title: Certifying Out-of-Domain Generalization for Blackbox Functions

abstract:
  Certifying the robustness of model performance under bounded data
distribution shifts has recently attracted intensive interests under the
umbrella of distributional robustness. However, existing techniques either make
strong assumptions on the model class and loss functions that can be certified,
such as smoothness expressed via Lipschitz continuity of gradients, or require
to solve complex optimization problems. As a result, the wider application of
these techniques is currently limited by its scalability and flexibility --
these techniques often do not scale to large-scale datasets with modern deep
neural networks or cannot handle loss functions which may be non-smooth, such
as the 0-1 loss. In this paper, we focus on the problem of certifying
distributional robustness for black box models and bounded losses, without
other assumptions. We propose a novel certification framework given bounded
distance of mean and variance of two distributions. Our certification technique
scales to ImageNet-scale datasets, complex models, and a diverse range of loss
functions. We then focus on one specific application enabled by such
scalability and flexibility, i.e., certifying out-of-domain generalization for
large neural networks and loss functions such as accuracy and AUC. We
experimentally validate our certification method on a number of datasets,
ranging from ImageNet, where we provide the first non-vacuous certified
out-of-domain generalization, to smaller classification tasks where we are able
to compare with the state-of-the-art and show that our method performs
considerably better.


title:
State-of-the-Art Methods for Exposure-Health Studies: results from the
  Exposome Data Challenge Event

abstract:
  The exposome recognizes that individuals are exposed simultaneously to a
multitude of different environmental factors and takes a holistic approach to
the discovery of etiological factors for disease. However, challenges arise
when trying to quantify the health effects of complex exposure mixtures.
Analytical challenges include dealing with high dimensionality, studying the
combined effects of these exposures and their interactions, integrating causal
pathways, and integrating omics layers. To tackle these challenges, ISGlobal
Exposome Hub held a data challenge event open to researchers from all over the
world and from all expertises. Analysts had a chance to compete and apply
state-of-the-art methods on a common partially simulated exposome dataset
(based on real case data from the HELIX project) with multiple correlated
exposure variables (P>100) arising from general and personal environments at
different time points, biological molecular data (multi-omics: DNA methylation,
gene expression, proteins, metabolomics) and multiple clinical phenotypes in
1301 mother-child pairs. Most of the methods presented included feature
selection or feature reduction to deal with the high dimensionality of the
exposome dataset. Several approaches explicitly searched for combined effects
of exposures and/or their interactions using linear index models or response
surface methods, including Bayesian methods. Other methods dealt with the
multi-omics dataset in mediation analyses using multiple-step approaches. Here
we discuss the statistical models and provide the data and codes used, so that
analysts have examples of implementation and can learn how to use these
methods. Overall, the exposome data challenge presented a unique opportunity
for researchers from different disciplines to create and share methods, setting
a new standard for open science in the exposome and environmental health field.


title:
Domain Decomposition in space-time of 4D-VAR Data Assimilation problem:
  a case study on the ROMS software

abstract:
  Domain Decomposition of 4D-VAR Data Assimilation (DD-4DVAR) is made up of
decomposition of the spate-time domain, solution of reduced forecast model and
minimization of local 4D-VAR functionals. Relying on the existing software
implementation of ROMS software, we describe main components of DD-4D VAR DA
method, highlighting the topics that we will should address both on the
mathematical problem underlying ROMS and the MPI-based code implementation of
the ROMS-IS4DVAR formulation.


title:
How to build a cognitive map: insights from models of the hippocampal
  formation

abstract:
  Learning and interpreting the structure of the environment is an innate
feature of biological systems, and is integral to guiding flexible behaviours
for evolutionary viability. The concept of a cognitive map has emerged as one
of the leading metaphors for these capacities, and unravelling the learning and
neural representation of such a map has become a central focus of neuroscience.
While experimentalists are providing a detailed picture of the neural substrate
of cognitive maps in hippocampus and beyond, theorists have been busy building
models to bridge the divide between neurons, computation, and behaviour. These
models can account for a variety of known representations and neural phenomena,
but often provide a differing understanding of not only the underlying
principles of cognitive maps, but also the respective roles of hippocampus and
cortex. In this Perspective, we bring many of these models into a common
language, distil their underlying principles of constructing cognitive maps,
provide novel (re)interpretations for neural phenomena, suggest how the
principles can be extended to account for prefrontal cortex representations
and, finally, speculate on the role of cognitive maps in higher cognitive
capacities.


title:
On the properties of the exceptional set for the randomized Euler and
  Runge-Kutta schemes

abstract:
  We show that the probability of the exceptional set decays exponentially for
a broad class of randomized algorithms approximating solutions of ODEs,
admitting a certain error decomposition. This class includes randomized
explicit and implicit Euler schemes, and the randomized two-stage Runge-Kutta
scheme (under inexact information). We design a confidence interval for the
exact solution of an IVP and perform numerical experiments to illustrate the
theoretical results.


title: A Recent Survey of Event Triggered Control of Nonlinear Systems

abstract:
  The control systems are an essential part of every engineering system in any
industrial application. The basic purpose of controls is to manage the internal
operations of the system and detect any unwanted or uncertain situation.
Failure in any system may be caused by various reasons and need to be
restricted to save other components of the system. event-triggered control
systems manage errors and failures by identifying particular events caused by
failure and then mitigate the effect of that failure as much as possible. These
systems have become prominent due to their smooth and autonomous way of
detecting and minimizing risk in any system. The input to state stability
property and a perfectly stabilized control are assumed for applying
event-triggered control systems. Switching topologies can be utilized as an
alternative to ISS assumptions to design triggering events. There are some
issues related to this approach like non-linearity of systems, multi-agent
communication, heterogeneity and Zeno effect in systems while monitoring,
controlling faults and taking feedbacks. The efficiency of neural networks and
fuzzy technologies are considered in this regard which are investigated by many
researchers in the last decade. This survey reviews all work related to
event-triggered control systems, their applications, challenges and possible
solutions. The adaptability of these controls is evaluated based on the
solutions available as well as the applicability of solutions proposed by
researchers.


title:
Pathway Towards Optical Cycling and Laser Cooling of Functionalized
  Arenes

abstract:
  Rapid and repeated photon cycling has enabled precision metrology and the
development of quantum information systems using a variety of atoms and simple
molecules. Extending optical cycling to structurally complex molecules would
provide new capabilities in these areas, as well as in ultracold chemistry.
Increased molecular complexity, however, makes realizing closed optical
transitions more difficult. Building on the already established strong optical
cycling of diatomic, linear triatomic, and symmetric top molecules, recent
theoretical and experimental work has indicated that cycling will be extendable
to phenol containing molecules, as well as other asymmetric species. The
paradigm for these systems is the use of an optical cycling center bonded to a
molecular ligand. Theory has suggested that cycling may be extended to even
larger ligands, like naphthalene, pyrene and coronene. Here, we study the
optical excitation and vibrational branching of the molecules CaO-2-naphthyl,
SrO-2-naphthyl and CaO-1-naphthyl and find only weak decay to excited
vibrational states, indicating a promising path to full quantum control and
laser cooling of large arene-based molecules.


title:
Stabilization of a multi-frequency open cavity flow with
  gradient-enriched machine learning control

abstract:
  We stabilize an open cavity flow experiment to 1% of its original fluctuation
level. For the first time, a multi-modal feedback control is automatically
learned for this configuration. The key enabler is automatic in-situ
optimization of control laws with machine learning augmented by a gradient
descent algorithm, named gradient-enriched machine learning control (Cornejo
Maceda et al. 2021, gMLC). gMLC is shown to learn one order of magnitude faster
than MLC (Duriez et al. 2017, MLC). The physical interpretation of the feedback
mechanism is assisted by a novel cluster-based control law visualization for
flow dynamics and corresponding actuation commands. Starting point of the
control experiment are two unforced open cavity benchmark configurations: a
narrow-bandwidth regime with a single dominant frequency and a mode-switching
regime where two frequencies compete. The feedback control commands the DBD
actuator located at the leading edge. The flow is monitored by a downstream
hot-wire sensor over the trailing edge. The feedback law is optimized with
respect to the monitored fluctuation level. As reference, the self-oscillations
of the mixing layer are mitigated with steady actuation. Then, feedback
controller is optimized with gMLC. As expected, feedback control outperforms
steady actuation by achieving both, a better amplitude reduction and a
significantly smaller actuation power, about about 1% of the actuation energy
required for similarly effective steady forcing. Intriguingly, optimized laws
learned for one regime performs well for the other untested regime as well. The
proposed control strategy can be expected to be applicable for many other shear
flow experiments.


title: Around Smale's 14th problem

abstract:
  We consider the Lorenz equations, a system of three dimensional ordinary
differential equations modeling atmospheric convection. As the equations are
chaotic and hard to study, a simpler "geometric model" has been introduced in
the seventies. One of the classical problems in dynamical systems is to relate
the original equations to the simplified geometric model. This has been
achieved numerically by Tucker. Here we show that for a different parameter we
can prove the relation to the geometric model analytically, by elementary
geometric means.


title: On upper bounds for the first $\ell^2$-Betti number

abstract:
  This article presents a method for proving upper bounds for the first
$\ell^2$-Betti number of groups using only the geometry of the Cayley graph. As
an application we prove that Burnside groups of large prime exponent have
vanishing first $\ell^2$-Betti number.
  Our approach extends to generalizations of $\ell^2$-Betti numbers, that are
defined using characters. We illustrate this flexibility by generalizing
results of Thom-Peterson on q-normal subgroups to this setting.


