title:
Estimation of the incubation period and generation time of SARS-CoV-2
  Alpha and Delta variants from contact tracing data

abstract:
  Background. During 2021, the COVID-19 pandemic was characterized by the
emergence of lineages with increased fitness. For most of these variants,
quantitative information is scarce on epidemiological quantities such as the
incubation period and generation time, which are critical for both public
health decisions and scientific research. Method. We analyzed a dataset
collected during contact tracing activities in the province of Reggio Emilia,
Italy, throughout 2021. We determined the distributions of the incubation
period using information on negative PCR tests and the date of last exposure
from 282 symptomatic cases. We estimated the distributions of the intrinsic
generation time (the time between the infection dates of an infector and its
secondary cases under a fully susceptible population) using a Bayesian
inference approach applied to 4,435 SARS-CoV-2 cases clustered in 1,430
households where at least one secondary case was recorded. Results. We
estimated a mean incubation period of 4.9 days (95% credible intervals, CrI,
4.4-5.4; 95 percentile of the mean distribution: 1-12) for Alpha and 4.5 days
(95%CrI 4.0-5.0; 95 percentile: 1-10) for Delta. The intrinsic generation time
was estimated to have a mean of 6.0 days (95% CrI 5.6-6.4; 95 percentile: 1-15)
for Alpha and of 6.6 days (95%CrI 6.0-7.3; 95 percentile: 1-18) for Delta. The
household serial interval was 2.6 days (95%CrI 2.4-2.7) for Alpha and 2.4 days
(95%CrI 2.2-2.6) for Delta, and the estimated proportion of pre-symptomatic
transmission was 54-55% for both variants. Conclusions. These results indicate
limited differences in the incubation period and intrinsic generation time of
SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages.


title: Dark Matter Physics from the CMB-S4 Experiment

abstract:
  The nature of dark matter is one of the major puzzles of fundamental physics,
integral to the understanding of our universe across almost every epoch. The
search for dark matter takes place at different energy scales, and use data
ranging from particle colliders to astrophysical surveys. We focus here on
CMB-S4, a future ground-based Cosmic Microwave Background (CMB) experiment,
which is expected to provide exquisite measurements of the CMB temperature and
polarization anisotropies. These measurements (on their own and in combination
with other surveys) will allow for new means to shed light on the nature of
dark matter.


title: Optimal Aggregation Strategies for Social Learning over Graphs

abstract:
  Adaptive social learning is a useful tool for studying distributed
decision-making problems over graphs. This paper investigates the effect of
combination policies on the performance of adaptive social learning strategies.
Using large-deviation analysis, it first derives a bound on the probability of
error and characterizes the optimal selection for the Perron eigenvectors of
the combination policies. It subsequently studies the effect of the combination
policy on the transient behavior of the learning strategy by estimating the
adaptation time in the small signal-to-noise ratio regime. In the process, it
is discovered that, interestingly, the influence of the combination policy on
the transient behavior is insignificant, and thus it is more critical to employ
policies that enhance the steady-state performance. The theoretical conclusions
are illustrated by means of computer simulations.


title: Does inflation squeeze cosmological perturbations?

abstract:
  There seems to exist agreement about the fact that inflation squeezes the
quantum state of cosmological perturbations and entangles modes with
wavenumbers $\vec k$ and $-\vec k$. Paradoxically, this result has been used to
justify both the classicality as well as the quantumness of the primordial
perturbations at the end of inflation. We reexamine this question and point out
that the definition of two-mode squeezing of the modes $\vec k$ and $-\vec k$
used in previous work rests on choices that are only justified for systems with
time-independent Hamiltonians and finitely many degrees of freedom. We argue
that for quantum fields propagating on generic time-dependent
Friedmann-Lema\^itre-Robertson-Walker backgrounds, the notion of squeezed
states is subject to ambiguities, which go hand in hand with the ambiguity in
the definition of particles. In other words, we argue that the question "does
the cosmic expansion squeeze and entangle modes with wavenumbers $\vec k$ and
$-\vec k$?" contains the same ambiguity as the question "does the cosmic
expansion create particles?". When additional symmetries are present, like in
the (quasi) de Sitter-like spacetimes used in inflationary models, one can
resolve the ambiguities, and we find that the answer to the question in the
title turns out to be in the negative. We further argue that this fact does not
make the state of cosmological perturbations any less quantum, at least when
deviations from Gaussianity can be neglected.


title: Toward Ethical AIED

abstract:
  This paper presents the key conclusions to the forthcoming edited book on The
Ethics of Artificial Intelligence in Education: Practices, Challenges and
Debates (August 2022, Routlege). As well as highlighting the key contributions
to the book, it discusses the key questions and the grand challenges for the
field of AI in Education (AIED)in the context of ethics and ethical practices
within the field. The book itself presents diverse perspectives from outside
and from within the AIED as a way of achieving a broad perspective in the key
ethical issues for AIED and a deep understanding of work conducted to date by
the AIED community.


title: A New Learning Paradigm for Stochastic Configuration Network: SCN+

abstract:
  Learning using privileged information (LUPI) paradigm, which pioneered
teacher-student interaction mechanism, makes the learning models use additional
information in training stage. This paper is the first to propose an
incremental learning algorithm with LUPI paradigm for stochastic configuration
network (SCN), named SCN+. This novel algorithm can leverage privileged
information into SCN in the training stage, which provides a new method to
train SCN. Moreover, the convergences have been studied in this paper. Finally,
experimental results indicate that SCN+ indeed performs favorably.


title: Observational Imprints of Our Lost Twin Anti-Universe

abstract:
  We consider observational consequences of the entanglement between our
universe and a hypothetical twin anti-universe in the third quantization scheme
of the canonical quantum gravity. Based on our previous investigations we
select some special form of the interuniversal interaction which allows the
entanglement entropy of the pair of universes to diverge at some critical
points of their classical evolution. We find that the modification of the
cosmic microwave background (CMB) power spectrum due to the entanglement with
our twin anti-universe is enlarged for small modes $k$ and small multipole
numbers $l$ with the Planck constraint onto the interaction coupling constant
$\lambda_o\lesssim\mathcal{O}(10^{-56})$. Some other coupling functions which
allow more critical points are also briefly commented on in the context of
their observational effect on CMB and other observations.


title:
Computation and optimization of the Purcell factor of an open tunable
  Fabry-Perot microcavity

abstract:
  The spontaneous emission of atoms can be controlled by placing them between
two mirrors that form an optical cavity. Rapid advances in material processing
techniques in the last 10 years have made it possible to fabricate microscopic
optical cavities that can be finely tuned into resonance with the emitter. This
has enabled progress in single-photon sources, nano-lasers and spectroscopy of
new nano-emitters such as semiconductor quantum dots and Nitrogen-vacancy
centres in diamond. Here we introduce a step-by-step method for computing the
Purcell factor of the latest generation of open tunable Fabry-Perot
microcavities used in micro-photoluminescence studies. We discuss how the
Purcell factor can be optimised as a function of the cavity's geometry and find
the conditions for resonance with the emitter. Subtleties such as the optical
properties of the emitter and penetration depth of the electric field into the
cavity mirrors are also considered.


title:
Neural Forecasting of the Italian Sovereign Bond Market with Economic
  News

abstract:
  In this paper we employ economic news within a neural network framework to
forecast the Italian 10-year interest rate spread. We use a big, open-source,
database known as Global Database of Events, Language and Tone to extract
topical and emotional news content linked to bond markets dynamics. We deploy
such information within a probabilistic forecasting framework with
autoregressive recurrent networks (DeepAR). Our findings suggest that a deep
learning network based on Long-Short Term Memory cells outperforms classical
machine learning techniques and provides a forecasting performance that is over
and above that obtained by using conventional determinants of interest rates
alone.


title: A representation-theoretic approach to $qq$-characters

abstract:
  We raise the question of whether (a slightly generalized notion of)
$qq$-characters can be constructed purely representation-theoretically. In the
main example of the quantum toroidal $\mathfrak{gl}_1$ algebra, geometric
engineering of adjoint matter produces an explicit vertex operator
$\mathsf{RR}$ which computes certain $qq$-characters, namely Hirzebruch
$\chi_y$-genera, completely analogously to how the R-matrix $\mathsf{R}$
computes $q$-characters. We prove the independence of preferred direction for
the refined vertex in this and more general non-toric settings.


title: Impression Allocation and Policy Search in Display Advertising

abstract:
  In online display advertising, guaranteed contracts and real-time bidding
(RTB) are two major ways to sell impressions for a publisher. For large
publishers, simultaneously selling impressions through both guaranteed
contracts and in-house RTB has become a popular choice. Generally speaking, a
publisher needs to derive an impression allocation strategy between guaranteed
contracts and RTB to maximize its overall outcome (e.g., revenue and/or
impression quality). However, deriving the optimal strategy is not a trivial
task, e.g., the strategy should encourage incentive compatibility in RTB and
tackle common challenges in real-world applications such as unstable traffic
patterns (e.g., impression volume and bid landscape changing). In this paper,
we formulate impression allocation as an auction problem where each guaranteed
contract submits virtual bids for individual impressions. With this
formulation, we derive the optimal bidding functions for the guaranteed
contracts, which result in the optimal impression allocation. In order to
address the unstable traffic pattern challenge and achieve the optimal overall
outcome, we propose a multi-agent reinforcement learning method to adjust the
bids from each guaranteed contract, which is simple, converging efficiently and
scalable. The experiments conducted on real-world datasets demonstrate the
effectiveness of our method.


title: Born rule in a timeless universe

abstract:
  In canonical quantum gravity, if the quantum state of a closed universe
contains an individual history, than it must contain, with the same
probability, the time-reversed history as well. We show that applied to the
conditional probability interpretation of a timeless universe, this statement
is consistent with the Born rule.


