title:
Adding Connectionist Temporal Summarization into Conformer to Improve
  Its Decoder Efficiency For Speech Recognition

abstract:
  The Conformer model is an excellent architecture for speech recognition
modeling that effectively utilizes the hybrid losses of connectionist temporal
classification (CTC) and attention to train model parameters. To improve the
decoding efficiency of Conformer, we propose a novel connectionist temporal
summarization (CTS) method that reduces the number of frames required for the
attention decoder fed from the acoustic sequences generated by the encoder,
thus reducing operations. However, to achieve such decoding improvements, we
must fine-tune model parameters, as cross-attention observations are changed
and thus require corresponding refinements. Our final experiments show that,
with a beamwidth of 4, the LibriSpeech's decoding budget can be reduced by up
to 20% and for FluentSpeech data it can be reduced by 11%, without losing ASR
accuracy. An improvement in accuracy is even found for the LibriSpeech
"test-other" set. The word error rate (WER) is reduced by 6\% relative at the
beam width of 1 and by 3% relative at the beam width of 4.


title:
Inelastic Cotunneling Resonances in the Coulomb-Blockade Transport in
  Donor-Atom Transistors

abstract:
  We report finite-bias characteristics of electrical transport through
phosphorus donors in silicon nanoscale transistors, in which we observe
inelastic-cotunneling current in the Coulomb blockade region. The cotunneling
current appears like a resonant-tunneling current peak emerging from the
excited state at the crossover between blockade and non-blockade regions. These
cotunneling features are unique, since the inelastic-cotunneling currents have
so far been reported either as a broader hump or as a continuous increment of
current. This finding is ascribed purely due to excitation-related inelastic
cotunneling involving the ground and excited states. Theoretical calculations
were performed for a two-level quantum dot, supporting our experimental
observation.


title: Direct measurement of atomic entanglement via cavity photon statistics

abstract:
  We propose an experimental scheme for the measurement of entanglement between
two two-level atoms. Our scheme requires one of the two entangled atoms to
interact with a cavity field dispersively, and we show that by measuring the
zero time-delay second-order coherence function of the cavity field, one can
measure the concurrence of an arbitrary Bell-like atomic two-qubit state. As
our scheme requires only one of the atoms to interact with the measured cavity,
the entanglement quantification becomes independent of the location of the
other atom. Therefore, our scheme can have important implications for
entanglement quantification in distributed quantum systems.


title: A note on one-sided recognizable morphisms

abstract:
  We revisit the notion of one-sided recognizability of morphisms and its
relation to two-sided recognizability.


title:
On the fast assemblage of finite element matrices with application to
  nonlinear heat transfer problems

abstract:
  The finite element method is a well-established method for the numerical
solution of partial differential equations (PDEs), both linear and nonlinear.
However, the repeated reassemblage of finite element matrices for nonlinear
PDEs is frequently pointed out as one of the bottlenecks in the computations.
The second bottleneck being the large and numerous linear systems to be solved
arising from the use of Newton's method to solve nonlinear systems of
equations. In this paper, we will address the first issue. We will see how
under mild assumptions the assemblage procedure may be rewritten using a
completely loop-free algorithm. Our approach leads to a small matrix-matrix
multiplication for which we may count on highly optimized algorithms.


title:
Quantifying relation between mobility patterns and socioeconomic status
  of dockless sharing-bike users

