title:
The multiplicity of the doubly charmed state $T_{cc} ^+$ in heavy-ion
  collisions

abstract:
  We study the evolution of the doubly charmed state $T_{cc}^+$ in a hot hadron
gas produced in the late stage of heavy-ion collisions. We use effective
Lagrangians to calculate the thermally averaged cross sections of $T_{cc}^+$
production in reactions such as $ D^{(*)} D^{(*)} \rightarrow T_{cc}^+ \pi,
T_{cc}^+ \rho $ and its absorption in the corresponding inverse processes. We
then solve the rate equation to follow the time evolution of the $T_{cc}^+$
multiplicity, and determine how it is affected by the considered reactions
during the expansion of the hadronic matter. We compare the evolution of the
$T_{cc}^+$ abundance treated as a hadronic $S$-wave molecule and as a
tetraquark state. Our results show that the tetraquark yield increases by a
factor of about 2 at freeze-out, but it is still one order of magnitude smaller
than the final yield of molecules formed from hadron coalescence. Also, the
results indicate that $N_{T_{cc}}$ is more affected by interactions with
hadronic medium than $ N_{X(3872)} $ in similar conditions. We also show that
yields depend very weakly on the system size, represented by $\mathcal{N} =
\left[ d N_{ch} / d \eta (\eta < 0.5)\right]^{1/3}$.


title: Information Design in Concave Games

abstract:
  We study information design in games with a continuum of actions such that
the players' payoffs are concave in their own actions. A designer chooses an
information structure--a joint distribution of a state and a private signal of
each player. The information structure induces a Bayesian game and is evaluated
according to the expected designer's payoff under the equilibrium play.
  We develop a method that facilitates the search for an optimal information
structure, i.e., one that cannot be outperformed by any other information
structure, however complex. We show an information structure is optimal
whenever it induces the strategies that can be implemented by an incentive
contract in a dual, principal-agent problem which aggregates marginal payoffs
of the players in the original game. We use this result to establish the
optimality of Gaussian information structures in settings with quadratic
payoffs and a multivariate normally distributed state. We analyze the details
of optimal structures in a differentiated Bertrand competition and in a
prediction game.


title: Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysis

abstract:
  Citations are generally analyzed using only quantitative measures while
excluding qualitative aspects such as sentiment and intent. However,
qualitative aspects provide deeper insights into the impact of a scientific
research artifact and make it possible to focus on relevant literature free
from bias associated with quantitative aspects. Therefore, it is possible to
rank and categorize papers based on their sentiment and intent. For this
purpose, larger citation sentiment datasets are required. However, from a time
and cost perspective, curating a large citation sentiment dataset is a
challenging task. Particularly, citation sentiment analysis suffers from both
data scarcity and tremendous costs for dataset annotation. To overcome the
bottleneck of data scarcity in the citation analysis domain we explore the
impact of out-domain data during training to enhance the model performance. Our
results emphasize the use of different scheduling methods based on the use
case. We empirically found that a model trained using sequential data
scheduling is more suitable for domain-specific usecases. Conversely, shuffled
data feeding achieves better performance on a cross-domain task. Based on our
findings, we propose an end-to-end trainable multi-task model that covers the
sentiment and intent analysis that utilizes out-domain datasets to overcome the
data scarcity.


title:
Learning Infomax and Domain-Independent Representations for Causal
  Effect Inference with Real-World Data

abstract:
  The foremost challenge to causal inference with real-world data is to handle
the imbalance in the covariates with respect to different treatment options,
caused by treatment selection bias. To address this issue, recent literature
has explored domain-invariant representation learning based on different domain
divergence metrics (e.g., Wasserstein distance, maximum mean discrepancy,
position-dependent metric, and domain overlap). In this paper, we reveal the
weaknesses of these strategies, i.e., they lead to the loss of predictive
information when enforcing the domain invariance; and the treatment effect
estimation performance is unstable, which heavily relies on the characteristics
of the domain distributions and the choice of domain divergence metrics.
Motivated by information theory, we propose to learn the Infomax and
Domain-Independent Representations to solve the above puzzles. Our method
utilizes the mutual information between the global feature representations and
individual feature representations, and the mutual information between feature
representations and treatment assignment predictions, in order to maximally
capture the common predictive information for both treatment and control
groups. Moreover, our method filters out the influence of instrumental and
irrelevant variables, and thus it effectively increases the predictive ability
of potential outcomes. Experimental results on both the synthetic and
real-world datasets show that our method achieves state-of-the-art performance
on causal effect inference. Moreover, our method exhibits reliable prediction
performances when facing data with different characteristics of data
distributions, complicated variable types, and severe covariate imbalance.


title: Limitations of nearest-neighbour quantum networks

abstract:
  Quantum communication research has in recent years shifted to include
multi-partite networks for which questions of quantum network routing naturally
emerge. To understand the potential for multi-partite routing, we focus on the
most promising architectures for future quantum networks - those connecting
nodes close to each other. Nearest-neighbour networks such as rings, lines, and
grids, have been studied under different communication scenarios to facilitate
the sharing of quantum resources, especially in the presence of bottlenecks. We
here analyze the potential of nearest-neighbour quantum networks and identify
some serious limitations, by demonstrating that rings and lines cannot overcome
common bottleneck communication problems.


title:
Policy Evaluation for Temporal and/or Spatial Dependent Experiments in
  Ride-sourcing Platforms

abstract:
  Policy evaluation based on A/B testing has attracted considerable interest in
digital marketing, but such evaluation in ride-sourcing platforms (e.g., Uber
and Didi) is not well studied primarily due to the complex structure of their
temporal and/or spatial dependent experiments. Motivated by policy evaluation
in ride-sourcing platforms, the aim of this paper is to establish causal
relationship between platform's policies and outcomes of interest under a
switchback design. We propose a novel potential outcome framework based on a
temporal varying coefficient decision process (VCDP) model to capture the
dynamic treatment effects in temporal dependent experiments. We further
characterize the average treatment effect by decomposing it as the sum of
direct effect (DE) and indirect effect (IE). We develop estimation and
inference procedures for both DE and IE. Furthermore, we propose a
spatio-temporal VCDP to deal with spatiotemporal dependent experiments. For
both VCDP models, we establish the statistical properties (e.g., weak
convergence and asymptotic power) of our estimation and inference procedures.
We conduct extensive simulations to investigate the finite-sample performance
of the proposed estimation and inference procedures. We examine how our VCDP
models can help improve policy evaluation for various dispatching and
dispositioning policies in Didi.


title:
Geometric phases acquired for a two-level atom coupled to fluctuating
  vacuum scalar fields due to linear acceleration and circular motion

abstract:
  In open quantum systems, we study the geometric phases acquired for a
two-level atom coupled to a bath of fluctuating vacuum scalar fields due to
linear acceleration and circular motion without and with a boundary. In free
space, as we amplify acceleration, the geometric phase acquired purely due to
linear acceleration case firstly is smaller than circular acceleration case in
the ultra-relativistic limit for the initial atomic state
$\theta\in(0,\frac{\pi}{2})\cup(\frac{\pi}{2},\pi)$, and then equal to the
circular acceleration case in a certain acceleration, and finally larger than
the circular acceleration case. This result is different from the case of a
bath of fluctuating vacuum electromagnetic fields. Considering the initial
atomic state $\theta \in(0,\pi)$, we find that the geometric phase acquired
purely due to linear acceleration always equal to the circular acceleration
case for the certain acceleration. Adding a reflecting boundary, we observe
that a larger value of geometric phase can be obtained compared to the absence
of a boundary. Besides, the geometric phase fluctuates along $z$, and the
maximum of geometric phase is closer to the boundary for a larger acceleration.
We also find that geometric phases can be acquired purely due to linear
acceleration case and circular acceleration case with $\theta \in(0,\pi)$ for a
smaller $z$.


title:
Resolving molecular diffusion and aggregation of antibody proteins with
  megahertz X-ray free-electron laser pulses

abstract:
  X-ray free-electron lasers (XFELs) with megahertz repetition rate can provide
novel insights into structural dynamics of biological macromolecule solutions.
However, very high dose rates can lead to beam-induced dynamics and structural
changes due to radiation damage. Here, we probe the dynamics of dense antibody
protein (Ig-PEG) solutions using megahertz X-ray photon correlation
spectroscopy (MHz-XPCS) at the European XFEL. By varying the total dose and
dose rate, we identify a regime for measuring the motion of proteins in their
first coordination shell, quantify XFEL-induced effects such as driven motion,
and map out the extent of agglomeration dynamics. The results indicate that for
dose rates below $1.16\,\mathrm{kGy\mu s^{-1}}$ in a time window up to
$10\,\mathrm{\mu s}$, it is possible to capture the protein dynamics before the
onset of beam induced aggregation. We refer to this approach as "correlation
before aggregation" and demonstrate that MHz-XPCS bridges an important
spatio-temporal gap in measurement techniques for biological samples.


title: Hierarchical Perceiver

abstract:
  General perception systems such as Perceivers can process arbitrary
modalities in any combination and are able to handle up to a few hundred
thousand inputs. They achieve this generality by exclusively using global
attention operations. This however hinders them from scaling up to the inputs
sizes required to process raw high-resolution images or video. In this paper,
we show that some degree of locality can be introduced back into these models,
greatly improving their efficiency while preserving their generality. To scale
them further, we introduce a self-supervised approach that enables learning
dense low-dimensional positional embeddings for very large signals. We call the
resulting model a Hierarchical Perceiver (HiP). HiP retains the ability to
process arbitrary modalities, but now at higher-resolution and without any
specialized preprocessing, improving over flat Perceivers in both efficiency
and accuracy on the ImageNet, Audioset and PASCAL VOC datasets.


