title: Targeted influence maximization in complex networks

abstract:
  Many real-world applications based on spreading processes in complex networks
aim to deliver information to specific target nodes. However, it remains
challenging to optimally select a set of spreaders to initiate the spreading
process. In this paper, we study the targeted influence maximization problem
using a susceptible-infected-recovered (SIR) model as an example. Formulated as
a combinatorial optimization, the objective is to identify a given number of
spreaders that can maximize the influence over target nodes while minimize the
influence over non-target nodes. To find a practical solution to this
optimization problem, we develop a theoretical framework based on a message
passing process and perform a stability analysis on the equilibrium solution
using non-backtracking (NB) matrices. We propose that the spreaders can be
selected by imposing optimal perturbation on the equilibrium solution for the
subgraph consisting of the target nodes and their multi-step nearest neighbors
while avoiding such perturbation on the complement graph that excludes target
nodes from the original network. We further introduce a metric, termed targeted
collective influence, for each node to identify influential spreaders for
targeted spreading processes. The proposed method, validated in both synthetic
and real-world networks, outperforms other competing heuristic approaches. Our
results provide a framework for analyzing the targeted influence maximization
problem and a practical method to identify spreaders in real-world
applications.


title:
Multi-Modal Fusion for Sensorimotor Coordination in Steering Angle
  Prediction

abstract:
  Imitation learning is employed to learn sensorimotor coordination for
steering angle prediction in an end-to-end fashion requires expert
demonstrations. These expert demonstrations are paired with environmental
perception and vehicle control data. The conventional frame-based RGB camera is
the most common exteroceptive sensor modality used to acquire the environmental
perception data. The frame-based RGB camera has produced promising results when
used as a single modality in learning end-to-end lateral control. However, the
conventional frame-based RGB camera has limited operability in illumination
variation conditions and is affected by the motion blur. The event camera
provides complementary information to the frame-based RGB camera. This work
explores the fusion of frame-based RGB and event data for learning end-to-end
lateral control by predicting steering angle. In addition, how the
representation from event data fuse with frame-based RGB data helps to predict
the lateral control robustly for the autonomous vehicle. To this end, we
propose DRFuser, a novel convolutional encoder-decoder architecture for
learning end-to-end lateral control. The encoder module is branched between the
frame-based RGB data and event data along with the self-attention layers.
Moreover, this study has also contributed to our own collected dataset
comprised of event, frame-based RGB, and vehicle control data. The efficacy of
the proposed method is experimentally evaluated on our collected dataset, Davis
Driving dataset (DDD), and Carla Eventscape dataset. The experimental results
illustrate that the proposed method DRFuser outperforms the state-of-the-art in
terms of root-mean-square error (RMSE) and mean absolute error (MAE) used as
evaluation metrics.


title:
Continuous-Time Analysis of AGM via Conservation Laws in Dilated
  Coordinate Systems

abstract:
  We analyze continuous-time models of accelerated gradient methods through
deriving conservation laws in dilated coordinate systems. Namely, instead of
analyzing the dynamics of $X(t)$, we analyze the dynamics of
$W(t)=t^\alpha(X(t)-X_c)$ for some $\alpha$ and $X_c$ and derive a conserved
quantity, analogous to physical energy, in this dilated coordinate system.
Through this methodology, we recover many known continuous-time analyses in a
streamlined manner and obtain novel continuous-time analyses for OGM-G, an
acceleration mechanism for efficiently reducing gradient magnitude that is
distinct from that of Nesterov. Finally, we show that a semi-second-order
symplectic Euler discretization in the dilated coordinate system leads to an
$\mathcal{O}(1/k^2)$ rate on the standard setup of smooth convex minimization,
without any further assumptions such as infinite differentiability.


title: Non-Boussinesq subgrid-scale model with dynamic tensorial coefficients

abstract:
  A major drawback of Boussinesq-type subgrid-scale stress models used in
large-eddy simulations is the inherent assumption of alignment between
large-scale strain rates and filtered subgrid-stresses. A priori analyses using
direct numerical simulation (DNS) data has shown that this assumption is
invalid locally as subgrid-scale stresses are poorly correlated with the
large-scale strain rates [Bardina et al., AIAA 1980; Meneveau and Liu, Ann.
Rev. Fluid Mech. 2002]. In the present work, a new, non-Boussinesq
subgrid-scale model is presented where the model coefficients are computed
dynamically. Some previous non-Boussinesq models have observed issues in
providing adequate dissipation of turbulent kinetic energy [e.g.: Bardina et
al., AIAA 1980; Clark et al. J. Fluid Mech., 1979; Stolz and Adams, Phys. of
Fluids, 1999]; however, the present model is shown to provide sufficient
dissipation using dynamic coefficients. Modeled subgrid-scale Reynolds stresses
satisfy the consistency requirements of the governing equations for LES, vanish
in laminar flow and at solid boundaries, and have the correct asymptotic
behavior in the near-wall region of a turbulent boundary layer.
  The new model, referred to as the dynamic tensor-coefficient Smagorinsky
model (DTCSM), has been tested in simulations of canonical flows: decaying and
forced homogeneous isotropic turbulence (HIT), and wall-modeled turbulent
channel flow at high Reynolds numbers; the results show favorable agreement
with DNS data. In order to assess the performance of DTCSM in more complex
flows, wall-modeled simulations of high Reynolds number flow over a Gaussian
bump exhibiting smooth-body flow separation are performed. Predictions of
surface pressure and skin friction, compared against DNS and experimental data,
show improved accuracy from DTCSM in comparison to the existing static
coefficient (Vreman) and dynamic Smagorinsky model.


title: Construction du hens\'elis\'e d'un corps valu\'e

abstract:
  We give an explicit construction of the henselization of a valued field, with
a constructive proof. It is analogous to the construction of the real closure
of a discrete ordered field.
  Nous donnons une construction explicite, et constructivement prouv\'ee, du
hens\'elis\'e d'un corps valu\'e. Cette construction peut \^etre consid\'er\'ee
comme l'analogue, dans le cas valu\'e, de la construction de la cl\^oture
r\'e\'eelle d'un corps ordonn\'e.


title:
Generalized Taylor formulae, computations in real closed valued fields
  and quantifier elimination

abstract:
  We use generalized Taylor formulae in order to give some simple constructions
in the real closure of an \ovfz. We deduce a new, simple quantifier elimination
algorithm for \rcvfs and some theorems about constructible subsets of real
valuative affine space.


title:
Software Architecture for Quantum Computing Systems -- A Systematic
  Review

abstract:
  Quantum computing systems rely on the principles of quantum mechanics to
perform a multitude of computationally challenging tasks more efficiently than
their classical counterparts. The architecture of software-intensive systems
can empower architects who can leverage architecture-centric processes,
practices, description languages, etc., to model, develop, and evolve quantum
computing software (quantum software for short) at higher abstraction levels.
We conducted a systematic literature review (SLR) to investigate (i)
architectural process, (ii) modeling notations, (iii) architecture design
patterns, (iv) tool support, and (iv) challenging factors for quantum software
architecture. Results of the SLR indicate that quantum software represents a
new genre of software-intensive systems; however, existing processes and
notations can be tailored to derive the architecting activities and develop
modeling languages for quantum software. Quantum bits (Qubits) mapped to
Quantum gates (Qugates) can be represented as architectural components and
connectors that implement quantum software. Tool-chains can incorporate
reusable knowledge and human roles (e.g., quantum domain engineers, quantum
code developers) to automate and customize the architectural process. Results
of this SLR can facilitate researchers and practitioners to develop new
hypotheses to be tested, derive reference architectures, and leverage
architecture-centric principles and practices to engineer emerging and next
generations of quantum software.


title:
On the preferred extensions of argumentation frameworks: bijections with
  naive extensions

abstract:
  This paper deals with the problem of finding the preferred extensions of an
argumentation framework by means of a bijection with the naive extensions of
another framework. First we consider the case where an argumentation framework
is naive-realizable: its naive and preferred extensions are equal. Recognizing
naive-realizable argumentation frameworks is hard, but we show that it is
tractable for frameworks with bounded in-degree. Next, we give a bijection
between the preferred extensions of an argumentation framework being
admissible-closed (the intersection of two admissible sets is admissible) and
the naive extensions of another framework on the same set of arguments. On the
other hand, we prove that identifying admissible-closed argumentation
frameworks is coNP-complete. At last, we introduce the notion of irreducible
self-defending sets as those that are not the union of others. It turns out
there exists a bijection between the preferred extensions of an argumentation
framework and the naive extensions of a framework on its irreducible
self-defending sets. Consequently, the preferred extensions of argumentation
frameworks with some lattice properties can be listed with polynomial delay and
polynomial space.


title: Exact values for unbalanced Zarankiewicz numbers

abstract:
  For positive integers $s$, $t$, $m$ and $n$, the Zarankiewicz number
$Z_{s,t}(m,n)$ is defined to be the maximum number of edges in a bipartite
graph with parts of sizes $m$ and $n$ that has no complete biparitite subgraph
containing $s$ vertices in the part of size $m$ and $t$ vertices in the part of
size $n$. A simple argument shows that, for each $t \geq 2$,
$Z_{2,t}(m,n)=(t-1)\binom{m}{2}+n$ when $n \geq (t-1)\binom{m}{2}$. Here, for
large $m$, we determine the exact value of $Z_{2,t}(m,n)$ in almost all of the
remaining cases where $n=\Theta(tm^2)$. We establish a new family of upper
bounds on $Z_{2,t}(m,n)$ which complement a family already obtained by Roman.
We then prove that the floor of the best of these bounds is almost always
achieved. We also show that there are cases in which this floor cannot be
achieved and others in which determining whether it is achieved is likely a
very hard problem. Our results are proved by viewing the problem through the
lens of linear hypergraphs and our constructions make use of existing results
on edge decompositions of dense graphs.


title: Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer

abstract:
  Text spotting end-to-end methods have recently gained attention in the
literature due to the benefits of jointly optimizing the text detection and
recognition components. Existing methods usually have a distinct separation
between the detection and recognition branches, requiring exact annotations for
the two tasks. We introduce TextTranSpotter (TTS), a transformer-based approach
for text spotting and the first text spotting framework which may be trained
with both fully- and weakly-supervised settings. By learning a single latent
representation per word detection, and using a novel loss function based on the
Hungarian loss, our method alleviates the need for expensive localization
annotations. Trained with only text transcription annotations on real data, our
weakly-supervised method achieves competitive performance with previous
state-of-the-art fully-supervised methods. When trained in a fully-supervised
manner, TextTranSpotter shows state-of-the-art results on multiple benchmarks.


