobile
robots in challenging environments, especially if they are left to operate
unattended under limited on-board power. This paper reports on first results of
an energy-aware path planner, which can provide estimates of the driving energy
consumption and energy recovery of a robot traversing complex uneven terrains.
Energy is estimated over trajectories making use of a self-supervised learning
approach, in which the robot autonomously learns how to correlate perceived
terrain point clouds to energy consumption and recovery. A novel feature of the
method is the use of 1D convolutional neural network to analyse the terrain
sequentially in the same temporal order as it would be experienced by the robot
when moving. The performance of the proposed approach is assessed in simulation
over several digital terrain models collected from real natural scenarios, and
is compared with a heuristic inclination-based energy model. We show evidence
of the benefit of our method to increase the overall prediction r2 score by
66.8% and to reduce the driving energy consumption over planned paths by 5.5%.


Abstract:
  Topological groupoids admit various types of morphisms. We push these notions
to the level of continuous groupoid actions to obtain various types of groupoid
action morphisms. Some dynamical properties and their relation to these
morphisms are studied. Among them are recurrence, various forms of
transitivity, minimality, limit, recurrent, periodic and almost periodic
points.


Abstract:
  Deep-learning models such as Convolutional Neural Networks (CNN) and Long
Short-Term Memory (LSTM) have been successfully used for process-mining tasks.
They have achieved better performance for different predictive tasks than
traditional approaches. We extend the existing body of research by testing four
different variants of Graph Neural Networks (GNN) and a fully connected
Multi-layer Perceptron (MLP) with dropout for the tasks of predicting the
nature and timestamp of the next process activity. In contrast to existing
studies, we evaluate our models' performance at different stages of a process,
determined by quartiles of the number of events and normalized quarters of the
case duration. This provides new insights into the performance of a prediction
model, as they behave differently at different stages of a business-process.
Interestingly, our experiments show that the simple MLP often outperforms more
sophisticated deep-learning models in both prediction tasks. We argue that care
needs to be taken when applying automated process-prediction techniques at
different stages of a process. We further argue that researchers should reflect
their results with strong baselines methods like simple MLPs.


Abstract:
  Image quality assessment (IQA) models aim to establish a quantitative
relationship between visual images and their perceptual quality by human
observers. IQA modeling plays a special bridging role between vision science
and engineering practice, both as a test-bed for vision theories and
computational biovision models, and as a powerful tool that could potentially
make profound impact on a broad range of image processing, computer vision, and
computer graphics applications, for design, optimization, and evaluation
purposes. IQA research has enjoyed an accelerated growth in the past two
decades. Here we present an overview of IQA methods from a Bayesian
perspective, with the goals of unifying a wide spectrum of IQA approaches under
a common framework and providing useful references to fundamental concepts
accessible to vision scientists and image processing practitioners. We discuss
the implications of the successes and limitations of modern IQA methods for
biological vision and the prospect for vision science to inform the design of
future artificial vision systems.


Abstract:
  We prove Fermat's Last Theorem using the philosophical and mathematical
knowledge of 1637, when French mathematician Pierre de Fermat claimed to have a
proof of his conjecture. Our strategy consists in setting three variables of
the Fermat's equation as integers, and then we evaluate if the remaining
variable can be an integer as well. We assert that at least an irrational
number is needed to satisfy Fermat's equation. We confirm that Fermat's Last
Theorem is valid.


Abstract:
  Dynamical systems methods are used to investigate a cosmological model with
non-minimally coupled scalar field and asymptotically quadratic potential
function. We found that for values of the non-minimal coupling constant
parameter $\frac{3}{16}<\xi<\frac{1}{4}$ there exists an unstable asymptotic de
Sitter state, free from a parallelly propagated singularity for
$\frac{5}{24}\le\xi<\frac{1}{4}$, giving rise to non-singular beginning of the
universe. The energy density associated with this state depends on value of the
non-minimal coupling constant and can be much smaller than the Planck energy
density. For $\xi=\frac{1}{4}$ we found that the initial state is in form of
the static Einstein universe. Proposed evolutional model, contrary to the
seminal Starobinsky's model, do not depend on the specific choice of initial
conditions in phase space, moreover, a small change in the model parameters do
not change the evolution thus the model is generic and structurally stable. The
values of the non-minimal coupling constant can indicate for a new fundamental
symmetry in the gravitational theory. We show that Jordan frame and Einstein
frame formulation of the theory are physically nonequivalent.


Abstract:
  We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field
integrating knowledge and methods from neurotechnology, artificial
intelligence, and quantum computing. The objective is to develop an enhanced
connectivity between the human brain and quantum computers for a variety of
disruptive applications. We foresee the emergence of hybrid classical-quantum
networks of wetware and hardware nodes, mediated by machine learning techniques
and brain-machine interfaces. QBraiNs will harness and transform in
unprecedented ways arts, science, technologies, and entrepreneurship, in
particular activities related to medicine, Internet of humans, intelligent
devices, sensorial experience, gaming, Internet of things, crypto trading, and
business.


Abstract:
  Sr$_2$CuO$_2$Cl$_2$ (SCOC) is a model undoped cuprate with $I4/mmm$
crystallographic symmetry, and a simple magnetic space group $C_Amca$ with
associated magnetic point group $mmm1'$. However, recent second harmonic
spectroscopy in the antiferromagnetic phase has challenged this picture,
suggesting instead a magnetic point group $4/mm'm'$ that co-exists with the
antiferromagnetism and breaks the two orthogonal mirror planes containing the
tetragonal $c$-axis. Here, we analyze the symmetry of SCOC in light of the
second harmonic results, and discuss possible ground states that are consistent
with the data.


Abstract:
  Excitonic resonance and binding energies can be altered by controlling the
environmental screening of the attractive Coulomb potential. Although this
screening response is often assumed to be static, the time evolution of the
excitonic quasiparticles manifests a frequency-dependence in its Coulomb
screening efficacy. In this letter, we investigate a ground (1s) and first
excited exciton state (2s) in a multi-layered transition metal dichalcogenide
(MoS$_2$) upon ultrafast photo-excitation. We explore the dynamic screening
effects on the latter and show its resonance frequency is the relevant
frequency at which screening from the smaller-sized 1s counterparts is
effective. Our finding sheds light on new avenues of external tuning on
excitonic properties.