title: Protecting GNSS-based Services using Time Offset Validation

abstract:
  Global navigation satellite systems (GNSS) provide pervasive accurate
positioning and timing services for a large gamut of applications, from Time
based One-Time Passwords (TOPT), to power grid and cellular systems. However,
there can be security concerns for the applications due to the vulnerability of
GNSS. It is important to observe that GNSS receivers are components of
platforms, in principle having rich connectivity to different network
infrastructures. Of particular interest is the access to a variety of timing
sources, as those can be used to validate GNSS-provided location and time.
Therefore, we consider off-the-shelf platforms and how to detect if the GNSS
receiver is attacked or not, by cross-checking the GNSS time and time from
other available sources. First, we survey different technologies to analyze
their availability, accuracy, and trustworthiness for time synchronization.
Then, we propose a validation approach for absolute and relative time.
Moreover, we design a framework and experimental setup for the evaluation of
the results. Attacks can be detected based on WiFi supplied time when the
adversary shifts the GNSS provided time, more than 23.942us; with Network Time
Protocol (NTP) supplied time when the adversary-induced shift is more than
2.046ms. Consequently, the proposal significantly limits the capability of an
adversary to manipulate the victim GNSS receiver.


title: Optical Shadows of Rotating Bardeen AdS Black Holes

abstract:
  Using the Hamilton-Jacobi method, we study certain optical shadows of
rotating Bardeen-AdS black holes in the presence of internal and external field
contributions. Precisely, we examine the shadow using one-dimensional real
curves. By establishing the equations of motion of such AdS black holes without
external dark sectors, we first analyze the shadow configurations in terms of a
reduced moduli space involving only internal parameter contributions including
the charge of the nonlinear electrodynamics. Among others, we find that this
parameter serves as a geometric quantity controlling the shadow shape. Then, we
graphically discuss the associated astronomical observables. Enlarging the
moduli space, we analyze the behaviors of the quintessential black hole
solutions in terms of the involved parameters controlling the dark field
sector. Concretely, we observe that the dark energy not only affects the size
but also deforms the shadow shape. Finally, we provide a possible link with
observations from Event Horizon Telescope by showing certain constraints on the
involved moduli space in the light of the M87$^*$ picture.


title:
Impact of Atrial Fibrillation on Left Atrium Haemodynamics: A
  Computational Fluid Dynamics Study

abstract:
  We analyze left atrium haemodynamics, highlighting differences among healthy
individuals and patients affected by atrial fibrillation. The computational
study is based on patient-specific geometries of the left atria to simulate
blood flow dynamics. We devise a novel procedure aimed at recovering the
boundary conditions for the 3D haemodynamics simulations, particularly useful
in absence of specific ones provided by clinical measurements. With this aim,
we introduce a parametric definition of the atria displacement, and we employ a
closed-loop lumped parameter model of the whole cardiocirculatory system
conveniently tuned on the basis of the patient characteristics. We evaluate a
number of fluid dynamics indicators for the atrial haemodynamics, validating
our numerical results in terms of several clinical measurements; we investigate
the impact of geometrical and clinical features on the risk of thrombosis. To
analyse the correlation of thrombus formation with atrial fibrillation,
coherently with the medical evidence, we propose a novel indicator, which we
call age stasis and that arises from the combination of Eulerian and Lagrangian
quantities. This indicator identifies regions where the slow flow cannot rinse
the chamber properly, accumulating stale blood particles and creating optimal
conditions for clot formation.

categories: physics.flu-dyn cs.NA math.NA physics.med-ph


title: Quantum Internet Protocol Stack: a Comprehensive Survey

abstract:
  Classical Internet evolved exceptionally during the last five decades, from a
network comprising a few static nodes in the early days to a leviathan
interconnecting billions of devices. This has been possible by the separation
of concern principle, for which the network functionalities are organized as a
stack of layers, each providing some communication functionalities through
specific network protocols. In this survey, we aim at highlighting the
impossibility of adapting the classical Internet protocol stack to the Quantum
Internet, due to the marvels of quantum mechanics. Indeed, the design of the
Quantum Internet requires a major paradigm shift of the whole protocol stack
for harnessing the peculiarities of quantum entanglement and quantum
information. In this context, we first overview the relevant literature about
Quantum Internet protocol stack. Then, stemming from this, we sheds the light
on the open problems and required efforts toward the design of an effective and
complete Quantum Internet protocol stack. To the best of authors' knowledge, a
survey of this type is the first of its own. What emerges from this analysis is
that the Quantum Internet, though still in its infancy, is a disruptive
technology whose design requires an inter-disciplinary effort at the border
between quantum physics, computer and telecommunications engineering.


title: Qualitative analysis on the critical points of the Robin function

abstract:
  Let $\Omega\subset\mathbb{R}^N$ be a smooth bounded domain with $N\ge2$ and
$\Omega_\epsilon=\Omega\backslash B(P,\epsilon)$ where $B(P,\epsilon)$ is the
ball centered at $P\in\Omega$ and radius $\epsilon$. In this paper, we
establish the number, location and non-degeneracy of critical points of the
Robin function in $\Omega_\epsilon$ for $\epsilon$ small enough. We will show
that the location of $P$ plays a crucial role on the existence and multiplicity
of the critical points. The proof of our result is a consequence of delicate
estimates on the Green function near to $\partial B(P,\epsilon)$. Some
applications to compute the exact number of solutions of related well-studied
nonlinear elliptic problems will be showed.


title:
Interplay between atomic alignment and orientation fluctuations in the
  spin noise spectroscopy of metastable helium

abstract:
  We perform spin noise spectroscopy experiments in metastable helium atoms at
room temperature, with a probe light whose frequency is blue detuned from the
D0 line. Both circular birefringence fluctuations (Faraday noise) and linear
birefringence fluctuations (ellipticity noise) are explored theoretically and
experimentally. In particular, in the latter case, it is shown that two noise
resonances are isolated at the Larmor frequency and at twice the Larmor
frequency. The behavior of these resonances with the orientation of the probe
field polarization is analyzed and explained for large probe detunings.
Moreover, the simple structure of metastable helium allows us to probe, model
and explain the changes in the behavior of these peaks when the probe detuning
is reduced, inducing an optical pumping, which leads to circular and linear
dichroisms.


title: DEMO: Relay/Replay Attacks on GNSS signals

abstract:
  Global Navigation Satellite Systems (GNSS) are ubiquitously relied upon for
positioning and timing. Detection and prevention of attacks against GNSS have
been researched over the last decades, but many of these attacks and
countermeasures were evaluated based on simulation. This work contributes to
the experimental investigation of GNSS vulnerabilities, implementing a
relay/replay attack with off-the-shelf hardware. Operating at the signal level,
this attack type is not hindered by cryptographically protected transmissions,
such as Galileo's Open Signals Navigation Message Authentication (OS-NMA). The
attack we investigate involves two colluding adversaries, relaying signals over
large distances, to effectively spoof a GNSS receiver. We demonstrate the
attack using off-the-shelf hardware, we investigate the requirements for such
successful colluding attacks, and how they can be enhanced, e.g., allowing for
finer adversarial control over the victim receiver.


title: Relational Algebra and Calculus with SQL Null Values

abstract:
  The logic of nulls in databases has been subject of investigation since their
introduction in Codd's Relational Model, which is the foundation of the SQL
standard. We show a logical characterisation of a first-order fragment of SQL
with null values, by first focussing on a simple extension with null values of
standard relational algebra, which captures exactly the SQL fragment, and then
proposing two different domain relational calculi, in which the null value is a
term of the language but it does not appear as an element of the semantic
interpretation domain of the logics. In one calculus, a relation can be seen as
a set of partial tuples, while in the other (equivalent) calculus, a relation
is horizontally decomposed as a set of relations each one holding regular total
tuples. We extend Codd's theorem by proving the equivalence of the relational
algebra with both domain relational calculi in presence of SQL null values.


title: POSTER: Detecting GNSS misbehaviour with high-precision clocks

abstract:
  To mitigate spoofing attacks targeting global navigation satellite systems
(GNSS) receivers, one promising method is to rely on alternative time sources,
such as network-based synchronization, in order to detect clock offset
discrepancies caused by GNSS attacks. However, in case of no network
connectivity, such validation references would not be available. A viable
option is to rely on a local time reference; in particular, precision hardware
clock ensembles of chip-scale thermally stable oscillators with extended
holdover capabilities. We present a preliminary design and results towards a
custom device capable of providing a stable reference, with smaller footprint
and cost compared to traditional precision clocks. The system is fully
compatible with existing receiver architecture, making this solution feasible
for most industrial scenarios. Further integration with network-based
synchronization can provide a complete time assurance system, with high short-
and long-term stability.


title:
Some Wadati-Konno-Ichikawa type integrable systems and their
  constructions

abstract:
  A standard-form Wadati-Konno-Ichikawa(WKI) type integrable hierarchy is
derived from a corresponding matrix spectral problem associated with the Lie
algebra sl(2, R). Each equation in the resulting hierarchy has a bi-Hamiltonian
structure furnished by the trace identity. Then, the higher grading affine
algebraic construction of some special cases is proposed. We also show that
eneralized short pulse equation arises naturally from the negative WKI flow.


title:
On the influence of water on THz vibrational spectral features of
  molecular crystals

abstract:
  The nanoscale structure of molecular assemblies plays a major role in many
($\mu$)-biological mechanisms. Molecular crystals are one of the most simple of
these assemblies and are widely used in a variety of applications from
pharmaceuticals and agrochemicals, to nutraceuticals and cosmetics. The
collective vibrations in such molecular crystals can be probed using terahertz
spectroscopy, providing unique characteristic spectral fingerprints. However,
the association of the spectral features to the crystal conformation, crystal
phase and its environment is a difficult task. We present a combined
computationalexperimental study on the incorporation of water in lactose
molecular crystals, and show how simulations can be used to associate spectral
features in the THz region to crystal conformations and phases. Using periodic
DFT simulations of lactose molecular crystals, the role of water in the
observed lactose THz spectrum is clarified, presenting both direct and indirect
contributions. A specific experimental setup is built to allow the controlled
heating and corresponding dehydration of the sample, providing the monitoring
of the crystal phase transformation dynamics. Besides the observation that
lactose phases and phase transformation appear to be more complex than
previously thoughtincluding several crystal forms in a single phase and a
non-negligible water content in the so-called anhydrous phasewe draw two main
conclusions from this study. Firstly, THz modes are spread over more than one
molecule and require periodic computation rather than a gas-phase one.
Secondly, hydration water does not only play a perturbative role but also
participates in the facilitation of the THz vibrations.