title:
Benchmarking intra-nuclear cascade models for neutrino scattering with
  relativistic optical potentials

abstract:
  The description of final-state interactions (FSI) in the large phase space
probed in neutrino experiments poses a great challenge. In neutrino
experiments, which operate under semi-inclusive conditions, cascade models are
commonly used for this task, while under exclusive conditions FSI can be
treated with relativistic optical potentials (ROP). We formulate conditions
under which the ROP approach and cascade model can be directly compared. We
feed the NEUT cascade with events from a relativistic distorted-wave impulse
approximation calculation that uses the real part of an optical potential. Cuts
on the missing energy of the resulting events are applied to define a set of
events that can be directly compared to RDWIA calculations with the full
optical potential. The NEUT cascade and ROP agree for proton kinetic energies
$T_p > 150$ MeV for carbon, oxygen and calcium nuclei when a realistic nuclear
density is used to introduce events in the cascade. For $T_p < 100$ MeV the ROP
and NEUT cross sections differ in shape and differences in magnitude are larger
than 50 \%. Single transverse variables allow to distinguish different
approaches to FSI, but due to a large non-QE contribution the comparison to T2K
data does not give an unambiguous view of FSI. We discuss electron scattering
and argue that with a cut in missing energy FSI can be studied with minimal
confounding factors in e.g. $e4\nu$. The agreement of the ROP and NEUT for T2K
conditions lends confidence to these models as a tool in oscillation analyses
for sufficiently large nucleon kinetic energies. These results urge for caution
when a cascade model is applied for small nucleon energies. The assessment of
model assumptions relevant to this region are strongly encouraged. This paper
provides novel constraints on cascade models from proton-nucleus scattering
that can be easily applied to other neutrino event generators.


title:
Machine Learning and Artificial Intelligence in Next-Generation Wireless
  Network

abstract:
  Due to the advancement in technologies, the next-generation wireless network
will be very diverse, complicated, and according to the changed demands of the
consumers. The current network operator methodologies and approaches are
traditional and cannot help the next generation networks to utilize their
resources most appropriately. The limited capability of the traditional tools
will not allow the network providers to fulfill the demands of the network's
subscribers in the future. Therefore, this paper will focus on machine
learning, automation, artificial intelligence, and big data analytics for
improving the capacity and effectiveness of next-generation wireless networks.
The paper will discuss the role of these new technologies in improving the
service and performance of the network providers in the future. The paper will
find out that machine learning, big data analytics, and artificial intelligence
will help in making the next-generation wireless network self-adaptive,
self-aware, prescriptive, and proactive. At the end of the paper, it will be
provided that future wireless network operators cannot work without shifting
their operational framework to AI and machine learning technologies.


title:
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive
  Principal

abstract:
  Principal-Agent (PA) problems describe a broad class of economic
relationships characterized by misaligned incentives and asymmetric
information. The Principal's problem is to find optimal incentives given the
available information, e.g., a manager setting optimal wages for its employees.
Whereas the Principal is often assumed rational, comparatively little is known
about solutions when the Principal is boundedly rational, especially in the
sequential setting, with multiple Agents, and with multiple information
channels. Here, we develop RIRL, a deep reinforcement learning framework that
solves such complex PA problems with a rationally inattentive Principal. Such a
Principal incurs a cost for paying attention to information, which can model
forms of bounded rationality. We use RIRL to analyze rich economic phenomena in
manager-employee relationships. In the single-step setting, 1) RIRL yields
wages that are consistent with theoretical predictions; and 2) non-zero
attention costs lead to simpler but less profitable wage structures, and
increased Agent welfare. In a sequential setting with multiple Agents, RIRL
shows opposing consequences of the Principal's inattention to different
information channels: 1) inattention to Agents' outputs closes wage gaps based
on ability differences; and 2) inattention to Agents' efforts induces a social
dilemma dynamic in which Agents work harder, but essentially for free.
Moreover, RIRL reveals non-trivial relationships between the Principal's
inattention and Agent types, e.g., if Agents are prone to sub-optimal effort
choices, payment schedules are more sensitive to the Principal's attention
cost. As such, RIRL can reveal novel economic relationships and enables
progress towards understanding the effects of bounded rationality in dynamic
settings.


title:
Review on the stabilization of non linear systems achieved by output
  feedback control technique

abstract:
  Stability and control of a non-linear system represent an important system
configuration that frequently arises in practical engineering. Stability covers
a vast range of systems that do not obey the superposition principle and
applies to more real-world systems because all real control systems are
non-linear. For efficient stabilization of these systems, a great number of
researches have been proposed. This paper surveys some well-known facts as well
as some recent developments and different strategies for the topic of
stabilization of non-linear systems by output feedback control techniques.


title:
Twisted extreme trans-Neptunian orbital parameter space: statistically
  significant asymmetries confirmed

abstract:
  Asymmetric debris discs have been found around stars other than the Sun;
asymmetries are sometimes attributed to perturbations induced by unseen
planets. The presence or absence of asymmetries in our own trans-Neptunian belt
remains controversial. The study of sensitive tracers in a sample of objects
relatively free from the perturbations exerted by the four known giant planets
and most stellar flybys may put an end to this debate. The analysis of the
distribution of the mutual nodal distances of the known extreme trans-Neptunian
objects (ETNOs) that measure how close two orbits may get to each other could
be such a game changer. Here, we use a sample of 51 ETNOs together with random
shufflings of this sample and two unbiased scattered-disc orbital models to
confirm a statistically significant (62 sigma) asymmetry between the shortest
mutual ascending and descending nodal distances as well as the existence of
multiple highly improbably (p < 0.0002) correlated pairs of orbits with mutual
nodal distances as low as 0.2 au at 152 au from the Solar system's barycentre
or 1.3 au at 339 au. We conclude that these findings fit best with the notion
that trans-Plutonian planets exist.


title: Variational Nearest Neighbor Gaussian Processes

abstract:
  Variational approximations to Gaussian processes (GPs) typically use a small
set of inducing points to form a low-rank approximation to the covariance
matrix. In this work, we instead exploit a sparse approximation of the
precision matrix. We propose variational nearest neighbor Gaussian process
(VNNGP), which introduces a prior that only retains correlations within K
nearest-neighboring observations, thereby inducing sparse precision structure.
Using the variational framework, VNNGP's objective can be factorized over both
observations and inducing points, enabling stochastic optimization with a time
complexity of O($K^3$). Hence, we can arbitrarily scale the inducing point
size, even to the point of putting inducing points at every observed location.
We compare VNNGP to other scalable GPs through various experiments, and
demonstrate that VNNGP (1) can dramatically outperform low-rank methods, and
(2) is less prone to overfitting than other nearest neighbor methods.


title: Green Vehicle Routing Problem: State of the Art and Future Directions

abstract:
  Green vehicle routing problem (GVRP) aims to consider greenhouse gas
emissions reduction, while routing the vehicles. It can be either through
adopting Alternative Fuel Vehicles (AFVs) or with existing conventional fossil
fuel vehicles in fleets. GVRP also takes into account environmental
sustainability in transportation and logistics. We critically review several
variations and specializations of GVRP to address issues related to charging,
pickup, delivery, and energy consumption. Starting with the concepts and
definitions of GVRP, we summarize the key elements and contributors to GVRP
publications. Afterward, the issues regarding each category of green vehicle
routing are reviewed, based on which key future research directions and
challenges are suggested. It was observed that the main focus of previous
publications is on the operational level routing decision and not the supply
chain issues. The majority of publications used metaheuristic methods, while
overlooking the emerging machine learning methods. We envision that in addition
to machine learning, reinforcement learning, distributed systems, the internet
of vehicles (IoV), and new fuel technologies have a strong role in developing
the GVRP research further.


title:
QoS-SLA-Aware Adaptive Genetic Algorithm for Multi-Request Offloading in
  Integrated Edge-Cloud Computing in Internet of Vehicles

abstract:
  The Internet of Vehicles over Vehicular Ad-hoc Networks is an emerging
technology enabling the development of smart city applications focused on
improving traffic safety, traffic efficiency, and the overall driving
experience. These applications have stringent requirements detailed in Service
Level Agreement.
  Since vehicles have limited computational and storage capabilities,
applications requests are offloaded onto an integrated edge-cloud computing
system. Existing offloading solutions focus on optimizing the application's
Quality of Service (QoS) in terms of execution time, and respecting a single
SLA constraint. They do not consider the impact of overlapped multi-requests
processing nor the vehicle's varying speed. This paper proposes a novel
Artificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA)
to optimize the application's execution time for multi-request offloading in a
heterogeneous edge-cloud computing system, which considers the impact of
processing multi-requests overlapping and dynamic vehicle speed. The proposed
genetic algorithm integrates an adaptive penalty function to assimilate the SLA
constraints regarding latency, processing time, deadline, CPU, and memory
requirements. Numerical experiments and analysis compare our QoS-SLA-AGA to
random offloading, and baseline genetic-based approaches. Results show
QoS-SLA-AGA executes the requests 1.22 times faster on average compared to the
random offloading approach and with 59.9% fewer SLA violations. In contrast,
the baseline genetic-based approach increases the requests' performance by 1.14
times, with 19.8% more SLA violations.

categories: cs.NI cs.AI cs.DC cs.NA cs.NE cs.PF math.NA


title: Power logit regression for modeling bounded data

abstract:
  The main purpose of this paper is to introduce a new class of regression
models for bounded continuous data, commonly encountered in applied research.
The models, named the power logit regression models, assume that the response
variable follows a distribution in a wide, flexible class of distributions with
three parameters, namely the median, a dispersion parameter and a skewness
parameter. The paper offers a comprehensive set of tools for likelihood
inference and diagnostic analysis, and introduces the new R package PLreg.
Applications with real and simulated data show the merits of the proposed
models, the statistical tools, and the computational package.


title:
Understanding the Role of Context in Creating Enjoyable Co-Located
  Interactions

abstract:
  In recent years, public discourse has blamed digital technologies for making
people feel "alone together," distracting us from engaging with one another,
even when we are interacting in-person. We argue that in order to design
technologies that foster and augment co-located interactions, we need to first
understand the context in which enjoyable co-located socialization takes place.
We address this gap by surveying and interviewing over 1,000 U.S.-based
participants to understand what, where, with whom, how, and why people enjoy
spending time in-person. Our findings suggest that people enjoy engaging in
everyday activities with individuals with whom they have strong social ties
because it helps enable nonverbal cues, facilitate spontaneity, support
authenticity, encourage undivided attention, and leverage the physicality of
their bodies and the environment. We conclude by providing a set of
recommendations for designers interested in creating co-located technologies
that encourage social engagement and relationship building.