abstract:
  Bikes are among the healthiest, greenest, and most affordable means of
transportation for a better future city, but mobility patterns of riders with
different income were rarely studied due to limitations on collecting data.
Newly emergent dockless bike-sharing platforms that record detailed information
regarding each trip provide us a unique opportunity. Attribute to its better
usage flexibility and accessibility, dockless bike-sharing platforms are
booming over the past a few years worldwide and reviving the riding fashion in
cities. In this work, by exploiting massive riding records in two megacities
from a dockless bike-sharing platform, we reveal that individual mobility
patterns, including radius of gyration and average travel distance, are similar
among users with different income, which indicates that human beings all follow
similar physical rules. However, collective mobility patterns, including
average range and diversity of visitation, and commuting directions, all
exhibit different behaviors and spatial patterns across income categories.
Hotspot locations that attract more cycling activities are quite different over
groups, and locations where users reside are of a low user ratio for both
higher and lower income groups. Lower income groups are inclined to visit less
flourishing locations, and commute towards the direction to the city center in
both cities, and of a smaller mobility diversity in Beijing but a larger
diversity in Shanghai. In addition, differences on mobility patterns among
socioeconomic categories are more evident in Beijing than in Shanghai. Our
findings would be helpful on designing better promotion strategies for dockless
bike-sharing platforms and towards the transition to a more sustainable green
transportation.


title:
SoundBeam: Target sound extraction conditioned on sound-class labels and
  enrollment clues for increased performance and continuous learning

abstract:
  In many situations, we would like to hear desired sound events (SEs) while
being able to ignore interference. Target sound extraction (TSE) aims at
tackling this problem by estimating the sound of target SE classes in a mixture
while suppressing all other sounds. We can achieve this with a neural network
that extracts the target SEs by conditioning it on clues representing the
target SE classes. Two types of clues have been proposed, i.e., target SE class
labels and enrollment sound samples similar to the target sound. Systems based
on SE class labels can directly optimize embedding vectors representing the SE
classes, resulting in high extraction performance. However, extending these
systems to the extraction of new SE classes not encountered during training is
not easy. Enrollment-based approaches extract SEs by finding sounds in the
mixtures that share similar characteristics to the enrollment. These approaches
do not explicitly rely on SE class definitions and can thus handle new SE
classes. In this paper, we introduce a TSE framework, SoundBeam, that combines
the advantages of both approaches. We also perform an extensive evaluation of
the different TSE schemes using synthesized and real mixtures, which shows the
potential of SoundBeam.


title:
Towards Semi-Supervised Learning of Automatic Post-Editing:
  Data-Synthesis by Infilling Mask with Erroneous Tokens

abstract:
  Semi-supervised learning that leverages synthetic training data has been
widely adopted in the field of Automatic post-editing (APE) to overcome the
lack of human-annotated training data. In that context, data-synthesis methods
to create high-quality synthetic data have also received much attention.
Considering that APE takes machine-translation outputs containing translation
errors as input, we propose a noising-based data-synthesis method that uses a
mask language model to create noisy texts through substituting masked tokens
with erroneous tokens, yet following the error-quantity statistics appearing in
genuine APE data. In addition, we propose corpus interleaving, which is to
combine two separate synthetic data by taking only advantageous samples, to
further enhance the quality of the synthetic data created with our noising
method. Experimental results reveal that using the synthetic data created with
our approach results in significant improvements in APE performance upon using
other synthetic data created with different existing data-synthesis methods.


title:
Sim-to-Real Learning of Robust Compliant Bipedal Locomotion on Torque
  Sensor-Less Gear-Driven Humanoid

abstract:
  In deep reinforcement learning, sim-to-real is the mainstream method as it
needs a large number of trials, however, it is challenging to transfer trained
policy due to reality gap. In particular, it is known that the characteristics
of actuators in leg robots have a considerable influence on the reality gap,
and this is also noticeable in high reduction ratio gears. Therefore, we
propose a new simulation model of high reduction ratio gears to reduce the
reality gap. The instability of the bipedal locomotion causes the sim-to-real
transfer to fail catastrophically, making system identification of the physical
parameters of the simulation difficult. Thus, we also propose a system
identification method that utilizes the failure experience. The realistic
simulations obtained by these improvements allow the robot to perform compliant
bipedal locomotion by reinforcement learning. The effectiveness of the method
is verified using a actual biped robot, ROBOTIS-OP3, and the sim-to-real
transferred policy archived to stabilize the robot under severe disturbances
and walk on uneven terrain without force and torque sensors.