title:
Coupled multiphysics analysis of a 4-vane RFQ accelerator under high
  power operation

abstract:
  The radiofrequency resonant four-quadrant mechanical structure of a 4-vane
Radio Frequency Quadrupole (RFQ) has a high quality factor and narrow
bandwidth, resulting in high sensitivity to frequency detuning caused by
thermal expansion under high power operation, so minimization of frequency
errors and affording dynamic tuning are very important design issues. Here we
describe an optimization approach to figure out a suitable cooling design for a
4-vane RFQ in steady state. Besides, we investigate how the accelerator
responds in transient thermal analysis that could help to guide commissioning
and reduce frequency detuning. Multiphysics analysis utilized with CST and
ANSYS for a recently developed 176 MHz 4-vane RFQ is taken as an example. This
RFQ will dissipate 211 kW when reaching an inter-vane voltage of 80 kV, which
is required for an acceleration of an 80-mA proton from 65 keV to 2.5 MeV in
5.3 meters.


title: Confident Neural Network Regression with Bootstrapped Deep Ensembles

abstract:
  With the rise of the popularity and usage of neural networks, trustworthy
uncertainty estimation is becoming increasingly essential. In this paper we
present a computationally cheap extension of Deep Ensembles for a regression
setting called Bootstrapped Deep Ensembles that explicitly takes the effect of
finite data into account using a modified version of the parametric bootstrap.
We demonstrate through a simulation study that our method has comparable or
better prediction intervals and superior confidence intervals compared to Deep
Ensembles and other state-of-the-art methods. As an added bonus, our method is
better capable of detecting overfitting than standard Deep Ensembles.


title: The near exact bin covering problem

abstract:
  We present a new generalization of the bin covering problem that is known to
be a strongly NP-hard problem. In our generalization there is a positive
constant $\Delta$, and we are given a set of items each of which has a positive
size. We would like to find a partition of the items into bins. We say that a
bin is near exact covered if the total size of items packed into the bin is
between $1$ and $1+\Delta$. Our goal is to maximize the number of near exact
covered bins. If $\Delta=0$ or $\Delta>0$ is given as part of the input, our
problem is shown here to have no approximation algorithm with a bounded
asymptotic approximation ratio (assuming that $P\neq NP$). However, for the
case where $\Delta>0$ is seen as a constant, we present an asymptotic fully
polynomial time approximation scheme (AFPTAS) that is our main contribution.


title:
Modeling and Analysis of Heat Transfer and Fluid Flow Mechanisms in
  Nanofluid Filled Enclosures Irradiated from Below

abstract:
  Radiation driven transport mechanisms are ubiquitous in many natural flows
and industrial processes. To mimic and to better understand these processes,
recently, radiatively heated nanofluid filled enclosures have been extensively
researched. The present work is essentially a determining step in quantifying
and understanding the transport mechanisms involved in such enclosures. In
particular, a two dimensional square nanofluid filled enclosure irradiated from
the bottom has been investigated in laminar flow situation. Effects of
nanofluid optical depth, inclination angle of the enclosure, incident flux, and
boundary conditions (adiabatic and isothermal) have been investigated.
Moreover, the temperature and flow fields have been carefully analyzed in the
situation ranging from volumetric to mixed to surface absorption modes. Under
adiabatic boundary conditions, steady state is unconditionally achieved
irrespective of the incident flux magnitude (varied between 5Wm-2 to 50Wm-2),
enclosure inclination angle (varied between 0 to 60 degrees) and mode of
absorption (surface, mixed or volumetric). However, in case of isothermal
boundaries; onset of natural convection and its transition into transient
regime is significantly impacted by the mode of absorption and the enclosure
inclination angle.


title:
An ES-BGK model for diatomic gases with correct relaxation rates for
  internal energies

abstract:
  We propose a new ES-BGK model for diatomic gases which allows for
translational-rotational and translational-vibrational energy exchanges, as
given by Landau-Teller and Jeans relaxation equations. This model is consistent
with the general definition of the vibrational and rotational collision numbers
that are also commonly used in DSMC solvers. It is proved to satisfy the
H-theorem and to give the correct transport coefficients, up to the volume
viscosity.


title: On the functors associated with beaded open Jacobi diagrams

abstract:
  Morphisms in the linear category A of Jacobi diagrams in handlebodies give
rise to interesting contravariant functors on the category gr of
finitely-generated free groups, encoding part of the composition structure of
the category A. These functors correspond, via an equivalence of categories
given by Powell, to functors given by beaded open Jacobi diagrams. We study the
polynomiality of these functors and whether they are outer functors. These
results are inspired by and generalize previous results obtained by Katada.


title:
Quantum walk in a reinforced free-energy landscape: Quantum annealing
  with reinforcement

abstract:
  Providing an optimal path to a quantum annealing algorithm is key to find
good approximate solutions to computationally hard optimization problems.
Reinforcement is one of the strategies that can be used to circumvent the
exponentially small energy gaps of the system in the annealing process. Here a
time-dependent reinforcement term is added to Hamiltonian in order to give
lower energies to the most probable states of the evolving system. In this
study, we take a local entropy in the configuration space for the reinforcement
and apply the algorithm to a number of easy and hard optimization problems. The
reinforced algorithm performs better than the standard quantum annealing
algorithm in the quantum search problem where the optimal parameters behave
very differently depending on the number of solutions. Moreover, the
reinforcement can change the discontinuous phase transitions of the mean-field
p-spin model ($p>2$) to a continuous transition. The algorithm's performance in
the binary perceptron problem is also superior to that of the standard quantum
annealing algorithm, which already works better than a classical simulated
annealing algorithm.


title: Integral points of bounded height on a certain toric variety

abstract:
  We determine an asymptotic formula for the number of integral points of
bounded height on a certain toric variety, which is incompatible with part of a
preprint by Chambert-Loir and Tschinkel. We provide an alternative
interpretation of the asymptotic formula we get. To do so, we construct an
analogue of Peyre's constant $\alpha$ and describe its relation to a new
obstruction to the Zariski density of integral points in certain regions of
varieties.


title: Sound Adversarial Audio-Visual Navigation

abstract:
  Audio-visual navigation task requires an agent to find a sound source in a
realistic, unmapped 3D environment by utilizing egocentric audio-visual
observations. Existing audio-visual navigation works assume a clean environment
that solely contains the target sound, which, however, would not be suitable in
most real-world applications due to the unexpected sound noise or intentional
interference. In this work, we design an acoustically complex environment in
which, besides the target sound, there exists a sound attacker playing a
zero-sum game with the agent. More specifically, the attacker can move and
change the volume and category of the sound to make the agent suffer from
finding the sounding object while the agent tries to dodge the attack and
navigate to the goal under the intervention. Under certain constraints to the
attacker, we can improve the robustness of the agent towards unexpected sound
attacks in audio-visual navigation. For better convergence, we develop a joint
training mechanism by employing the property of a centralized critic with
decentralized actors. Experiments on two real-world 3D scan datasets, Replica,
and Matterport3D, verify the effectiveness and the robustness of the agent
trained under our designed environment when transferred to the clean
environment or the one containing sound attackers with random policy. Project:
\url{https://yyf17.github.io/SAAVN}.


title:
A tensor network discriminator architecture for classification of
  quantum data on quantum computers

abstract:
  We demonstrate the use of matrix product state (MPS) models for
discriminating quantum data on quantum computers using holographic algorithms,
focusing on classifying a translationally invariant quantum state based on $L$
qubits of quantum data extracted from it. We detail a process in which data
from single-shot experimental measurements are used to optimize an isometric
tensor network, the tensors are compiled into unitary quantum operations using
greedy compilation heuristics, parameter optimization on the resulting quantum
circuit model removes the post-selection requirements of the isometric tensor
model, and the resulting quantum model is inferenced on either product state or
entangled quantum data. We demonstrate our training and inference architecture
on a synthetic dataset of six-site single-shot measurements from the bulk of a
one-dimensional transverse field Ising model (TFIM) deep in its
antiferromagnetic and paramagnetic phases. We experimentally evaluate models on
Quantinuum's H1-2 trapped ion quantum computer using entangled input data
modeled as translationally invariant, bond dimension 4 MPSs across the known
quantum phase transition of the TFIM. Using linear regression on the
experimental data near the transition point, we find predictions for the
critical transverse field of $h=0.962$ and $0.994$ for tensor network
discriminators of bond dimension $\chi=2$ and $\chi=4$, respectively. These
predictions compare favorably with the known transition location of $h=1$
despite training on data far from the transition point. Our techniques identify
families of short-depth variational quantum circuits in a data-driven and
hardware-aware fashion and robust classical techniques to precondition the
model parameters, and can be adapted beyond machine learning to myriad
applications of tensor networks on quantum computers, such as quantum
simulation and error correction.


title:
Variation Aware Training of Hybrid Precision Neural Networks with 28nm
  HKMG FeFET Based Synaptic Core

abstract:
  This work proposes a hybrid-precision neural network training framework with
an eNVM based computational memory unit executing the weighted sum operation
and another SRAM unit, which stores the error in weight update during back
propagation and the required number of pulses to update the weights in the
hardware. The hybrid training algorithm for MLP based neural network with 28 nm
ferroelectric FET (FeFET) as synaptic devices achieves inference accuracy up to
95% in presence of device and cycle variations. The architecture is primarily
evaluated using behavioral or macro-model of FeFET devices with experimentally
calibrated device variations and we have achieved accuracies compared to
floating-point implementations.


title: Distributed Sparse Multicategory Discriminant Analysis

abstract:
  This paper proposes a convex formulation for sparse multicategory linear
discriminant analysis and then extend it to the distributed setting when data
are stored across multiple sites. The key observation is that for the purpose
of classification it suffices to recover the discriminant subspace which is
invariant to orthogonal transformations. Theoretically, we establish
statistical properties ensuring that the distributed sparse multicategory
linear discriminant analysis performs as good as the centralized version after
{a few rounds} of communications. Numerical studies lend strong support to our
methodology and theory.