title:
DistrEdge: Speeding up Convolutional Neural Network Inference on
  Distributed Edge Devices

abstract:
  As the number of edge devices with computing resources (e.g., embedded GPUs,
mobile phones, and laptops) increases, recent studies demonstrate that it can
be beneficial to collaboratively run convolutional neural network (CNN)
inference on more than one edge device. However, these studies make strong
assumptions on the devices' conditions, and their application is far from
practical. In this work, we propose a general method, called DistrEdge, to
provide CNN inference distribution strategies in environments with multiple IoT
edge devices. By addressing heterogeneity in devices, network conditions, and
nonlinear characters of CNN computation, DistrEdge is adaptive to a wide range
of cases (e.g., with different network conditions, various device types) using
deep reinforcement learning technology. We utilize the latest embedded AI
computing devices (e.g., NVIDIA Jetson products) to construct cases of
heterogeneous devices' types in the experiment. Based on our evaluations,
DistrEdge can properly adjust the distribution strategy according to the
devices' computing characters and the network conditions. It achieves 1.1 to 3x
speedup compared to state-of-the-art methods.


title: Non-uniqueness times for the maximizer of the KPZ fixed point

abstract:
  Let $\mathfrak h_t$ be the KPZ fixed point started from any initial condition
that guarantees $\mathfrak h_t$ has a maximum at every time $t$ almost surely.
For any fixed $t$, almost surely $\max \mathfrak h_t$ is uniquely attained.
However, there are exceptional times $t \in (0, \infty)$ when $\max \mathfrak
h_t$ is achieved at multiple points. Let $\mathcal T_k \subset (0, \infty)$
denote the set of times when $\max \mathfrak h_t$ is achieved at exactly $k$
points. We show that almost surely $\mathcal T_2$ has Hausdorff dimension $2/3$
and is dense, $\mathcal T_3$ has Hausdorff dimension $1/3$ and is dense,
$\mathcal T_4$ has Hausdorff dimension $0$, and there are no times when $\max
\mathfrak h_t$ is achieved at $5$ or more points. This resolves two conjectures
of Corwin, Hammond, Hegde, and Matetski.


title:
Lost in translation: the Abelian affine connection (in the coincident
  gauge)

abstract:
  The simplest i.e. the Abelian i.e. the commutative i.e. the integrable i.e.
the flat and torsion-free i.e. the symmetric teleparallel affine connection has
been considered in many recent works in the literature. Such an affine
connection is characterised by the property that it can be vanished by a
general coordinate transformation, by fixing the so called coincident gauge.
This article focuses on the subtleties involved in the applications of the
coincident gauge.


title: Bias-tailored quantum LDPC codes

abstract:
  Bias-tailoring allows quantum error correction codes to exploit qubit noise
asymmetry. Recently, it was shown that a modified form of the surface code, the
XZZX code, exhibits considerably improved performance under biased noise. In
this work, we demonstrate that quantum low density parity check codes can be
similarly bias-tailored. We introduce a bias-tailored lifted product code
construction that provides the framework to expand bias-tailoring methods
beyond the family of 2D topological codes. We present examples of bias-tailored
lifted product codes based on classical quasi-cyclic codes and numerically
assess their performance using a belief propagation plus ordered statistics
decoder. Our Monte Carlo simulations, performed under asymmetric noise, show
that bias-tailored codes achieve several orders of magnitude improvement in
their error suppression relative to depolarising noise.


title: Interstellar reddening correction using He I lines

abstract:
  We present a method to derive the logarithmic extinction coefficient in
optical wavelengths using the emission lines of HeI. Using this procedure we
can avoid selection biases when studying regions with different surface
brightness and we can obtain better measurements of temperature lines, for
example [SIII]6312.


title: Investigating Network Parameters in Neural-Network Quantum States

abstract:
  Recently, quantum-state representation using artificial neural networks has
started to be recognized as a powerful tool. However, due to the black-box
nature of machine learning, it is difficult to analyze what machine learns or
why it is powerful. Here, by applying one of the simplest neural networks, the
restricted Boltzmann machine (RBM), to the ground-state representation of the
one-dimensional (1D) transverse-field Ising (TFI) model, we make an attempt to
directly analyze the optimized network parameters. In the RBM optimization, a
zero-temperature quantum state is mapped onto a finite-temperature classical
state of the extended Ising spins that constitute the RBM. We find that the
quantum phase transition from the ordered phase to the disordered phase in the
1D TFI model by increasing the transverse field is clearly reflected in the
behaviors of the optimized RBM parameters and hence in the finite-temperature
phase diagram of the classical RBM Ising system. The present finding of a
correspondence between the neural-network parameters and quantum phases
suggests that a careful investigation of the neural-network parameters may
provide a new route to extracting nontrivial physical insights from the
neural-network wave functions.

categories: quant-ph cond-mat.dis-nn cond-mat.str-el physics.comp-ph


title: Controlling the CERN Experimental Area Beams

abstract:
  The CERN fixed target experimental areas are composed of more than 8 km of
beam lines with around 800 devices used to define and monitor the beam
parameters. Each year more than 140 groups of users come to perform experiments
in these areas, with a need to control and access the data from these devices.
The software to allow this therefore has to be simple and robust, and be able
to control and read out all types of beam devices. This contribution describes
the functionality of the beam line control system, CESAR, and its evolution.
This includes all the features that can be used by the beam line physicists,
operators, and device experts that work in the experimental areas. It also
underlines the flexibility that the software provides to the experimental users
for control of their beam line, allowing them to manage this in a very easy and
independent way. This contribution also covers the on-going work of providing
MAD-X support to CESAR to achieve an easier way of integrating beam optics. An
overview of the on-going software migration of the Experimental Areas is also
given.


title: Using Sulfur as metallicity tracer in galaxies

abstract:
  We review the present methodology for the use of Sulfur as global metallicity
tracer in galaxies, which allows performing a complete abundance analysis using
mainly the red to near infrared spectral region, and extending the range of
directly derived abundances up to 5 times the S solar photospheric value. The
empirical calibration of Sulfur via the S23 parameter is also reviewed.


title:
Local Minimum Principle for an Optimal Control Problem with a Nonregular
  Mixed Constraint

abstract:
  We consider the simplest optimal control problem with one nonregular mixed
inequality constraint, i.e. when its gradient in the control can vanish on the
zero surface. Using the Dubovitskii--Milyutin theorem on the approximate
separation of convex cones, we prove a first or der necessary condition for a
weak minimum in the form of the so-called local minimum principle, which is
formulated in terms of functions of bounded variation, integrable functions,
and Lebesgue--Stieltjes measures, and does not use functionals on the space of
measurable bounded functions. Two illustrative examples are given. The work is
based on results by Milyutin.


title:
The relationship between sentiment score and COVID-19 cases in the
  United States

abstract:
  The coronavirus disease (COVID-19) continues to have devastating effects
across the globe. No nation has been free from the uncertainty brought by this
pandemic. The health, social and economic tolls associated with it are causing
strong emotions and spreading fear in people of all ages, genders, and races.
Since the beginning of the COVID-19 pandemic, many have expressed their
feelings and opinions related to a wide range of aspects of their lives via
Twitter. In this study, we consider a framework for extracting sentiment scores
and opinions from COVID-19 related tweets. We connect users' sentiment with
COVID-19 cases across the USA and investigate the effect of specific COVID-19
milestones on public sentiment. The results of this work may help with the
development of pandemic-related legislation, serve as a guide for scientific
work, as well as inform and educate the public on core issues related to the
pandemic.


title: Towards Coherent and Consistent Use of Entities in Narrative Generation

abstract:
  Large pre-trained language models (LMs) have demonstrated impressive
capabilities in generating long, fluent text; however, there is little to no
analysis on their ability to maintain entity coherence and consistency. In this
work, we focus on the end task of narrative generation and systematically
analyse the long-range entity coherence and consistency in generated stories.
First, we propose a set of automatic metrics for measuring model performance in
terms of entity usage. Given these metrics, we quantify the limitations of
current LMs. Next, we propose augmenting a pre-trained LM with a dynamic entity
memory in an end-to-end manner by using an auxiliary entity-related loss for
guiding the reads and writes to the memory. We demonstrate that the dynamic
entity memory increases entity coherence according to both automatic and human
judgment and helps preserving entity-related information especially in settings
with a limited context window. Finally, we also validate that our automatic
metrics are correlated with human ratings and serve as a good indicator of the
quality of generated stories.


title:
Multi-Output Physics-Informed Neural Networks for Forward and Inverse
  PDE Problems with Uncertainties

abstract:
  Physics-informed neural networks (PINNs) have recently been used to solve
various computational problems which are governed by partial differential
equations (PDEs). In this paper, we propose a multi-output physics-informed
neural network (MO-PINN) which can provide solutions with uncertainty
distributions for both forward and inverse PDE problems with noisy data. In
this framework, the uncertainty arising from the noisy data is first translated
into multiple measurements regarding the prior noise distribution using the
bootstrap method, and then the outputs of neural networks are designed to
satisfy the measurements as well as the underlying physical laws.The posterior
estimation of target parameters can be obtained at the end of training, which
can be further used for uncertainty quantification and decision making. In this
paper, MO-PINNs are demonstrated with a series of numerical experiments
including both linear and nonlinear, forward and inverse problems. The results
show that MO-PINN is able to provide accurate predictions with noisy data.In
addition, we also demonstrate that the prediction and posterior distributions
from MO-PINNs are consistent with the solutions from traditional a finite
element method (FEM) solver and Monte Carlo methods given the same data and
prior knowledge. Finally, we show that additional statistical knowledge can be
incorporated into the training to improve the prediction if available.