title:
A Robust Approach for the Decomposition of High-Energy-Consuming
  Industrial Loads with Deep Learning

abstract:
  The knowledge of the users' electricity consumption pattern is an important
coordinating mechanism between the utility company and the electricity
consumers in terms of key decision makings. The load decomposition is therefore
crucial to reveal the underlying relationship between the load consumption and
its characteristics. However, load decomposition is conventionally performed on
the residential and commercial loads, and adequate consideration has not been
given to the high-energy-consuming industrial loads leading to inefficient
results. This paper thus focuses on the load decomposition of the industrial
park loads (IPL). The commonly used parameters in a conventional method are
however inapplicable in high-energy-consuming industrial loads. Therefore, a
more robust approach is developed comprising a three-algorithm model to achieve
this goal on the IPL. First, the improved variational mode decomposition (IVMD)
algorithm is introduced to denoise the training data of the IPL and improve its
stability. Secondly, the convolutional neural network (CNN) and simple
recurrent units (SRU) joint algorithms are used to achieve a non-intrusive and
non-invasive decomposition process of the IPL using a double-layer deep
learning network based on the IPL characteristics. Specifically, CNN is used to
extract the IPL data characteristics while the improved long and short-term
memory (LSTM) network, SRU, is adopted to develop the decomposition model and
further train the load data. Through the robust decomposition process, the
underlying relationship in the load consumption is extracted. The results
obtained from the numerical examples show that this approach outperforms the
state-of-the-art in the conventional decomposition process.


title:
Quantum Physisorption of Methane and Carbon Dioxide within Nanoporous
  Materials

abstract:
  Although numerous investigations reveal the gas physisorption characteristics
of porous materials and a variety of theories have also established to describe
gas physisorption during the past century, the essence of physisorption
behavior of gas within nanoscale space is still indistinct. We find that the
physisorption behavior of complex molecular system of methane and carbon
dioxide within nanoporous materials exhibits a quantum effect. Based on this
quantum effect, we established a physisorption equation from the perspective of
quantum mechanics to re-understand the basic principles of gas physisorption
within nanopores. Energy level transition triggers gas physisorption, and
non-uniform spatial distribution of energy-quantized molecules within nanopores
dominates the gas physisorption behavior. The spatial distribution of gas
molecules can be adjusted by temperature, pressure and potential energy field.
This result contributes to understand and predict the physisorption behavior of
CH4 and CO2 within nanoporous materials.


title:
Dynamical analysis of the infection status in diverse communities due to
  COVID-19 using a modified SIR model

abstract:
  In this article, we model and study the spread of COVID-19 in Germany, Japan,
India and highly impacted states in India, i.e., in Delhi, Maharashtra, West
Bengal, Kerala and Karnataka. We consider recorded data published in
Worldometers and COVID-19 India websites from April 2020 to July 2021,
including periods of interest where these countries and states were hit
severely by the pandemic. Our methodology is based on the classic
susceptible-infected-removed (SIR) model and can track the evolution of
infections in communities, where we (a) allow for the susceptible and infected
populations to be reset at times where surges, outbreaks or secondary waves
appear in the recorded data sets, (b) consider the parameters in the SIR model
that represent the effective transmission and recovery rates to be functions of
time and (c) estimate the number of deaths by combining the model solutions
with the recorded data sets to approximate them between consecutive surges,
outbreaks or secondary waves, providing a more accurate estimate. We report on
the status of the current infections in these countries and states, and the
infections and deaths in India and Japan. Our model can adapt to the recorded
data and can be used to explain them and importantly, to forecast the number of
infected, recovered, removed and dead individuals, as well as it can estimate
the effective infection and recovery rates as functions of time, assuming an
outbreak occurs at a given time. The latter can be used to forecast the future
reproduction number and together with the forecast on the number of infected
and dead individuals, our approach can be used to suggest the implementation of
intervention strategies and mitigation policies to keep at bay the number of
infected and dead individuals. This can help reduce the impact of the spread
around the world and improve the wellbeing of people.


title:
A Mixed Integer Programming Approach for Verifying Properties of
  Binarized Neural Networks

abstract:
  Many approaches for verifying input-output properties of neural networks have
been proposed recently. However, existing algorithms do not scale well to large
networks. Recent work in the field of model compression studied binarized
neural networks (BNNs), whose parameters and activations are binary. BNNs tend
to exhibit a slight decrease in performance compared to their full-precision
counterparts, but they can be easier to verify. This paper proposes a simple
mixed integer programming formulation for BNN verification that leverages
network structure. We demonstrate our approach by verifying properties of BNNs
trained on the MNIST dataset and an aircraft collision avoidance controller. We
compare the runtime of our approach against state-of-the-art verification
algorithms for full-precision neural networks. The results suggest that the
difficulty of training BNNs might be worth the reduction in runtime achieved by
our verification algorithm.


title:
Strategy Complexity of Point Payoff, Mean Payoff and Total Payoff
  Objectives in Countable MDPs

abstract:
  We study countably infinite Markov decision processes (MDPs) with real-valued
transition rewards. Every infinite run induces the following sequences of
payoffs: 1. Point payoff (the sequence of directly seen transition rewards), 2.
Mean payoff (the sequence of the sums of all rewards so far, divided by the
number of steps), and 3. Total payoff (the sequence of the sums of all rewards
so far). For each payoff type, the objective is to maximize the probability
that the $\liminf$ is non-negative. We establish the complete picture of the
strategy complexity of these objectives, i.e., how much memory is necessary and
sufficient for $\varepsilon$-optimal (resp. optimal) strategies. Some cases can
be won with memoryless deterministic strategies, while others require a step
counter, a reward counter, or both.


title:
Probabilistic forecasts of wind power generation in regions with complex
  topography using deep learning methods: An Arctic case

abstract:
  The energy market relies on forecasting capabilities of both demand and power
generation that need to be kept in dynamic balance. Today, when it comes to
renewable energy generation, such decisions are increasingly made in a
liberalized electricity market environment, where future power generation must
be offered through contracts and auction mechanisms, hence based on forecasts.
The increased share of highly intermittent power generation from renewable
energy sources increases the uncertainty about the expected future power
generation. Point forecast does not account for such uncertainties. To account
for these uncertainties, it is possible to make probabilistic forecasts. This
work first presents important concepts and approaches concerning probabilistic
forecasts with deep learning. Then, deep learning models are used to make
probabilistic forecasts of day-ahead power generation from a wind power plant
located in Northern Norway. The performance in terms of obtained quality of the
prediction intervals is compared for different deep learning models and sets of
covariates. The findings show that the accuracy of the predictions improves
when historical data on measured weather and numerical weather predictions
(NWPs) were included as exogenous variables. This allows the model to
auto-correct systematic biases in the NWPs using the historical measurement
data. Using only NWPs, or only measured weather as exogenous variables, worse
prediction performances were obtained.


title:
Web Mining to Inform Locations of Charging Stations for Electric
  Vehicles

abstract:
  The availability of charging stations is an important factor for promoting
electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for
city planners, the crucial question is where to place charging stations so that
they reach a large utilization. Here, we hypothesize that the utilization of EV
charging stations is driven by the proximity to points-of-interest (POIs), as
EV owners have a certain limited willingness to walk between charging stations
and POIs. To address our research question, we propose the use of web mining:
we characterize the influence of different POIs from OpenStreetMap on the
utilization of charging stations. For this, we present a tailored interpretable
model that takes into account the full spatial distributions of both the POIs
and the charging stations. This allows us then to estimate the distance and
magnitude of the influence of different POI types. We evaluate our model with
data from approx. 300 charging stations and 4,000 POIs in Amsterdam,
Netherlands. Our model achieves a superior performance over state-of-the-art
baselines and, on top of that, is able to offer an unmatched level of
interpretability. To the best of our knowledge, no previous paper has
quantified the POI influence on charging station utilization from real-world
usage data by estimating the spatial proximity in which POIs are relevant. As
such, our findings help city planners in identifying effective locations for
charging stations.


title: IntroSurvey of representation theory

abstract:
  There could be thousands of Introductions/Surveys of representation theory,
given that it is an enormous field. This is just one of them, quite personal
and informal. It has an increasing level of difficulty; the first part is
intended for final year undergrads. We explain some basics of representation
theory, notably Schur-Weyl duality and representations of the symmetric group.
We then do the quantum version, introduce Kazhdan-Lusztig theory, quantum
groups and their categorical versions. We then proceed to a survey of some
recent advances in modular representation theory. We finish with twenty open
problems and a song of despair.


title: Open-source Tools for Training Resources -- OTTR

abstract:
  Data science and informatics tools are developing at a blistering rate, but
their users often lack the educational background or resources to efficiently
apply the methods to their research. Training resources often deprecate because
their maintenance is not prioritized by funding, giving teams little time to
devote to such endeavors. Our group has developed Open-source Tools for
Training Resources (OTTR) to offer greater efficiency and flexibility for
creating and maintaining online course content. OTTR empowers creators to
customize their work and allows for a simple workflow to publish using multiple
platforms. OTTR allows content creators to publish material to multiple massive
online learner communities using familiar rendering mechanics. OTTR allows the
incorporation of pedagogical practices like formative and summative assessments
in the form of multiple choice questions and fill in the blank problems that
are automatically graded. No local installation of any software is required to
begin creating content with OTTR. Thus far, 15 courses have been created with
OTTR repository template. By using the OTTR system, the maintenance workload
for updating these courses across platforms has been drastically reduced.


title:
Projective dimension and Castelnuovo-Mumford regularity of t-spread
  ideals

abstract:
  We study some algebraic invariants of $t$-spread ideals, $t\ge 1$, such as
the projective dimension and the Castelnuovo-Mumford regularity, by means of
well-known graded resolutions. We state upper bounds for these invariants and,
furthermore, we identify a special class of t-spread ideals for which such
bounds are optimal.


title:
Interpretability for Language Learners Using Example-Based Grammatical
  Error Correction

abstract:
  Grammatical Error Correction (GEC) should not focus only on high accuracy of
corrections but also on interpretability for language learning. However,
existing neural-based GEC models mainly aim at improving accuracy, and their
interpretability has not been explored. A promising approach for improving
interpretability is an example-based method, which uses similar retrieved
examples to generate corrections. In addition, examples are beneficial in
language learning, helping learners understand the basis of grammatically
incorrect/correct texts and improve their confidence in writing. Therefore, we
hypothesize that incorporating an example-based method into GEC can improve
interpretability as well as support language learners. In this study, we
introduce an Example-Based GEC (EB-GEC) that presents examples to language
learners as a basis for a correction result. The examples consist of pairs of
correct and incorrect sentences similar to a given input and its predicted
correction. Experiments demonstrate that the examples presented by EB-GEC help
language learners decide to accept or refuse suggestions from the GEC output.
Furthermore, the experiments also show that retrieved examples improve the
accuracy of corrections.