title: Generalizing Dirichlet-to-Neumann operators

abstract:
  The aim of this paper is to study the Dirichlet-to-Neumann operators in the
context of Dirichlet forms and especially to figure out their probabilistic
counterparts. Regarding irreducible Dirichlet forms, we will show that the
Dirichlet-to-Neumann operators for them are associated with the trace Dirichlet
forms corresponding to the time changed processes on the boundary. Furthermore,
the Dirichlet-to-Neumann operators for perturbations of Dirichlet forms will be
also explored. It turns out that for typical cases such a Dirichlet-to-Neumann
operator corresponds to a quasi-regular positivity preserving (symmetric)
coercive form, so that there exists a family of Markov processes associated
with it via Doob's $h$-transformations.


title:
Learning-informed parameter identification in nonlinear time-dependent
  PDEs

abstract:
  We introduce and analyze a method of learning-informed parameter
identification for partial differential equations (PDEs) in an all-at-once
framework. The underlying PDE model is formulatedin a rather general setting
with three unknowns: physical parameter, state and nonlinearity. Inspired by
advances in machine learning, we approximate the nonlinearity via a neural
network, whose parameters are learned from measurement data. The later is
assumed to be given as noisy observations of the unknown state, and both the
state and the physical parameters are identified simultaneously with the
parameters of the neural network. Moreover, diverging from the classical
approach, the proposed all-at-once setting avoids constructing the
parameter-to-state map by explicitly handling the state as additional variable.
The practical feasibility of the proposed method is confirmed with experiments
using two different algorithmic settings: A function-space algorithm based on
analytic adjoints as well as a purely discretized setting using standard
machine learning algorithms.


title:
Remaining Useful Life Prediction Using Temporal Deep Degradation Network
  for Complex Machinery with Attention-based Feature Extraction

abstract:
  The precise estimate of remaining useful life (RUL) is vital for the
prognostic analysis and predictive maintenance that can significantly reduce
failure rate and maintenance costs. The degradation-related features extracted
from the sensor streaming data with neural networks can dramatically improve
the accuracy of the RUL prediction. The Temporal deep degradation network
(TDDN) model is proposed to make the RUL prediction with the
degradation-related features given by the one-dimensional convolutional neural
network (1D CNN) feature extraction and attention mechanism. 1D CNN is used to
extract the temporal features from the streaming sensor data. Temporal features
have monotonic degradation trends from the fluctuating raw sensor streaming
data. Attention mechanism can improve the RUL prediction performance by
capturing the fault characteristics and the degradation development with the
attention weights. The performance of the TDDN model is evaluated on the public
C-MAPSS dataset and compared with the existing methods. The results show that
the TDDN model can achieve the best RUL prediction accuracy in complex
conditions compared to current machine learning models. The degradation-related
features extracted from the high-dimension sensor streaming data demonstrate
the clear degradation trajectories and degradation stages that enable TDDN to
predict the turbofan-engine RUL accurately and efficiently.


title:
Nanomechanical Design Strategy for Single-Mode Optomechanical
  Measurement

abstract:
  The motion of a mechanical resonator is intrinsically decomposed over a
collection of normal modes of vibration. When the resonator is used as a
sensor, its multimode nature often deteriorates or limits its performance and
sensitivity. This challenge is frequently encountered in state-of-the-art
optomechanical sensing platforms. We present a mechanical design strategy that
ensures that optomechanical measurements can retrieve information on a single
mechanical degree of freedom, and implement it in a sliced photonic crystal
nanobeam resonator. A spectral design approach is used to make mechanical
symmetries robust against practical disorder. The effectiveness of the method
is evaluated by deriving a relevant figure of merit for continuous and pulsed
measurement application scenarios. The method can be employed in any mechanical
design that presents unwanted spurious mechanical modes. In the nanobeam
platform, we experimentally show an increase of the signal to noise ratio of
the mode of interest over the first spurious mode by four orders of magnitudes.


title:
Honour Thesis: A Joint Value at Risk and Expected Shortfall Combination
  Framework and its Applications in the Cryptocurrency Market

abstract:
  Value at risk and expected shortfall are increasingly popular tail risk
measures in the financial risk management field. Both academia and financial
institutions are working to improve tail risk forecasts in order to meet the
requirements of the Basel Capital Accord; it states that one purpose of risk
management and measuring risk accuracy is, since extreme movements cannot
always be avoided, financial institutions can prepare for these extreme returns
by capital allocation, and putting aside the appropriate amount of capital so
as to avoid default in times of extreme price or index movements. Forecast
combination has drawn much attention, as a combined forecast can outperform the
individual forecasts under certain conditions. We propose two methodology, one
is a semiparametric combination framework that can jointly produce combined
value at risk and expected shortfall forecasts, another one is a parametric
regression framework named as Quantile-ES regression that can produce combined
expected shortfall forecasts. The favourability of the semiparametric
combination framework has been presented via an empirical study - application
in cryptocurrency markets with high-frequency data where the necessity of risk
management application increases as the cryptocurrency market becomes more
popular and mature. Additionally, the general framework of the parametric
Quantile-ES regression has been presented via a simulation study, whereas it
still need to be improved in the future. The contributions of this work include
but are not limited to the enabling of the combination of expected shortfall
forecasts and the application of risk management procedures in the
cryptocurrency market with high-frequency data.


title:
Translational Quantum Machine Intelligence for Modeling Tumor Dynamics
  in Oncology

abstract:
  Quantifying the dynamics of tumor burden reveals useful information about
cancer evolution concerning treatment effects and drug resistance, which play a
crucial role in advancing model-informed drug developments (MIDD) towards
personalized medicine and precision oncology. The emergence of Quantum Machine
Intelligence offers unparalleled insights into tumor dynamics via a quantum
mechanics perspective. This paper introduces a novel hybrid quantum-classical
neural architecture named $\eta-$Net that enables quantifying quantum dynamics
of tumor burden concerning treatment effects. We evaluate our proposed neural
solution on two major use cases, including cohort-specific and patient-specific
modeling. In silico numerical results show a high capacity and expressivity of
$\eta-$Net to the quantified biological problem. Moreover, the close connection
to representation learning - the foundation for successes of modern AI, enables
efficient transferability of empirical knowledge from relevant cohorts to
targeted patients. Finally, we leverage Bayesian optimization to quantify the
epistemic uncertainty of model predictions, paving the way for $\eta-$Net
towards reliable AI in decision-making for clinical usages.


title: Strong Cohomological rigidity of Bott manifolds

abstract:
  We show that the strong cohomological rigidity conjecture for Bott manifolds
is true. Namely, any graded cohomology ring isomorphism between two Bott
manifolds is induced by a diffeomorphism.


title:
A Deep Learning Approach to Predicting Ventilator Parameters for
  Mechanically Ventilated Septic Patients

abstract:
  We develop a deep learning approach to predicting a set of ventilator
parameters for a mechanically ventilated septic patient using a long and short
term memory (LSTM) recurrent neural network (RNN) model. We focus on short-term
predictions of a set of ventilator parameters for the septic patient in
emergency intensive care unit (EICU). The short-term predictability of the
model provides attending physicians with early warnings to make timely
adjustment to the treatment of the patient in the EICU. The patient specific
deep learning model can be trained on any given critically ill patient, making
it an intelligent aide for physicians to use in emergent medical situations.


title:
Simulation of crack propagation through voxel-based, heterogeneous
  structures based on eigenerosion and finite cells

abstract:
  This paper presents an algorithm for the efficient simulation of ductile
crack propagation through heterogeneous structures, as e.g. metallic
microstructures, which are given as voxel data. These kinds of simulations are
required for e.g., the numerical investigation of wear mechanisms at small
length scales, which is still a challenging task in engineering. The basic idea
of the proposed algorithm is to combine the advantages of the Finite Cell
Method allowing for a convenient integration of heterogeneous finite element
problems with the eigenerosion approach to still enable the mesh-independent
simulation of crack propagation. The major component is to switch from finite
subcells to finite elements wherever the crack progresses, thereby
automatically adaptively refining at the crack tip by managing the newly
appearing nodes as hanging nodes. Technically relevant problems of crack
propagation at the microscale are mostly linked with sub-critical crack growth
where the crack moves fast and stepwise with subsequent load cycles. Therefore,
inertia may become important which is why dynamics are taken into account by
spreading the mass of the eroded elements to the nodes to avoid a loss in mass
resulting from the erosion procedure. Furthermore, a certain treatment for the
finite cell decomposition is considered in order to ensure efficiency and
accuracy. The numerical framework as well as the voxel decomposition techniques
are analyzed in detail in different three-dimensional numerical examples to
show the performance of the proposed approach.


title:
MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an
  Exponential Convergence Rate

abstract:
  The fluctuation effect of gradient expectation and variance caused by
parameter update between consecutive iterations is neglected or confusing by
current mainstream gradient optimization algorithms.Using this fluctuation
effect, combined with the stratified sampling strategy, this paper designs a
novel \underline{M}emory \underline{S}tochastic s\underline{T}ratified Gradient
Descend(\underline{MST}GD) algorithm with an exponential convergence rate.
Specifically, MSTGD uses two strategies for variance reduction: the first
strategy is to perform variance reduction according to the proportion p of used
historical gradient, which is estimated from the mean and variance of sample
gradients before and after iteration, and the other strategy is stratified
sampling by category. The statistic \ $\bar{G}_{mst}$\ designed under these two
strategies can be adaptively unbiased, and its variance decays at a geometric
rate. This enables MSTGD based on $\bar{G}_{mst}$ to obtain an exponential
convergence rate of the form $\lambda^{2(k-k_0)}$($\lambda\in (0,1)$,k is the
number of iteration steps,$\lambda$ is a variable related to proportion
p).Unlike most other algorithms that claim to achieve an exponential
convergence rate, the convergence rate is independent of parameters such as
dataset size N, batch size n, etc., and can be achieved at a constant step
size.Theoretical and experimental results show the effectiveness of MSTGD


title:
Surface waves and bulk Ruderman mode of a bosonic superfluid vortex
  crystal in the lowest Landau level

abstract:
  We determine and analyze collective normal modes of a finite disk-shaped
two-dimensional vortex crystal formed in a compressible bosonic superfluid in
an artificial magnetic field. Using the microscopic Gross-Pitaevskii theory in
the lowest Landau level approximation, we generate vortex crystal ground states
and solve the Bogoliubov-de Gennes equations for small amplitude collective
oscillations. We find chiral surface waves that propagate at frequencies larger
than those of the bulk Tkachenko modes. Furthermore, we study low frequency
bulk excitations and identify a torsional Ruderman mode, which we find is
well-described by a previously developed low-energy effective field theory.