title: Algorithmic Fairness Datasets: the Story so Far

abstract:
  Data-driven algorithms are being studied and deployed in diverse domains to
support critical decisions, directly impacting on people's well-being. As a
result, a growing community of algorithmic fairness researchers has been
investigating the equity of existing algorithms and proposing novel ones,
advancing the understanding of the risks and opportunities of automated
decision-making for different populations. Algorithmic fairness progress hinges
on data, which can be used appropriately only if adequately documented.
Unfortunately, the algorithmic fairness community, as a whole, suffers from a
collective data documentation debt caused by a lack of information on specific
resources (opacity) and scatteredness of available information (sparsity). In
this work, we survey over two hundred datasets employed in algorithmic fairness
research, producing standardized and searchable documentation for each of them,
along with in-depth documentation for the three most popular fairness datasets,
namely Adult, COMPAS and German Credit. These documentation efforts support
multiple contributions. Firstly, we summarize the merits and limitations of
popular algorithmic fairness datasets, questioning their suitability as
general-purpose fairness benchmarks. Secondly, we document hundreds of
available alternatives, annotating their domain and supported fairness tasks,
to assist dataset users in task-oriented and domain-oriented search. Finally,
we analyze these resources from the perspective of five important data curation
topics: anonymization, consent, inclusivity, labeling of sensitive attributes,
and transparency. We discuss different approaches and levels of attention to
these topics, making them tangible, and distill them into a set of best
practices for the curation of novel datasets.


title: Review of automated time series forecasting pipelines

abstract:
  Time series forecasting is fundamental for various use cases in different
domains such as energy systems and economics. Creating a forecasting model for
a specific use case requires an iterative and complex design process. The
typical design process includes the five sections (1) data pre-processing, (2)
feature engineering, (3) hyperparameter optimization, (4) forecasting method
selection, and (5) forecast ensembling, which are commonly organized in a
pipeline structure. One promising approach to handle the ever-growing demand
for time series forecasts is automating this design process. The present paper,
thus, analyzes the existing literature on automated time series forecasting
pipelines to investigate how to automate the design process of forecasting
models. Thereby, we consider both Automated Machine Learning (AutoML) and
automated statistical forecasting methods in a single forecasting pipeline. For
this purpose, we firstly present and compare the proposed automation methods
for each pipeline section. Secondly, we analyze the automation methods
regarding their interaction, combination, and coverage of the five pipeline
sections. For both, we discuss the literature, identify problems, give
recommendations, and suggest future research. This review reveals that the
majority of papers only cover two or three of the five pipeline sections. We
conclude that future research has to holistically consider the automation of
the forecasting pipeline to enable the large-scale application of time series
forecasting.


title:
Developer Load Normalization Using Iterative Kuhn-Munkres Algorithm: An
  Optimization Triaging Approach

abstract:
  Bug triage can be defined as the process of assigning a developer to a bug
report. The duty of the bug triage team is to study the developers profiles
well in order to make an appropriate match between the developers and the
incoming bug reports. Thus, this process is a vital step in issue management
system. In fact, the number of bug reports submitted every day is gradually
increasing which affects the developer workload. Thus, the triage team should
consider this factor in distributing the bugs and because of the manual
approach, many developers are burden. In particular, triaging bug reports
without considering the workload does not only affect the developers workload
but also leads to an increase in the number of unaddressed bug reports. As a
result, the fixing time of the reported bugs will relatively increase. Unlike
other researchers who focus on automating the bug triage and ignoring the
developer workload, in this work, we handle the triaging process from a
different perspective. The proposed approach focuses on how to optimize the bug
fixing time by normalizing the developer load in an automating system. To
evaluate our work, we use 26,317 bug reports from different bug repositories.
Results shows that our work outperforms other systems in terms of optimizing
the bug total fixing time and normalizing developer load.


title:
The Near Infrared Imager and Slitless Spectrograph for the James Webb
  Space Telescope -- II. Wide Field Slitless Spectroscopy

abstract:
  We present the wide field slitless spectroscopy mode of the NIRISS instrument
on the James Webb Space Telescope. This mode employs two orthogonal
low-resolution (resolving power $\approx 150$) grisms in combination with a set
of six blocking filters in the wavelength range 0.8 to $2.3\,\mu$m to provide a
spectrum of almost every source across the field-of-view. When combined with
the low background, high sensitivity and high spatial resolution afforded by
the telescope, this mode will enable unprecedented studies of the structure and
evolution of distant galaxies. We describe the performance of the as-built
hardware relevant to this mode and expected imaging and spectroscopic
sensitivity. We discuss operational and calibration procedures to obtain the
highest quality data. As examples of the observing mode usage, we present
details of two planned Guaranteed Time Observations programs: The Canadian
NIRISS Unbiased Cluster Survey (CANUCS) and The NIRISS Survey for Young Brown
Dwarfs and Rogue Planets.


title:
High-Fidelity Ion State Detection Using Trap-Integrated Avalanche
  Photodiodes

abstract:
  Integrated technologies greatly enhance the prospects for practical quantum
information processing and sensing devices based on trapped ions. High-speed
and high-fidelity ion state readout is critical for any such application.
Integrated detectors offer significant advantages for system portability and
can also greatly facilitate parallel operations if a separate detector can be
incorporated at each ion-trapping location. Here we demonstrate ion quantum
state detection at room temperature utilizing single-photon avalanche diodes
(SPADs) integrated directly into the substrate of silicon ion trapping chips.
We detect the state of a trapped $^{88}\text{Sr}^{+}$ ion via fluorescence
collection with the SPAD, achieving $99.92(1)\%$ average fidelity in 450
$\mu$s, opening the door to the application of integrated state detection to
quantum computing and sensing utilizing arrays of trapped ions.


title: Multivariate Algorithmics for Eliminating Envy by Donating Goods

abstract:
  Fairly dividing a set of indivisible resources to a set of agents is of
utmost importance in some applications. However, after an allocation has been
implemented the preferences of agents might change and envy might arise. We
study the following problem to cope with such situations: Given an allocation
of indivisible resources to agents with additive utility-based preferences, is
it possible to socially donate some of the resources (which means removing
these resources from the allocation instance) such that the resulting modified
allocation is envy-free (up to one good). We require that the number of deleted
resources and/or the caused utilitarian welfare loss of the allocation are
bounded. We conduct a thorough study of the (parameterized) computational
complexity of this problem considering various natural and problem-specific
parameters (e.g., the number of agents, the number of deleted resources, or the
maximum number of resources assigned to an agent in the initial allocation) and
different preference models, including unary and 0/1-valuations. In our
studies, we obtain a rich set of (parameterized) tractability and
intractability results and discover several surprising contrasts, for instance,
between the two closely related fairness concepts envy-freeness and
envy-freeness up to one good and between the influence of the parameters
maximum number and welfare of the deleted resources.


title: A User-Friendly Environment for Battery Data Science

abstract:
  We report a user-friendly software environment for battery data science. It
is designed to streamline data management, data cleaning, and data analysis to
help bridge the gap between the domain expertise of most battery scientists and
the tools needed as the field becomes increasingly data intensive. The software
solution suitable for ingesting battery test data from disparate sources. By
aggregating data in an intelligent way, users can streamline routine data
analysis tasks and leverage Jupyter Notebook functionality to build advanced
scripts and analytics, thereby making battery engineering teams more
productive.


title: MV-Datalog+-: Effective Rule-based Reasoning with Uncertain Observations

abstract:
  Modern applications combine information from a great variety of sources.
Oftentimes, some of these sources, like Machine-Learning systems, are not
strictly binary but associated with some degree of (lack of) confidence in the
observation. We propose MV-Datalog and MV-Datalog+- as extensions of Datalog
and Datalog+-, respectively, to the fuzzy semantics of infinite-valued
Lukasiewicz logic L as languages for effectively reasoning in scenarios where
such uncertain observations occur. We show that the semantics of MV-Datalog
exhibits similar model-theoretic properties as Datalog. in particular, we show
that (fuzzy) entailment can be defined in terms of an analogue of minimal
models and give a characterisation, and proof of the uniqueness of such minimal
models. On the basis of this characterisation, we propose similar many-valued
semantics for rules with existential quantification in the head, extending
Datalog+-.


title: FORML: Learning to Reweight Data for Fairness

abstract:
  Deployed machine learning models are evaluated by multiple metrics beyond
accuracy, such as fairness and robustness. However, such models are typically
trained to minimize the average loss for a single metric, which is typically a
proxy for accuracy. Training to optimize a single metric leaves these models
prone to fairness violations, especially when the population of sub-groups in
the training data are imbalanced. This work addresses the challenge of jointly
optimizing fairness and predictive performance in the multi-class
classification setting by introducing Fairness Optimized Reweighting via
Meta-Learning (FORML), a training algorithm that balances fairness constraints
and accuracy by jointly optimizing training sample weights and a neural
network's parameters. The approach increases fairness by learning to weight
each training datum's contribution to the loss according to its impact on
reducing fairness violations, balancing the contributions from both over- and
under-represented sub-groups. We empirically validate FORML on a range of
benchmark and real-world classification datasets and show that our approach
improves equality of opportunity fairness criteria over existing
state-of-the-art reweighting methods by approximately 1% on image
classification tasks and by approximately 5% on a face attribute prediction
task. This improvement is achieved without pre-processing data or
post-processing model outputs, without learning an additional weighting
function, and while maintaining accuracy on the original predictive metric.


title:
Are EU Climate and Energy Package 20-20-20 targets achievable and
  compatible? Evidence from the impact of renewables on electricity prices

abstract:
  This paper studies the realizability and compatibility of the three CEP2020
targets, focusing on electricity prices. We study the impact of renewables and
other fundamental determinants on wholesale and household retail electricity
prices in ten EU countries from 2008 to 2016. Increases in production from
renewables decrease wholesale electricity prices in all countries. As decreases
in prices should promote consumption, an apparent contradiction emerges between
the target of an increase in renewables and the target of a reduction in
consumption. However, the impact of renewables on the non-energy part of
household wholesale electricity prices is positive in six countries. Therefore,
decreases in wholesale prices, that may compromise the CEP2020 target of
decrease in consumption, do not necessarily translate into lower household
retail prices.


title:
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in
  Contextual Bandits

abstract:
  Methods for offline A/B testing and counterfactual learning are seeing rapid
adoption in search and recommender systems, since they allow efficient reuse of
existing log data. However, there are fundamental limits to using existing log
data alone, since the counterfactual estimators that are commonly used in these
methods can have large bias and large variance when the logging policy is very
different from the target policy being evaluated. To overcome this limitation,
we explore the question of how to design data-gathering policies that most
effectively augment an existing dataset of bandit feedback with additional
observations for both learning and evaluation. To this effect, this paper
introduces Minimum Variance Augmentation Logging (MVAL), a method for
constructing logging policies that minimize the variance of the downstream
evaluation or learning problem. We explore multiple approaches to computing
MVAL policies efficiently, and find that they can be substantially more
effective in decreasing the variance of an estimator than na\"ive approaches.