title: Exploring Transformer's potential on automatic piano transcription

abstract:
  Most recent research about automatic music transcription (AMT) uses
convolutional neural networks and recurrent neural networks to model the
mapping from music signals to symbolic notation. Based on a high-resolution
piano transcription system, we explore the possibility of incorporating another
powerful sequence transformation tool -- the Transformer -- to deal with the
AMT problem. We argue that the properties of the Transformer make it more
suitable for certain AMT subtasks. We confirm the Transformer's superiority on
the velocity detection task by experiments on the MAESTRO dataset and a
cross-dataset evaluation on the MAPS dataset. We observe a performance
improvement on both frame-level and note-level metrics after introducing the
Transformer network.


title:
Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent
  Large Spatial-Temporal Data-Driven Approach -- Part 1

abstract:
  In this study, a novel coordinative scheduling optimization approach is
proposed to enhance port efficiency by reducing average wait time and
turnaround time. The proposed approach consists of enhanced particle swarm
optimization (ePSO) as kernel and augmented firefly algorithm (AFA) as global
optimal search. Two paradigm methods of the proposed approach are investigated,
which are batch method and rolling horizon method. The experimental results
show that both paradigm methods of proposed approach can effectively enhance
port efficiency. The average wait time could be significantly reduced by 86.0%
- 95.5%, and the average turnaround time could eventually save 38.2% - 42.4%
with respect to historical benchmarks. Moreover, the paradigm method of rolling
horizon could reduce to 20 mins on running time over 3-month datasets, rather
than 4 hrs on batch method at corresponding maximum performance.


title: The insidious bicategory of algebra bundles

abstract:
  In this paper we construct a bicategory of (super) algebra bundles over a
smooth manifold, where the 1-morphisms are bundles of bimodules. The main point
is that naive definitions of bimodule bundles will not lead to a well-defined
composition law in such a bicategory, at least not if non-invertible bimodules
and non-semisimple algebras are desired. This problem has not been addressed so
far in the literature. We develop a complete solution, and also address
symmetric monoidal structures as well as the corresponding questions of
dualizability.


title: Dragged surfaces. On the accretion tori in the ergoregion

abstract:
  We discuss the conditions for the existence of extended matter configurations
orbiting in the ergoregion or close to the outer ergosurface of the Kerr black
hole ("dragged" configurations). The corotating tori under consideration are
perfect fluid configurations with barotropic equation of state, orbiting on the
equatorial plane of the central Kerr black hole. The possibility of magnetized
tori with a toroidal magnetic field is also discussed. Indications on the
attractors where dragged tori can be observed are provided with the analysis of
the fluid characteristics and geometrical features, relevant for the tori
stability and their observations. QPOs emissions from the inner edges of the
dragged tori are also discussed. We argue that the smaller dragged tori could
be subjected to a characteristic instability, effect of the frame-dragging.
This possibility is thoroughly explored. This can finally lead to the
destruction of the torus (disk exfoliation) which can combine with accretion
and processes present in the regions very close to the black hole horizon. Tori
are characterized according to the central attractor dimensionless spin. These
structures can be observed orbiting black holes with dimensionless spin a >
0.9897M.


title: Minimal subsystems of given mean dimension in Bernstein spaces

abstract:
  In this paper, we study the shift on the space of uniformly bounded
continuous functions band-limited in a given compact interval with the standard
topology of tempered distributions. We give a constructive proof of the
existence of minimal subsystems with any given mean dimension strictly less
than twice its band-width. A version of real-valued function spaces is
considered as well.