title: Overview of the physics prospects in MOMENT

abstract:
  MuOn-decay MEdium-baseline NeuTrino beam facility (MOMENT) is a recently
proposed neutrino oscillation experiment that is currently under consideration
in China. Based on a novel accelerator concept, MOMENT is capable of delivering
15 MW neutrino beam produced with the decay of positively and negatively
charged muons. In this study, the physics performance of MOMENT is briefly
reviewed in its baseline setup, involving a 150 km baseline length and large
gadolinium-doped Water Cherenkov detector. The prospects are discussed in the
case of precision measurements on the Dirac CP phase $\delta_{CP}$, which
MOMENT is shown to be able to measure by about 8$^\circ$...18$^\circ$
resolution at 1$\sigma$ CL. It is examined how MOMENT performs as an
independent experiment and also as a complementary facility to the future
superbeam experiments.


title: Support Vectors and Gradient Dynamics for Implicit Bias in ReLU Networks

abstract:
  Understanding implicit bias of gradient descent has been an important goal in
machine learning research. Unfortunately, even for a single-neuron ReLU
network, it recently proved impossible to characterize the implicit
regularization with the square loss by an explicit function of the norm of
model parameters. In order to close the gap between the existing theory and the
intriguing empirical behavior of ReLU networks, here we examine the gradient
flow dynamics in the parameter space when training single-neuron ReLU networks.
Specifically, we discover implicit bias in terms of support vectors in ReLU
networks, which play a key role in why and how ReLU networks generalize well.
Moreover, we analyze gradient flows with respect to the magnitude of the norm
of initialization, and show the impact of the norm in gradient dynamics.
Lastly, under some conditions, we prove that the norm of the learned weight
strictly increases on the gradient flow.


title: Inference with System W Satisfies Syntax Splitting

abstract:
  In this paper, we investigate inductive inference with system W from
conditional belief bases with respect to syntax splitting. The concept of
syntax splitting for inductive inference states that inferences about
independent parts of the signature should not affect each other. This was
captured in work by Kern-Isberner, Beierle, and Brewka in the form of
postulates for inductive inference operators expressing syntax splitting as a
combination of relevance and independence; it was also shown that c-inference
fulfils syntax splitting, while system P inference and system Z both fail to
satisfy it. System W is a recently introduced inference system for nonmonotonic
reasoning that captures and properly extends system Z as well as c-inference.
We show that system W fulfils the syntax splitting postulates for inductive
inference operators by showing that it satisfies the required properties of
relevance and independence. This makes system W another inference operator
besides c-inference that fully complies with syntax splitting, while in
contrast to c-inference, also extending rational closure.


title: Dynamic computations inside the algebraic closure of a valued field

abstract:
  We explain how to compute in the algebraic closure of a valued field. These
computations heavily rely on the \NPAz. They are made in the same spirit as the
dynamic algebraic closure of a field. They give a concrete content to the
theorem saying that a valued field does have an algebraically closed valued
extension. The algorithms created for that purpose can be used to perform an
effective quantifier elimination for algebraically closed valued fields, which
relies on a very natural geometric idea.


title: Supercooled Jahn-Teller Ice

abstract:
  When the spins on the frustrated pyrochlore lattice obey the celebrated
2-$in$-2-$out$ ice rule, they stay in a correlated disordered phase down to
zero temperature. Similarly, if the atomic ions on the pyrochlore lattice move
in and outwards of the tetrahedra, they may obey a constraint resembling the
ice rule. We microscopically derive a model for such lattice-displacement
degree of freedom by taking account of a Jahn-Teller (JT) effect. The model
aims to describe a family of molybdates $A_2$Mo$_2$O$_7$ ($A =$Y, Dy, Tb), that
forms a pyrochlore lattice based on Mo$^{4+}$ ions. The JT effect occurs when
lattice distortions reduce the symmetry of the local crystal field, resulting
in the orbital-energy-splitting that causes the local energy gain. Unlike the
standard JT effect, the JT energy is determined neither uniquely nor locally by
a single Mo$^{4+}$ ion and the surrounding oxygens but depends on the
displacements of Mo$^{4+}$ ions in neighboring tetrahedra. This situation is
described in our model by the 2nd and 3rd neighbor interactions comparably as
strong as the nearest neighbor interactions of standard ice. As a result, the
energy landscape of the degenerate ice state is altered: a crystalline-like
ground state coexists with a highly quasi-degenerate ice-like liquid state. The
latter is made up of a 2-$in$-2-$out$ ice network and persistent defects that
spread dynamically throughout the system. Our Monte Carlo simulations show that
this liquid remains remarkably stable down to low temperatures by avoiding the
putative first order transition, which can be viewed as a "supercooled JT ice".
The relaxation in the supercooled JT Ice state exhibits glassy dynamics with a
plateau structure resembling the dynamics in glass-forming molecular liquids.


title:
Dilated convolutional neural network-based deep reference picture
  generation for video compression

abstract:
  Motion estimation and motion compensation are indispensable parts of inter
prediction in video coding. Since the motion vector of objects is mostly in
fractional pixel units, original reference pictures may not accurately provide
a suitable reference for motion compensation. In this paper, we propose a deep
reference picture generator which can create a picture that is more relevant to
the current encoding frame, thereby further reducing temporal redundancy and
improving video compression efficiency. Inspired by the recent progress of
Convolutional Neural Network(CNN), this paper proposes to use a dilated CNN to
build the generator. Moreover, we insert the generated deep picture into
Versatile Video Coding(VVC) as a reference picture and perform a comprehensive
set of experiments to evaluate the effectiveness of our network on the latest
VVC Test Model VTM. The experimental results demonstrate that our proposed
method achieves on average 9.7% bit saving compared with VVC under low-delay P
configuration.


title: Multi-Access Coded Caching Schemes from Maximal Cross Resolvable Designs

abstract:
  We study the problem of multi-access coded caching (MACC): a central server
has $N$ files, $K$ ($K \leq N$) caches each of which stores $M$ out of the $N$
files, $K$ users each of which demands one out of the $N$ files, and each user
accesses $z$ caches. The objective is to jointly design the placement,
delivery, and user-to-cache association, to optimize the achievable rate. This
problem has been extensively studied in the literature under the assumption
that a user accesses only one cache. However, when a user accesses more caches,
this problem has been studied only under the assumption that a user accesses
$z$ consecutive caches with a cyclic wrap-around over the boundaries. A natural
question is how other user-to-cache associations fare against the cyclic
wrap-around user-to-cache association. A bipartite graph can describe a general
user-to-cache association. We identify a class of bipartite graphs that, when
used as a user-to-cache association, achieves either a lesser rate or a lesser
subpacketization than all other existing MACC schemes using a cyclic
wrap-around user-to-cache association. The placement and delivery strategy of
our MACC scheme is constructed using a combinatorial structure called maximal
cross resolvable design.


title:
Lattice studies of the $Sp(4)$ gauge theory with two fundamental and
  three antisymmetric Dirac fermions

abstract:
  We consider the $Sp(4)$ gauge theory coupled to $N_f=2$ fundamental and
$n_f=3$ antisymmetric flavours of Dirac fermions in four dimensions. This
theory serves as the microscopic origin for composite Higgs models with
$SU(4)/Sp(4)$ coset, supplemented by partial top compositeness. We study
numerically its lattice realisation, and couple the fundamental plaquette
action to Wilson-Dirac fermions in mixed representations, by adopting a
(rational) hybrid Monte Carlo method, to perform non-trivial tests of the
properties of the resulting lattice theory.
  We find evidence of a surface (with boundaries) of first-order bulk phase
transitions in the three-dimensional space of bare parameters (one coupling and
two masses). Explicit evaluation of the Dirac eigenvalues confirms the expected
patterns of global symmetry breaking. After investigating finite volume effects
in the weak-coupling phase of the theory, for the largest available lattice we
study the mass spectra of the lightest spin-0 and spin-1 flavoured mesons
composed of fermions in each representation, and of the lightest half-integer
spin composite particle made of fermions in different representations -- the
chimera baryon. This work sets the stage for future systematical studies of the
non-perturbative dynamics in phenomenologically relevant regions of parameter
space.


title:
Electricity Consumption Forecasting for Out-of-distribution Time-of-Use
  Tariffs

abstract:
  In electricity markets, retailers or brokers want to maximize profits by
allocating tariff profiles to end consumers. One of the objectives of such
demand response management is to incentivize the consumers to adjust their
consumption so that the overall electricity procurement in the wholesale
markets is minimized, e.g. it is desirable that consumers consume less during
peak hours when cost of procurement for brokers from wholesale markets are
high. We consider a greedy solution to maximize the overall profit for brokers
by optimal tariff profile allocation. This in-turn requires forecasting
electricity consumption for each user for all tariff profiles. This forecasting
problem is challenging compared to standard forecasting problems due to
following reasons: i. the number of possible combinations of hourly tariffs is
high and retailers may not have considered all combinations in the past
resulting in a biased set of tariff profiles tried in the past, ii. the
profiles allocated in the past to each user is typically based on certain
policy. These reasons violate the standard i.i.d. assumptions, as there is a
need to evaluate new tariff profiles on existing customers and historical data
is biased by the policies used in the past for tariff allocation. In this work,
we consider several scenarios for forecasting and optimization under these
conditions. We leverage the underlying structure of how consumers respond to
variable tariff rates by comparing tariffs across hours and shifting loads, and
propose suitable inductive biases in the design of deep neural network based
architectures for forecasting under such scenarios. More specifically, we
leverage attention mechanisms and permutation equivariant networks that allow
desirable processing of tariff profiles to learn tariff representations that
are insensitive to the biases in the data and still representative of the task.


title: Criteria for finite time blow up for a system of Klein-Gordon equations

abstract:
  We give three conditions on initial data for the blowing up of the
corresponding solutions to some system of Klein-Gordon equations on the three
dimensional Euclidean space. We first use Levine's concavity argument to show
that the negativeness of energy leads to the blowing up of local solutions in
finite time. For the data of positive energy, we give a sufficient condition so
that the corresponding solution blows up in finite time. This condition
embodies datum with arbitrarily large energy. At last we use Payne-Sattinger's
potential well argument to classify the datum with energy not so large (to be
exact, below the ground states) into two parts: one part consists of datum
leading to blowing-up solutions in finite time, while the other part consists
of datum that leads to the global solutions.