title:
MDMMT-2: Multidomain Multimodal Transformer for Video Retrieval, One
  More Step Towards Generalization

abstract:
  In this work we present a new State-of-The-Art on the text-to-video retrieval
task on MSR-VTT, LSMDC, MSVD, YouCook2 and TGIF obtained by a single model.
Three different data sources are combined: weakly-supervised videos,
crowd-labeled text-image pairs and text-video pairs. A careful analysis of
available pre-trained networks helps to choose the best prior-knowledge ones.
We introduce three-stage training procedure that provides high transfer
knowledge efficiency and allows to use noisy datasets during training without
prior knowledge degradation. Additionally, double positional encoding is used
for better fusion of different modalities and a simple method for non-square
inputs processing is suggested.


title:
Incremental Control System Design and Flight Tests of a Micro-Coaxial
  Rotor UAV

abstract:
  In this paper, the incremental nonlinear dynamic inversion (INDI) method is
applied to the control system design of coaxial rotor UAVs. The aerodynamic
uncertainty and anti-disturbance problems are solved in the control system
design. The designed controller gives the UAV excellent flight performance and
control robustness. An incremental gain method (IGM) is proposed for the delay
problem of the state derivative. This method has the advantages of less
calculation load and simple parameter adjustment, which provides excellent
convenience for applying INDI controllers to coaxial rotor UAVs. The principle
of IGM is demonstrated by the stability analysis method of the discrete system,
and the parameter selection strategy of the IGM is analyzed in detail by
simulation. The advantages of the controller design method are verified by
comparative flight tests of nonlinear dynamic inversion (NDI) and INDI. The
experimental results show that the average trajectory tracking error of INDI is
only 58.3% of that of NDI under the same wind speed. When the angular
acceleration delay is less than 0.06 s, IGM can easily keep the system stable.
In addition, INDI has better robustness under obvious model errors and strong
input disturbance.


title: Functions Beyond Multiple Polylogarithms for Precision Collider Physics

abstract:
  Feynman diagrams constitute one of the essential ingredients for making
precision predictions for collider experiments. Yet, while the simplest Feynman
diagrams can be evaluated in terms of multiple polylogarithms -- whose
properties as special functions are well understood -- more complex diagrams
often involve integrals over complicated algebraic manifolds. Such diagrams
already contribute at NNLO to the self-energy of the electron, $t \bar{t}$
production, $\gamma \gamma$ production, and Higgs decay, and appear at two
loops in the planar limit of maximally supersymmetric Yang-Mills theory. This
makes the study of these more complicated types of integrals of
phenomenological as well as conceptual importance.
  In this white paper contribution to the Snowmass community planning exercise,
we provide an overview of the state of research on Feynman diagrams that
involve special functions beyond multiple polylogarithms, and highlight a
number of research directions that constitute essential avenues for future
investigation.


title:
Experimental Searches for Muon to Electron Conversion in a Nucleus:
  COMET, DeeMe, and Mu2e. A Contributed paper for Snowmass 21

abstract:
  Searches for charged lepton flavor violation (CLFV) offer deep probes for a
range of new physics scenarios, such as super-symmetric models, theories
involving scalar leptoquarks or additional Higgs doublets, and models
explaining the neutrino mass hierarchy and the matter-antimatter asymmetry of
the universe via leptogenesis. The coherent, neutrinoless conversion of a muon
to an electron in the field of a nucleus, $\mu^{-}+N\rightarrow e^{-} +N$, is
one example of a muonic CLFV process which has sensitivity to this new physics.
This paper details three experiments: COMET, DeeMe and Mu2e which will search
for $\mu^{-}+N\rightarrow e^{-} +N$ in the coming decade. These experiments
offer sensitivity up to an effective new physics mass scale of
$\mathcal{O}(10^{4}$ TeV/c$^{2}$), going far beyond what can be achieved in
direct, collider-based, searches. The theoretical motivation, designs and
anticipated timelines for these three experiments are presented. These
experiments are a crucial part of a global search for CLFV. Continued support
for all planned experimental searches for muonic CLFV is strongly encouraged.


title:
Towards Unifying the Label Space for Aspect- and Sentence-based
  Sentiment Analysis

abstract:
  The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims
to determine the sentiment polarity towards targeted aspect terms occurring in
the sentence. The development of the ABSA task is very much hindered by the
lack of annotated data. To tackle this, the prior works have studied the
possibility of utilizing the sentiment analysis (SA) datasets to assist in
training the ABSA model, primarily via pretraining or multi-task learning. In
this article, we follow this line, and for the first time, we manage to apply
the Pseudo-Label (PL) method to merge the two homogeneous tasks. While it seems
straightforward to use generated pseudo labels to handle this case of label
granularity unification for two highly related tasks, we identify its major
challenge in this paper and propose a novel framework, dubbed as
Dual-granularity Pseudo Labeling (DPL). Further, similar to PL, we regard the
DPL as a general framework capable of combining other prior methods in the
literature. Through extensive experiments, DPL has achieved state-of-the-art
performance on standard benchmarks surpassing the prior work significantly.


title:
Snowmass White Paper: Quantum Computing Systems and Software for
  High-energy Physics Research

abstract:
  Quantum computing offers a new paradigm for advancing high-energy physics
research by enabling novel methods for representing and reasoning about
fundamental quantum mechanical phenomena. Realizing these ideals will require
the development of novel computational tools for modeling and simulation,
detection and classification, data analysis, and forecasting of high-energy
physics (HEP) experiments. While the emerging hardware, software, and
applications of quantum computing are exciting opportunities, significant gaps
remain in integrating such techniques into the HEP community research programs.
Here we identify both the challenges and opportunities for developing quantum
computing systems and software to advance HEP discovery science. We describe
opportunities for the focused development of algorithms, applications,
software, hardware, and infrastructure to support both practical and
theoretical applications of quantum computing to HEP problems within the next
10 years.


title:
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its
  Warehouse Applications

abstract:
  We study two state-of-the-art solutions to the multi-agent pickup and
delivery (MAPD) problem based on different principles -- multi-agent
path-finding (MAPF) and multi-agent reinforcement learning (MARL).
Specifically, a recent MAPF algorithm called conflict-based search (CBS) and a
current MARL algorithm called shared experience actor-critic (SEAC) are
studied. While the performance of these algorithms is measured using quite
different metrics in their separate lines of work, we aim to benchmark these
two methods comprehensively in a simulated warehouse automation environment.


title: Human Attention Detection Using AM-FM Representations

abstract:
  Human activity detection from digital videos presents many challenges to the
computer vision and image processing communities. Recently, many methods have
been developed to detect human activities with varying degree of success. Yet,
the general human activity detection problem remains very challenging,
especially when the methods need to work 'in the wild' (e.g., without having
precise control over the imaging geometry). The thesis explores phase-based
solutions for (i) detecting faces, (ii) back of the heads, (iii) joint
detection of faces and back of the heads, and (iv) whether the head is looking
to the left or the right, using standard video cameras without any control on
the imaging geometry. The proposed phase-based approach is based on the
development of simple and robust methods that rely on the use of Amplitude
Modulation- Frequency Modulation (AM-FM) models. The approach is validated
using video frames extracted from the Advancing Out-of-school Learning in
Mathematics and Engineering (AOLME) project. The dataset consisted of 13,265
images from ten students looking at the camera, and 6,122 images from five
students looking away from the camera. For the students facing the camera, the
method was able to correctly classify 97.1% of them looking to the left and
95.9% of them looking to the right. For the students facing the back of the
camera, the method was able to correctly classify 87.6% of them looking to the
left and 93.3% of them looking to the right. The results indicate that AM-FM
based methods hold great promise for analyzing human activity videos.


title: DialMed: A Dataset for Dialogue-based Medication Recommendation

abstract:
  Medication recommendation is a crucial task for intelligent healthcare
systems. Previous studies mainly recommend medications with electronic health
records(EHRs). However, some details of interactions between doctors and
patients may be ignored in EHRs, which are essential for automatic medication
recommendation. Therefore, we make the first attempt to recommend medications
with the conversations between doctors and patients. In this work, we construct
DialMed, the first high-quality dataset for medical dialogue-based medication
recommendation task. It contains 11,996 medical dialogues related to 16 common
diseases from 3 departments and 70 corresponding common medications.
Furthermore, we propose a Dialogue structure and Disease knowledge aware
Network(DDN), where a graph attention network is utilized to model the dialogue
structure and the knowledge graph is used to introduce external disease
knowledge. The extensive experimental results demonstrate that the proposed
method is a promising solution to recommend medications with medical dialogues.
The dataset and code are available at https://github.com/Hhhhhhhzf/DialMed.


title:
Analysing the accuracy of asymptotic approximations in incomplete
  contact problems

abstract:
  The error incurred in the representation of the contact pressure at the edges
of incomplete contacts by first order asymptotes is treated, and the maximum
value of the relative error found for a range of geometries, both symmetric and
non-symmetric. Shear tractions are excited by both the application of a shear
force and the application of bulk tension in one body. An asymptotic
representation of the shear traction distribution under conditions of full
stick is presented.