categories: cond-mat.quant-gas nlin.PS physics.atom-ph quant-ph


title: Homotopy Cartan calculus and inner deformations of $A_\infty$-algebras

abstract:
  We consider inner deformations of families of $A_\infty$-algebras. With the
help of noncommutative Cartan's calculus, we prove the invariance of Hochschild
(co)homology under inner deformations. The invariance also holds for cyclic
cohomology classes that satisfy some additional conditions. Applications to
dg-algebras and QFT problems are briefly discussed.


title: Tunable critical Casimir forces counteract Casimir-Lifshitz attraction

abstract:
  Casimir forces in quantum electrodynamics emerge between microscopic metallic
objects because of the confinement of the vacuum electromagnetic fluctuations
occurring even at zero temperature. Their generalization at finite temperature
and in material media are referred to as Casimir--Lifshitz forces. These forces
are typically attractive, leading to the widespread problem of stiction between
the metallic parts of micro- and nanodevices. Recently, repulsive Casimir
forces have been experimentally realized but their reliance on specialized
materials prevents their dynamic control and thus limits their further
applicability. Here, we experimentally demonstrate that repulsive critical
Casimir forces, which emerge in a critical binary liquid mixture upon
approaching the critical temperature, can be used to actively control
microscopic and nanoscopic objects with nanometer precision. We demonstrate
this by using critical Casimir forces to prevent the stiction caused by the
Casimir--Lifshitz forces. We study a microscopic gold flake above a flat
gold-coated substrate immersed in a critical mixture. Far from the critical
temperature, stiction occurs because of dominant Casimir--Lifshitz forces. Upon
approaching the critical temperature, however, we observe the emergence of
repulsive critical Casimir forces that are sufficiently strong to counteract
stiction. This experimental demonstration can accelerate the development of
micro- and nanodevices by preventing stiction as well as providing active
control and precise tunability of the forces acting between their constituent
parts.


title:
Investigating Older Adults' Attitudes towards Crisis Informatics Tools:
  Opportunities for Enhancing Community Resilience during Disasters

abstract:
  The world population is projected to rapidly age over the next 30 years.
Given the increasing digital technology adoption amongst older adults,
researchers have investigated how technology can support aging populations.
However, little work has examined how technology can support older adults
during crises, despite increasingly common natural disasters, public health
emergencies, and other crisis scenarios in which older adults are especially
vulnerable. Addressing this gap, we conducted focus groups with older adults
residing in coastal locations to examine to what extent they felt technology
could support them during emergencies. Our findings characterize participants'
desire for tools that enhance community resilience-local knowledge,
preparedness, community relationships, and communication, that help communities
withstand disasters. Further, older adults' crisis technology preferences were
linked to their sense of control, social relationships, and digital readiness.
We discuss how a focus on community resilience can yield crisis technologies
that more effectively support older adults.


title: Towards Noncommutative Quantum Reality

abstract:
  The implications of the physical theory of quantum mechanics on the question
of realism is much a subject of sustaining interest, while the background
questions among physicists on how to think about all the theoretical notion and
`interpretation' of the theory remains controversial. Through a careful
analysis of the theoretical notions with the help of modern mathematical
perspectives, we give here a picture of quantum mechanics, as the basic theory
for `nonrelativistic' particle dynamics, that can be seen as being as much
about the physical reality as classical mechanics itself. The key is to fully
embrace the noncommutativity of the theory and see it as a notion about the
reality of physical quantities. Quantum reality is then just a noncommutative
version of the classical reality.


title:
Electronic and magnetic properties of silicene monolayer under bi-axial
  mechanical strain: a first-principles study

abstract:
  Mechanical control of electronic and magnetic properties of 2D Van-der-Waals
heterostructures gives new possibilities for further development of spintronics
and information-related technologies. Using the density functional theory, we
investigate the structural, electronic, and magnetic properties of silicene
monolayer with substituted Chromium atoms and under a small biaxial strain
($-6\%< \epsilon < 8\%$). Our results indicate that the Cr-doped silicene
nanosheets without strain have magnetic metallic, half-metallic or
semiconducting properties depending on the type of substitution. We also show
that the magnetic moments associated with the monomer and vertical dimer
substitutions change very weakly with strain. However, the magnetic moment
associated with the horizontal dimer substitution decreases when either
compressive or tensile strain is applied to the system. Additionally, we show
that the largest semiconductor band-gap is approximately 0.13 eV under zero
strain for the vertical Cr-doped silicene. Finally, biaxial compressive strain
leads to irregular changes in the magnetic moment for Cr vertical dimer
substitution.


title: Transformation Coding: Simple Objectives for Equivariant Representations

abstract:
  We present a simple non-generative approach to deep representation learning
that seeks equivariant deep embedding through simple objectives. In contrast to
existing equivariant networks, our transformation coding approach does not
constrain the choice of the feed-forward layer or the architecture and allows
for an unknown group action on the input space. We introduce several such
transformation coding objectives for different Lie groups such as the
Euclidean, Orthogonal and the Unitary groups. When using product groups, the
representation is decomposed and disentangled. We show that the presence of
additional information on different transformations improves disentanglement in
transformation coding. We evaluate the representations learnt by transformation
coding both qualitatively and quantitatively on downstream tasks, including
reinforcement learning.


title:
Convergence Analysis of Structure-Preserving Numerical Methods Based on
  Slotboom Transformation for the Poisson--Nernst--Planck Equations

abstract:
  The analysis of structure-preserving numerical methods for the
Poisson--Nernst--Planck (PNP) system has attracted growing interests in recent
years. In this work, we provide an optimal rate convergence analysis and error
estimate for finite difference schemes based on the Slotboom reformulation.
Different options of mobility average at the staggered mesh points are
considered in the finite-difference spatial discretization, such as the
harmonic mean, geometric mean, arithmetic mean, and entropic mean. A
semi-implicit temporal discretization is applied, which in turn results in a
non-constant coefficient, positive-definite linear system at each time step. A
higher order asymptotic expansion is applied in the consistency analysis, and
such a higher order consistency estimate is necessary to control the discrete
maximum norm of the concentration variables. In convergence estimate, the
harmonic mean for the mobility average, which turns out to bring lots of
convenience in the theoretical analysis, is taken for simplicity, while other
options of mobility average would also lead to the desired error estimate, with
more technical details involved. As a result, an optimal rate convergence
analysis on concentrations, electric potential, and ionic fluxes is derived,
which is the first such results for the structure-preserving numerical schemes
based on the Slotboom reformulation. It is remarked that the convergence
analysis leads to a theoretical justification of the conditional energy
dissipation analysis, which relies on the maximum norm bounds of the
concentration and the gradient of the electric potential. Some numerical
results are also presented to demonstrate the accuracy and structure-preserving
performance of the associated schemes.


title: Broadband single-mode planar waveguides in monolithic 4H-SiC

abstract:
  Color-center defects in silicon carbide promise opto-electronic quantum
applications in several fields, such as computing, sensing and communication.
In order to scale down and combine these functionalities with the existing
silicon device platforms, it is crucial to consider SiC integrated optics. In
recent years many examples of SiC photonic platforms have been shown, like
photonic crystal cavities, film-on-insulator waveguides and micro-ring
resonators. However, all these examples rely on separating thin films of SiC
from substrate wafers. This introduces significant surface roughness, strain
and defects in the material, which greatly affects the homogeneity of the
optical properties of color centers. Here we present and test a method for
fabricating monolithic single-crystal integrated-photonic devices in SiC:
tuning optical properties via charge carrier concentration. We fabricated
monolithic SiC n-i-n and p-i-n junctions where the intrinsic layer acts as
waveguide core, and demonstrate the waveguide functionality for these samples.
The propagation losses are below 14 dB/cm. These waveguide types allow for
addressing color-centers over a broad wavelength range with low strain-induced
inhomogeneity of the optical-transition frequencies. Furthermore, we expect
that our findings open the road to fabricating waveguides and devices based on
p-i-n junctions, which will allow for integrated electrostatic and radio
frequency (RF) control together with high-intensity optical control of defects
in silicon carbide.

categories: physics.optics cond-mat.mes-hall physics.app-ph quant-ph


title:
Multi-bit quantum random number generator from path-entangled single
  photons

abstract:
  Measurement outcomes on quantum systems exhibit inherent randomness and are
fundamentally nondeterministic. This has enabled quantum physics to set new
standards for the generation of true randomness with significant applications
in the fields of cryptography, statistical simulations, and modeling of the
nondeterministic behavior in various other fields. In this work, we present a
scheme for the generation of multi-bit random numbers using path-entangled
single photons. Without losing their intrinsic randomness, the protocol allows
us to engineer the distribution from which we sample random numbers. For the
experimental demonstration, we use single photons generated using spontaneous
parametric down-conversion (SPDC), and assign a multi-bit commitment along the
path. One-bit and two-bit random numbers are then generated from measuring
entangled states in the path basis. In addition to passing the NIST tests for
randomness, we also demonstrate the certification of quantumness and
self-certification of quantum random number generator (QRNG) using Clauser,
Horne, Shimony and Holt (CHSH) inequality violation. The path-entangled states
can generate higher bitrates compared to heralded single photon or entangled
photon schemes which are limited by the coincidence counts. The scheme involves
distribution of photons along multiple paths resulting in multiple bits from
one photon and avoids the limitation imposed by the detection dead time of one
detector. We demonstrate this by generating a high rate of about 80 Mbps when
the single photon detector saturates at around 28 Mcps.