title:
Dynamical Quantum Phase Transitions in Strongly Correlated
  Two-Dimensional Spin Lattices Following a Quench

abstract:
  Dynamical quantum phase transitions are at the forefront of current efforts
to understand quantum matter out of equilibrium. Except for a few exactly
solvable models, predictions of these critical phenomena typically rely on
advanced numerical methods. However, those approaches are mostly restricted to
one dimension, making investigations of two-dimensional systems highly
challenging. Here, we present evidence of dynamical quantum phase transitions
in strongly correlated spin lattices in two dimensions. To this end, we apply
our recently developed cumulant method [Phys. Rev. X 11, 041018 (2021)] to
determine the zeros of the Loschmidt amplitude in the complex plane of time and
predict the crossing points of the thermodynamic lines of zeros with the
real-time axis, where dynamical quantum phase transitions occur. We find the
critical times of a two-dimensional quantum Ising lattice and the Heisenberg
model with ferromagnetic or antiferromagnetic couplings. We also show how
dynamical quantum phase transitions can be predicted by measuring the initial
energy fluctuations, for example, in quantum simulators or other engineered
quantum systems.


title: What Does it Mean for a Language Model to Preserve Privacy?

abstract:
  Natural language reflects our private lives and identities, making its
privacy concerns as broad as those of real life. Language models lack the
ability to understand the context and sensitivity of text, and tend to memorize
phrases present in their training sets. An adversary can exploit this tendency
to extract training data. Depending on the nature of the content and the
context in which this data was collected, this could violate expectations of
privacy. Thus there is a growing interest in techniques for training language
models that preserve privacy. In this paper, we discuss the mismatch between
the narrow assumptions made by popular data protection techniques (data
sanitization and differential privacy), and the broadness of natural language
and of privacy as a social norm. We argue that existing protection methods
cannot guarantee a generic and meaningful notion of privacy for language
models. We conclude that language models should be trained on text data which
was explicitly produced for public use.


title:
The co-evolution of molecular hydrogen and the grain size distribution
  in an isolated galaxy

abstract:
  Understanding the evolution of dust and molecular hydrogen (H$_2$) is a
critical aspect of galaxy evolution, as they affect star formation and the
spectral energy distribution of galaxies. We use the
$N$-body/smoothed-particle-hydrodynamics code {\sc Gadget-4} to compute the
evolution of dust and H$_2$ in a suite of numerical simulations of an isolated
Milky-Way-like galaxy. The evolution of the full grain size distribution (GSD)
is solved by sampling the grain size on a logarithmically spaced grid with 30
bins. The evolution of a primordial chemistry network with twelve species is
solved consistently with the hydrodynamic evolution of the system, including
star formation, metal and energy ejections from stars into the interstellar
medium through supernova feedback and stellar winds. The formation model for
H$_2$ considers the GSD and photo-dissociation through the UV radiation of
young stars. We identify the processes needed for producing a sizeable amount
of H$_2$, verify that the resulting star formation law in the later stages of
galaxy evolution is consistent with observations of local spirals, and show
that our model manages to produce a galactic molecular gas fraction in line
with observations of Milky-Way-like galaxies. We stress the importance of the
co-evolution of the GSD and H$_2$, as models assuming a fixed MRN shape for the
GSD overestimate the production of H$_2$ in regimes where the dust abundance is
dominated by large grains and underestimate it in the regime where the dust is
dominated by small grains, both of which are realized in simulations of dust
evolution.


title: Unsupervised HDR Imaging: What Can Be Learned from a Single 8-bit Video?

abstract:
  Recently, Deep Learning-based methods for inverse tone-mapping standard
dynamic range (SDR) images to obtain high dynamic range (HDR) images have
become very popular. These methods manage to fill over-exposed areas
convincingly both in terms of details and dynamic range. Typically, these
methods, to be effective, need to learn from large datasets and to transfer
this knowledge to the network weights. In this work, we tackle this problem
from a completely different perspective. What can we learn from a single SDR
video? With the presented zero-shot approach, we show that, in many cases, a
single SDR video is sufficient to be able to generate an HDR video of the same
quality or better than other state-of-the-art methods.


title:
A Laser-ARPES View of the 2D Electron Systems at LaAlO3/SrTiO3 and
  Al/SrTiO3 Interfaces

abstract:
  We have measured the electronic structure of the two-dimensional electron
system (2DES) found at the Al/SrTiO3 (Al/STO) and LaAlO3/SrTiO3 (LAO/STO)
interfaces by means of laser angle resolved photoemission spectroscopy, taking
advantage of the large photoelectron escape depth at low photon energy to probe
these buried interfaces. We demonstrate the possibility of tuning the
electronic density in Al/STO by varying the Al layer thickness and show that
the electronic structure evolution is well described by self-consistent tight
binding supercell calculations, but differs qualitatively from a rigid band
shift model. We show that both 2DES are strongly coupled to longitudinal
optical phonons, in agreement with previous reports of a polaronic ground state
in similar STO based 2DESs. Tuning the electronic density in Al/STO to match
that of LAO/STO and comparing both systems, we estimate that the intrinsic
LAO/STO 2DES has a bare band width of ~ 60 meV and a carrier density of ~ 6
10^13 cm-2.


title:
Partial controllability of network dynamical systems with unilateral
  inputs

abstract:
  Our ability to control network dynamical systems is often hindered by
constraints on the number and nature of the available control actions, which
make controlling the whole network unfeasible. In this manuscript, we focus on
the case where unilateral inputs are exerted on a subset of the network nodes.
Leveraging the observation that, different from the case of subsystems,
unilateral node reachability and controllability are equivalent, we provide
conditions for a given node subset to be unilaterally controllable. The
theoretical findings are then employed to develop a computationally efficient
heuristic to select the nodes where the unilateral inputs should be injected


title:
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale
  Contrastive Learning Approach

abstract:
  Anomaly detection from graph data is an important data mining task in many
applications such as social networks, finance, and e-commerce. Existing efforts
in graph anomaly detection typically only consider the information in a single
scale (view), thus inevitably limiting their capability in capturing anomalous
patterns in complex graph data. To address this limitation, we propose a novel
framework, graph ANomaly dEtection framework with Multi-scale cONtrastive
lEarning (ANEMONE in short). By using a graph neural network as a backbone to
encode the information from multiple graph scales (views), we learn better
representation for nodes in a graph. In maximizing the agreements between
instances at both the patch and context levels concurrently, we estimate the
anomaly score of each node with a statistical anomaly estimator according to
the degree of agreement from multiple perspectives. To further exploit a
handful of ground-truth anomalies (few-shot anomalies) that may be collected in
real-life applications, we further propose an extended algorithm, ANEMONE-FS,
to integrate valuable information in our method. We conduct extensive
experiments under purely unsupervised settings and few-shot anomaly detection
settings, and we demonstrate that the proposed method ANEMONE and its variant
ANEMONE-FS consistently outperform state-of-the-art algorithms on six benchmark
datasets.


title:
Controlling energy conservation in quantum dynamics with independently
  moving basis functions: Application to Multi-Configuration Ehrenfest

abstract:
  Application of the time-dependent variational principle to a linear
combination of frozen-width Gaussians describing the nuclear wavefunction
provides a formalism where the total energy is conserved. The computational
downside of this formalism is that trajectories of individual Gaussians are
solutions of a coupled system of differential equations, limiting
implementation to serial propagation algorithms. To allow for parallelization
and acceleration of the computation, independent trajectories based on
simplified equations of motion were suggested. Unfortunately, within practical
realizations involving finite Gaussian bases, this simplification leads to
breaking the energy conservation. We offer a solution for this problem by using
Lagrange multipliers to ensure the energy and norm conservation regardless of
basis function trajectories or basis completeness. We illustrate our approach
within the Multi-Configuration Ehrenfest method considering a linear vibronic
coupling model.


title: Extended Anderson Criticality in Heavy-Tailed Neural Networks

abstract:
  We investigate the emergence of complex dynamics in networks with
heavy-tailed connectivity by developing a non-Hermitian random matrix theory.
We uncover the existence of an extended critical regime of spatially
multifractal fluctuations between the quiescent and active phases. This
multifractal critical phase combines features of localization and
delocalization and differs from the edge of chaos in classical networks by the
appearance of universal hallmarks of Anderson criticality over an extended
region in phase space. We show that the rich nonlinear response properties of
the extended critical regime can account for a variety of neural dynamics such
as the diversity of timescales, providing a computational advantage for
persistent classification in a reservoir setting.


title:
MusIAC: An extensible generative framework for Music Infilling
  Applications with multi-level Control

abstract:
  We present a novel music generation framework for music infilling, with a
user friendly interface. Infilling refers to the task of generating musical
sections given the surrounding multi-track music. The proposed
transformer-based framework is extensible for new control tokens as the added
music control tokens such as tonal tension per bar and track polyphony level in
this work. We explore the effects of including several musically meaningful
control tokens, and evaluate the results using objective metrics related to
pitch and rhythm. Our results demonstrate that adding additional control tokens
helps to generate music with stronger stylistic similarities to the original
music. It also provides the user with more control to change properties like
the music texture and tonal tension in each bar compared to previous research
which only provided control for track density. We present the model in a Google
Colab notebook to enable interactive generation.


title: An inductive-recursive universe generic for small families

abstract:
  We show that it is possible to construct a universe in all Grothendieck topoi
with injective codes a la Pujet and Tabareau which is nonetheless generic for
small families. As a trivial consequence, we show that their observational type
theory admits interpretations in Grothendieck topoi suitable for use as
internal languages.


title: An Improved EPA based Receiver Design for Uplink LDPC Coded SCMA System

abstract:
  Sparse code multiple access (SCMA) is an emerging paradigm for efficient
enabling of massive connectivity in future machine-type communications (MTC).
In this letter, we conceive the uplink transmissions of the low-density parity
check (LDPC) coded SCMA system. Traditional receiver design of LDPC-SCMA
system, which is based on message passing algorithm (MPA) for multiuser
detection followed by individual LDPC decoding, may suffer from the drawback of
the high complexity and large decoding latency, especially when the system has
large codebook size and/or high overloading factor. To address this problem, we
introduce a novel receiver design by applying the expectation propagation
algorithm (EPA) to the joint detection and decoding (JDD) involving an
aggregated factor graph of LDPC code and sparse codebooks. Our numerical
results demonstrate the superiority of the proposed EPA based JDD receiver over
the conventional Turbo receiver in terms of both significantly lower complexity
and faster convergence rate without noticeable error rate performance
degradation.