title: On Presburger arithmetic extended with non-unary counting quantifiers

abstract:
  We consider a first-order logic for the integers with addition. This logic
extends classical first-order logic by modulo-counting, threshold-counting, and
exact-counting quantifiers, all applied to tuples of variables. Further, the
residue in modulo-counting quantifiers is given as a term. Our main result
shows that satisfaction for this logic is decidable in two-fold exponential
space. If only threshold- and exact-counting quantifiers are allowed, we prove
an upper bound of alternating two-fold exponential time with linearly many
alternations. This latter result almost matches Berman's exact complexity of
first-order logic without counting quantifiers.
  To obtain these results, we first translate threshold- and exact-counting
quantifiers into classical first-order logic in polynomial time (which already
proves the second result). To handle the remaining modulo-counting quantifiers
for tuples, we first reduce them in doubly exponential time to modulo-counting
quantifiers for single elements. For these quantifiers, we provide a quantifier
elimination procedure similar to Reddy and Loveland's procedure for first-order
logic and analyse the growth of coefficients, constants, and moduli appearing
in this process. The bounds obtained this way allow to replace quantification
in the original formula to integers of bounded size which then implies the
first result mentioned above.
  Our logic is incomparable with the logic considered recently by Chistikov et
al. They allow more general counting operations in quantifiers, but only unary
quantifiers. The move from unary to non-unary quantifiers is non-trivial,
since, e.g., the non-unary version of the H\"artig quantifier results in an
undecidable theory.


title:
Taylor Estimates for the laws of pinned Bessel bridges, and Integration
  by Parts

abstract:
  In this article, we extend the integration by parts formulae for the laws of
Bessel bridges obtained in previous work with Zambotti, by showing that these
formulae hold for very general test functionals on $L^{2}(0,1)$. A key step
consists in establishing new Taylor estimates on the laws of pinned Bessel
bridges.


title:
BioBART: Pretraining and Evaluation of A Biomedical Generative Language
  Model

abstract:
  Pretrained language models have served as important backbones for natural
language processing. Recently, in-domain pretraining has been shown to benefit
various domain-specific downstream tasks. In the biomedical domain, natural
language generation (NLG) tasks are of critical importance, while understudied.
Approaching natural language understanding (NLU) tasks as NLG achieves
satisfying performance in the general domain through constrained language
generation or language prompting. We emphasize the lack of in-domain generative
language models and the unsystematic generative downstream benchmarks in the
biomedical domain, hindering the development of the research community. In this
work, we introduce the generative language model BioBART that adapts BART to
the biomedical domain. We collate various biomedical language generation tasks
including dialogue, summarization, entity linking, and named entity
recognition. BioBART pretrained on PubMed abstracts has enhanced performance
compared to BART and set strong baselines on several tasks. Furthermore, we
conduct ablation studies on the pretraining tasks for BioBART and find that
sentence permutation has negative effects on downstream tasks.


title: Superexchange liquefaction of strongly correlated lattice dipolar bosons

abstract:
  Optical lattices with dipolar bosons provide a unique platform for
fundamental studies of the extended Bose-Hubbard model. The interplay between
anisotropic long-range interaction, strong on-site repulsion and periodic
confinement give rise to unexpected phases of matter that have not been
explored so far in experiments. We propose a mechanism that stabilizes quantum
liquids in strongly interacting dipolar gases in a one-dimensional optical
lattice. The mechanism is based on the enhanced role of the superexchange
processes close to a self-bound Mott insulator state. To support this finding,
we contrast our perturbative calculations with full density-matrix
renormalization group simulations and characterize the properties of different
phases of the system. Finally, we analyze the speed of sound of the model that
exhibits a non-trivial behavior owing to the breaking of Galilean invariance.
We argue that an experimental detection of this unknown quantum liquid could
provide a fingerprint of the superexchange process and open intriguing
possibilities of investigating non-Galilean invariant liquids.


title:
On the considerations adopted by Breidze and Traczyk towards the
  faithfulness of Burau representation for $n=4$

abstract:
  This work discusses the open problem of the faithfulness of the reduced Burau
representation for $n=4$. Birman showed that in order to prove this
representation is faithful, it is sufficient to find two matrices $A$ and $B$
that generate a free group of rank 2. Breidze and Traczyk proved that $A^{3}$
and $B^{3}$ generate the free group of rank 2. In our work, we show that
$A^{2}$ and $B^{2}$ generate the free group of rank 2.