title: Letters of the Alphabet: Discovering Natural Feature Sets

abstract:
  Deep learning networks find intricate features in large datasets using the
backpropagation algorithm. This algorithm repeatedly adjusts the network
connections.' weights and examining the "hidden" nodes behavior between the
input and output layer provides better insight into how neural networks create
feature representations. Experiments built on each other show that activity
differences computed within a layer can guide learning. A simple neural network
is used, which includes a data set comprised of the alphabet letters, where
each letter forms 81 input nodes comprised of 0 and 1s and a single hidden
layer and an output layer. The first experiment explains how the hidden layers
in this simple neural network represent the input data's features. The second
experiment attempts to reverse-engineer the neural network to find the
alphabet's natural feature sets. As the network interprets features, we can
understand how it derives the natural feature sets for a given data. This
understanding is essential to delve deeper into deep generative models, such as
Boltzmann machines. Deep generative models are a class of unsupervised deep
learning algorithms. The primary function of deep generative models is to find
the natural feature sets for a given data set.


title:
EF-Train: Enable Efficient On-device CNN Training on FPGA Through Data
  Reshaping for Online Adaptation or Personalization

abstract:
  Conventionally, DNN models are trained once in the cloud and deployed in edge
devices such as cars, robots, or unmanned aerial vehicles (UAVs) for real-time
inference. However, there are many cases that require the models to adapt to
new environments, domains, or new users. In order to realize such domain
adaption or personalization, the models on devices need to be continuously
trained on the device. In this work, we design EF-Train, an efficient DNN
training accelerator with a unified channel-level parallelism-based convolution
kernel that can achieve end-to-end training on resource-limited low-power
edge-level FPGAs. It is challenging to implement on-device training on
resource-limited FPGAs due to the low efficiency caused by different memory
access patterns among forward, backward propagation, and weight update.
Therefore, we developed a data reshaping approach with intra-tile continuous
memory allocation and weight reuse. An analytical model is established to
automatically schedule computation and memory resources to achieve high energy
efficiency on edge FPGAs. The experimental results show that our design
achieves 46.99 GFLOPS and 6.09GFLOPS/W in terms of throughput and energy
efficiency, respectively.


title: A Survey of Vision-Language Pre-Trained Models

abstract:
  As Transformer evolved, pre-trained models have advanced at a breakneck pace
in recent years. They have dominated the mainstream techniques in natural
language processing (NLP) and computer vision (CV). How to adapt pre-training
to the field of Vision-and-Language (V-L) learning and improve the performance
on downstream tasks becomes a focus of multimodal learning. In this paper, we
review the recent progress in Vision-Language Pre-Trained Models (VL-PTMs). As
the core content, we first briefly introduce several ways to encode raw images
and texts to single-modal embeddings before pre-training. Then, we dive into
the mainstream architectures of VL-PTMs in modeling the interaction between
text and image representations. We further present widely-used pre-training
tasks, after which we introduce some common downstream tasks. We finally
conclude this paper and present some promising research directions. Our survey
aims to provide multimodal researchers a synthesis and pointer to related
research.


title:
An end-to-end predict-then-optimize clustering method for intelligent
  assignment problems in express systems

abstract:
  Express systems play important roles in modern major cities. Couriers serving
for the express system pick up packages in certain areas of interest (AOI)
during a specific time. However, future pick-up requests vary significantly
with time. While the assignment results are generally static without changing
with time. Using the historical pick-up request number to conduct AOI
assignment (or pick-up request assignment) for couriers is thus unreasonable.
Moreover, even we can first predict future pick-up requests and then use the
prediction results to conduct the assignments, this kind of two-stage method is
also impractical and trivial, and exists some drawbacks, such as the best
prediction results might not ensure the best clustering results. To solve these
problems, we put forward an intelligent end-to-end predict-then-optimize
clustering method to simultaneously predict the future pick-up requests of AOIs
and assign AOIs to couriers by clustering. At first, we propose a deep
learning-based prediction model to predict order numbers on AOIs. Then a
differential constrained K-means clustering method is introduced to cluster
AOIs based on the prediction results. We finally propose a one-stage end-to-end
predict-then-optimize clustering method to assign AOIs to couriers reasonably,
dynamically, and intelligently. Results show that this kind of one-stage
predict-then-optimize method is beneficial to improve the performance of
optimization results, namely the clustering results. This study can provide
critical experiences for predict-and-optimize related tasks and intelligent
assignment problems in express systems.


title:
Incentive Mechanism Design for Joint Resource Allocation in
  Blockchain-based Federated Learning

abstract:
  Blockchain-based federated learning (BCFL) has recently gained tremendous
attention because of its advantages such as decentralization and privacy
protection of raw data. However, there has been few research focusing on the
allocation of resources for clients in BCFL. In the BCFL framework where the FL
clients and the blockchain miners are the same devices, clients broadcast the
trained model updates to the blockchain network and then perform mining to
generate new blocks. Since each client has a limited amount of computing
resources, the problem of allocating computing resources into training and
mining needs to be carefully addressed. In this paper, we design an incentive
mechanism to assign each client appropriate rewards for training and mining,
and then the client will determine the amount of computing power to allocate
for each subtask based on these rewards using the two-stage Stackelberg game.
After analyzing the utilities of the model owner (MO) (i.e., the BCFL task
publisher) and clients, we transform the game model into two optimization
problems, which are sequentially solved to derive the optimal strategies for
both the MO and clients. Further, considering the fact that local training
related information of each client may not be known by others, we extend the
game model with analytical solutions to the incomplete information scenario.
Extensive experimental results demonstrate the validity of our proposed
schemes.


title: Single-Leg Revenue Management with Advice

abstract:
  Single-leg revenue management is a foundational problem of revenue management
that has been particularly impactful in the airline and hotel industry: Given
$n$ units of a resource, e.g. flight seats, and a stream of
sequentially-arriving customers segmented by fares, what is the optimal online
policy for allocating the resource. Previous work focused on designing
algorithms when forecasts are available, which are not robust to inaccuracies
in the forecast, or online algorithms with worst-case performance guarantees,
which can be too conservative in practice. In this work, we look at the
single-leg revenue management problem through the lens of the
algorithms-with-advice framework, which attempts to optimally incorporate
advice/predictions about the future into online algorithms. In particular, we
characterize the Pareto frontier that captures the tradeoff between consistency
(performance when advice is accurate) and competitiveness (performance when
advice is inaccurate) for every advice. Moreover, we provide an online
algorithm that always achieves performance on this Pareto frontier. We also
study the class of protection level policies, which is the most widely-deployed
technique for single-leg revenue management: we provide an algorithm to
incorporate advice into protection levels that optimally trades off consistency
and competitiveness. Moreover, we empirically evaluate the performance of these
algorithms on synthetic data. We find that our algorithm for protection level
policies performs remarkably well on most instances, even if it is not
guaranteed to be on the Pareto frontier in theory.


title:
Experimental evidence for long-distance electrodynamic intermolecular
  forces

abstract:
  Both classical and quantum electrodynamics predict the existence of
dipole-dipole long-range electrodynamic intermolecular forces; however, these
have never been hitherto experimentally observed. The discovery of completely
new and unanticipated forces acting between biomolecules could have
considerable impact on our understanding of the dynamics and functioning of the
molecular machines at work in living organisms. Here, using two independent
experiments, on the basis of different physical effects detected by
fluorescence correlation spectroscopy and terahertz spectroscopy, respectively,
we demonstrate experimentally the activation of resonant electrodynamic
intermolecular forces. This is an unprecedented experimental proof of principle
of a physical phenomenon that, having been observed for biomacromolecules and
with long-range action (up to 1000 Angstroms), could be of importance for
biology. In addition to thermal fluctuations that drive molecular motion
randomly, these resonant (and thus selective) electrodynamic forces may
contribute to molecular encounters in the crowded cellular space.


title:
Recognizing Concepts and Recognizing Musical Themes. A Quantum Semantic
  Analysis

abstract:
  How are abstract concepts and musical themes recognized on the basis of some
previous experience? It is interesting to compare the different behaviors of
human and of artificial intelligences with respect to this problem. Generally,
a human mind that abstracts a concept (say, table) from a given set of known
examples creates a table-Gestalt: a kind of vague and out of focus image that
does not fully correspond to a particular table with well determined features.
A similar situation arises in the case of musical themes. Can the construction
of a gestaltic pattern, which is so natural for human minds, be taught to an
intelligent machine? This problem can be successfully discussed in the
framework of a quantum approach to pattern recognition and to machine learning.
The basic idea is replacing classical data sets with quantum data sets, where
either objects or musical themes can be formally represented as pieces of
quantum information, involving the uncertainties and the ambiguities that
characterize the quantum world. In this framework, the intuitive concept of
Gestalt can be simulated by the mathematical concept of positive centroid of a
given quantum data set. Accordingly, the crucial problem "how can we classify a
new object or a new musical theme (we have listened to) on the basis of a
previous experience?" can be dealt with in terms of some special quantum
similarity-relations. Although recognition procedures are different for human
and for artificial intelligences, there is a common method of "facing the
problems" that seems to work in both cases.


title: New kind of condensation of Bose particles through stimulated processes

abstract:
  We show that stimulated scattering of an isolated system of N Bose particles
with initially broad energy distribution can yield condensation of particles
into excited collective state in which most of the bosons occupy one or several
modes. During condensation, the total particle number and energy are conserved,
while entropy of the system grows. Onset of condensation occurs at a critical
particle occupation number when spectrum narrowing due to stimulated processes
overcomes spectrum broadening due to diffusion. This differs from Bose-Einstein
condensation in which particles undergo condensation into the equilibrium state
due to thermalization processes.


title: Gradient Based Activations for Accurate Bias-Free Learning

abstract:
  Bias mitigation in machine learning models is imperative, yet challenging.
While several approaches have been proposed, one view towards mitigating bias
is through adversarial learning. A discriminator is used to identify the bias
attributes such as gender, age or race in question. This discriminator is used
adversarially to ensure that it cannot distinguish the bias attributes. The
main drawback in such a model is that it directly introduces a trade-off with
accuracy as the features that the discriminator deems to be sensitive for
discrimination of bias could be correlated with classification. In this work we
solve the problem. We show that a biased discriminator can actually be used to
improve this bias-accuracy tradeoff. Specifically, this is achieved by using a
feature masking approach using the discriminator's gradients. We ensure that
the features favoured for the bias discrimination are de-emphasized and the
unbiased features are enhanced during classification. We show that this simple
approach works well to reduce bias as well as improve accuracy significantly.
We evaluate the proposed model on standard benchmarks. We improve the accuracy
of the adversarial methods while maintaining or even improving the unbiasness
and also outperform several other recent methods.