title: Cyclical Curriculum Learning

abstract:
  Artificial neural networks (ANN) are inspired by human learning. However,
unlike human education, classical ANN does not use a curriculum. Curriculum
Learning (CL) refers to the process of ANN training in which examples are used
in a meaningful order. When using CL, training begins with a subset of the
dataset and new samples are added throughout the training, or training begins
with the entire dataset and the number of samples used is reduced. With these
changes in training dataset size, better results can be obtained with
curriculum, anti-curriculum, or random-curriculum methods than the vanilla
method. However, a generally efficient CL method for various architectures and
data sets is not found. In this paper, we propose cyclical curriculum learning
(CCL), in which the data size used during training changes cyclically rather
than simply increasing or decreasing. Instead of using only the vanilla method
or only the curriculum method, using both methods cyclically like in CCL
provides more successful results. We tested the method on 18 different data
sets and 15 architectures in image and text classification tasks and obtained
more successful results than no-CL and existing CL methods. We also have shown
theoretically that it is less erroneous to apply CL and vanilla cyclically
instead of using only CL or only vanilla method. The code of Cyclical
Curriculum is available at
https://github.com/CyclicalCurriculum/Cyclical-Curriculum.


title:
Comment on "Self-Consistent-Field Method for Correlated Many-Electron
  Systems with an Entropic Cumulant Energy"

abstract:
  In [Phys. Rev. Lett. 128, 013001 (2022)] a novel ground state method was
proposed. It has been suggested that this $i$-DMFT would be a method within
one-particle reduced density matrix functional theory (DMFT), capable of
describing accurately molecules at various geometries with an
information-theoretical nature. We reassess this work and its suggestions from
a conceptual and practical point of view, leading to the following conclusions:
i) A method which assigns to each molecule $\mathcal{M}$ its own functional
$\mathcal{F}_{\!\mathcal{M}}$ is not a functional theory (striking violation of
"universality") ii) even for the simplest systems $i$-DMFT yields incorrect
one-particle reduced density matrices and iii) the use of an
information-theoretical concept to describe molecular dissociation limits was
not essential. The latter insight may help to fix the deficiency of $i$-DMFT to
not reproduce correctly the smaller occupation numbers and thus to not recover
the important dynamic correlations.


title: Inverse medium scattering for a nonlinear Helmholtz equation

abstract:
  We discuss a time-harmonic inverse scattering problem for a nonlinear
Helmholtz equation with compactly supported inhomogeneous scattering objects
that are described by a nonlinear refractive index in unbounded free space.
Assuming the knowledge of a nonlinear far field operator, which maps Herglotz
incident waves to the far field patterns of corresponding solutions of the
nonlinear scattering problem, we show that the nonlinear index of refraction is
uniquely determined. We also generalize two reconstruction methods, a
factorization method and a monotonicity method, to recover the support of such
nonlinear scattering objects. Numerical results illustrate our theoretical
findings.


title: Spin Hall magnetoresistance effect from a disordered interface

abstract:
  The Spin Hall magnetoresistance (SMR) emerged as a reference tool to
investigate the magnetic properties of materials with an all-electrical set-up.
Its sensitivity to the magnetization of thin films and surfaces may turn it
into a valuable technique to characterize Van der Waals magnetic materials,
which support long range magnetic order in atomically thin layers. However,
realistic surfaces can be affected by defects and disorder, which may result in
unexpected artifacts in the SMR, rather than the sole appearance of electrical
noise. Here, we study the SMR response of heterostructures combining a platinum
(Pt) thin film with the Van der Waals antiferromagnet MnPSe3 and observe a
robust SMR-like signal, which turns out to originate from the presence of
strong interfacial disorder in the system. We use transmission electron
microscopy (TEM) to characterize the interface between MnPSe3 and Pt, revealing
the formation of a few-nanometer-thick platinum-chalcogen amorphous layer. The
analysis of the transport and TEM measurements suggests that the signal arises
from a disordered magnetic system formed at the Pt/MnPSe3 interface, washing
out the interaction between the spins of the Pt electrons and the MnPSe3
magnetic lattice. Our results show that damaged interfaces can yield an
important contribution to SMR, questioning a widespread assumption on the role
of disorder in such measurements.


title:
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural
  Network for Internet Traffic Classification

abstract:
  Traffic classification, i.e. the identification of the type of applications
flowing in a network, is a strategic task for numerous activities (e.g.,
intrusion detection, routing). This task faces some critical challenges that
current deep learning approaches do not address. The design of current
approaches do not take into consideration the fact that networking hardware
(e.g., routers) often runs with limited computational resources. Further, they
do not meet the need for faithful explainability highlighted by regulatory
bodies. Finally, these traffic classifiers are evaluated on small datasets
which fail to reflect the diversity of applications in real commercial
settings. Therefore, this paper introduces a Lightweight, Efficient and
eXplainable-by-design convolutional neural network (LEXNet) for Internet
traffic classification, which relies on a new residual block (for lightweight
and efficiency purposes) and prototype layer (for explainability). Based on a
commercial-grade dataset, our evaluation shows that LEXNet succeeds to maintain
the same accuracy as the best performing state-of-the-art neural network, while
providing the additional features previously mentioned. Moreover, we
demonstrate that LEXNet significantly reduces the model size and inference time
compared to the state-of-the-art neural networks with explainability-by-design
and post hoc explainability methods. Finally, we illustrate the explainability
feature of our approach, which stems from the communication of detected
application prototypes to the end-user, and we highlight the faithfulness of
LEXNet explanations through a comparison with post hoc methods.


title:
The enumeration of meet-irreducible elements based on hierarchical
  decompositions of implicational bases

abstract:
  We study the well-known problem of translating between two representations of
closure systems, namely implicational bases and meet-irreducible elements.
Albeit its importance, the problem is open. In this paper, we introduce splits
of an implicational base. It is a partitioning operation of the implications
which we recursively apply to obtain a binary tree representing a decomposition
of the implicational base. We show that this decomposition can be conducted in
polynomial time and space in the size of the input implicational base. Focusing
on the case of acyclic splits, we obtain a recursive characterization of the
meet-irreducible elements of the associated closure system. We use this
characterization and hypergraph dualization to derive new results for the
translation problem in acyclic convex geometries.


title: Interplay of capillary and Marangoni flows in micropillar evaporation

abstract:
  The evaporation from a micropillar evaporator is a problem governed by
various interfacial phenomena such as the capillarity-induced liquid flow,
thin-film evaporation intensifying near the contact lines, and
thermocapillarity-induced Marangoni flow. However, past research has not been
able to assess the effect of Marangoni flow due to the missing coupling between
cell- and device-level modeling. In this work, we develop a comprehensive model
for the evaporation from a micropillar evaporator by coupling the liquid flow
with the energy transfer in both liquid and solid domains at both cell- and
device-levels. The model is successfully validated with previous experiments.
When the Marangoni number is sufficiently high, the model identifies a sharp
reduction in the evaporator temperature attributed to the thermocapillary
convection creating circulations beneath the liquid-vapor interface, which
results in the formation of periodic reverse surface flows on the interface.
This temperature drop cannot be identified when thermocapillarity is switched
off in the model and the model's prediction substantially deviates from
experimental measurement. Therefore, the current study reveals a hitherto
unexplored role of Marangoni flow in the evaporation of water from micropillar
evaporators.


title:
Hybridization of Capsule and LSTM Networks for unsupervised anomaly
  detection on multivariate data

abstract:
  Deep learning techniques have recently shown promise in the field of anomaly
detection, providing a flexible and effective method of modelling systems in
comparison to traditional statistical modelling and signal processing-based
methods. However, there are a few well publicised issues Neural Networks (NN)s
face such as generalisation ability, requiring large volumes of labelled data
to be able to train effectively and understanding spatial context in data. This
paper introduces a novel NN architecture which hybridises the
Long-Short-Term-Memory (LSTM) and Capsule Networks into a single network in a
branched input Autoencoder architecture for use on multivariate time series
data. The proposed method uses an unsupervised learning technique to overcome
the issues with finding large volumes of labelled training data. Experimental
results show that without hyperparameter optimisation, using Capsules
significantly reduces overfitting and improves the training efficiency.
Additionally, results also show that the branched input models can learn
multivariate data more consistently with or without Capsules in comparison to
the non-branched input models. The proposed model architecture was also tested
on an open-source benchmark, where it achieved state-of-the-art performance in
outlier detection, and overall performs best over the metrics tested in
comparison to current state-of-the art methods.


title: A Sonification of the zCOSMOS Galaxy Dataset

abstract:
  Sonification is the transformation of data into acoustic signals, achievable
through different techniques. Sonification can be defined as a way to represent
data values and relations as perceivable sounds, aiming at facilitating their
communication and interpretation. Like data visualization provides meaning via
images, sonification conveys meaning via sound. Sonification approaches are
useful in a number of scenario. A first case is the possibility to receive
information while keeping other sensory channels free, like in medical
environment, in driving experience, etc. Another scenario addresses an easier
recognition of patterns when data present high dimensionality and cardinality.
Finally, sonification can be applied to presentation and dissemination
initiatives, also with artistic goals. The zCOSMOS dataset contains detailed
data about almost 20000 galaxies, describing the evolution of a relatively
small portion of the universe in the last 10 million years in terms of galaxy
mass, absolute luminosity, redshift, distance, age, and star formation rate.
The present paper proposes a sonification for the mentioned dataset, with the
following goals: i) providing a general description of the dataset, accessible
via sound, which could also make unnoticed patterns emerge; ii) realizing an
artistic but scientifically accurate sonic portrait of a portion of the
universe, thus filling the gap between art and science in the context of
scientific dissemination and so-called "edutainment"; iii) adding value to the
dataset, since also scientific data and achievements must be considered as a
cultural heritage that needs to be preserved and enhanced. Both scientific and
technological aspects of the sonification are addressed.