title:
Second-order differential equation with indefinite and repulsive
  singularities

abstract:
  This paper concerns a second-order differential equation with indefinite and
repulsive singularities. It is the first time to study differential equation
containing both indefinite and repulsive singularities simultaneously. A set of
sufficient conditions are obtained for the existence of positive periodic
solutions. The theoretical underpinnings of this paper are the positivity of
Green's function and fixed point theorem in cones. Our results improve and
extend the results in previous literatures. Finally, three examples and their
numerical simulations (phase diagrams and time diagrams of periodic solutions)
are given to show the effectiveness of our conclusions.


title: On the $ P_3 $-hull numbers of $ q $-Kneser graphs and Grassmann graphs

abstract:
  Let $S$ be an $n$-dimensional vector space over the finite field
$\mathbb{F}_q$, where $q$ is necessarily a prime power. Denote $K_q(n,k)$
(resp. $J_q(n,k)$) to be the \emph{$q$-Kneser graph} (resp. \emph{Grassmann
graph}) for $k\geq 1$ whose vertices are the $k$-dimensional subspaces of $S$
and two vertices $v_1$ and $v_2$ are adjacent if $\dim(v_1\cap v_2)=0$ (resp.
$\dim(v_1\cap v_2)=k-1$). We consider the infection spreading in the $ q
$-Kneser graphs and the Grassmann graphs: a vertex gets infected if it has at
least two infected neighbors. In this paper, we compute the $ P_3 $-hull
numbers of $K_q(n,k)$ and $J_q(n,k)$ respectively, which is the minimum size of
a vertex set that eventually infects the whole graph.


title: Generic Stability and Modes of Convergence

abstract:
  We study generically stable types/measures in both classical and continuous
logics, and their connection with randomization and modes of convergence of
types/measures.


title: A posteriori learning for quasi-geostrophic turbulence parametrization

abstract:
  The use of machine learning to build subgrid parametrizations for climate
models is receiving growing attention. State-of-the-art strategies address the
problem as a supervised learning task and optimize algorithms that predict
subgrid fluxes based on information from coarse resolution models. In practice,
training data are generated from higher resolution numerical simulations
transformed in order to mimic coarse resolution simulations. By essence, these
strategies optimize subgrid parametrizations to meet so-called $\textit{a
priori}$ criteria. But the actual purpose of a subgrid parametrization is to
obtain good performance in terms of $\textit{a posteriori}$ metrics which imply
computing entire model trajectories. In this paper, we focus on the
representation of energy backscatter in two dimensional quasi-geostrophic
turbulence and compare parametrizations obtained with different learning
strategies at fixed computational complexity. We show that strategies based on
$\textit{a priori}$ criteria yield parametrizations that tend to be unstable in
direct simulations and describe how subgrid parametrizations can alternatively
be trained end-to-end in order to meet $\textit{a posteriori}$ criteria. We
illustrate that end-to-end learning strategies yield parametrizations that
outperform known empirical and data-driven schemes in terms of performance,
stability and ability to apply to different flow configurations. These results
support the relevance of differentiable programming paradigms for climate
models in the future.


title:
Optical Signatures of the Coupling between Excitons and Charge Transfer
  States in Linear Molecular Aggregates

abstract:
  Charge Transfer (CT) has enjoyed continuous interest due to increasing
experimental control over molecular structure leading to applications in, for
example, photovoltaics and hydrogen production. In this paper, we investigate
the effect of CT states on the absorption spectrum of linear molecular
aggregates using a scattering matrix technique that allows us to deal with
arbitrarily large systems. The presented theory performs well for both strong
and weak mixing of exciton and CT states, bridging the gap between previously
employed methods which are applicable in only one of these limits. In
experimental spectra the homogeneous linewidth is often too large to resolve
all optically allowed transitions individually, resulting in a characteristic
two-peak absorption spectrum in both the weak- and strong-coupling regime.
Using the scattering matrix technique we examine the contributions of free and
bound states in detail. We conclude that the skewness of the high-frequency
peak may be used as a new way to identify the exciton-CT-state coupling
strength.