title:
A holistic approach to understand Helium enrichment in Interplanetary
  coronal mass ejections: New insights

abstract:
  Despite helium abundance (AHe = nH/nHe) is ~ 8 % at the solar
photospheric/chromospheric heights, AHe can be found to exceed 8% in
interplanetary coronal mass ejections (ICMEs) on many occasions. Although
various factors like interplanetary shocks, chromospheric evaporation and
"sludge removal" have been separately invoked in the past to address the AHe
enhancements in ICMEs, none of these processes could explain the variability of
AHe in ICMEs comprehensively. Based on extensive analysis of 275 ICME events,
we show that there is a solar activity variation of ICME averaged AHe values.
The investigation also reveals that the first ionization potential effect as
well as coronal temperature are not the major contributing factors for AHe
enhancements in ICMEs. Investigation on concurrent solar flares and ICME events
for 63 cases reveals that chromospheric evaporation in tandem with
gravitational settling determine the AHe enhancements and variabilities beyond
8% in ICMEs. While chromospheric evaporation releases the helium from
chromosphere into the corona, the gravitationally settled helium is thrown out
during the ICMEs.We show that the intensity and timing of the preceding flares
from the same active region from where the CME erupts are important factors to
understand the AHe enhancements in ICMEs.


title:
Systems Biology: Identifiability analysis and parameter identification
  via systems-biology informed neural networks

abstract:
  The dynamics of systems biological processes are usually modeled by a system
of ordinary differential equations (ODEs) with many unknown parameters that
need to be inferred from noisy and sparse measurements. Here, we introduce
systems-biology informed neural networks for parameter estimation by
incorporating the system of ODEs into the neural networks. To complete the
workflow of system identification, we also describe structural and practical
identifiability analysis to analyze the identifiability of parameters. We use
the ultridian endocrine model for glucose-insulin interaction as the example to
demonstrate all these methods and their implementation.


title: Seeded Database Matching Under Noisy Column Repetitions

abstract:
  The re-identification or de-anonymization of users from anonymized data
through matching with publicly-available correlated user data has raised
privacy concerns, leading to the complementary measure of obfuscation in
addition to anonymization. Recent research provides a fundamental understanding
of the conditions under which privacy attacks are successful, either in the
presence of obfuscation or synchronization errors stemming from the sampling of
time-indexed databases. This paper presents a unified framework considering
both obfuscation and synchronization errors and investigates the matching of
databases under noisy column repetitions. By devising replica detection and
seeded deletion detection algorithms, and using information-theoretic tools,
sufficient conditions for successful matching are derived. It is shown that a
seed size logarithmic in the row size is enough to guarantee the detection of
all deleted columns. It is also proved that this sufficient condition is
necessary, thus characterizing the database matching capacity of database
matching under noisy column repetitions.


title:
RipsNet: a general architecture for fast and robust estimation of the
  persistent homology of point clouds

abstract:
  The use of topological descriptors in modern machine learning applications,
such as Persistence Diagrams (PDs) arising from Topological Data Analysis
(TDA), has shown great potential in various domains. However, their practical
use in applications is often hindered by two major limitations: the
computational complexity required to compute such descriptors exactly, and
their sensitivity to even low-level proportions of outliers. In this work, we
propose to bypass these two burdens in a data-driven setting by entrusting the
estimation of (vectorization of) PDs built on top of point clouds to a neural
network architecture that we call RipsNet. Once trained on a given data set,
RipsNet can estimate topological descriptors on test data very efficiently with
generalization capacity. Furthermore, we prove that RipsNet is robust to input
perturbations in terms of the 1-Wasserstein distance, a major improvement over
the standard computation of PDs that only enjoys Hausdorff stability, yielding
RipsNet to substantially outperform exactly-computed PDs in noisy settings. We
showcase the use of RipsNet on both synthetic and real-world data. Our
open-source implementation is publicly available at
https://github.com/hensel-f/ripsnet and will be included in the Gudhi library.


title:
Quantum coherence dynamics of displaced squeezed thermal state in a
  Non-Markovian environment

abstract:
  The dynamical behavior of quantum coherence of a displaced squeezed thermal
state in contact with an external bath is discussed in the present work. We use
a Fano-Anderson type of Hamiltonian to model the environment and solve the
quantum Langevin equation. From the solution of the quantum Langevin equation
we obtain the Green's functions which are used to calculate the expectation
value of the quadrature operators which are in turn used to construct the
covariance matrix. We use a relative entropy based measure to calculate the
quantum coherence of the mode. The single mode squeezed thermal state is
studied in the Ohmic, sub-Ohmic and the super-Ohmic limits for different values
of the mean photon number. In all these limits, we find that when the coupling
between the system and the environment is weak, the coherence decays
monotonically and exhibit a Markovian nature. When the system and the
environment are strongly coupled, we observe that the evolution is initially
Markovian and after some time it becomes non-Markovian. The non-Markovian
effect is due to the environmental back action on the system. Finally, we also
present the steady state dynamics of the coherence in the long time limit in
both low and high temperature regime. We find that the qualitative behavior
remains the same in both the low and high temperature limits. But quantitative
values differ because the coherence in the system is lower due to thermal
decoherence.


title:
Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal
  Graph Convolutional Neural Networks

abstract:
  The ability to identify and temporally segment fine-grained actions in motion
capture sequences is crucial for applications in human movement analysis.
Motion capture is typically performed with optical or inertial measurement
systems, which encode human movement as a time series of human joint locations
and orientations or their higher-order representations. State-of-the-art action
segmentation approaches use multiple stages of temporal convolutions. The main
idea is to generate an initial prediction with several layers of temporal
convolutions and refine these predictions over multiple stages, also with
temporal convolutions. Although these approaches capture long-term temporal
patterns, the initial predictions do not adequately consider the spatial
hierarchy among the human joints. To address this limitation, we present
multi-stage spatial-temporal graph convolutional neural networks (MS-GCN). Our
framework decouples the architecture of the initial prediction generation stage
from the refinement stages. Specifically, we replace the initial stage of
temporal convolutions with spatial-temporal graph convolutions, which better
exploit the spatial configuration of the joints and their temporal dynamics.
Our framework was compared to four strong baselines on five tasks. Experimental
results demonstrate that our framework achieves state-of-the-art performance.


title:
Moir\'e-like superlattice generated van Hove singularities in strained
  CuO$_2$ double layer

abstract:
  While it is known that the double-layer Bi$_2$Sr$_2$CaCu$_2$O$_{8+y}$ (BSCCO)
cuprate superconductor exhibits an incommensurate superlattice (IS), the effect
of IS on the electronic structure remains elusive. The interlayer interaction
produces a double-peaked van Hove singularity (VHS) in the density of states
(called bilayer splitting) which is strongly changed by the incommensurate
period of the superlattice controlled by the misfit strain between different
atomic layers in BSCCO. Recently the wave vector $\textbf{q} = \epsilon
\textbf{b}$ (where $\textbf{b}$ is the reciprocal lattice vector of the average
lattice) of the superlattice in a high-quality single crystal has been measured
with high precision [N.~Poccia et al., Phys. Rev. Materials $\mathbf{4}$,
114007 (2020)]. This work reports the calculation of the VHS bilayer splitting
in a {\it quasi-commensurate} phase with a rational number $\epsilon=9/43$ for
the pure BSCCO crystal. We extend the calculation to the devil's staircase
observed in the high entropy perovskite
Bi$_2$Sr$_2$Ca$_{1-x}$Y$_x$Cu$_2$O$_{8+y}$ (BSCYCO), where the wave vector
$\epsilon =9/\eta$ changes in the range $36 > \eta > 43$ for $1>x>0$ controlled
by the lattice misfit strain. Our results are of high relevance where the
chemical potential is tuned in the optimum and overdoped regime where the
superconductor to metal transition takes place. The similarity of the complex
VHS splitting due to the devil's staircase in mismatched CuO$_2$ bilayer with
VHS due to moir{\'e} lattice in strained twisted graphene. This makes
mismatched CuO$_2$ bilayer quite promising for constructing quantum devices
with tuned physical characteristics.


title: Can machines solve general queueing systems?

abstract:
  In this paper, we analyze how well a machine can solve a general problem in
queueing theory. To answer this question, we use a deep learning model to
predict the stationary queue-length distribution of an $M/G/1$ queue (Poisson
arrivals, general service times, one server). To the best of our knowledge,
this is the first time a machine learning model is applied to a general
queueing theory problem. We chose $M/G/1$ queue for this paper because it lies
"on the cusp" of the analytical frontier: on the one hand exact solution for
this model is available, which is both computationally and mathematically
complex. On the other hand, the problem (specifically the service time
distribution) is general. This allows us to compare the accuracy and efficiency
of the deep learning approach to the analytical solutions.
  The two key challenges in applying machine learning to this problem are (1)
generating a diverse set of training examples that provide a good
representation of a "generic" positive-valued distribution, and (2)
representations of the continuous distribution of service times as an input. We
show how we overcome these challenges.
  Our results show that our model is indeed able to predict the stationary
behavior of the $M/G/1$ queue extremely accurately: the average value of our
metric over the entire test set is $0.0009$. Moreover, our machine learning
model is very efficient, computing very accurate stationary distributions in a
fraction of a second (an approach based on simulation modeling would take much
longer to converge). We also present a case-study that mimics a real-life
setting and shows that our approach is more robust and provides more accurate
solutions compared to the existing methods. This shows the promise of extending
our approach beyond the analytically solvable systems (e.g., $G/G/1$ or
$G/G/c$).


title: Database Matching Under Column Repetitions

abstract:
  Motivated by synchronization errors in the sampling of time-indexed
databases, matching of random databases under random column repetitions
(including deletions) is investigated. Column histograms are used as a
permutation-invariant feature to detect the repetition pattern, whose
asymptotic-uniqueness is proved using information-theoretic tools. Repetition
detection is followed by a row matching scheme. Considering this overall
scheme, sufficient conditions for successful database matching in terms of the
database growth rate are derived. A modified version of Fano's inequality leads
to a tight necessary condition for successful matching, establishing the
matching capacity under column repetitions. This capacity is equal to the
erasure bound, which assumes the repetition locations are known a-priori.