title: On Semialgebraic Range Reporting

abstract:
  In the problem of semialgebraic range searching, we are to preprocess a set
of points in $\mathbb{R}^D$ such that the subset of points inside a
semialgebraic region described by $O(1)$ polynomial inequalities of degree
$\Delta$ can be found efficiently.
  Relatively recently, several major advances were made on this problem. Using
algebraic techniques, "near-linear space" structures [AMS13,MP15] with almost
optimal query time of $Q(n)=O(n^{1-1/D+o(1)})$ were obtained. For "fast query"
structures (i.e., when $Q(n)=n^{o(1)}$), it was conjectured that a structure
with space $S(n) = O(n^{D+o(1)})$ is possible. The conjecture was refuted
recently by Afshani and Cheng [AC21]. In the plane, they proved that $S(n) =
\Omega(n^{\Delta+1 - o(1)}/Q(n)^{(\Delta+3)\Delta/2})$ which shows
$\Omega(n^{\Delta+1-o(1)})$ space is needed for $Q(n) = n^{o(1)}$. While this
refutes the conjecture, it still leaves a number of unresolved issues: the
lower bound only works in 2D and for fast queries, and neither the exponent of
$n$ or $Q(n)$ seem to be tight even for $D=2$, as the current upper bound is
$S(n) = O(n^{\boldsymbol{m}+o(1)}/Q(n)^{(\boldsymbol{m}-1)D/(D-1)})$ where
$\boldsymbol{m}=\binom{D+\Delta}{D}-1 = \Omega(\Delta^D)$ is the maximum number
of parameters to define a monic degree-$\Delta$ $D$-variate polynomial, for any
$D,\Delta=O(1)$.
  In this paper, we resolve two of the issues: we prove a lower bound in
$D$-dimensions and show that when $Q(n)=n^{o(1)}+O(k)$,
$S(n)=\Omega(n^{\boldsymbol{m}-o(1)})$, which is almost tight as far as the
exponent of $n$ is considered in the pointer machine model. When considering
the exponent of $Q(n)$, we show that the analysis in [AC21] is tight for $D=2$,
by presenting matching upper bounds for uniform random point sets. This shows
either the existing upper bounds can be improved or a new fundamentally
different input set is needed to get a better lower bound.


title: Extending depolarized DLS measurements to turbid samples

abstract:
  The application of dynamic light scattering to soft matter systems has
strongly profited from advanced approaches such as the so-called modulated 3D
cross correlation technique (mod3D-DLS) that suppress contributions from
multiple scattering, and can therefore be used for the characterization of
turbid samples. Here we now extend the possibilities of this technique to allow
for depolarized light scattering (Mod3D-DDLS) and thus obtain information on
both translational and rotational diffusion, which is important for the
characterization of anisotropic particles. We describe the required optical
design and test the performance of the approach for increasingly turbid samples
using well defined anisotropic colloidal models systems. Our measurements
demonstrate that 3D-DDLS experiments can be performed successfully for samples
with a reduced transmission due to multiple scattering as low as 1\%. We
compare the results from this approach with those obtained by standard DDLS
experiments, and point out the importance of using an appropriate optical
design when performing depolarized dynamic light scattering experiments with
turbid systems.


title: A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory

abstract:
  Trajectory prediction has gained great attention and significant progress has
been made in recent years. However, most works rely on a key assumption that
each video is successfully preprocessed by detection and tracking algorithms
and the complete observed trajectory is always available. However, in complex
real-world environments, we often encounter miss-detection of target agents
(e.g., pedestrian, vehicles) caused by the bad image conditions, such as the
occlusion by other agents. In this paper, we address the problem of trajectory
prediction from incomplete observed trajectory due to miss-detection, where the
observed trajectory includes several missing data points. We introduce a
two-block RNN model that approximates the inference steps of the Bayesian
filtering framework and seeks the optimal estimation of the hidden state when
miss-detection occurs. The model uses two RNNs depending on the detection
result. One RNN approximates the inference step of the Bayesian filter with the
new measurement when the detection succeeds, while the other does the
approximation when the detection fails. Our experiments show that the proposed
model improves the prediction accuracy compared to the three baseline
imputation methods on publicly available datasets: ETH and UCY ($9\%$ and $7\%$
improvement on the ADE and FDE metrics). We also show that our proposed method
can achieve better prediction compared to the baselines when there is no
miss-detection.


title: Dynamical dimensional reduction in multi-valued Hamiltonians

abstract:
  Several interesting physical systems, such as the Lovelock extension of
General Relativity in higher dimensions, classical time crystals, k-essence
fields, Horndeski theories, compressible fluids, and nonlinear electrodynamics,
have apparent ill defined sympletic structures, due to the fact that their
Hamiltonians are multi-valued functions of the momenta. In this paper, the
dynamical evolution generated by such Hamiltonians is described as a degenerate
dynamical system, whose sympletic form does not have a constant rank, allowing
novel features and interpretations not present in previous investigations. In
particular, it is shown how the multi-valuedness is associated with a dynamical
mechanism of dimensional reduction, as some degrees of freedom turn into gauge
symmetries when the system degenerates.


title:
The equation $X^\top AX=B$ with $B$ skew-symmetric: How much of a
  bilinear form is skew-symmetric?

abstract:
  Given a bilinear form on $\mathbb C^n$, represented by a matrix $A\in\mathbb
C^{n\times n}$, the problem of finding the largest dimension of a subspace of
$\mathbb C^n$ such that the restriction of $A$ to this subspace is a
non-degenerate skew-symmetric bilinear form is equivalent to finding the size
of the largest invertible skew-symmetric matrix $B$ such that the equation
$X^\top AX=B$ is consistent (here $X^\top$ denotes the transpose of the matrix
$X$). In this paper, we provide a characterization, by means of a necessary and
sufficient condition, for the matrix equation $X^\top AX=B$ to be consistent
when $B$ is a skew-symmetric matrix. This condition is valid for most matrices
$A\in\mathbb C^{n\times n}$. To be precise, the condition depends on the
canonical form for congruence (CFC) of the matrix $A$, which is a direct sum of
blocks of three types. The condition is valid for all matrices $A$ except those
whose CFC contains blocks, of one of the types, with size smaller than $3$.
However, we show that the condition is necessary for all matrices $A$.


title: Kronecker anomalies and gravitational striction

abstract:
  We study quantum field theories in which the number of degrees of freedom
changes discontinuously across the momentum space. This discontinuity which we
call "Kronecker anomaly" leads to non-local effective actions and can be
represented as a theory with the random, self-tuning coupling constants.


title:
Detection of Electromagnetic Signal Injection Attacks on Actuator
  Systems

abstract:
  An actuator is a device that converts electricity into another form of
energy, typically physical movement. They are absolutely essential for any
system that needs to impact or modify the physical world, and are used in
millions of systems of all sizes, all over the world, from cars and spacecraft
to factory control systems and critical infrastructure. An actuator is a "dumb
device" that is entirely controlled by the surrounding electronics, e.g., a
microcontroller, and thus cannot authenticate its control signals or do any
other form of processing. The problem we look at in this paper is how the wires
that connect an actuator to its control electronics can act like antennas,
picking up electromagnetic signals from the environment. This makes it possible
for a remote attacker to wirelessly inject signals (energy) into these wires to
bypass the controller and directly control the actuator.
  To detect such attacks, we propose a novel detection method that allows the
microcontroller to monitor the control signal and detect attacks as a deviation
from the intended value. We have managed to do this without requiring the
microcontroller to sample the signal at a high rate or run any signal
processing. That makes our defense mechanism practical and easy to integrate
into existing systems. Our method is general and applies to any type of
actuator (provided a few basic assumptions are met), and can deal with
adversaries with arbitrarily high transmission power. We implement our
detection method on two different practical systems to show its generality,
effectiveness, and robustness.


title:
Mermin and Svetlichny inequalities for non-projective measurement
  observables

abstract:
  The necessary and sufficient criteria for violating the Mermin and Svetlichny
inequalities by arbitrary three-qubit states are presented. Several attempts
have been made, earlier, to find such criteria, however, those extant criteria
are neither tight for most of the instances, nor fully general. We generalize
the existing criteria for Mermin and Svetlichny inequalities which are valid
for the local projective measurement observables as well as for the arbitrary
ones. We obtain the maximal achievable bounds of the Mermin and Svetlichny
operators with unbiased measurement observables for arbitrary three-qubit
states and with arbitrary observables for three-qubit states having maximally
mixed marginals. We find that for certain ranges of measurement strengths, it
is possible to violate Mermin and Svetlichny inequalities only by biased
measurement observables. The necessary and sufficient criteria of violating any
one of the six possible Mermin and Svetlichny inequalities are also derived.


title:
The stable cooperations of Morava K-Theory and the fiber product of
  automorphism groups of formal group laws

abstract:
  The Hopf algebra $K(n)_*(K(n))$, the stable cooperations of Morava $K$-theory
at an odd prime, is well-known. Whereas the main part of $K(n)_*(K(n))$
corepresents the group-valued functor consisting of the automorphisms of a
formal group law of height $n$, relations between the whole structure of
$K(n)_*(K(n))$ and formal group laws have not been investigated well. Firstly,
we constitute the fiber product of two natural transformations between the
automorphism groups of formal group laws, and the corepresenting Hopf algebra
$C_*$. Next, we construct a Hopf algebra homomorphism from $C_*$ to
$K(n)_*(K(n))$ via Boardman's stable comodule algebras. By Yagita's results and
the algebra structure of $K(n)_*(K(n))$, we see that the homomorphism is an
isomorphism. The isomorphism clarifies the relationship between the Hopf
algebra structure of $K(n)_*(K(n))$ including the exterior algebra part and
formal group laws.


title: Privatized Graph Federated Learning

abstract:
  Federated learning is a semi-distributed algorithm, where a server
communicates with multiple dispersed clients to learn a global model. The
federated architecture is not robust and is sensitive to communication and
computational overloads due to its one-master multi-client structure. It can
also be subject to privacy attacks targeting personal information on the
communication links. In this work, we introduce graph federated learning (GFL),
which consists of multiple federated units connected by a graph. We then show
how graph homomorphic perturbations can be used to ensure the algorithm is
differentially private. We conduct both convergence and privacy theoretical
analyses and illustrate performance by means of computer simulations.


title: The parton distribution function in a pion with Minkowskian dynamics

abstract:
  The parton distribution of the pion is obtained for the first time from the
solution of a dynamical equation in Minkowski space. The adopted equation is
the homogeneous Bethe-Salpeter one with a ladder kernel, described in terms of
i) constituent quarks and gluons degrees of freedom, and ii) an extended
quark-gluon vertex. The masses of quark and gluon as well as the
interaction-vertex scale have been chosen in a range suggested by lattice QCD
calculations, and calibrated to reproduce both pion mass and decay constant.
Beside the full parton distribution, we have also calculated the contribution
from the light-front valence wave function, corresponding to the lowest Fock
component in the expansion of the pion state. After applying an evolution with
an effective charge and a LO splitting function, a detailed and inspiring
comparison with both the extracted experimental data ( with and without
resummation effects) and other recent calculations obtained in different
frameworks is presented. Interestingly, in a wide region of
longitudinal-momentum fraction, the parton distribution function receives
sizable contributions from the higher Fock-components of the pion state at the
initial scale, while approaching the tail the light-front valence component
dominates, as expected. Moreover, an exponent $\sim$ 3 is found suitable for
describing the tail at the scale 5.2 GeV.