title: Stability of Non-linear Neural Feedback Loops using Sum of Squares

abstract:
  Neural network controllers have the potential to improve the performance of
feedback systems compared to traditional controllers, due to their ability to
act as general function approximators. However, quantifying their safety and
robustness properties has proven challenging due to the non-linearities of the
activation functions inside the neural network. A key robustness indicator is
certifying the stability properties of the feedback system and providing a
region of attraction, which has been addressed in previous literature. However,
these works only address linear systems or require one to abstract the plant
non-linearities and bound them using slope and sector constraints. In this
paper we use a Sum of Squares programming framework to compute the stability of
non-linear systems with neural network controllers directly. Within this
framework, we can propose higher order candidate Lyapunov functions with richer
structures that are able to better capture the dynamics of the non-linear
system and the nonlinearities in the neural network. We are also able to
analyse these systems in continuous time, whereas other methods rely on
discretising the system. These higher order Lyapunov functions are used in
conjunction with higher order multipliers on the inequality and equality
constraints that bound the neural network input-output properties. The volume
of the region of attraction computed is increased compared to other methods,
allowing for better safety guarantees on the stability of the system. We are
also able to easily analyse non-linear polynomial systems, which is not
possible to do with other methods. We are also able to conduct robustness
analysis on the parameter uncertainty. We show the benefits of our method using
numerical examples.


title:
Revealing A Trans-Planckian World Solves The Cosmological Constant
  Problem

abstract:
  The Planck scale has been believed to be an unpassable wall. Putting a cutoff
there and thinking of it as a quantized spacetime entity shows that. However,
this is exactly the cause of many problems in quantum gravity. The cosmological
constant problem also comes down to the problem of how to describe a
trans-Planckian world by a continuum theory, which shall be renormalizable and
background free. Here, we show that quantizing gravity in a diffeomorphism
invariant method in which background freedom arises asymptotically as an exact
conformal symmetry, zero-point energy vanishes identically. Thus, there is no
problem with the cosmological constant, which is a physical constant, namely a
renormalization group invariant. We also argue that new quanta based on the
background freedom emerge and spacetime is discretized dynamically. Primordial
fluctuations will be originated from such quanta.


title:
A model in which the Separation principle holds for a given effective
  projective Sigma-class

abstract:
  In this paper, we prove the following: If $n\ge3$, there is a generic
extension of $L$ -- the constructible universe -- in which it is true that the
Separation principle holds for both effective (lightface) classes
$\varSigma^1_n$ and $\varPi^1_n$ for sets of integers. The result was announced
long ago by Leo Harrington with a sketch of the proof for $n=3$; its full proof
has never been presented. Our methods are based on a countable product of
almost-disjoint forcing notions independent in the sense of Jensen--Solovay.


title: SuperNet in Neural Architecture Search: A Taxonomic Survey

abstract:
  Deep Neural Networks (DNN) have made significant progress in a wide range of
visual recognition tasks such as image classification, object detection, and
semantic segmentation. The evolution of convolutional architectures has led to
better performance by incurring expensive computational costs. In addition,
network design has become a difficult task, which is labor-intensive and
requires a high level of domain knowledge. To mitigate such issues, there have
been studies for a variety of neural architecture search methods that
automatically search for optimal architectures, achieving models with
impressive performance that outperform human-designed counterparts. This survey
aims to provide an overview of existing works in this field of research and
specifically focus on the supernet optimization that builds a neural network
that assembles all the architectures as its sub models by using weight sharing.
We aim to accomplish that by categorizing supernet optimization by proposing
them as solutions to the common challenges found in the literature: data-side
optimization, poor rank correlation alleviation, and transferable NAS for a
number of deployment scenarios.


title:
In situ controllable magnetic phases in doped twisted bilayer
  transition-metal dichalcogenides

abstract:
  We study the electronic structure of hole-doped transition metal
dichalcogenides for small twist-angels, where the onsite repulsion is extremely
strong. Using unbiased diagrammatic Monte Carlo simulations, we find evidence
for magnetic correlations which are driven by delocalization and can be
controlled in situ via the dielectric environment. For weak spin-orbit
coupling, we find that the moderately doped system becomes anti-ferromagnetic,
whilst the regime of strong spin-orbit coupling features ferromagnetic
correlations. We show that this behavior is accurately predicted by an
analytical theory based on moment expansion of the Hamiltonian, and analysis of
corresponding particle trajectories.