categories: cs.SD astro-ph.IM cs.CY eess.AS physics.ed-ph physics.soc-ph


title:
Necessary and sufficient conditions for identifiability in the admixture
  model

abstract:
  We consider M SNP data from N individuals who are an admixture of K unknown
ancient populations. Let $\Pi_{si}$ be the frequency of the reference allele of
individual i at SNP s. So the number of reference alleles at SNP s for a
diploid individual is binomially distributed with parameters 2 and $\Pi_{si}$.
We suppose $\Pi_{si}=\sum_{k=1}^KF_{sk}Q_{ki}$, where $F_{sk}$ is the allele
frequency of SNP s in population k and $Q_{ki}$ is the proportion of population
k in the ancestry of individual i.
  I am interested in the identifiability of F and Q, up to a relabelling of the
ancient populations. Under what conditions, when $\Pi =F^1Q^1=F^2Q^2$ are $F^1$
and $F^2$ and $Q^1$ and $Q^2$ equal? I show that the anchor condition (Cabreros
and Storey, 2019) on one matrix together with an independence condition on the
other matrix is sufficient for identifiability.
  I will argue that the proof of the necessary condition in Cabreros and
Storey, 2019 is incorrect, and I will provide a correct proof, which in
addition does not require knowledge of the number of ancestral populations.
  I will also provide abstract necessary and sufficient conditions for
identifiability. I will show that one cannot deviate substantially from the
anchor condition without losing identifiability. Finally, I show necessary and
sufficient conditions for identifiability for the non-admixed case.


title:
Designing a Social Media Analytics Dashboard for Government Agency
  Crisis Communications

abstract:
  Social media have become a valuable source for extracting data about societal
crises and an important outlet to disseminate official information. Government
agencies are increasingly turning to social media to use it as a mouthpiece in
times of crisis. Gaining intelligence through social media analytics, however,
remains a challenge for government agencies, e.g. due to a lack of training and
instruments. To mitigate this shortcoming, government agencies need tools that
support them in analysing social media data for the public good. This paper
presents a design science research approach that guides the development of a
social media analytics dashboard for a regional government agency. Preliminary
results from a workshop and the resulting design of a first prototype are
reported. A user-friendly and responsive design that is secure, flexible, and
quick in use could identified as requirements, as well as information display
of regional discussion statistics, sentiment, and emerging topics.


title:
Injectivity of non-singular planar maps with disconnecting curves in the
  eigenvalues space

abstract:
  Fessler and Gutierrez \cite{Fe,Gu} proved that if a non-singular planar map
has Jacobian matrix without eigenvalues in $(0,+\infty)$, then it is injective.
We prove that the same holds replacing $(0,+\infty)$ with any unbounded curve
disconnecting the upper (lower) complex half-plane. Additionally we prove that
a Jacobian map $(P,Q)$ is injective if $P_x + Q_y$ is not a surjective
function.


title:
Microlocal analysis for Gelfand--Shilov spaces. Part I: Anisotropic wave
  front sets

abstract:
  We introduce an anisotropic global wave front set of Gelfand--Shilov
ultradistributions with different indices for regularity and decay at infinity.
The concept is defined by the lack of super-exponential decay along power type
curves in the phase space of the short-time Fourier transform. This wave front
set captures the phase space behaviour of oscillations of even power monomial
type, a k a chirp signals. A microlocal result is proved with respect to
pseudodifferential operators with symbol classes that give rise to continuous
operators on Gelfand--Shilov spaces. We determine the wave front set of certain
series of derivatives of the Dirac delta, and exponential functions.


title: Condensed matter physics in big discrete time crystals

abstract:
  We review the application of discrete time crystals created in a
Bose-Einstein condensate (BEC) of ultracold atoms bouncing resonantly on an
oscillating mirror to the investigation of condensed matter phenomena in the
time dimension. Such a bouncing BEC system can exhibit dramatic breaking of
time-translation symmetry, allowing the creation of discrete time crystals
having up to about 100 temporal lattice sites and suitable for hosting a broad
range of temporal condensed matter phenomena. We first consider single-particle
condensed matter phenomena in the time dimension which include Anderson
localization due to temporal disorder, topological time crystals, and
quasi-crystal structures in time. We then discuss many-body temporal condensed
matter phenomena including Mott insulator phases in time, many-body
localization in time, many-body topological time crystals and time crystals
having long-range exotic interactions. We also discuss the construction of two
(or three) dimensional time lattices, involving the bouncing of a BEC between
two (or three) orthogonal oscillating mirrors and between two oscillating
mirrors oriented at 45-degrees. The latter configuration supports a versatile
M\"obius strip geometry which can host a variety of two-dimensional time
lattices including a honeycomb time lattice and a Lieb square time lattice.
Finally, we discuss the construction of a six-dimensional time-space lattice
which is based on periodically driven BECs trapped in a three-dimensional
optical lattice.


title:
First-principles investigation of interfacial reconstruction in
  epitaxial SrTiO$_3$/Si photocathodes

abstract:
  Epitaxial SrTiO$_3$ (STO) on Si is nowadays the benchmark initial platform
for the further addition of functional oxides on Si. Starting the growth of STO
on a Sr-passivated Si substrate with 1/2 monolayer (ML) Sr coverage and a (1
$\times$ 2) reconstructed Si surface with rows of Si dimers, the final
STO/Sr/Si stack exhibits 1 ML Sr coverage and a (1 $\times$ 1) Si surface
without dimer. Using first-principles density functional theory calculations,
we investigate how the interface evolves from 1/2 ML to 1 ML Sr coverage,
concluding that the latter is indeed most stable and that the reconstruction of
the interface takes place during the early stage of the layer-by-layer
deposition. Going further, we determine the band alignment of the final stable
interface and assess its potential interest as photocathode for water
reduction.


title: Insertion Time of Random Walk Cuckoo Hashing below the Peeling Threshold

abstract:
  Most hash tables have an insertion time of $O(1)$, possibly qualified as
expected and/or amortised. While insertions into cuckoo hash tables indeed seem
to take $O(1)$ expected time in practice, only polylogarithmic guarantees are
proven in all but the simplest of practically relevant cases. Given the
widespread use of cuckoo hashing to implement compact dictionaries and Bloom
filter alternatives, closing this gap is an important open problem for
theoreticians.
  In this paper, we show that random walk insertions into cuckoo hash tables
take $O(1)$ expected amortised time when any number $k \geq 3$ of hash
functions is used and the load factor is below the corresponding peeling
threshold (e.g. $\approx 0.81$ for $k = 3$). To our knowledge, this is the
first meaningful guarantee for constant time insertion for cuckoo hashing that
works for $k \in \{3,\dots,9\}$.
  In addition to being useful in its own right, we hope that our key-centred
analysis method can be a stepping stone on the path to the true end goal:
$O(1)$ time insertions for all load factors below the load threshold (e.g.
$\approx 0.91$ for $k = 3$).


title: Trace field degrees of Abelian differentials

abstract:
  We prove that every even number $2 \le 2d \le 2g$ is realised as the degree
of a Thurston-Veech pseudo-Anosov stretch factor in every connected component
of every stratum of the moduli space of Abelian differentials.


title:
The Risks, Benefits, and Consequences of Prepublication Moderation:
  Evidence from 17 Wikipedia Language Editions

abstract:
  Many online communities rely on postpublication moderation where contributors
-- even those that are perceived as being risky -- are allowed to publish
material immediately without review. An alternative arrangement involves
moderating content before publication. A range of communities have argued
against prepublication moderation by suggesting that it makes contributing less
enjoyable for new members and that it will distract established community
members with extra moderation work. We present an empirical analysis of the
effects of a prepublication review system called \textit{FlaggedRevs} that was
deployed by several Wikipedia language editions. We used panel data from 17
large Wikipedia editions to test a series of hypotheses related to the effect
of the system on activity levels and contribution quality within the affected
communities. We found that the system was very effective at keeping low-quality
contributions from ever becoming visible. Although there is some evidence that
the system discouraged participation among unregistered users, our analysis
suggests that the system's effects on contribution volume and quality were
moderate at most. Our findings imply that concerns regarding the major negative
effects of prepublication moderation systems on contribution quality, project
productivity, and community sustainability may be overstated.


title: Lightning: Scaling the GPU Programming Model Beyond a Single GPU

abstract:
  The GPU programming model is primarily aimed at the development of
applications that run one GPU. However, this limits the scalability of GPU code
to the capabilities of a single GPU in terms of compute power and memory
capacity. To scale GPU applications further, a great engineering effort is
typically required: work and data must be divided over multiple GPUs by hand,
possibly in multiple nodes, and data must be manually spilled from GPU memory
to higher-level memories.
  We present Lightning: a framework that follows the common GPU programming
paradigm but enables scaling to large problems with ease. Lightning supports
multi-GPU execution of GPU kernels, even across multiple nodes, and seamlessly
spills data to higher-level memories (main memory and disk). Existing CUDA
kernels can easily be adapted for use in Lightning, with data access
annotations on these kernels allowing Lightning to infer their data
requirements and the dependencies between subsequent kernel launches. Lightning
efficiently distributes the work/data across GPUs and maximizes efficiency by
overlapping scheduling, data movement, and kernel execution when possible.
  We present the design and implementation of Lightning, as well as
experimental results on up to 32 GPUs for eight benchmarks and one real-world
application. Evaluation shows excellent performance and scalability, such as a
speedup of 57.2x over the CPU using Lighting with 16 GPUs over 4 nodes and 80
GB of data, far beyond the memory capacity of one GPU.


title:
The Factorial-Basis Method for Finding Definite-Sum Solutions of Linear
  Recurrences With Polynomial Coefficients

abstract:
  The problem of finding a nonzero solution of a linear recurrence $Ly = 0$
with polynomial coefficients where $y$ has the form of a definite
hypergeometric sum, related to the Inverse Creative Telescoping Problem of
[14][Sec. 8], has now been open for three decades. Here we present an algorithm
(implemented in a SageMath package) which, given such a recurrence and a
quasi-triangular, shift-compatible factorial basis $\mathcal{B} = \langle
P_k(n)\rangle_{k=0}^\infty$ of the polynomial space $\mathbb{K}[n]$ over a
field $\mathbb{K}$ of characteristic zero, computes a recurrence satisfied by
the coefficient sequence $c = \langle c_k\rangle_{k=0}^\infty$ of the solution
$y_n = \sum_{k=0}^\infty c_kP_k(n)$ (where, thanks to the quasi-triangularity
of $\mathcal{B}$, the sum on the right terminates for each $n \in \mathbb{N}$).
More generally, if $\mathcal{B}$ is $m$-sieved for some $m \in \mathbb{N}$, our
algorithm computes a system of $m$ recurrences satisfied by the $m$-sections of
the coefficient sequence $c$. If an explicit nonzero solution of this system
can be found, we obtain an explicit nonzero solution of $Ly = 0$.