title: Fast Online Video Super-Resolution with Deformable Attention Pyramid

abstract:
  Video super-resolution (VSR) has many applications that pose strict causal,
real-time, and latency constraints, including video streaming and TV. We
address the VSR problem under these settings, which poses additional important
challenges since information from future frames is unavailable. Importantly,
designing efficient, yet effective frame alignment and fusion modules remain
central problems. In this work, we propose a recurrent VSR architecture based
on a deformable attention pyramid (DAP). Our DAP aligns and integrates
information from the recurrent state into the current frame prediction. To
circumvent the computational cost of traditional attention-based methods, we
only attend to a limited number of spatial locations, which are dynamically
predicted by the DAP. Comprehensive experiments and analysis of the proposed
key innovations show the effectiveness of our approach. We significantly reduce
processing time and computational complexity in comparison to state-of-the-art
methods, while maintaining a high performance. We surpass state-of-the-art
method EDVR-M on two standard benchmarks with a speed-up of over $3\times$.


title: Tail Risk of Electricity Futures

abstract:
  This paper compares the in-sample and out-of-sample performance of several
models for computing the tail risk of one-month and one-year electricity
futures contracts traded in the NordPool, French, German, and Spanish markets
in 2008-2017. As measures of tail risk, we use the one-day-ahead Value-at-Risk
(VaR) and the Expected Shortfall (ES). With VaR, the AR (1)-GARCH (1,1) model
with Student-t distribution is the best-performing specification with 88% cases
in which the Fisher test accepts the model, with a success rate of 94% in the
left tail and of 81% in the right tail. The model passes the test of model
adequacy in the 100% of the cases in the NordPool and German markets, but only
in the 88% and 63% of the cases in the Spanish and French markets. With ES,
this model passes the test of model adequacy in 100% of cases in all markets.
Historical Simulation and Quantile Regression-based approaches misestimate tail
risks. The right-hand tail of the returns is more difficult to model than the
left-hand tail and therefore financial regulators and the administrators of
futures markets should take these results into account when setting additional
regulatory capital requirements and margin account regulations to short
positions.


title:
Ultrafast Collective Excited State Dynamics of a Virus-supported
  Fluorophore Antenna

abstract:
  Radiation brightening was recently observed in a multi-fluorophore-conjugated
brome mosaic virus (BMV) particle, at room temperature under pulsed excitation.
Based on its nonlinear dependence on the number of fluorophores, the origins of
the phenomenon were attributed to a collective relaxation. However, the
mechanism remains unknown. We present ultrafast transient absorption and
fluorescence spectroscopic studies which shed new light on the collective
nature of the relaxation dynamics in such radiation-brightened,
multi-fluorophore particles. Our findings indicate that the emission dynamics
is consistent with a superradiance mechanism. The ratio between the rates of
competing radiative and non-radiative relaxation pathways depends on the number
of fluorophores per virus. We also discuss the evidence of coherent
oscillations in the transient absorption trace from multi-fluorophore
conjugated which last for $\sim100$s of picoseconds, at room temperature. The
findings suggest that small icosahedral virus shells provide a unique
biological scaffold for developing non-classical, deep subwavelength light
sources, and may open new realms for the development of photonic probes for
medical imaging applications.


title: Anomalous Hall effect at half filling in twisted bilayer graphene

abstract:
  Magic-angle twisted bilayer graphene (tBLG) has been studied extensively
owing to its wealth of symmetry-broken phases, correlated Chern insulators,
orbital magnetism, and superconductivity. In particular, the anomalous Hall
effect (AHE) has been observed at odd integer filling factors ($\nu=1$ and $3$)
in a small number of tBLG devices, indicating the emergence of a zero-field
orbital magnetic state with spontaneously broken time-reversal symmetry.
However, the AHE is typically not anticipated at half filling ($\nu=2$) owing
to competing intervalley coherent states, as well as spin-polarized and valley
Hall states that are favored by an intervalley Hund's coupling. Here, we
present measurements of two tBLG devices with twist angles slightly away from
the magic angle (0.96$^{\circ}$ and 1.20$^{\circ}$), in which we report the
surprising observation of the AHE at $\nu=+2$ and $-2$, respectively. These
findings imply that a valley-polarized phase can become the ground state at
half filling in tBLG rotated slightly away from the magic angle. Our results
reveal the emergence of an unexpected ground state in the
intermediately-coupled regime ($U/W \sim 1$, where $U$ is the strength of
Coulomb repulsion and $W$ is the bandwidth), in between the strongly-correlated
insulator and weakly-correlated metal, highlighting the need to develop a more
complete understanding of tBLG away from the strongly-coupled limit.


title: Universal Statistical Simulator

abstract:
  The Quantum Fourier Transform is a famous example in quantum computing for
being the first demonstration of a useful algorithm in which a quantum computer
is exponentially faster than a classical computer. However when giving an
explanation of the speed up, understanding computational complexity of a
classical calculation has to be taken on faith. Moreover, the explanation also
comes with the caveat that the current classical calculations might be
improved. In this paper we present a quantum computer code for a Galton Board
Simulator that is exponentially faster than a classical calculation using an
example that can be intuitively understood without requiring an understanding
of computational complexity. We demonstrate a straight forward implementation
on a quantum computer, using only three types of quantum gate, which calculates
$2^n$ trajectories using $\mathcal{O} (n^2)$ resources. The circuit presented
here also benefits from having a lower depth than previous Quantum Galton
Boards, and in addition, we show that it can be extended to a universal
statistical simulator which is achieved by removing pegs and altering the
left-right ratio for each peg.


title:
WatchAuth: User Authentication and Intent Recognition in Mobile Payments
  using a Smartwatch

abstract:
  In this paper, we show that the tap gesture, performed when a user 'taps' a
smartwatch onto an NFC-enabled terminal to make a payment, is a biometric
capable of implicitly authenticating the user and simultaneously recognising
intent-to-pay. The proposed system can be deployed purely in software on the
watch without requiring updates to payment terminals. It is agnostic to
terminal type and position and the intent recognition portion does not require
any training data from the user. To validate the system, we conduct a user
study (n=16) to collect wrist motion data from users as they interact with
payment terminals and to collect long-term data from a subset of them (n=9) as
they perform daily activities. Based on this data, we identify optimum gesture
parameters and develop authentication and intent recognition models, for which
we achieve EERs of 0.08 and 0.04, respectively.


title: Time-zero Efficiency of European Power Derivatives Markets

abstract:
  We study time-zero efficiency of electricity derivatives markets. By
time-zero efficiency is meant a sequence of prices of derivatives contracts
having the same underlying asset but different times to maturity which implies
that prices comply with a set of efficiency conditions that prevent profitable
time-zero arbitrage opportunities. We investigate whether statistical tests,
based on the law of one price, and trading rules, based on price differentials
and no-arbitrage violations, are useful for assessing time-zero efficiency. We
apply tests and trading rules to daily data of three European power markets:
Germany, France and Spain. In the case of the German market, after considering
liquidity availability and transaction costs, results are not inconsistent with
time-zero efficiency. However, in the case of the French and Spanish markets,
limitations in liquidity and representativeness are challenges that prevent
definite conclusions. Liquidity in French and Spanish markets should improve by
using pricing and marketing incentives. These incentives should attract more
participants into the electricity derivatives exchanges and should encourage
them to settle OTC trades in clearinghouses. Publication of statistics on
prices, volumes and open interest per type of participant should be promoted.


title:
Stellar feedback in M83 as observed with MUSE -- I. Overview, an
  unprecedented view of the stellar and gas kinematics and evidence of
  outflowing gas

abstract:
  We present a large VLT/MUSE mosaic (3.8 x 3.8 kpc) of the nearby spiral
galaxy M83, with a spatial resolution ~20 pc. We obtained the kinematics of the
stars and ionised gas, and compared them with molecular gas kinematics from
ALMA CO(2-1). We separated the ionised gas into HII regions and diffuse ionised
gas (DIG) and determined the fraction of Ha luminosity originating from the DIG
(f_DIG). We observe that both stars and gas trace the galactic disk rotation,
as well as a fast-rotating nuclear component, likely connected to secular
processes driven by the galactic bar. In the gas kinematics, we observe a
stream east of the nucleus, redshifted with respect to the disk. The stream is
surrounded by an extended ionised gas region with enhanced velocity dispersion
and a high ionisation state, which is largely consistent with being ionised by
slow shocks. We interpret this feature as either the superposition of the disk
and an extraplanar layer of DIG, or as a bar-driven inflow of shocked gas. A
double Gaussian component fit to the Ha line also reveals the presence of a
nuclear biconic structure whose axis of symmetry is perpendicular to the bar.
The two cones appear blue- and redshifted along the line of sight and stand out
for having an Ha emission separated by up to 200 km s-1 from that of the disk,
and a high velocity dispersion ~80-200 km s-1. At the far end of the cones, we
observe that the gas is consistent with being ionised by shocks. These features
had never been observed before in M83; we postulate that they are tracing a
starburst-driven outflow shocking into the surrounding ISM. Finally, we obtain
f_DIG ~ 13% in our field of view. We inspect the emission of the HII regions
and DIG in `BPT' diagrams, finding that in HII regions photoionisation accounts
for 99.8% of the Ha flux, whereas the DIG has a mixed contribution from
photoionisation (94.9%) and shocks (5.1%). [abridged]


title: Graphene nanodrums as valleytronic devices

abstract:
  We investigate the electronic transport in graphene nanoelectromechanical
resonators (GrNEMS), known also as graphene nanodrums or nanomembranes. We
demonstrate that these devices, despite small values of out-of-plane strain,
between $0.1$ and $1\%$, can be used as efficient and robust valley polarizers
and filters. Their working principle is based on the pseudomagnetic field
generated by the strain of the graphene membrane. They work for ballistic
electron beams as well as for strongly dispersed ones and can be also used as
electron beam collimators due to the focusing effect of the pseudomagnetic
field. We show additionally that the current flow can be estimated by
semiclassical trajectories which represent a computationally efficient tool for
predicting the functionality of the devices.