title: The Duality of SONC: Advances in Circuit-based Certificates

abstract:
  The cone of sums of nonnegative circuits (SONCs) is a subset of the cone of
nonnegative polynomials / exponential sums, which has been studied extensively
in recent years. In this article, we construct a subset of the SONC cone which
we call the DSONC cone. The DSONC cone can be seen as an extension of the dual
SONC cone; membership can be tested via linear programming. We show that the
DSONC cone is a proper, full-dimensional cone, we provide a description of its
extreme rays, and collect several properties that parallel those of the SONC
cone. Moreover, we show that functions in the DSONC cone cannot have real
zeros, which yields that DSONC cone does not intersect the boundary of the SONC
cone. Furthermore, we discuss the intersection of the DSONC cone with the SOS
and SDSOS cones. Finally, we show that circuit functions in the boundary of the
DSONC cone are determined by points of equilibria, which hence are the
analogues to singular points in the primal SONC cone, and relate the DSONC cone
to tropical geometry.


title: Network Shuffling: Privacy Amplification via Random Walks

abstract:
  Recently, it is shown that shuffling can amplify the central differential
privacy guarantees of data randomized with local differential privacy. Within
this setup, a centralized, trusted shuffler is responsible for shuffling by
keeping the identities of data anonymous, which subsequently leads to stronger
privacy guarantees for systems. However, introducing a centralized entity to
the originally local privacy model loses some appeals of not having any
centralized entity as in local differential privacy. Moreover, implementing a
shuffler in a reliable way is not trivial due to known security issues and/or
requirements of advanced hardware or secure computation technology.
  Motivated by these practical considerations, we rethink the shuffle model to
relax the assumption of requiring a centralized, trusted shuffler. We introduce
network shuffling, a decentralized mechanism where users exchange data in a
random-walk fashion on a network/graph, as an alternative of achieving privacy
amplification via anonymity. We analyze the threat model under such a setting,
and propose distributed protocols of network shuffling that is straightforward
to implement in practice. Furthermore, we show that the privacy amplification
rate is similar to other privacy amplification techniques such as uniform
shuffling. To our best knowledge, among the recently studied intermediate trust
models that leverage privacy amplification techniques, our work is the first
that is not relying on any centralized entity to achieve privacy amplification.


title: Global Update Guided Federated Learning

abstract:
  Federated learning protects data privacy and security by exchanging models
instead of data. However, unbalanced data distributions among participating
clients compromise the accuracy and convergence speed of federated learning
algorithms. To alleviate this problem, unlike previous studies that limit the
distance of updates for local models, we propose global-update-guided federated
learning (FedGG), which introduces a model-cosine loss into local objective
functions, so that local models can fit local data distributions under the
guidance of update directions of global models. Furthermore, considering that
the update direction of a global model is informative in the early stage of
training, we propose adaptive loss weights based on the update distances of
local models. Numerical simulations show that, compared with other advanced
algorithms, FedGG has a significant improvement on model convergence accuracies
and speeds. Additionally, compared with traditional fixed loss weights,
adaptive loss weights enable our algorithm to be more stable and easier to
implement in practice.


title: Mixed lattice structures and cone projections

abstract:
  Problems related to projections on closed convex cones are frequently
encountered in optimization theory and related fields. To study these problems,
various unifying ideas have been introduced, including asymmetric vector-valued
norms and certain generalized lattice-like operations. We propose a new
perspective on these studies by describing how the problem of cone projection
can be formulated using an order-theoretic formalism developed in this paper.
The underlying mathematical structure is a partially ordered vector space that
generalizes the notion of a vector lattice by using two partial orderings and
having certain lattice-type properties with respect to these orderings. In this
note we introduce a generalization of these so-called mixed lattice spaces, and
we show how such structures arise quite naturally in some of the applications
mentioned above.