title: Derivatives in Proton CT

abstract:
  Algorithmic derivatives can be useful to quantify uncertainties and optimize
parameters using computer simulations. Whether they actually are, depends on
how "well-linearizable" the program is.
  Proton computed tomography (pCT) is a medical imaging technology with the
potential to increase the spatial accuracy of the dose delivered in proton-beam
radiotherapy. The Bergen pCT collaboration is developing and constructing a
digital tracking calorimeter (DTC) to measure the position, direction and
energy of protons after they passed through a patient, and a software pipeline
to process these data into a pCT image.
  We revisit the software pipeline from the perspective of algorithmic
differentiation (AD). In the early subprocedures, several obstacles such as
discrete variables or frequent discontinuities were identified, and are
probably tackled best by using surrogate models. The model-based iterative
reconstruction (MBIR) subprocedure in the end seems to be AD-ready, and we
propose changes in the AD workflow that can reduce the memory consumption in
reverse mode.


title:
Stellar mergers as the origin of the blue main-sequence band in young
  star clusters

abstract:
  Recent high-quality Hubble Space Telescope (HST) photometry shows that the
main sequences (MS) stars of young star clusters form two discrete components
in the color-magnitude diagram (CMD). Based on their distribution in the CMD,
we show that stars of the blue MS component can be understood as slow rotators
originating from stellar mergers. We derive the masses of the blue MS stars,
and find that they follow a nearly flat mass function, which supports their
unusual formation path. Our results imply that the cluster stars gain their
mass in two different ways, by disk accretion leading to rapid rotation,
contributing to the red MS, or by binary merger leading to slow rotation and
populating the blue MS. We also derive the approximate merger time of the
individual stars of the blue MS component, and find a strong early peak in the
merger rate, with a lower level merger activity prevailing for tens of Myr.
This supports recent binary formation models, and explains new velocity
dispersion measurements for members of young star clusters. Our findings shed
new light on the origin of the bi-modal mass, spin, and magnetic field
distributions of main-sequence stars.


title: On characterising assemblages in Einstein-Podolsky-Rosen scenarios

abstract:
  Characterising non-classical quantum phenomena is crucial not only from a
fundamental perspective, but also to better understand its capabilities for
information processing and communication tasks. In this work, we focus on
exploring the characterisation of Einstein-Podolsky-Rosen inference (a.k.a.
steering): a signature of non-classicality manifested when one or more parties
in a Bell scenario have their systems and measurements described by quantum
theory, rather than being treated as black boxes. We propose a way of
characterising common-cause assemblages from the correlations that arise when
the trusted party performs tomographically-complete measurements on their share
of the experiment, and discuss the advantages and challenges of this approach.
Within this framework, we show that so-called almost quantum assemblages
satisfy the principle of macroscopic noncontextuality, and demonstrate that a
subset of almost quantum correlations recover almost quantum assemblages in
this approach.


title: Improved bounds for randomly colouring simple hypergraphs

abstract:
  We study the problem of sampling almost uniform proper $q$-colourings in
$k$-uniform simple hypergraphs with maximum degree $\Delta$. For any $\delta >
0$, if $k \geq\frac{20(1+\delta)}{\delta}$ and $q \geq
100\Delta^{\frac{2+\delta}{k-4/\delta-4}}$, the running time of our algorithm
is $\tilde{O}(\mathrm{poly}(\Delta k)\cdot n^{1.01})$, where $n$ is the number
of vertices. Our result requires fewer colours than previous results for
general hypergraphs (Jain, Pham, and Voung, 2021; He, Sun, and Wu, 2021), and
does not require $\Omega(\log n)$ colours unlike the work of Frieze and Anastos
(2017).


title:
A note on the spectral analysis of matrix sequences via GLT momentary
  symbols: from all-at-once solution of parabolic problems to distributed
  fractional order matrices

abstract:
  The first focus of this paper is the characterization of the spectrum and the
singular values of the coefficient matrix stemming from the discretization with
space-time grid for a parabolic diffusion problem and from the approximation of
distributed order fractional equations. For this purpose we will use the
classical GLT theory and the new concept of GLT momentary symbols. The first
permits to describe the singular value or eigenvalue asymptotic distribution of
the sequence of the coefficient matrices, the latter permits to derive a
function, which describes the singular value or eigenvalue distribution of the
matrix of the sequence, even for small matrix-sizes but under given
assumptions. The note is concluded with a list of open problems, including the
use of our machinery in the study of iteration matrices, especially those
concerning multigrid-type techniques.


title: Boosting jittering jets by neutrino heating in core collapse supernovae

abstract:
  I estimate the energy that neutrino heating add to the outflow that jets
induce in the collapsing core material in core collapse supernovae (CCSNe), and
find that this energy crudely doubles the energy that the jets deposit into the
core. I consider the jittering jets explosion mechanism where there are several
stochastic jet-launching episodes, each lasting for about 0.01-0.1 seconds. The
collapsing core material pass through the stalled shock at about 100 km and
then slowly flows onto the proto-neutron star (NS). I assume that the proto-NS
launches jittering jets, and that the jets break out from the stalled shock. I
examine the boosting process by which the high-pressure gas inside the stalled
shock, the gain region material, expands alongside the jets and does work on
the material that the jets shock, the cocoon. This work is crudely equal to the
energy that the original jets carry. I argue that the coupling between
instabilities, stochastic rotation, magnetic fields, and jittering jets lead to
most CCSN explosions. In other cases, the pre-collapse core is rapidly rotating
and therefore ordered rotation replaces stochastic rotation and fix jets
replace jittering jets.


title:
Polynomial bounds for chromatic number. V. Excluding a tree of radius
  two and a complete multipartite graph

abstract:
  The Gy\'arf\'as-Sumner conjecture says that for every forest $H$ and every
integer $k$, if $G$ is $H$-free and does not contain a clique on $k$ vertices
then it has bounded chromatic number. (A graph is $H$-free if it does not
contain an induced copy of $H$.) Kierstead and Penrice proved it for trees of
radius at most two, but otherwise the conjecture is known only for a few simple
types of forest. More is known if we exclude a complete bipartite subgraph
instead of a clique: R\"odl showed that, for every forest $H$, if $G$ is
$H$-free and does not contain $K_{t,t}$ as a subgraph then it has bounded
chromatic number. In an earlier paper with Sophie Spirkl, we strengthened
R\"odl's result, showing that for every forest $H$, the bound on chromatic
number can be taken to be polynomial in $t$. In this paper, we prove a related
strengthening of the Kierstead-Penrice theorem, showing that for every tree $H$
of radius two and every integer $d\ge 2$, if $G$ is $H$-free and does not
contain as a subgraph the complete $d$-partite graph with parts of cardinality
$t$, then its chromatic number is at most polynomial in $t$.


title:
First principles calculations of double resonance Raman spectra for
  monolayer MoTe$_2$

abstract:
  Since double resonance Raman (DRR) spectra are laser-energy dependent, the
first-principles calculations of DRR for two-dimensional materials are
challenging. Here, the DRR spectrum of monolayer MoTe$_2$ is calculated by
home-made program, in which we combine {\em ab-initio}
density-functional-theory calculations with the electron-phonon Wannier (EPW)
method. Within the fourth-order perturbation theory, we are able to quantify
not only the electron-photon matrix elements within the dipole approximation,
but also the electron-phonon matrix elements using the Wannier functions. The
reasonable agreement between the calculated and experimental Raman spectra is
achieved, in which we reproduce some distinctive features of transition metal
dichalcogenides (TMDCs) from graphene (for example, the dominant intervalley
process involving an electron or a hole). Furthermore, we perform an analysis
of the possible DRR modes over the Brillouin zone, highlighting the role of
low-symmetry points. Raman tensors for some DRR modes are given by first
principles calculations from which laser polarization dependence is obtained.


title:
New perspectives on asymmetric bending behavior: A lesson learned from
  leaves

abstract:
  Designing materials or structures that can achieve asymmetric shape-shifting
in response to symmetrically switching stimuli is a promising approach to
enhance the locomotion performance of soft actuators/robots. Inspired by the
geometry of slender leaves of many plants, we find that the thin-walled beam
with a U-shaped cross section exhibits asymmetric deformation behaviors under
bending with opposite orientations. Although this novel mechanical property has
been long noticed and utilized in some applications, its mechanism is still
unclear so far. In this study, we attribute this asymmetric bending behavior of
thin-walled U-shaped beams to the buckling of sidewalls caused by the
bending-induced compressive effect. Based on the Euler-Bernoulli beam theory
and Kirchhoff-Love thin plate theory, a simple but efficient model is
established to derive the critical moment for the sidewall buckling in a
semi-analytical form. Finite element analysis simulations and experiments are
employed to validate the theoretical foundations of our findings. The results
of our work not only shed light on the mechanics underlying the asymmetric
bending behavior of thin-walled U-shaped beams, but also open up new avenues
for the structure design of high-performance soft actuators/robots and other
novel devices.


title: Controlling Confusion via Generalisation Bounds

abstract:
  We establish new generalisation bounds for multiclass classification by
abstracting to a more general setting of discretised error types. Extending the
PAC-Bayes theory, we are hence able to provide fine-grained bounds on
performance for multiclass classification, as well as applications to other
learning problems including discretisation of regression losses. Tractable
training objectives are derived from the bounds. The bounds are uniform over
all weightings of the discretised error types and thus can be used to bound
weightings not foreseen at training, including the full confusion matrix in the
multiclass classification case.


title: Spin-triplet superconductivity in Weyl nodal-line semimetals

abstract:
  Topological semimetals are three dimensional materials with
symmetry-protected massless bulk excitations. As a special case, Weyl
nodal-line semimetals are realized in materials either having no inversion or
broken time-reversal symmetry and feature bulk nodal lines. The 111-family of
materials, LaNiSi, LaPtSi and LaPtGe (all lacking inversion symmetry), belong
to this class. Here, by combining muon-spin rotation and relaxation with
thermodynamic measurements, we find that these materials exhibit a fully-gapped
superconducting ground state, while spontaneously breaking time-reversal
symmetry at the superconducting transition. Since time-reversal symmetry is
essential for protecting the normal-state topology, its breaking upon entering
the superconducting state should remarkably result in a topological phase
transition. By developing a minimal model for the normal-state band structure
and assuming a purely spin-triplet pairing, we show that the superconducting
properties across the family can be described accurately. Our results
demonstrate that the 111-family reported here provides an ideal test-bed for
investigating the rich interplay between the exotic properties of Weyl
nodal-line fermions and unconventional superconductivity.