title:
Memristor-based cryogenic programmable DC sources for scalable in-situ
  quantum-dot control

abstract:
  Current quantum systems based on spin qubits are controlled by classical
electronics located outside the cryostat at room temperature. This approach
creates a major wiring bottleneck, which is one of the main roadblocks toward
truly scalable quantum computers. Thus, we propose a scalable memristor-based
programmable DC source that could be used to perform biasing of quantum dots
inside of the cryostat (i.e. in-situ). This novel cryogenic approach would
enable to control the applied voltage on the electrostatic gates by programming
the resistance of the memristors, thus storing in the latter the appropriate
conditions to form the quantum dots. In this study, we first demonstrate
multilevel resistance programming of a TiO2-based memristors at 4.2 K, an
essential feature to achieve voltage tunability of the memristor-based DC
source. We then report hardwarebased simulations of the electrical performance
of the proposed DC source. A cryogenic TiO2-based memristor model fitted on our
experimental data at 4.2 K was used to show a 1 V voltage range and 100 uV
in-situ memristor-based DC source. Finally, we simulate the biasing of double
quantum dots enabling sub-2 minutes in-situ charge stability diagrams. This
demonstration is a first step towards more advanced cryogenic applications for
resistive memories such as cryogenic control electronics for quantum computers.


title:
Consistent and scalable Bayesian joint variable and graph selection for
  disease diagnosis leveraging functional brain network

abstract:
  We consider the joint inference of regression coefficients and the inverse
covariance matrix for covariates in high-dimensional probit regression, where
the predictors are both relevant to the binary response and functionally
related to one another. A hierarchical model with spike and slab priors over
regression coefficients and the elements in the inverse covariance matrix is
employed to simultaneously perform variable and graph selection. We establish
joint selection consistency for both the variable and the underlying graph when
the dimension of predictors is allowed to grow much larger than the sample
size, which is the first theoretical result in the Bayesian literature. A
scalable Gibbs sampler is derived that performs better in high-dimensional
simulation studies compared with other state-of-art methods. We illustrate the
practical impact and utilities of the proposed method via a functional MRI
dataset, where both the regions of interest with altered functional activities
and the underlying functional brain network are inferred and integrated
together for stratifying disease risk.


title: Automatic Compiler-Based Data Structure Generation

abstract:
  Optimizing compilers are mainly equipped to optimize control flow. The
optimization of data structures is left to the programmer and it is the
programmer's responsibility to design the data structures to suit the target
hardware. Very specific data structures are required to exploit certain
hardware features, such as cache line size, address alignment, vector width,
and memory hierarchy specifics. Because optimizing compilers do not target data
structures, these features are explicitly encoded in program specifications.
This leads to convoluted programs that obscure the essence of the computation
from the compiler, in turn causing compiler analysis techniques to break down
and hampering compiler optimizations from being applied.
  To solve this problem, we propose to move towards the specification of
programs without explicitly specifying the data structure. The compiler will
automatically generate actual data structures and executable code starting from
this specification. In this paper, we introduce a compiler-based framework to
support this automatic generation of data structures, allowing the compiler to
go beyond the optimization of solely control flow and also target the way data
is organized and accessed. As a case study of the effectiveness of this
framework, we present a detailed description and experimental results of the
application of the proposed techniques to automatically generate data
structures for sparse matrix computations. We show that this way sparse data
structures can be generated that were up till now only specified by hand and
that automatically instantiated routines and corresponding data structures can
be found that outperform implementations of three existing sparse algebra
libraries.


title:
Bayesian inference on hierarchical nonlocal priors in generalized linear
  models

abstract:
  Variable selection methods with nonlocal priors have been widely studied in
linear regression models, and their theoretical and empirical performances have
been reported. However, the crucial model selection properties for hierarchical
nonlocal priors in high-dimensional generalized linear regression have rarely
been investigated. In this paper, we consider a hierarchical nonlocal prior for
high-dimensional logistic regression models and investigate theoretical
properties of the posterior distribution. Specifically, a product moment (pMOM)
nonlocal prior is imposed over the regression coefficients with an
Inverse-Gamma prior on the tuning parameter. Under standard regularity
assumptions, we establish strong model selection consistency in a
high-dimensional setting, where the number of covariates is allowed to increase
at a sub-exponential rate with the sample size. We implement the Laplace
approximation for computing the posterior probabilities, and a modified shotgun
stochastic search procedure is suggested for efficiently exploring the model
space. We demonstrate the validity of the proposed method through simulation
studies and an RNA-sequencing dataset for stratifying disease risk.


title: Disentangled Representation Learning for Text-Video Retrieval

abstract:
  Cross-modality interaction is a critical component in Text-Video Retrieval
(TVR), yet there has been little examination of how different influencing
factors for computing interaction affect performance. This paper first studies
the interaction paradigm in depth, where we find that its computation can be
split into two terms, the interaction contents at different granularity and the
matching function to distinguish pairs with the same semantics. We also observe
that the single-vector representation and implicit intensive function
substantially hinder the optimization. Based on these findings, we propose a
disentangled framework to capture a sequential and hierarchical representation.
Firstly, considering the natural sequential structure in both text and video
inputs, a Weighted Token-wise Interaction (WTI) module is performed to decouple
the content and adaptively exploit the pair-wise correlations. This interaction
can form a better disentangled manifold for sequential inputs. Secondly, we
introduce a Channel DeCorrelation Regularization (CDCR) to minimize the
redundancy between the components of the compared vectors, which facilitate
learning a hierarchical representation. We demonstrate the effectiveness of the
disentangled representation on various benchmarks, e.g., surpassing CLIP4Clip
largely by +2.9%, +3.1%, +7.9%, +2.3%, +2.8% and +6.5% R@1 on the MSR-VTT,
MSVD, VATEX, LSMDC, AcitivityNet, and DiDeMo, respectively.


title: RCL: Recurrent Continuous Localization for Temporal Action Detection

abstract:
  Temporal representation is the cornerstone of modern action detection
techniques. State-of-the-art methods mostly rely on a dense anchoring scheme,
where anchors are sampled uniformly over the temporal domain with a discretized
grid, and then regress the accurate boundaries. In this paper, we revisit this
foundational stage and introduce Recurrent Continuous Localization (RCL), which
learns a fully continuous anchoring representation. Specifically, the proposed
representation builds upon an explicit model conditioned with video embeddings
and temporal coordinates, which ensure the capability of detecting segments
with arbitrary length. To optimize the continuous representation, we develop an
effective scale-invariant sampling strategy and recurrently refine the
prediction in subsequent iterations. Our continuous anchoring scheme is fully
differentiable, allowing to be seamlessly integrated into existing detectors,
e.g., BMN and G-TAD. Extensive experiments on two benchmarks demonstrate that
our continuous representation steadily surpasses other discretized counterparts
by ~2% mAP. As a result, RCL achieves 52.92% mAP@0.5 on THUMOS14 and 37.65% mAP
on ActivtiyNet v1.3, outperforming all existing single-model detectors.


title: Energy-Energy Correlators for Precision QCD

abstract:
  In this contribution to the Proceedings of the US Community Study on the
Future of Particle Physics (Snowmass 2021) we review recent progress in the
evaluation and application of the Energy-Energy Correlator (EEC) event shape
observable in $e^+e^-$ annihilation, hadronic collisions, and deep inelastic
scattering. The importance of EEC as a precision probe of the perturbative and
non perturbative aspects of QCD dynamics is emphasized. It can be used to
extract the strong coupling constant and to constrain TMD distribution
functions. Closely related energy-correlation shape variables have also been
used to tag boosted objects produced in high energy collisions. The
opportunities to study EEC at the future Electron-Ion Collider are also
highlighted.


title:
WSSAMNet: Weakly Supervised Semantic Attentive Medical Image
  Registration Network

abstract:
  We present WSSAMNet, a weakly supervised method for medical image
registration. Ours is a two step method, with the first step being the
computation of segmentation masks of the fixed and moving volumes. These masks
are then used to attend to the input volume, which are then provided as inputs
to a registration network in the second step. The registration network computes
the deformation field to perform the alignment between the fixed and the moving
volumes. We study the effectiveness of our technique on the BraTSReg challenge
data against ANTs and VoxelMorph, where we demonstrate that our method performs
competitively.


title:
Modelling variability in vibration-based PBSHM via a generalised
  population form

abstract:
  Structural health monitoring (SHM) has been an active research area for the
last three decades, and has accumulated a number of critical advances over that
period, as can be seen in the literature. However, SHM is still facing
challenges because of the paucity of damage-state data, operational and
environmental fluctuations, repeatability issues, and changes in boundary
conditions. These issues present as inconsistencies in the captured features
and can have a huge impact on the practical implementation, but more
critically, on the generalisation of the technology. Population-based SHM has
been designed to address some of these concerns by modelling and transferring
missing information using data collected from groups of similar structures.
  In this work, vibration data were collected from four healthy,
nominally-identical, full-scale composite helicopter blades. Manufacturing
differences (e.g., slight differences in geometry and/or material properties),
among the blades presented as variability in their structural dynamics, which
can be very problematic for SHM based on machine learning from vibration data.
This work aims to address this variability by defining a general model for the
frequency response functions of the blades, called a form, using mixtures of
Gaussian processes.