title:
Convex Loss Functions for Contextual Pricing with Observational
  Posted-Price Data

abstract:
  We study an off-policy contextual pricing problem where the seller has access
to samples of prices which customers were previously offered, whether they
purchased at that price, and auxiliary features describing the customer and/or
item being sold. This is in contrast to the well-studied setting in which
samples of the customer's valuation (willingness to pay) are observed. In our
setting, the observed data is influenced by the historic pricing policy, and we
do not know how customers would have responded to alternative prices. We
introduce suitable loss functions for this pricing setting which can be
directly optimized to find an effective pricing policy with expected revenue
guarantees without the need for estimation of an intermediate demand function.
We focus on convex loss functions. This is particularly relevant when linear
pricing policies are desired for interpretability reasons, resulting in a
tractable convex revenue optimization problem. We further propose generalized
hinge and quantile pricing loss functions, which price at a multiplicative
factor of the conditional expected value or a particular quantile of the
valuation distribution when optimized, despite the valuation data not being
observed. We prove expected revenue bounds for these pricing policies
respectively when the valuation distribution is log-concave, and provide
generalization bounds for the finite sample case. Finally, we conduct
simulations on both synthetic and real-world data to demonstrate that this
approach is competitive with, and in some settings outperforms,
state-of-the-art methods in contextual pricing.


title: Subtyping brain diseases from imaging data

abstract:
  The imaging community has increasingly adopted machine learning (ML) methods
to provide individualized imaging signatures related to disease diagnosis,
prognosis, and response to treatment. Clinical neuroscience and cancer imaging
have been two areas in which ML has offered particular promise. However, many
neurologic and neuropsychiatric diseases, as well as cancer, are often
heterogeneous in terms of their clinical manifestations, neuroanatomical
patterns or genetic underpinnings. Therefore, in such cases, seeking a single
disease signature might be ineffectual in delivering individualized precision
diagnostics. The current chapter focuses on ML methods, especially
semi-supervised clustering, that seek disease subtypes using imaging data. Work
from Alzheimer Disease and its prodromal stages, psychosis, depression, autism,
and brain cancer are discussed. Our goal is to provide the readers with a broad
overview in terms of methodology and clinical applications.


title: Locally EFX Allocations Over a Graph

abstract:
  The fairness notion of envy-free up to any good (EFX) has recently gained
popularity in the fair allocation literature. However, few positive results
about EFX allocations exist, with the existence of such allocations alone being
an open question. In this work, we study an intuitive relaxation of EFX
allocations: our allocations are only required to satisfy the EFX constraint
for certain pre-defined pairs of agents. Since these problem instances can be
represented using an undirected graph where the vertices correspond to the
agents and each edge represents an EFX constraint, we call such allocations
graph EFX ($G$-EFX) allocations. We show that $G$-EFX allocations exist for
three different classes of graphs -- two of them generalize the star $K_{1,
n-1}$ and the third generalizes the three-edge path $P_4$. We also present and
evaluate an algorithm using problem instances from Spliddit to show that
$G$-EFX allocations are likely to exist for larger classes of graphs like long
paths $P_n$.


title:
Provably convergent quasistatic dynamics for mean-field two-player
  zero-sum games

abstract:
  In this paper, we study the problem of finding mixed Nash equilibrium for
mean-field two-player zero-sum games. Solving this problem requires optimizing
over two probability distributions. We consider a quasistatic Wasserstein
gradient flow dynamics in which one probability distribution follows the
Wasserstein gradient flow, while the other one is always at the equilibrium.
Theoretical analysis are conducted on this dynamics, showing its convergence to
the mixed Nash equilibrium under mild conditions. Inspired by the continuous
dynamics of probability distributions, we derive a quasistatic Langevin
gradient descent method with inner-outer iterations, and test the method on
different problems, including training mixture of GANs.


title:
A Semi-supervised Learning Approach with Two Teachers to Improve
  Breakdown Identification in Dialogues

abstract:
  Identifying breakdowns in ongoing dialogues helps to improve communication
effectiveness. Most prior work on this topic relies on human annotated data and
data augmentation to learn a classification model. While quality labeled
dialogue data requires human annotation and is usually expensive to obtain,
unlabeled data is easier to collect from various sources. In this paper, we
propose a novel semi-supervised teacher-student learning framework to tackle
this task. We introduce two teachers which are trained on labeled data and
perturbed labeled data respectively. We leverage unlabeled data to improve
classification in student training where we employ two teachers to refine the
labeling of unlabeled data through teacher-student learning in a bootstrapping
manner. Through our proposed training approach, the student can achieve
improvements over single-teacher performance. Experimental results on the
Dialogue Breakdown Detection Challenge dataset DBDC5 and Learning to Identify
Follow-Up Questions dataset LIF show that our approach outperforms all previous
published approaches as well as other supervised and semi-supervised baseline
methods.


title: Optical Field Characterization using Off-axis Digital Holography

abstract:
  Angular resolved digital holography is presented as a technique for real-time
characterization of the full optical field (amplitude and phase) of
space-division multiplexing components and fibers, here a 6-mode
photonic-lantern is characterized.


title:
A Solomonic Solution to Ownership Disputes: An Application to Blockchain
  Front-Running

abstract:
  Blockchain front-running involves multiple agents, other than the legitimate
agent, claiming a payment from performing a contract. It arises because of the
public nature of blockchain transactions and potential network congestion. This
paper notes that disputes over payments are similar to classic ownership
disputes (such as King Solomon's dilemma). We propose a simultaneous report
mechanism that resolves Solomon's dilemma (using only ordinal preference
information) and also eliminates blockchain front-running. In each case, the
mechanism relies on threats to remove ownership from all claimants and
preferences from the legitimate claimant over allocations to other agents.


title:
Multiple Importance Sampling ELBO and Deep Ensembles of Variational
  Approximations

abstract:
  In variational inference (VI), the marginal log-likelihood is estimated using
the standard evidence lower bound (ELBO), or improved versions as the
importance weighted ELBO (IWELBO). We propose the multiple importance sampling
ELBO (MISELBO), a \textit{versatile} yet \textit{simple} framework. MISELBO is
applicable in both amortized and classical VI, and it uses ensembles, e.g.,
deep ensembles, of independently inferred variational approximations. As far as
we are aware, the concept of deep ensembles in amortized VI has not previously
been established. We prove that MISELBO provides a tighter bound than the
average of standard ELBOs, and demonstrate empirically that it gives tighter
bounds than the average of IWELBOs. MISELBO is evaluated in density-estimation
experiments that include MNIST and several real-data phylogenetic tree
inference problems. First, on the MNIST dataset, MISELBO boosts the
density-estimation performances of a state-of-the-art model, nouveau VAE.
Second, in the phylogenetic tree inference setting, our framework enhances a
state-of-the-art VI algorithm that uses normalizing flows. On top of the
technical benefits of MISELBO, it allows to unveil connections between VI and
recent advances in the importance sampling literature, paving the way for
further methodological advances. We provide our code at
\url{https://github.com/Lagergren-Lab/MISELBO}.


title:
Assessing the influence of French vaccine critics during the two first
  years of the COVID-19 pandemic

abstract:
  When the threat of COVID-19 became widely acknowledged, many hoped that this
epidemic would squash "the anti-vaccine movement". However, when vaccines
started arriving in rich countries at the end of 2020, it appeared that vaccine
hesitancy might be an issue even in the context of this major epidemic. Does it
mean that the mobilization of vaccine-critical activists on social media is one
of the main causes of this reticence to vaccinate against COVID-19? In this
paper, we wish to contribute to current work on vaccine hesitancy during the
COVID-19 epidemic by looking at one of the many mechanisms which can cause
reticence towards vaccines: the capacity of vaccine-critical activists to
influence a wider public on social media. We analyze the evolution of debates
over the COVID-19 vaccine on the French Twittosphere, during two first years of
the pandemic, with a particular attention to the spreading capacity of
vaccine-critical websites. We address two main questions: 1) Did
vaccine-critical contents gain ground during this period? 2) Who were the
central actors in the diffusion of these contents? While debates over vaccines
experienced a tremendous surge during this period, the share of
vaccine-critical contents in these debates remains stable except for a limited
number of short periods associated with specific events. Secondly, analyzing
the community structure of the re-tweets hyper-graph, we reconstruct the
mesoscale structure of the information flows, identifying and characterizing
the major communities of users. We analyze their role in the information
ecosystem: the largest right-wing community has a typical echo-chamber behavior
collecting all the vaccine-critical tweets from outside and recirculating it
inside the community. The smaller left-wing community is less permeable to
vaccine-critical contents but, has a large capacity to spread it once adopted.


title:
UAV/HAP-Assisted Vehicular Edge Computing in 6G: Where and What to
  Offload?

abstract:
  In the context of 6th generation (6G) networks, vehicular edge computing
(VEC) is emerging as a promising solution to let battery-powered ground
vehicles with limited computing and storage resources offload processing tasks
to more powerful devices. Given the dynamic vehicular environment, VEC systems
need to be as flexible, intelligent, and adaptive as possible. To this aim, in
this paper we study the opportunity to realize VEC via non-terrestrial networks
(NTNs), where ground vehicles offload resource-hungry tasks to Unmanned Aerial
Vehicles (UAVs), High Altitude Platforms (HAPs), or a combination of the two.
We define an optimization problem in which tasks are modeled as a Poisson
arrival process, and apply queuing theory to find the optimal offloading factor
in the system. Numerical results show that aerial-assisted VEC is feasible even
in dense networks, provided that high-capacity HAP/UAV platforms are available.