title: Perfect Matchings and Quantum Physics: Progress on Krenn's Conjecture

abstract:
  In 2017, Krenn reported that certain problems related to the perfect
matchings and colourings of graphs emerge out of studying the constructability
of general quantum states using modern photonic technologies. He realized that
if we can prove that the \emph{weighted matching index} of a graph, a parameter
defined in terms of perfect matchings and colourings of the graph is at most 2,
that could lead to exciting insights on the potential of resources of quantum
inference. Motivated by this, he conjectured that the {weighted matching index}
of any graph is at most 2. The first result on this conjecture was by Bogdanov,
who proved that the \emph{(unweighted) matching index} of graphs
(non-isomorphic to $K_4$) is at most 2, thus classifying graphs non-isomorphic
to $K_4$ into Type 0, Type 1 and Type 2. By definition, the weighted matching
index of Type 0 graphs is 0. We give a structural characterization for Type 2
graphs, using which we settle Krenn's conjecture for Type 2 graphs. Using this
characterization, we provide a simple $O(|V||E|)$ time algorithm to find the
unweighted matching index of any graph. In view of our work, Krenn's conjecture
remains to be proved only for Type 1 graphs. We give upper bounds for the
weighted matching index in terms of connectivity parameters for such graphs.
Using these bounds, for a slightly simplified version, we settle Krenn's
conjecture for the class of graphs with vertex connectivity at most 2 and the
class of graphs with maximum degree at most 4.
  Krenn has been publicizing his conjecture in various ways since 2017. He has
even declared a reward for a resolution of his conjecture. We hope that this
article will popularize the problem among computer scientists.


title:
The X-ray activity of the young solar-like star Kepler-63 and the
  structure of its corona

abstract:
  The X-ray satellite XMM-Newton has so far revealed coronal cycles in seven
solar-like stars. In this sample, the youngest stars $\epsilon$ Eridani (400
Myr) and $\iota$ Horologii (600 Myr) display the shortest X-ray cycles and the
smallest cycle amplitudes. The corona of $\epsilon$ Eridani was modelled in
terms of solar magnetic structures (active regions, cores of active regions and
flares) at varying filling factors. The study revealed that 65-95% of its
corona is covered with magnetic structures, and this was held responsible for
the low X-ray cycle amplitude. It was also hypothesized that the basal surface
coverage with magnetic structures may be higher on the corona of younger
solar-like stars. To investigate this hypothesis, we study the solar-like star
Kepler-63 in the X-rays. With an age of 210 Myr and a photospheric cycle of
1.27 yr, it is so far the youngest star observed in X-rays with the aim of
revealing a coronal cycle. In the long-term X-ray lightcurve we do not reveal a
periodic variation of the X-ray luminosity, but a factor two change would be
possible. As for the case of $\epsilon$ Eridani, we modelled the corona of
Kepler-63 with magnetic structures observed on the Sun. The study suggests that
100% of the corona is composed of cores and flares of Class M, justifying the
absence of an X-ray cycle and confirming the analogous results derived for
$\epsilon$ Eridani. Finally, we establish an empirical relation between the
cycle amplitude and the X-ray surface flux . From the absence of a coronal
cycle in Kepler-63 we infer that stars with higher X-ray flux than Kepler-63
must host a significant fraction of higher-energetic flares than those
necessary to model the corona of Kepler-63. Our study opens new ground for
studies of the solar-stellar analogy and the joint exploration of resolved and
unresolved variability in stellar X-ray lightcurves.


title:
A Partial Reciprocity-based Channel Prediction Framework for FDD massive
  MIMO with High Mobility

abstract:
  Massive multiple-input multiple-output (MIMO) is believed to deliver
unrepresented spectral efficiency gains for 5G and beyond. However, a practical
challenge arises during its commercial deployment, which is known as the "curse
of mobility". The performance of massive MIMO drops alarmingly when the
velocity level of user increases. In this paper, we tackle the problem in
frequency division duplex (FDD) massive MIMO with a novel Channel State
Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD)
wideband beamformer is proposed for channel training. Our idea consists in the
exploitation of the partial channel reciprocity of FDD and the
angle-delay-Doppler channel structure. More precisely, the base station (BS)
estimates the angle-delay-Doppler information of the UL channel based on UL
pilots using Matrix Pencil method. It then computes the wideband JADD
beamformers according to the extracted parameters. Afterwards, the user
estimates and feeds back some scalar coefficients for the BS to reconstruct the
predicted DL channel. Asymptotic analysis shows that the CSI prediction error
converges to zero when the number of BS antennas and the bandwidth increases.
Numerical results with industrial channel model demonstrate that our framework
can well adapt to high speed (350 km/h), large CSI delay (10 ms) and channel
sample noise.


title:
Dynamic crystallography reveals spontaneous anisotropy in thermoelectric
  GeTe

abstract:
  Peak performance in energy materials is associated with high symmetry and
atomic fluctuations. Indeed cubic thermoelectrics, phase change memories,
photovoltaics etc are often claimed to be highly disordered or 'intrinsically
nanostructured'. In GeTe and related IV-VI compounds, this is said to provide
the low thermal and high electronic conductivities needed for thermoelectric
applications. However, a general theory linking these empirical observations to
transport properties is lacking. Since conventional crystallography cannot
distinguish between disorder and atomic motions, we develop the energy-resolved
variable-shutter pair distribution function (vsPDF) technique. We show that the
atomic lattice of GeTe is nearly perfectly crystalline at all temperatures, but
hosts remarkably large and anisotropic dynamics. Along the <111>_c direction,
motion is almost uncorrelated, however, interactions are strengthened along the
<100>c direction. We show that this anisotropy naturally emerges from a
Ginzburg-Landau model which couples polarisation fluctuations through
long-range elastic interactions. We propose that the liquid-like optical
phonons screen charge carriers from scattering, as found in the lead halide
hybrid perovksites. This explains the contradictory properties of c-GeTe, which
conducts heat like a glass, yet electricity like a crystal. Coupling of the
resultant ferroelectric large polarons to elastic anisotropy is likely
ubiquitous in the IV-VI materials, ferroelectrics and photovoltaics, allowing
strain engineering of novel optoelectronic properties


title:
Generalized Lipschitz numbers, fine differentiability, and
  quasiconformal mappings

abstract:
  We introduce a generalized version of the local Lipschitz number
$\textrm{lip}\,u$, and show that it can be used to characterize Sobolev
functions $u\in W_{\textrm{loc}}^{1,p}(\mathbb R^n)$, $1\le p\le \infty$. This
concept turns out to be fruitful for studying, and for establishing new
connections between, a wide range of topics including fine differentiability,
Rademacher's theorem, regularity of maximal functions, quasiconformal mappings,
Alberti's rank one theorem, as well as generalizations to metric measure
spaces.


title: Shuffle Private Linear Contextual Bandits

abstract:
  Differential privacy (DP) has been recently introduced to linear contextual
bandits to formally address the privacy concerns in its associated personalized
services to participating users (e.g., recommendations). Prior work largely
focus on two trust models of DP: the central model, where a central server is
responsible for protecting users sensitive data, and the (stronger) local
model, where information needs to be protected directly on user side. However,
there remains a fundamental gap in the utility achieved by learning algorithms
under these two privacy models, e.g., $\tilde{O}(\sqrt{T})$ regret in the
central model as compared to $\tilde{O}(T^{3/4})$ regret in the local model, if
all users are unique within a learning horizon $T$. In this work, we aim to
achieve a stronger model of trust than the central model, while suffering a
smaller regret than the local model by considering recently popular shuffle
model of privacy. We propose a general algorithmic framework for linear
contextual bandits under the shuffle trust model, where there exists a trusted
shuffler in between users and the central server, that randomly permutes a
batch of users data before sending those to the server. We then instantiate
this framework with two specific shuffle protocols: one relying on privacy
amplification of local mechanisms, and another incorporating a protocol for
summing vectors and matrices of bounded norms. We prove that both these
instantiations lead to regret guarantees that significantly improve on that of
the local model, and can potentially be of the order $\tilde{O}(T^{3/5})$ if
all users are unique. We also verify this regret behavior with simulations on
synthetic data. Finally, under the practical scenario of non-unique users, we
show that the regret of our shuffle private algorithm scale as
$\tilde{O}(T^{2/3})$, which matches that the central model could achieve in
this case.


title: On change of measure inequalities for $f$-divergences

abstract:
  We propose new change of measure inequalities based on $f$-divergences (of
which the Kullback-Leibler divergence is a particular case). Our strategy
relies on combining the Legendre transform of $f$-divergences and the
Young-Fenchel inequality. By exploiting these new change of measure
inequalities, we derive new PAC-Bayesian generalisation bounds with a
complexity involving $f$-divergences, and holding in mostly unchartered
settings (such as heavy-tailed losses). We instantiate our results for the most
popular $f$-divergences.

categories: stat.ML cs.IT cs.LG math.IT math.PR math.ST stat.TH


title:
RIS-Aided Wireless Communications: Extra Degrees of Freedom via Rotation
  and Location Optimization

abstract:
  We consider the extra degree of freedom offered by the rotation of the
reconfigurable intelligent surface (RIS) plane and investigate its potential in
improving the performance of RIS-assisted wireless communication systems. By
considering radiation pattern modeling at all involved nodes, we first derive
the composite channel gain and present a closed-form upper bound for the system
ergodic capacity over cascade Rician fading channels. Then, we reconstruct the
composite channel gain by taking the rotations at the RIS plane, transmit
antenna, and receive antenna into account, and extract the optimal rotation
angles after investigating their impacts on the capacity. Moreover, we present
a location-dependent expression of the ergodic capacity and investigate the RIS
deployment strategy, i.e. the joint rotation adjustment and location selection.
Finally, simulation results verify the accuracy of the theoretical analyses and
deployment strategy. Although the RIS location has a big impact on the
performance, our results showcase that the RIS rotation plays a more important
role. In other words, we can obtain a considerable improvement by properly
rotating the RIS rather than moving it over a wide area. For instance, we can
achieve more than 200\% performance improvement through rotating the RIS by
42.14$^{\circ}$, while an 150\% improvement is obtained by shifting the RIS
over 400 meters.