title: EIT: Efficiently Lead Inductive Biases to ViT

abstract:
  Vision Transformer (ViT) depends on properties similar to the inductive bias
inherent in Convolutional Neural Networks to perform better on non-ultra-large
scale datasets. In this paper, we propose an architecture called Efficiently
lead Inductive biases to ViT (EIT), which can effectively lead the inductive
biases to both phases of ViT. In the Patches Projection phase, a convolutional
max-pooling structure is used to produce overlapping patches. In the
Transformer Encoder phase, we design a novel inductive bias introduction
structure called decreasing convolution, which is introduced parallel to the
multi-headed attention module, by which the embedding's different channels are
processed respectively. In four popular small-scale datasets, compared with
ViT, EIT has an accuracy improvement of 12.6% on average with fewer parameters
and FLOPs. Compared with ResNet, EIT exhibits higher accuracy with only 17.7%
parameters and fewer FLOPs. Finally, ablation studies show that the EIT is
efficient and does not require position embedding. Code is coming soon:
https://github.com/MrHaiPi/EIT


title:
Snowmass white paper: Quantum information in quantum field theory and
  quantum gravity

abstract:
  We present a summary of recent progress and remaining challenges in applying
the methods and ideas of quantum information theory to the study of quantum
field theory and quantum gravity. Important topics and themes include:
entanglement entropy in QFTs and what it reveals about RG flows, symmetries,
and phases; scrambling, information spreading, and chaos; state preparation and
complexity; classical and quantum simulation of QFTs; and the role of
information in holographic dualities. We also highlight the ways in which
quantum information science benefits from the synergy between the fields.


title: The X-ray coronae in NuSTAR bright active galactic nuclei

abstract:
  We present systematic and uniform analysis of NuSTAR data with 10-78 keV S/N
> 50, of a sample of 60 SWIFT BAT selected AGNs, 10 of which are radio-loud. We
measure their high energy cutoff Ecut or coronal temperature Te using three
different spectral models to fit their NuSTAR spectra, and show a threshold in
NuSTAR spectral S/N is essential for such measurements. High energy spectral
breaks are detected in the majority of the sample, and for the rest strong
constraints to Ecut or Te are obtained. Strikingly, we find extraordinarily
large Ecut lower limits ( > 400 keV, up to > 800 keV) in 10 radio-quiet
sources, whereas none in the radio-loud sample. Consequently and surprisingly,
we find significantly larger mean Ecut/Te of radio-quiet sources compared with
radio-loud ones. The reliability of these measurements are carefully inspected
and verified with simulations. We find a strong positive correlation between
Ecut and photon index {\Gamma}, which can not be attributed to the parameter
degeneracy. The strong dependence of Ecut on {\Gamma}, which could fully
account for the discrepancy of Ecut distribution between radio-loud and
radio-quiet sources, indicates the X-ray coronae in AGNs with steeper hard
X-ray spectra have on average higher temperature and thus smaller opacity.
However, no prominent correlation is found between Ecut and {\lambda}edd. In
the l-{\Theta} diagram, we find a considerable fraction of sources lie beyond
the boundaries of forbidden regions due to runaway pair production, posing
(stronger) challenges to various (flat) coronal geometries.


title: The Relation between X-ray and Ultraviolet Variability of Quasars

abstract:
  The relation between X-ray and UV/optical variability in AGNs has been
explored in many individual sources, however a large sample study is yet
absent. Through matching the XMM-Newton serendipitous X-ray and UV source
catalogs with SDSS quasars, we build a sample of 802 epoch-pairs of 525 quasars
showing clear variability in logFx-logFuv space. After correcting for the
effect of photometric noise, we find 35.6\pm2.1% of the epoch-pairs show
asynchronous variability between X-ray and UV (brightening in one band but
dimming in the other). This indicates only in 28.8\pm4.2% of the epoch-pairs
the X-ray and UV variability are intrinsically coordinated. The variability
synchronicity exhibits no dependence on physical parameters of quasars or the
time lag of the epoch-pairs, except for stronger variability tends to have
stronger synchronicity. Switches between synchronous and asynchronous
variability are also seen in individual sources. The poor coordination clearly
contradicts both the X-ray reprocessing model and the accretion rate variation
model for AGN variability. The ratios of the observed X-ray variability
amplitude to that in UV span a broad range and peak at ~ 2. The dominant
fraction of the ratios appear too small to be attributed to X-ray reprocessing,
and too large for accretion rate variation. The inhomogeneous disk model which
incorporates both X-ray and UV/optical variability in AGNs is favored by the
observed stochastic relation between X-ray and UV variations.


title:
Neural Message Passing for Objective-Based Uncertainty Quantification
  and Optimal Experimental Design

abstract:
  Real-world scientific or engineering applications often involve mathematical
modeling of complex uncertain systems with a large number of unknown
parameters. The complexity of such systems, and the enormous uncertainties
therein, typically make accurate model identification from the available data
infeasible. In such cases, it is desirable to represent the model uncertainty
in a Bayesian paradigm, based on which we can design robust operators that
maintain the best overall performance across all possible models and design
optimal experiments that can effectively reduce uncertainty to maximally
enhance the performance of such operators. While objective-based uncertainty
quantification (objective-UQ) based on MOCU (mean objective cost of
uncertainty) has been shown to provide effective means for quantifying and
handling uncertainty in complex systems, a major drawback has been the high
computational cost of estimating MOCU. In this work, we demonstrate for the
first time that one can design accurate surrogate models for efficient
objective-UQ via MOCU based on a data-driven approach. We adopt a neural
message passing model for surrogate modeling, which incorporates a novel
axiomatic constraint loss that penalizes an increase in the estimated system
uncertainty. As an illustrative example, we consider the optimal experimental
design (OED) problem for uncertain Kuramoto models, where the goal is to
predict the experiments that can most effectively enhance the robust
synchronization performance through uncertainty reduction. Through quantitative
performance assessment, we show that our proposed approach can accelerate
MOCU-based OED by four to five orders of magnitude, virtually without any
visible loss of performance compared to the previous state-of-the-art. The
proposed approach can be applied to general OED tasks, beyond the Kuramoto
model.


title:
Parabolic optimal control problems with combinatorial switching
  constraints -- Part I: Convex relaxations

abstract:
  We consider optimal control problems for partial differential equations where
the controls take binary values but vary over the time horizon, they can thus
be seen as dynamic switches. The switching patterns may be subject to
combinatorial constraints such as, e.g., an upper bound on the total number of
switchings or a lower bound on the time between two switchings. While such
combinatorial constraints are often seen as an additional complication that is
treated in a heuristic postprocessing, the core of our approach is to
investigate the convex hull of all feasible switching patterns in order to
define a tight convex relaxation of the control problem. The convex relaxation
is built by cutting planes derived from finite-dimensional projections, which
can be studied by means of polyhedral combinatorics. A numerical example for
the case of a bounded number of switchings shows that our approach can
significantly improve the dual bounds given by the straightforward continuous
relaxation, which is obtained by relaxing binarity constraints.


title: Probabilistic Constrained Bayesion Inversion for Transpiration Cooling

abstract:
  To enable safe operations in applications such as rocket combustion chambers,
the materials require cooling to avoid material damage. Here, transpiration
cooling is a promising cooling technique. Numerous studies investigate
possibilities to simulate and evaluate the complex cooling mechanism. One
naturally arising question is the amount of coolant required to ensure a safe
operation. To study this, we introduce an approach that determines the
posterior probability distribution of the Reynolds number using an inverse
problem and constraining the maximum temperature of the system under parameter
uncertainties. Mathematically, this chance inequality constraint is dealt with
by a generalized Polynomial Chaos expansion of the system. The posterior
distribution will be evaluated by different Markov Chain Monte Carlo based
methods. A novel method for the constrained case is proposed and tested among
others on two-dimensional transpiration cooling models.


title: The Alt-Phillips functional for negative powers

abstract:
  We develop the free boundary regularity for nonnegative minimizers of the
Alt-Phillips functional for negative power potentials
  $$\int_\Omega \left(\frac 1 2 |\nabla u|^2 + u^{\gamma} \chi_{\{u>0\}}\right)
\, dx, \quad \quad \gamma \in (-2,0),$$
  and establish a $\Gamma$-convergence result of the rescaled energies to the
perimeter functional as $\gamma \to -2$.


title:
Similarity-based prediction of Ejection Fraction in Heart Failure
  Patients

abstract:
  Biomedical research is increasingly employing real world evidence (RWE) to
foster discoveries of novel clinical phenotypes and to better characterize long
term effect of medical treatments. However, due to limitations inherent in the
collection process, RWE often lacks key features of patients, particularly when
these features cannot be directly encoded using data standards such as ICD-10.
Here we propose a novel data-driven statistical machine learning approach,
named Feature Imputation via Local Likelihood (FILL), designed to infer missing
features by exploiting feature similarity between patients. We test our method
using a particularly challenging problem: differentiating heart failure
patients with reduced versus preserved ejection fraction (HFrEF and HFpEF
respectively). The complexity of the task stems from three aspects: the two
share many common characteristics and treatments, only part of the relevant
diagnoses may have been recorded, and the information on ejection fraction is
often missing from RWE datasets. Despite these difficulties, our method is
shown to be capable of inferring heart failure patients with HFpEF with a
precision above 80% when considering multiple scenarios across two RWE datasets
containing 11,950 and 10,051 heart failure patients. This is an improvement
when compared to classical approaches such as logistic regression and random
forest which were only able to achieve a precision < 73%. Finally, this
approach allows us to analyse which features are commonly associated with HFpEF
patients. For example, we found that specific diagnostic codes for atrial
fibrillation and personal history of long-term use of anticoagulants are often
key in identifying HFpEF patients.


title: Vector dark radiation and gravitational-wave polarization

abstract:
  We consider conformal vector models which could play the role of a
cosmological dark radiation component. We analyse the propagation of
gravitational waves in the presence of this vector background and find a
suppression in the tensor transfer function at large scales. We also find that
although the cosmological background metric is isotropic, anisotropies are
imprinted in the tensor power spectrum. In addition, the presence of the
background vector fields induces a net polarization of the gravitational wave
background and, for certain configurations of the vector field, a linear to
circular polarization conversion. We also show that this kind of effects are
also present for vector models with more general potential terms.