title: Fractional series operators on discrete Hardy spaces

abstract:
  We estudy the $H^{p}(\mathbb{Z})$ - $\ell^{q}(\mathbb{Z})$ boundedness of the
fractional series operator $T_{\gamma}$ given by \[ (T_{\gamma}b)(j) = \sum_{i
\neq \pm j} \frac{b(i)}{|i-j|^{\alpha}|i+j|^{\beta}}, \] where $0 \leq \gamma <
1$, $\alpha, \beta > 0$ and $\alpha + \beta = 1 -\gamma$. By means of a
counter-example, we also show that the operator $T_{\gamma}$ is not bounded
from $H^{p}(\mathbb{Z})$ into $H^{q}(\mathbb{Z})$.


title:
A Deep Reinforcement Learning based Approach for NOMA-based Random
  Access Network with Truncated Channel Inversion Power Control

abstract:
  As a main use case of 5G and Beyond wireless network, the ever-increasing
machine type communications (MTC) devices pose critical challenges over MTC
network in recent years. It is imperative to support massive MTC devices with
limited resources. To this end, Non-orthogonal multiple access (NOMA) based
random access network has been deemed as a prospective candidate for MTC
network. In this paper, we propose a deep reinforcement learning (RL) based
approach for NOMA-based random access network with truncated channel inversion
power control. Specifically, each MTC device randomly selects a pre-defined
power level with a certain probability for data transmission. Devices are using
channel inversion power control yet subject to the upper bound of the
transmission power. Due to the stochastic feature of the channel fading and the
limited transmission power, devices with different achievable power levels have
been categorized as different types of devices. In order to achieve high
throughput with considering the fairness between all devices, two objective
functions are formulated. One is to maximize the minimum long-term expected
throughput of all MTC devices, the other is to maximize the geometric mean of
the long-term expected throughput for all MTC devices. A Policy based deep
reinforcement learning approach is further applied to tune the transmission
probabilities of each device to solve the formulated optimization problems.
Extensive simulations are conducted to show the merits of our proposed
approach.


title: Photoinduced prethermalization phenomena in correlated metals

abstract:
  We study prethermalization phenomena in weakly interacting Hubbard systems
after electric-field pump pulses with finite duration. We treat the Hubbard
interaction up to second order, applying the prethermalization paradigm for
time-dependent interaction protocols, and the electric field strength beyond
linear order. A scaling behavior with pulse duration is observed for the
absorbed energy as well as individual prethermalized momentum occupation
numbers, which we attribute to the leading quadratic orders in interaction and
electric field. We show that a pronounced non-thermal momentum distribution can
be created with pump pulses of suitable resonance frequencies, and discuss how
to distinguish them from thermal states.


title:
Reconciling the Contour-Improved and Fixed-Order Approaches for $\tau$
  Hadronic Spectral Moments I: Renormalon-Free Gluon Condensate Scheme

abstract:
  We propose a simple and easy-to-implement scheme for a renormalon-free gluon
condensate (GC) matrix element. Because it is based on a perturbative
subtraction at the level of the matrix element, with a freely adaptable
infrared factorization scale, it can be implemented with little effort for any
observable where the GC is relevant. The scheme depends on the renormalon norm
of the GC which has to be supplemented independently. We apply the new scheme
to the fixed-order (FOPT) and contour-improved (CIPT) perturbative expansions
of $\tau$ hadronic spectral function moments. These expansions exhibit a
long-standing discrepancy for moments used in high-precision determinations of
the strong coupling in the commonly used GC scheme that is not renormalon-free.
We show that the new scheme is capable of resolving the FOPT-CIPT discrepancy
problem. At the same time, the perturbative behaviour of the moments that
previously showed bad convergence properties and for which the non-perturbative
corrections from the GC are sizeable, is substantially improved. The new GC
scheme may provide a powerful theoretical tool for future phenomenological
applications.


title:
Validation of e$^+$e$^-$ Pair Production Total Cross Sections for Monte
  Carlo Particle Transport

abstract:
  Several sources of total cross sections for e$^+$e$^-$ pair production by
photon interaction with matter, used by major Monte Carlo codes for particle
transport, are quantitatively evaluated with respect to experimental
measurements collected from the literature. They include data libraries
deriving from theoretical calculations and analytical formulae representing
empirical fits to tabulations. Although most of these sources of cross sections
are based on one theoretical reference, the statistical data analysis
documented in the paper highlights differences in their compatibility with
experiment. The cross sections collected in the 1997 version of the Evaluated
Photon Data Library (EPDL) exhibit the lowest incompatibility with experiment;
the paper details the validation results for all the examined cross section
sources. The scarcity of experimental data does not allow the validation
process to discern pair production in the field of the nucleus and in the field
of atomic electrons; nevertheless, the paper documents relevant clues regarding
the contribution of the latter to reproduce experimental measurements.


title: Pion model with the Nakanishi Integral Representations

abstract:
  In the present work, we describe a model for the pion based on an analytic
expression for the Bethe-Salpeter (BSA) amplitude, combined with some
ingredients from Lattice QCD calculations. The running quark mass function $M
(p^2)$, used here, reproduces well the results of Lattice QCD calculations. The
analytical form of the running quark mass function contains a single time-like
pole, which implies in time-like poles of the dressed quark propagator. Such a
form allows to build the weight functions, $G_i(\gamma,z)$, for the Nakanishi
integral representation of each scalar function, $\chi_{i}(k,p)$, appearing in
the decomposition of the Bethe-Salpeter amplitude in terms of Dirac operators,
Such scalar amplitudes can also be used to obtain the pion valence light-front
wave function.


title: Observations of instability-driven nanojets

abstract:
  The recent discovery of nanojets by Antolin et al. (2021) represents
component magnetic reconnection in a braided field, thus clearly identifying
the reconnection-driven nanoflares. Due to their small scale (500 km in widths,
1500 km in lengths) and short timescales ($<$ 25 s), it is unclear how
pervasive nanojets are in the solar corona. In this paper, we present IRIS and
SDO observations of nanojets found in multiple coronal structures, namely in a
coronal loop powered by a blowout jet, and in two other coronal loops with
coronal rain. In agreement with previous findings, we observe that nanojets are
accompanied by small nanoflare-like intensity bursts in the (E)UV, have
velocities of 150-250 kms$^{-1}$ and occur transversely to the field line of
origin, which is sometimes observed to split. These nanojets are found to have
number densities of ~$10^{10}$ cm$^{-3}$, and kinetic and thermal energies
within the nanoflare range. We also observe that they often occur in clusters,
less than 1000 km apart and at similar times. In the blowout jet case study,
the Kelvin-Helmholtz instability (KHI) is directly identified as the
reconnection driver. For the other two loops, we find that both, the KHI and
the Rayleigh-Taylor instability (RTI) are likely to be the drivers. However, we
find that the KHI and RTI are each more likely in one of the other two cases.
These observations of nanojets in a variety of structures and environments
support nanojets being a general result of component reconnection, that are
driven here by dynamic instabilities.


title:
Additive manufacturing of fine-granularity optically-isolated plastic
  scintillator elements

abstract:
  Plastic scintillator detectors are used in high energy physics as well as for
diagnostic imaging in medicine, beam monitoring on hadron therapy, muon
tomography, dosimetry and many security applications. To combine particle
tracking and calorimetry it is necessary to build detectors with
three-dimensional granularity, i.e. small voxels of scintillator optically
isolated from each other. Recently, the 3DET collaboration demonstrated the
possibility to 3D print polystyrene-based scintillators with a light output
performance close to that obtained with standard production methods. In this
article, after providing a further characterization of the developed
scintillators, we show the first matrix of plastic scintillator cubes optically
separated by a white reflector material entirely 3D printed with fused
deposition modeling. This is a major milestone towards the 3D printing of the
first real particle detector. A discussion of the results as well as the next
steps in the R&D is also provided.


title: Adaptive Cut Selection in Mixed-Integer Linear Programming

abstract:
  Cut selection is a subroutine used in all modern mixed-integer linear
programming solvers with the goal of selecting a subset of generated cuts that
induce optimal solver performance. These solvers have millions of parameter
combinations, and so are excellent candidates for parameter tuning. Cut
selection scoring rules are usually weighted sums of different measurements,
where the weights are parameters. We present a parametric family of
mixed-integer linear programs together with infinitely many family-wide valid
cuts. Some of these cuts can induce integer optimal solutions directly after
being applied, while others fail to do so even if an infinite amount are
applied. We show for a specific cut selection rule, that any finite grid search
of the parameter space will always miss all parameter values, which select
integer optimal inducing cuts in an infinite amount of our problems. We propose
a variation on the design of existing graph convolutional neural networks,
adapting them to learn cut selection rule parameters. We present a
reinforcement learning framework for selecting cuts, and train our design using
said framework over MIPLIB 2017. Our framework and design show that adaptive
cut selection does substantially improve performance over a diverse set of
instances, but that finding a single function describing such a rule is
difficult. Code for reproducing all experiments is available at
https://github.com/Opt-Mucca/Adaptive-Cutsel-MILP.


title:
A framework for spatial heat risk assessment using a generalized
  similarity measure

abstract:
  In this study, we develop a novel framework to assess health risks due to
heat hazards across various localities (zip codes) across the state of Maryland
with the help of two commonly used indicators i.e. exposure and vulnerability.
Our approach quantifies each of the two aforementioned indicators by developing
their corresponding feature vectors and subsequently computes
indicator-specific reference vectors that signify a high risk environment by
clustering the data points at the tail-end of an empirical risk spectrum. The
proposed framework circumvents the information-theoretic entropy based
aggregation methods whose usage varies with different views of entropy that are
subjective in nature and more importantly generalizes the notion of
risk-valuation using cosine similarity with unknown reference points.


title:
Comparing Controller With the Hand Gestures Pinch and Grab for Picking
  Up and Placing Virtual Objects

abstract:
  Grabbing virtual objects is one of the essential tasks for Augmented,
Virtual, and Mixed Reality applications. Modern applications usually use a
simple pinch gesture for grabbing and moving objects. However, picking up
objects by pinching has disadvantages. It can be an unnatural gesture to pick
up objects and prevents the implementation of other gestures which would be
performed with thumb and index. Therefore it is not the optimal choice for many
applications. In this work, different implementations for grabbing and placing
virtual objects are proposed and compared. Performance and accuracy of the
proposed techniques are measured and compared.