title: Emergence of a superglass phase in the random hopping Bose-Hubbard model

abstract:
  We study an experimentally feasible system of strongly correlated bosons with
random hoppings, described by the infinite-range Bose-Hubbard model on a
lattice with hopping integrals given by independent random variables of
Gaussian distribution with non-zero mean. We solve this quantum model in the
thermodynamic limit, employing the replica method and the Trotter-Suzuki
formula. We find and describe a superglass phase that emerges at the interface
between glass and superfluid phases. Both glassy and long-range orderings are
present in the superglass and compete with each other, as revealed by the
anticorrelation of their order parameters. We present phase diagrams in various
cross-sections of the multidimensional space of system parameters. In selected
parameter subspaces, we compare the results to those of non-disordered,
diagonally-disordered, and once celebrated spin-glass systems.


title:
Temperature control of diffusive memristor hysteresis and artificial
  neuron spiking

abstract:
  Memristive devices are promising elements for energy-efficient neuromorphic
computing and future artificial intelligence systems. For diffusive memristors,
the device state switching occurs because of the sequential formation and
disappearance of conduction pillars between device terminals due to the drift
and diffusion of Ag nanoparticles in the dielectric matrix. This process is
governed by the application of the voltage to the device contacts. Here, both
in experiment and in theory we demonstrate that varying temperature offers an
efficient control of memristor states and charges transport in the device. We
found out that by raising and lowering the device temperature, one can reset
the memristor state as well as change the residual time the memristor stays in
high and low resistive states when the current spiking is generated in the
memristive circuit at a constant applied voltage. Our theoretical model
demonstrates a good qualitative agreement with the experiments and helps to
explain the effects reported.


title:
STEP: State Estimator for Legged Robots Using a Preintegrated foot
  Velocity Factor

abstract:
  We propose a novel state estimator for legged robots, STEP, achieved through
a novel preintegrated foot velocity factor. In the preintegrated foot velocity
factor, the usual non-slip assumption is not adopted. Instead, the end effector
velocity becomes observable by exploiting the body speed obtained from a stereo
camera. In other words, the preintegrated end effector's pose can be estimated.
Another advantage of our approach is that it eliminates the necessity for a
contact detection step, unlike the typical approaches. The proposed method has
also been validated in harsh-environment simulations and real-world experiments
containing uneven or slippery terrains.


title: Very Pwnable Network: Cisco AnyConnect Security Analysis

abstract:
  Corporate Virtual Private Networks (VPNs) enable users to work from home or
while traveling. At the same time, VPNs are tied to a company's network
infrastructure, forcing users to install proprietary clients for network
compatibility reasons. VPN clients run with high privileges to encrypt and
reroute network traffic. Thus, bugs in VPN clients pose a substantial risk to
their users and in turn the corporate network. Cisco, the dominating vendor of
enterprise network hardware, offers VPN connectivity with their AnyConnect
client for desktop and mobile devices. While past security research primarily
focused on the AnyConnect Windows client, we show that Linux and iOS are based
on different architectures and have distinct security issues. Our reverse
engineering as well as the follow-up design analysis and fuzzing reveal 13 new
vulnerabilities. Seven of these are located in the Linux client. The root cause
for privilege escalations on Linux is anchored so deep in the client's
architecture that it only got patched with a partial workaround. A similar
analysis on iOS uncovers three AnyConnect-specific bugs as well as three
general issues in iOS network extensions, which apply to all kinds of VPNs and
are not restricted to AnyConnect.


title:
Resummed lattice QCD equation of state at finite baryon density:
  strangeness neutrality and beyond

abstract:
  We calculate a resummed equation of state with lattice QCD simulations at
imaginary chemical potentials. This work presents a generalization of the
scheme introduced in 2102.06660 to the case of non-zero $\mu_S$, focusing on
the line of strangeness neutrality. We present results up to $\mu_B/T \leq 3.5$
on the strangeness neutral line $\left\langle S \right\rangle = 0$ in the
temperature range $130 \rm{MeV} \leq T \leq 280 \rm{MeV}$. We also extrapolate
the finite baryon density equation of state to small non-zero values of the
strangeness-to-baryon ratio $R=\left\langle S \right\rangle / \left\langle B
\right\rangle$. We perform a continuum extrapolation using lattice simulations
of the 4stout-improved staggered action with 8, 10, 12 and 16 timeslices.


title: Witten's Cubic Open String Field Theory on multiple $Dp$-branes

abstract:
  We study Witten's cubic open string field theory on multiple $Dp$-branes. On
multiple $Dp$-branes the string fields carry $U(N)$ group indices. Mapping the
string world-sheet onto the upper half complex plane and evaluating the
Polyakov string path integral of Witten's cubic open string, we obtain a Fock
space representation of one, two and three-string vertex operators. In the low
energy region, the system is described by massless gauge fields and massless
scalar fields, carrying $U(N)$ group indices. The two-string vertex induces
mass terms for the gauge fields and the scalar fields. The cubic interaction
reduces correctly to the cubic term of the non-Abelian gauge fields, cubic
interaction terms of the gauge fields, and the scalar fields on $(p+1)$
dimensional space. Thus, Witten's string field theory may be a useful tool to
study the quantum dynamics of open strings on multiple $Dp$-branes.


title:
Jarzyski's equality and Crooks' fluctuation theorem for general Markov
  chains

abstract:
  We define common thermodynamic concepts purely within the framework of
general Markov chains and derive Jarzynski's equality and Crooks' fluctuation
theorem in this setup. Regarding non-equilibrium thermodynamics, we provide a
new proof of Jarzynski's equality without physical reversibility assumptions.
Moreover, we discuss an asymmetry in the definition of work that appears in the
usual formulation of Crooks' fluctuation theorem, but only surfaces in the
non-continuous case. We show how this asymmetry can be cured, requiring,
however, a new condition regarding the energy protocol. We notice, because of
the way in which one can prepare a thermodynamic system in the Boltzmann
distribution, the new requirement is fulfilled by all previous experimental
setups supporting Crooks' fluctuation theorem.


title: A Quick Repair Facility for Debugging

abstract:
  Modern development environments provide a widely used auto-correction
facility for quickly repairing syntactic errors. Auto-correction cannot deal
with semantic errors, which are much more difficult to repair. Automated
program repair techniques, designed for repairing semantic errors, are not
well-suited for interactive use while debugging, as they typically assume the
existence of a high-quality test suite and take considerable time. To bridge
the gap, we developed ROSE, a tool to suggest quick-yet-effective repairs of
semantic errors during debugging. ROSE does not rely on a test suite. Instead,
it assumes a debugger stopping point where a problem is observed. It asks the
developer to quickly describe what is wrong, performs a light-weight fault
localization to identify potential responsible locations, and uses a
generate-and-validate strategy to produce and validate repairs. Finally, it
presents the results so the developer can choose and make the appropriate
repair. To assess its utility, we implemented a prototype of ROSE that works in
the Eclipse IDE and applied it to two benchmarks, QuixBugs and Defects4J, for
repair. ROSE was able to suggest correct repairs for 17 QuixBugs and 16
Defects4J errors in seconds.


title:
Sharp weighted log-Sobolev inequalities: characterization of equality
  cases and applications

abstract:
  By using optimal mass transport theory, we provide a direct proof to the
sharp $L^p$-log-Sobolev inequality $(p\geq 1)$ involving a log-concave
homogeneous weight on an open convex cone $E\subseteq \mathbb R^n$. The perk of
this proof is that it allows to characterize the extremal functions realizing
the equality cases in the $L^p$-log-Sobolev inequality. The characterization of
the equality cases is new for $p\geq n$ even in the unweighted setting and
$E=\mathbb R^n$. As an application, we provide a sharp weighted
hypercontractivity estimate for the Hopf-Lax semigroup related to the
Hamilton-Jacobi equation, characterizing also the equality cases.


title:
Universality of replica-symmetry breaking in the transverse field
  Sherrington-Kirkpatrick model

abstract:
  The existence theorem for replica-symmetry breaking (RSB) in the transverse
field Sherrington-Kirkpatrick (SK) model is extended to the model with a
general random exchange interactions. The relation between the expectation
value of the exchange interaction energy and the Duhamel correlation function
of spin operators can be obtained by an approximate integration by parts for
general random interactions. A lower bound on the ground state energy is
evaluated by the square root interpolation of the free energy per single spin.
In addition to the Falk-Bruch inequality, these explicit evaluations enable us
to prove that the variance of overlap between two replica spin operators does
not vanish under sufficiently weak transverse field in sufficiently low
temperature. The absence of the ferromagnetic long range order is also shown to
distinguish RSB from the $\mathbb Z_2$-symmetry breaking.


title:
Central limit theorems for generalized descents and generalized
  inversions in finite root systems

abstract:
  We consider generalized inversions and descents in finite Weyl groups. We
establish Coxeter-theoretic properties of indicator random variables of
positive roots such as the covariance of two such indicator random variables.
We then compute the variances of generalized inversions and descents in
classical types. We finally use the dependency graph method to prove central
limit theorems for general antichains in root posets and in particular for
generalized descents, and then for generalized inversions.


title: Longitudinal dynamics for mesons on the light cone

abstract:
  We survey a set of proposed light-front models for confinement in the valence
quark sector of the mesons and portray similarities as well as differences. We
present the spectroscopies for the light mesons that result from a selection of
longitudinal confinement forms. We note that the Sturm-Liouville theory
provides a unifying framework for many elements of this comparison.


title: Menger-type connectivity of line graphs of faulty hypercubes

abstract:
  A connected graph $G$ is called strongly Menger edge connected if $G$ has
min\{deg$_G(x)$, deg$_G(y)$\} edge-disjoint paths between any two distinct
vertices $x$ and $y$ in $G$. In this paper, we consider two types of strongly
Menger edge connectivity of the line graphs of $n$-dimensional hypercube-like
networks with faulty edges, namely the $m$-edge-fault-tolerant and
$m$-conditional edge-fault-tolerant strongly Menger edge connectivity. We show
that the line graph of any $n$-dimensional hypercube-like network is
$(2n-4)$-edge-fault-tolerant strongly Menger edge connected for $n\geq 3$ and
$(4n-10)$-conditional edge-fault-tolerant strongly Menger edge connected for
$n\geq 4$. The two bounds for the maximum number of faulty edges are best
possible.