title:
Sampling discretization error of integral norms for function classes
  with small smoothness

abstract:
  We consider infinitely dimensional classes of functions and instead of the
relative error setting, which was used in previous papers on the integral norm
discretization, we consider the absolute error setting. We demonstrate how
known results from two areas of research -- supervised learning theory and
numerical integration -- can be used in sampling discretization of the square
norm on different function classes. We prove a general result, which shows that
the sequence of entropy numbers of a function class in the uniform norm
dominates, in a certain sense, the sequence of errors of sampling
discretization of the square norm of this class. Then we use this result for
establishing new error bounds for sampling discretization of the square norm on
classes of multivariate functions with mixed smoothness.


title:
X-ray Spectroscopic Evidence of Charge Exchange Emission in the Disk of
  M51

abstract:
  In the disks of spiral galaxies, diffuse soft X-ray emission is known to be
strongly correlated with star-forming regions. However, this emission is not
simply from a thermal-equilibrium plasma and its origin remains greatly
unclear. In this work, we present an X-ray spectroscopic analysis of the
emission from a star-forming enhanced region off the nucleus of the spiral
galaxy M51, named as the northern hot spot (NHS) hereafter. We analyze the high
spectral resolution data from XMM-Newton/RGS observations and unambiguously
detect an abnormally high G-ratio ($3.2^{+6.9}_{-1.5}$) of the O VII K$\alpha$
triplet emission from the NHS, which is spatially confirmed by oxygen emission
line maps from the RGS data. A physical model consisting of a thermal plasma
and its charge exchange (CX) with cold gas gives an excellent description of
the high G-ratio and the entire RGS spectra. Complementary to the RGS data, the
Chandra/ACIS-S data are also analyzed because its high spatial resolution is
beneficial for decomposing the diffuse and discrete source contributions. Its
imaging spectra of the diffuse emission either from the NHS or other parts of
the M51 disk can be well interpreted by the same physical model. Though the
surface brightness of the diffuse emission from the NHS is about twice that
from other parts of the disk, the hot plasma has the similar temperature of
~0.34 keV and approximately solar metallicity. For both parts of the disk, the
CX contributes ~50% to the diffuse soft X-ray emission in the 7-30 angstrom
band, and has an interface area of about five times the geometric area of the
M51 disk. Our results suggest that the hot plasma can interact with the cold
interstellar medium efficiently, and have strong implications for our
understanding of soft X-ray emission as a tracer of the stellar feedback in
active star-forming galaxies.


title:
Dark Energy Survey Year 3 results: cosmological constraints from the
  analysis of cosmic shear in harmonic space

abstract:
  We present cosmological constraints from the analysis of angular power
spectra of cosmic shear maps based on data from the first three years of
observations by the Dark Energy Survey (DES Y3). Our measurements are based on
the pseudo-$C_\ell$ method and offer a view complementary to that of the
two-point correlation functions in real space, as the two estimators are known
to compress and select Gaussian information in different ways, due to scale
cuts. They may also be differently affected by systematic effects and
theoretical uncertainties, such as baryons and intrinsic alignments (IA),
making this analysis an important cross-check. In the context of $\Lambda$CDM,
and using the same fiducial model as in the DES Y3 real space analysis, we find
${S_8 \equiv \sigma_8 \sqrt{\Omega_{\rm m}/0.3} = 0.793^{+0.038}_{-0.025}}$,
which further improves to ${S_8 = 0.784\pm 0.026 }$ when including shear
ratios. This constraint is within expected statistical fluctuations from the
real space analysis, and in agreement with DES~Y3 analyses of non-Gaussian
statistics, but favors a slightly higher value of $S_8$, which reduces the
tension with the Planck cosmic microwave background 2018 results from
$2.3\sigma$ in the real space analysis to $1.5\sigma$ in this work. We explore
less conservative IA models than the one adopted in our fiducial analysis,
finding no clear preference for a more complex model. We also include small
scales, using an increased Fourier mode cut-off up to $k_{\rm max}={5}{h{\rm
Mpc}^{-1}}$, which allows to constrain baryonic feedback while leaving
cosmological constraints essentially unchanged. Finally, we present an
approximate reconstruction of the linear matter power spectrum at present time,
which is found to be about 20\% lower than predicted by Planck 2018, as
reflected by the $1.5\sigma$ lower $S_8$ value.


title: Proper Ehresmann semigroups

abstract:
  We propose a notion of a proper Ehresmann semigroup based on a
three-coordinate description of its generating elements governed by certain
labelled directed graphs with additional structure. The generating elements are
determined by their domain projection, range projection and $\sigma$-class,
where $\sigma$ denotes the minimum congruence that identifies all projections.
We prove a structure result on proper Ehresmann semigroups and show that every
Ehresmann semigroup has a proper cover. Our covering semigroup turns out to be
isomorphic to that from the work by Branco, Gomes and Gould and provides a new
view of the latter. Proper Ehresmann semigroups all of whose elements admit a
three-coordinate description are characterized in terms of partial multiactions
of monoids on semilattices. As a consequence we recover the two-coordinate
structure result on proper restriction semigroups.


title:
Flexure-based Environmental Compliance for High-speed Robotic Contact
  Tasks

abstract:
  The design of physical compliance -- its location, degree, and structure --
affects robot performance and robustness in contact-rich tasks. While
compliance is often used in the robot's joints, flange, or end-effector, this
paper proposes compliant structures in the environment, allowing safe and
robust contact while keeping the higher motion control bandwidth and precision
of high impedance robots. Compliance is here realized with flexures and
viscoelastic materials, which are integrated to several mechanisms to offer
structured compliance, such as a remote center of compliance. Additive
manufacturing with fused deposition modeling is used, allowing faster design
iteration and low-cost integration with standard industrial equipment.
Mechanical properties, including the total stiffness matrix, stiffness ratio,
and rotational precision, are analytically determined and compared to
experimental results. Three remote center of compliance (RCC) devices and a
1-DOF linear device are prototyped and tested in high-speed assembly tasks.


title: Formula for two-loop divergent part of 4-D Yang-Mills effective action

abstract:
  In the paper, we study the two-loop contribution to the effective action of
the four-dimensional quantum Yang-Mills theory. We derive a new formula for the
contribution in terms of three functions, formed from the Green's function
expansion near the diagonal. This result can be applied to different types of
regularization. Therefore, we test it by using the dimensional regularization
and cutoff ones and show the consistence with the results, obtained in other
works.


title: Szego condition, scattering, and vibration of Krein strings

abstract:
  We give a dynamical characterization of measures on the real line with finite
logarithmic integral. The general case is considered in the setting of
evolution groups generated by de Branges canonical systems. Obtained results
are applied to the Dirac operators and Krein strings.


title: Controlling the shape and topology of two-component colloidal membranes

abstract:
  Changes in the geometry and topology of self-assembled membranes underlie
diverse processes across cellular biology and engineering. Similar to lipid
bilayers, monolayer colloidal membranes have in-plane fluid-like dynamics and
out-of-plane bending elasticity. Their open edges and micron length scale
provide a tractable system to study the equilibrium energetics and dynamic
pathways of membrane assembly and reconfiguration. Here, we find that doping
colloidal membranes with short miscible rods transforms disk-shaped membranes
into saddle-shaped surfaces with complex edge structures. The saddle-shaped
membranes are well-approximated by Enneper's minimal surfaces. Theoretical
modeling demonstrates that their formation is driven by increasing positive
Gaussian modulus, which in turn is controlled by the fraction of short rods.
Further coalescence of saddle-shaped surfaces leads to diverse topologically
distinct structures, including catenoids, tri-noids, four-noids, and higher
order structures. At long time scales, we observe the formation of a
system-spanning, sponge-like phase. The unique features of colloidal membranes
reveal the topological transformations that accompany coalescence pathways in
real time. We enhance the functionality of these membranes by making their
shape responsive to external stimuli. Our results demonstrate a novel pathway
towards control of thin elastic sheets' shape and topology -- a pathway driven
by the emergent elasticity induced by compositional heterogeneity.


title:
The importance of the way in which supernova energy is distributed
  around young stellar populations in simulations of galaxies

abstract:
  Supernova (SN) feedback plays a crucial role in simulations of galaxy
formation. Because blastwaves from individual SNe occur on scales that remain
unresolved in modern cosmological simulations, SN feedback must be implemented
as a subgrid model. Differences in the manner in which SN energy is coupled to
the local interstellar medium and in which excessive radiative losses are
prevented have resulted in a zoo of models used by different groups. However,
the importance of the selection of resolution elements around young stellar
particles for SN feedback has largely been overlooked. In this work, we examine
various selection methods using the smoothed particle hydrodynamics code SWIFT.
We run a suite of isolated disk galaxy simulations of a Milky Way-mass galaxy
and small cosmological volumes, all with the thermal stochastic SN feedback
model used in the EAGLE simulations. We complement the original mass-weighted
neighbour selection with a novel algorithm guaranteeing that the SN energy
distribution is as close to isotropic as possible. Additionally, we consider
algorithms where the energy is injected into the closest, least dense, or most
dense neighbour. We show that different neighbour-selection strategies cause
significant variations in star formation rates, gas densities, wind mass
loading factors, and galaxy morphology. The isotropic method results in more
efficient feedback than the conventional mass-weighted selection. We conclude
that the manner in which the feedback energy is distributed among the
resolution elements surrounding a feedback event is as important as changing
the amount of energy by factors of a few.


title: A Bayesian approach to translators' reliability assessment

abstract:
  Translation Quality Assessment (TQA) is a process conducted by human
translators and is widely used, both for estimating the performance of
(increasingly used) Machine Translation, and for finding an agreement between
translation providers and their customers. While translation scholars are aware
of the importance of having a reliable way to conduct the TQA process, it seems
that there is limited literature that tackles the issue of reliability with a
quantitative approach. In this work, we consider the TQA as a complex process
from the point of view of physics of complex systems and approach the
reliability issue from the Bayesian paradigm. Using a dataset of translation
quality evaluations (in the form of error annotations), produced entirely by
the Professional Translation Service Provider Translated SRL, we compare two
Bayesian models that parameterise the following features involved in the TQA
process: the translation difficulty, the characteristics of the translators
involved in producing the translation, and of those assessing its quality - the
reviewers. We validate the models in an unsupervised setting and show that it
is possible to get meaningful insights into translators even with just one
review per translation; subsequently, we extract information like translators'
skills and reviewers' strictness, as well as their consistency in their
respective roles. Using this, we show that the reliability of reviewers cannot
be taken for granted even in the case of expert translators: a translator's
expertise can induce a cognitive bias when reviewing a translation produced by
another translator. The most expert translators, however, are characterised by
the highest level of consistency, both in translating and in assessing the
translation quality.