title: Technological Factors Influencing Videoconferencing and Zoom Fatigue

abstract:
  The paper presents a conceptual, multidimensional approach to understand the
technological factors that are assumed to or even have been proven to
contribute to what has been coined as Zoom Fatigue (ZF) or more generally
Videoconferencing Fatigue (VCF). With the advent of the Covid-19 pandemic, the
usage of VC services has drastically increased, leading to more and more
reports about the ZF or VCF phenomenon. The paper is motivated by the fact that
some of the media outlets initially starting the debate on what Zoom fatigue is
and how it can be avoided, as well as some of the scientific papers addressing
the topic, contain assumptions that are rather hypothetical and insufficiently
underpinned by scientific evidence. Most of these works are acknowledge the
lacking evidence and partly suggest directions for future research. This paper
intends to deepen the survey of VC-technology-related literature and to provide
more existing evidence, where possible, while reviewing some of the already
provided support or evidence for certain causal hypotheses. The technological
factors dimension and its identified sub-dimensions presented in this paper are
embedded within a more holistic four-dimensional conceptual factors model
describing the causes for ZF or VCF. The paper describing this overall
conceptual model is written by the same group of authors and currently under
revision for an Open Access Journal publication. The present paper expands on
the technological factors dimension descriptions provided in the overall model
paper and provides more detailed analyzes and concepts associated with how VC
technology may affect users' perception, cognitive load, interaction and
communication, possibly leading to stress, exhaustion and fatigue. The paper
currently is a living document which will be expanded further with regard to
the evidence for or against the impact of certain technological factors.


title: How to Leverage Unlabeled Data in Offline Reinforcement Learning

abstract:
  Offline reinforcement learning (RL) can learn control policies from static
datasets but, like standard RL methods, it requires reward annotations for
every transition. In many cases, labeling large datasets with rewards may be
costly, especially if those rewards must be provided by human labelers, while
collecting diverse unlabeled data might be comparatively inexpensive. How can
we best leverage such unlabeled data in offline RL? One natural solution is to
learn a reward function from the labeled data and use it to label the unlabeled
data. In this paper, we find that, perhaps surprisingly, a much simpler method
that simply applies zero rewards to unlabeled data leads to effective data
sharing both in theory and in practice, without learning any reward model at
all. While this approach might seem strange (and incorrect) at first, we
provide extensive theoretical and empirical analysis that illustrates how it
trades off reward bias, sample complexity and distributional shift, often
leading to good results. We characterize conditions under which this simple
strategy is effective, and further show that extending it with a simple
reweighting approach can further alleviate the bias introduced by using
incorrect reward labels. Our empirical evaluation confirms these findings in
simulated robotic locomotion, navigation, and manipulation settings.


title: Mean field limits of co-evolutionary heterogeneous networks

abstract:
  Many science phenomena are modelled as interacting particle systems (IPS)
coupled on static networks. In reality, network connections are far more
dynamic. Connections among individuals receive feedback from nearby individuals
and make changes to better adapt to the world. Hence, it is reasonable to model
myriad real-world phenomena as co-evolutionary (or adaptive) networks. In this
paper, we propose a rigorous formulation for limits of a sequence of
co-evolutionary Kuramoto oscillators coupled on heterogeneous co-evolutionary
networks, which receive feedback from the dynamics of the oscillators on the
networks. We show under mild conditions, the mean field limit (MFL) of the
co-evolutionary network exists and the sequence of co-evolutionary Kuramoto
networks converges to this MFL. Such MFL is described by solutions of a
generalized Vlasov type equation. We treat the graph limits as graph measures,
motivated by the recent work in [Kuehn, Xu. Vlasov equations on digraph
measures, arXiv:2107.08419, 2021]. Under a mild condition on the initial graph
measure, we show that the graph measures are positive over a finite time
interval. In comparison to the recently emerging works on MFLs of IPS coupled
on non-co-evolutionary networks (i.e., static networks or time-dependent
networks independent of the dynamics of the IPS), our work is the first to
rigorously address the MFL of a co-evolutionary network model. The approach is
based on our formulation of a generalization of the co-evolutionary network as
a hybrid system of ODEs and measure differential equations parametrized by a
vertex variable, together with an analogue of the variation of parameters
formula, as well as the generalized Neunzert's in-cell-particle method
developed in [Kuehn, Xu. Vlasov equations on digraph measures,
arXiv:2107.08419, 2021].


title: Default Supply Auctions in Electricity Markets: Challenges and Proposals

abstract:
  This paper studies premiums got by winning bidders in default supply
auctions, and speculation and hedging activities in power derivatives markets
in dates near auctions. Data includes fifty-six auction prices from 2007 to
2013, those of CESUR in the Spanish OMEL electricity market, and those of Basic
Generation Service auctions (PJM-BGS) in New Jersey's PJM market. Winning
bidders got an average ex-post yearly forward premium of 7% (CESUR) and 38%
(PJM-BGS). The premium using an index of futures prices is 1.08% (CESUR) and
24% (PJM-BGS). Ex-post forward premium is negatively related to the number of
bidders and spot price volatility. In CESUR, hedging-driven trading in power
derivatives markets predominates around auction dates, but in PJM-BGS,
speculation-driven trading prevails.


title:
The Impact of Vaccination on the Infection rate and the Severity of
  Covid-19

abstract:
  This study aims to statistically assess the effectiveness of vaccination
against SARS-CoV-2. It is indispensable to investigate the relationship between
Covid-19 deadliness and vaccination in order to study the impact of vaccine in
real-world. We studied rates of infection and death due to Covid-19 in
different countries with respect to their levels of vaccination. People who
received the required dose of vaccination were considered as fully vaccinated
in this study. Based on the percentage of fully vaccinated population,
countries were categorized into several groups. Though a high-level study on
the vaccine effectiveness may not provide much insight for individual level
differences, a global analysis is imperative to infer the influence of
vaccination as a controlling measure of the pandemic.


title:
Symmetric matrix representations of truncated Toeplitz operators on
  finite dimensional spaces

abstract:
  In this paper, we study matrix representations of truncated Toeplitz
operators with respect to orthonormal bases which are invariant under a
canonical conjugation map. In particular, we determine necessary and sufficient
conditions for when a 3-by-3 symmetric matrix is the matrix representation of a
truncated Toeplitz operator with respect to a given conjugation invariant
orthonormal basis. We specialise our result to the case when the conjugation
invariant orthonormal basis is a modified Clark basis. As a corollary to this
specialisation, we answer a previously stated open conjecture in the negative,
and show that not every unitary equivalence between a complex symmetric matrix
and a truncated Toeplitz operator arises from a modified Clark basis
representation. Finally, we show that a given 3-by-3 symmetric matrix is the
matrix representation of a truncated Toeplitz operator with respect to a
conjugation invariant orthonormal basis if and only if a specified system of
polynomial equations is satisfied with a real solution.


title: Pivot Gray Codes for the Spanning Trees of a Graph ft. the Fan

abstract:
  We consider the problem of listing all spanning trees of a graph $G$ such
that successive trees differ by pivoting a single edge around a vertex. Such a
listing is called a "pivot Gray code", and it has more stringent conditions
than known "revolving-door" Gray codes for spanning trees. Most revolving-door
algorithms employ a standard edge-deletion/edge-contraction recursive approach
which we demonstrate presents natural challenges when requiring the "pivot"
property. Our main result is the discovery of a greedy strategy to list the
spanning trees of the fan graph in a pivot Gray code order. It is the first
greedy algorithm for exhaustively generating spanning trees using such a
minimal change operation. The resulting listing is then studied to find a
recursive algorithm that produces the same listing in $O(1)$-amortized time
using $O(n)$ space. Additionally, we present $O(n)$-time algorithms for ranking
and unranking the spanning trees for our listing; an improvement over the
generic $O(n^3)$-time algorithm for ranking and unranking spanning trees of an
arbitrary graph. Finally, we discuss how our listing can be applied to find a
pivot Gray code for the wheel graph.


title: The Met Dataset: Instance-level Recognition for Artworks

abstract:
  This work introduces a dataset for large-scale instance-level recognition in
the domain of artworks. The proposed benchmark exhibits a number of different
challenges such as large inter-class similarity, long tail distribution, and
many classes. We rely on the open access collection of The Met museum to form a
large training set of about 224k classes, where each class corresponds to a
museum exhibit with photos taken under studio conditions. Testing is primarily
performed on photos taken by museum guests depicting exhibits, which introduces
a distribution shift between training and testing. Testing is additionally
performed on a set of images not related to Met exhibits making the task
resemble an out-of-distribution detection problem. The proposed benchmark
follows the paradigm of other recent datasets for instance-level recognition on
different domains to encourage research on domain independent approaches. A
number of suitable approaches are evaluated to offer a testbed for future
comparisons. Self-supervised and supervised contrastive learning are
effectively combined to train the backbone which is used for non-parametric
classification that is shown as a promising direction. Dataset webpage:
http://cmp.felk.cvut.cz/met/


title:
Sequential Learning of the Topological Ordering for the Linear
  Non-Gaussian Acyclic Model with Parametric Noise

abstract:
  Causal discovery, the learning of causality in a data mining scenario, has
been of strong scientific and theoretical interest as a starting point to
identify "what causes what?" Contingent on assumptions, it is sometimes
possible to identify an exact causal Directed Acyclic Graph (DAG), as opposed
to a Markov equivalence class of graphs that gives ambiguity of causal
directions. The focus of this paper is on one such case: a linear structural
equation model with non-Gaussian noise, a model known as the Linear
Non-Gaussian Acyclic Model (LiNGAM). Given a specified parametric noise model,
we develop a novel sequential approach to estimate the causal ordering of a
DAG. At each step of the procedure, only simple likelihood ratio scores are
calculated on regression residuals to decide the next node to append to the
current partial ordering. Under mild assumptions, the population version of our
procedure provably identifies a true ordering of the underlying causal DAG. We
provide extensive numerical evidence to demonstrate that our sequential
procedure is scalable to cases with possibly thousands of nodes and works well
for high-dimensional data. We also conduct an application to a single-cell gene
expression dataset to demonstrate our estimation procedure.