title:
On the Nash equilibrium of moment-matching GANs for stationary Gaussian
  processes

abstract:
  Generative Adversarial Networks (GANs) learn an implicit generative model
from data samples through a two-player game. In this paper, we study the
existence of Nash equilibrium of the game which is consistent as the number of
data samples grows to infinity. In a realizable setting where the goal is to
estimate the ground-truth generator of a stationary Gaussian process, we show
that the existence of consistent Nash equilibrium depends crucially on the
choice of the discriminator family. The discriminator defined from second-order
statistical moments can result in non-existence of Nash equilibrium, existence
of consistent non-Nash equilibrium, or existence and uniqueness of consistent
Nash equilibrium, depending on whether symmetry properties of the generator
family are respected. We further study the local stability and global
convergence of gradient descent-ascent methods towards consistent equilibrium.


title:
A bond counting model for accurate prediction of lattice parameter of
  bcc solid solution alloys

abstract:
  Lattice Parameter is an important material feature in High Entropy Alloy
(HEA) Design. Vegards Law is typically used to estimate lattice parameters but
is often inaccurate for metal alloys due to an inability to account for charge
transfer which can affect atomic volumes. The present study used ab initio
simulation to calculate bond lengths between atoms of dissimilar elements in B2
intermetallic compounds which was then combined with a bond counting model to
produce a model to estimate the lattice parameters of Refractory BCC HEAS. The
model was tested using a supercell method which modeled various random solid
solution HEAs. The proposed model produced lattice parameters with superior
accuracy to Vegards Law without the need for large DFT calculations or fitting
parameters. The proposed model had a root mean squared error (RMSE) of 0.006
Angstroms which is half that of Vegards Law RMSE (0.012 Angstrom).


title: Adversarial amplitude swap towards robust image classifiers

abstract:
  The vulnerability of convolutional neural networks (CNNs) to image
perturbations such as common corruptions and adversarial perturbations has
recently been investigated from the perspective of frequency. In this study, we
investigate the effect of the amplitude and phase spectra of adversarial images
on the robustness of CNN classifiers. Extensive experiments revealed that the
images generated by combining the amplitude spectrum of adversarial images and
the phase spectrum of clean images accommodates moderate and general
perturbations, and training with these images equips a CNN classifier with more
general robustness, performing well under both common corruptions and
adversarial perturbations. We also found that two types of overfitting
(catastrophic overfitting and robust overfitting) can be circumvented by the
aforementioned spectrum recombination. We believe that these results contribute
to the understanding and the training of truly robust classifiers.


title: Ethical and Fairness Implications of Model Multiplicity

abstract:
  While predictive models are a purely technological feat, they may operate in
a social context in which benign engineering choices entail unexpected
real-life consequences. Fairness -- pertaining both to individuals and groups
-- is one of such considerations; it surfaces when data capture protected
characteristics of people who may be discriminated upon these attributes. This
notion has predominantly been studied for a fixed predictive model, sometimes
under different classification thresholds, striving to identify and eradicate
its undesirable behaviour. Here we backtrack on this assumption and explore a
novel definition of fairness where individuals can be harmed when one predictor
is chosen ad hoc from a group of equally well performing models, i.e., in view
of model multiplicity. Since a person may be classified differently across
models that are otherwise considered equivalent, this individual could argue
for a model with a more favourable outcome, possibly causing others to be
adversely affected. We introduce this scenario with a two-dimensional example
based on linear classification; then investigate its analytical properties in a
broader context; and finally present experimental results on data sets popular
in fairness studies. Our findings suggest that such unfairness can be found in
real-life situations and may be difficult to mitigate with technical measures
alone, as doing so degrades certain metrics of predictive performance.


title: CAROL: Confidence-Aware Resilience Model for Edge Federations

abstract:
  In recent years, the deployment of large-scale Internet of Things (IoT)
applications has given rise to edge federations that seamlessly interconnect
and leverage resources from multiple edge service providers. The requirement of
supporting both latency-sensitive and compute-intensive IoT tasks necessitates
service resilience, especially for the broker nodes in typical broker-worker
deployment designs. Existing fault-tolerance or resilience schemes often lack
robustness and generalization capability in non-stationary workload settings.
This is typically due to the expensive periodic fine-tuning of models required
to adapt them in dynamic scenarios. To address this, we present a confidence
aware resilience model, CAROL, that utilizes a memory-efficient generative
neural network to predict the Quality of Service (QoS) for a future state and a
confidence score for each prediction. Thus, whenever a broker fails, we quickly
recover the system by executing a local-search over the broker-worker topology
space and optimize future QoS. The confidence score enables us to keep track of
the prediction performance and run parsimonious neural network fine-tuning to
avoid excessive overheads, further improving the QoS of the system. Experiments
on a Raspberry-Pi based edge testbed with IoT benchmark applications show that
CAROL outperforms state-of-the-art resilience schemes by reducing the energy
consumption, deadline violation rates and resilience overheads by up to 16, 17
and 36 percent, respectively.


title: Hadron spectroscopy at STCF

abstract:
  Despite that quantum chromodynamics, the theory of strong interaction, has
colorful quarks and gluons as its basic degrees of freedom, all fundamental
particles participating the strong interaction that can be directly detected in
experiments are colorless or color-singlet hadrons. This phenomenon is called
color confinement. Because of that, the study of hadron spectroscopy is
essential in improving our understanding of the nonperturbative regime of QCD
and to reveal the mechanism of color confinement. The high-luminosity
electron-positron collision machine under discussion, Super tau-Charm Facility
(STCF), can play an essential role in hadron spectroscopy by discovering new
hadron resonances and measuring properties of known hadrons with an
unprecedented precision. In the following, we will discuss briefly the topics
on hadron spectroscopy that will be investigated at STCF.


title:
Conservative Filtering for Heterogeneous Decentralized Data Fusion in
  Dynamic Robotic Systems

abstract:
  This paper presents a method for Bayesian multi-robot peer-to-peer data
fusion where any pair of autonomous robots hold non-identical, but overlapping
parts of a global joint probability distribution, representing real world
inference tasks (e.g., mapping, tracking). It is shown that in dynamic
stochastic systems, filtering, which corresponds to marginalization of past
variables, results in direct and hidden dependencies between variables not
mutually monitored by the robots, which might lead to an overconfident fused
estimate. The paper makes both theoretical and practical contributions by
providing (i) a rigorous analysis of the origin of the dependencies and and
(ii) a conservative filtering algorithm for heterogeneous data fusion in
dynamic systems that can be integrated with existing fusion algorithms. This
work uses factor graphs as an analysis tool and an inference engine. Each robot
in the network maintains a local factor graph and communicates only relevant
parts of it (a sub-graph) to its neighboring robot. We discuss the
applicability to various multi-robot robotic applications and demonstrate the
performance using a multi-robot multi-target tracking simulation, showing that
the proposed algorithm produces conservative estimates at each robot.


title: Interpretable Dysarthric Speaker Adaptation based on Optimal-Transport

abstract:
  This work addresses the mismatch problem between the distribution of training
data (source) and testing data (target), in the challenging context of
dysarthric speech recognition. We focus on Speaker Adaptation (SA) in command
speech recognition, where data from multiple sources (i.e., multiple speakers)
are available. Specifically, we propose an unsupervised Multi-Source Domain
Adaptation (MSDA) algorithm based on optimal-transport, called MSDA via
Weighted Joint Optimal Transport (MSDA-WJDOT). We achieve a Command Error Rate
relative reduction of 16% and 7% over the speaker-independent model and the
best competitor method, respectively. The strength of the proposed approach is
that, differently from any other existing SA method, it offers an interpretable
model that can also be exploited, in this context, to diagnose dysarthria
without any specific training. Indeed, it provides a closeness measure between
the target and the source speakers, reflecting their similarity in terms of
speech characteristics. Based on the similarity between the target speaker and
the healthy/dysarthric source speakers, we then define the healthy/dysarthric
score of the target speaker that we leverage to perform dysarthria detection.
This approach does not require any additional training and achieves a 95%
accuracy in the dysarthria diagnosis.


title: DIMUS: Super-Compact Dimuonium Spectroscopy Collider at Fermilab

abstract:
  While dimuonium $(\mu^+\mu^-)$ has not yet been observed, it is of utmost
fundamental interest. By virtue of the larger mass, dimuonium has greater
sensitivity to beyond the standard model effects than its cousins positronium
or muonium, both discovered long ago, while not suffering from large QCD
uncertainties. Dimuonium atoms can be created in $e^+e^-$ collisions with large
longitudinal momentum, allowing them to decay a small distance away from the
beam crossing point and avoid prompt backgrounds. We envision a unique
cost-effective and fast-timeline opportunity for copious production of
$(\mu^+\mu^-)$ atoms at the production threshold via a modest modification of
Fermilab's existing FAST/NML facility to arrange collisions of 408 MeV
electrons and positrons at a 75$^{\rm o}$ angle. This compact 23 m
circumference collider (DIMUS) will allow for precision tests of QED and open
the door for searches for new physics coupled to the muon. Fermilab's FAST/NML
is perfectly suited for DIMUS as there are existing SRF accelerators and
infrastructure, capable of producing high energy, high current electron and
positron beams, sufficient for $O(10^{32})\mathrm{cm}^2\mathrm{s}^{-1}$
luminosity and $\sim$0.5 million dimuons per year. The expansion will require
installation of a second SRF cryomodule, positron production and accumulation
system, fast injection/extraction kickers and two small circumference
intersecting rings. An approximately meter-sized detector with several layers
of modern pixelated silicon detector and crystal-based electromagnetic
calorimeters will ensure observation of the decays of dimuonium to
electron-positron pairs in presence of the Bhabba scattering background. An
expansion of the system to would extend the physics program of DIMUS to include
precision studies of rare processes with muons, pions, and $\eta$ mesons
produced in $e^{+}e^{-}$ collisions.