title: Implications of an extended dark energy model with massive neutrinos

abstract:
  Recently there has been reports of finding a lower bound on the neutrino mass
parameter ($\Sigma m_\nu$) when using ACT and SPTpol data however these bounds
on the $\Sigma m_\nu$ are still weaker for most case around at 1 $\sigma$
level. In this context, here in this work, we study the consequences of using
an enlarged four parameter dynamical dark energy equation of state on neutrino
mass parameter as well as on the Hubble and S8 tensions. The four parameter
dark energy equation of state incorporates a generic non-linear monotonic
evolution of the dark energy equation of state, where the four parameters are
the early and the present value of the equation of state, the transition scale
factor and the sharpness of the transition. We report that with
lensing-marginalized Planck + BAO + Pantheon and prior on absolute magnitude
$M_B$ and KIDS/Viking $S_8$ prior, the model favours a non-zero value for the
neutrino mass parameter at the most at $\sim 1 \sigma$ level ($\Sigma m_\nu
\sim 0.2 \pm 0.1$ eV). In this case this model also brings down the Hubble
tension to $\sim 2.5 \sigma$ level and the S8 tension to $\sim$ 1.5 $\sigma$
level. This model also provide a tighter constraints on the value of the dark
energy equation of state at present epoch $w_0$ ($w_0 \sim -0.945 \pm 0.055$)
in comparison to the CPL like parameterization.


title: A Hafnian PH-Pfaffian State for 5/2 Quantum Hall Effect

abstract:
  The PH-Pfaffian state having 1/2 central charge is consistent with the
thermal Hall conductance measurement of 5/2 fractional quantum Hall system, but
lacks support from the existing numerical results. In this paper we propose a
new state described by a wavefunction obtained by multiplying a Hafnian factor
to a PH-Pfaffian wavefunction. We call this new state the Hafnian PH-Pfaffian
state. In spherical geometry, the Hafnian PH-Pfaffian state has the same
magnetic flux number as the Pfaffian state, allowing a direct numerical
comparison between the two states. Results of exact diagonalization of finite
systems in the second Landau level show that the overlap of the exact ground
state with the Hafnian PH-Pfaffian state exceeds that with the Pfaffian state
when the short range component of the Coulomb interaction increases to a
certain level, lending a numerical support to the Hafnian PH-Pfaffian state. We
further show the Hafnian PH-Pfaffian state is mathematically identical to the
newly proposed compressed PH-Pfaffian state [arxiv: 2001.01915 (2020)] formed
by "compressing" the PH-Pfaffian state with two flux quanta removed to create
two abelian Laughlin type quasiparticles of the maximum avoidance from one
another. As the result we argue that the Hafnian PH-Pfaffian state has the same
central charge as the PH-Pfaffian state, and is therefore consistent with the
thermal Hall conductance measurement. Finally we present numerical results on
two new wavefunctions formed by increasing the relative angular momentum by two
for each of the paired composite fermions of the PH-Pfaffian state.


title:
A Family of Current References Based on 2T Voltage References:
  Demonstration in 0.18-$\mu$m with 0.1-nA PTAT and 1.1-$\mu$A CWT
  38-ppm/$^\circ$C Designs

abstract:
  The robustness of current and voltage references to process, voltage and
temperature (PVT) variations is paramount to the operation of integrated
circuits in real-world conditions. However, while recent voltage references can
meet most of these requirements with a handful of transistors, current
references remain rather complex, requiring significant design time and silicon
area. In this paper, we present a family of simple current references
consisting of a two-transistor (2T) ultra-low-power voltage reference, buffered
onto a voltage-to-current converter by a single transistor. Two topologies are
fabricated in a 0.18-$\mu$m partially-depleted silicon-on-insulator (SOI)
technology and measured over 10 dies. First, a 7T nA-range
proportional-to-absolute-temperature (PTAT) reference intended for
constant-$g_m$ biasing of subthreshold operational amplifiers demonstrates a
0.096-nA current with a line sensitivity (LS) of 1.48 %/V, a temperature
coefficient (TC) of 0.75 %/$^\circ$C, and a variability $(\sigma/\mu)$ of 1.66
%. Then, two 4T+1R $\mu$A-range constant-with-temperature (CWT) references with
(resp. without) TC calibration exhibit a 1.09-$\mu$A (resp. 0.99-$\mu$A)
current with a 0.21-%/V (resp. 0.20-%/V) LS, a 38-ppm/$^\circ$C (resp.
290-ppm/$^\circ$C) TC, and a 0.87-% (resp. 0.65-%) $(\sigma/\mu)$. In addition,
portability to common scaled CMOS technologies, such as 65-nm bulk and 28-nm
fully-depleted SOI, is discussed and validated through post-layout simulations.


title: Near-Optimal Learning of Extensive-Form Games with Imperfect Information

abstract:
  This paper resolves the open question of designing near-optimal algorithms
for learning imperfect-information extensive-form games from bandit feedback.
We present the first line of algorithms that require only
$\widetilde{\mathcal{O}}((XA+YB)/\varepsilon^2)$ episodes of play to find an
$\varepsilon$-approximate Nash equilibrium in two-player zero-sum games, where
$X,Y$ are the number of information sets and $A,B$ are the number of actions
for the two players. This improves upon the best known sample complexity of
$\widetilde{\mathcal{O}}((X^2A+Y^2B)/\varepsilon^2)$ by a factor of
$\widetilde{\mathcal{O}}(\max\{X, Y\})$, and matches the information-theoretic
lower bound up to logarithmic factors. We achieve this sample complexity by two
new algorithms: Balanced Online Mirror Descent, and Balanced Counterfactual
Regret Minimization. Both algorithms rely on novel approaches of integrating
\emph{balanced exploration policies} into their classical counterparts. We also
extend our results to learning Coarse Correlated Equilibria in multi-player
general-sum games.


title:
m-CUBES An efficient and portable implementation of multi-dimensional
  integration for gpus

abstract:
  The task of multi-dimensional numerical integration is frequently encountered
in physics and other scientific fields, e.g., in modeling the effects of
systematic uncertainties in physical systems and in Bayesian parameter
estimation. Multi-dimensional integration is often time-prohibitive on CPUs.
Efficient implementation on many-core architectures is challenging as the
workload across the integration space cannot be predicted a priori. We propose
m-Cubes, a novel implementation of the well-known Vegas algorithm for execution
on GPUs. Vegas transforms integration variables followed by calculation of a
Monte Carlo integral estimate using adaptive partitioning of the resulting
space. m-Cubes improves performance on GPUs by maintaining relatively uniform
workload across the processors. As a result, our optimized Cuda implementation
for Nvidia GPUs outperforms parallelization approaches proposed in past
literature. We further demonstrate the efficiency of m-Cubes by evaluating a
six-dimensional integral from a cosmology application, achieving significant
speedup and greater precision than the CUBA library's CPU implementation of
VEGAS. We also evaluate m-Cubes on a standard integrand test suite. m-Cubes
outperforms the serial implementations of the Cuba and GSL libraries by orders
of magnitude speedup while maintaining comparable accuracy. Our approach yields
a speedup of at least 10 when compared against publicly available Monte Carlo
based GPU implementations. In summary, m-Cubes can solve integrals that are
prohibitively expensive using standard libraries and custom implementations. A
modern C++ interface header-only implementation makes m-Cubes portable,
allowing its utilization in complicated pipelines with easy to define stateful
integrals. Compatibility with non-Nvidia GPUs is achieved with our initial
implementation of m-Cubes using the Kokkos framework.


title:
Bifurcation analysis for axisymmetric capillary water waves with
  vorticity and swirl

abstract:
  We study steady axisymmetric water waves with general vorticity and swirl,
subject to the influence of surface tension. This can be formulated as an
elliptic free boundary problem in terms of Stokes' stream function. A change of
variables allows us to overcome the generic coordinate-induced singularities
and to cast the problem in the form "identity plus compact", which is amenable
to Rabinowitz' global bifurcation theorem, while no restrictions regarding the
absence of stagnation points in the flow have to be made. Within the scope of
this new formulation, local and global solution curves, bifurcating from
laminar flows with a flat surface, are constructed.


title:
Classification of doubly periodic untwisted (p,q)-weaves by their
  crossing number

abstract:
  A weave is the lift to the Euclidean thickened plane of a set of infinitely
many planar crossed geodesics, that can be characterized by a number of sets of
threads describing the organization of the non-intersecting curves, together
with a set of crossing sequences representing the entanglements. In this paper,
the classification of a specific class of doubly periodic weaves, called
untwisted (p,q)-weaves, is done by their crossing number, which is the minimum
number of crossings that can possibly be found in a unit cell of its infinite
weaving diagrams. Such a diagram can be considered as a particular type of
quadrivalent periodic planar graph with an over or under information at each
vertex, whose unit cell corresponds to a link diagram in a thickened torus.
Moreover, considering that a weave is not uniquely defined by its sets of
threads and its crossing sequences, we also specify the notion of equivalence
classes by introducing a new parameter, called crossing matrix.


title:
On the Convergence of Inexact Predictor-Corrector Methods for Linear
  Programming

abstract:
  Interior point methods (IPMs) are a common approach for solving linear
programs (LPs) with strong theoretical guarantees and solid empirical
performance. The time complexity of these methods is dominated by the cost of
solving a linear system of equations at each iteration. In common applications
of linear programming, particularly in machine learning and scientific
computing, the size of this linear system can become prohibitively large,
requiring the use of iterative solvers, which provide an approximate solution
to the linear system. However, approximately solving the linear system at each
iteration of an IPM invalidates the theoretical guarantees of common IPM
analyses. To remedy this, we theoretically and empirically analyze (slightly
modified) predictor-corrector IPMs when using approximate linear solvers: our
approach guarantees that, when certain conditions are satisfied, the number of
IPM iterations does not increase and that the final solution remains feasible.
We also provide practical instantiations of approximate linear solvers that
satisfy these conditions for special classes of constraint matrices using
randomized linear algebra.