title: A Library of Synthetic X-ray Spectra for Fitting Tidal Disruption Events

abstract:
  We present a pre-tabulated model for the X-ray spectral fitting of tidal
disruption events (TDEs). The model is constructed by ray-tracing stationary
general relativistic slim disks, including gravitational redshift, Doppler, and
lensing effects self-consistently. We used earlier versions to fit individual
TDE flares. Here we introduce the pre-tabulated version to reduce computational
expense and make the model more accessible to the community. This version
constructs synthetic X-ray spectra by interpolation. The resulting error in the
synthetic flux estimates is $<10\%$ (it can rise to $40\%$ when the disk is
nearly edge-on). We refit the X-ray spectra of ASASSN-14li and ASASSN-15oi with
this model, successfully reproducing our earlier constraints on black hole mass
$M_\bullet$ and spin $a_\bullet$ from the full (non-interpolated) ray-tracing
code. We use mock spectra to explore the degeneracies among parameters. First,
we find that higher $a_\bullet$, lower $M_\bullet$, edge-on inclination angles,
and super-Eddington spectra offer tighter constraints on $M_\bullet$ and
$a_\bullet$. Second, the constraining power of X-ray spectra on $M_\bullet$ and
$a_\bullet$ increases as a power-law with the number of X-ray counts; the index
of the power law is higher for higher accretion rates, higher $a_\bullet$, and
smaller $M_\bullet$. Third, multi-epoch X-ray spectra partially break the large
degeneracy between $M_\bullet$ and $a_\bullet$. Fourth, the time-dependent
level of X-ray absorption can be reasonably constrained from spectral fitting.
The model and slim disk code are available here
(https://ope123456789.wixsite.com/slimdisk).


title:
Electron microscopy probing electron-photon interactions in SiC
  nanowires with ultra-wide energy and momentum match

abstract:
  Nanoscale materials usually can trap light and strongly interact with it
leading to many photonic device applications. The light-matter interactions are
commonly probed by optical spectroscopy, which, however, have some limitations
such as diffraction-limited spatial resolution, tiny momentum transfer and
non-continuous excitation/detection. In this work, using scanning transmission
electron microscopy-electron energy loss spectroscopy (STEM-EELS) with
ultra-wide energy and momentum match and sub-nanometer spatial resolution, we
study the optical microcavity resonant spectroscopy in a single SiC nanowire.
The longitudinal Fabry-Perot (FP) resonating modes and the transverse
whispering-gallery modes (WGMs) are simultaneously excited and detected, which
span from near-infrared (~ 1.2 {\mu}m) to ultraviolet (~ 0.2 {\mu}m) spectral
regime and the momentum transfer can be ranging up to 108 cm{^{-1}}. The size
effects on the resonant spectra of nanowires are also revealed. Moreover, the
nanoscale decay length of resonant EELS is revealed, which is contributed by
the strongly localized electron-photon interactions in the SiC nanowire. This
work provides a new alternative technique to investigate the optical resonating
spectroscopy of a single nanowire structure and to explore the light-matter
interactions in dielectric nanostructures, which is also promising for
modulating free electrons via photonic structures.


title:
The strong coupling $g_{X J/\psi\phi}$ of $X(4700) \to J/\psi \phi$ in
  the light-cone sum rules

abstract:
  We assign the scalar tetraquark and the D-wave tetraquark state for $X(4700)$
and calculate the width of the decay $X(4700)$ $\to J/\psi \phi$ within the
framework of light-cone sum rules. The strong coupling $g_{X J/\psi \phi}$ is
obtained by considering the technique of soft-meson approximation. We also
investigate the mass and the decay constant of $X(4700)$ in the framework of
SVZ sum rules. Our prediction for the mass is in agreement with the
experimental measurement, and that for the decay width of $X(4700)$ $\to J/\psi
\phi$ support the possibility that $X(4700)$ could be a scalar tetraquark state
if $X(4700)$ $\to J/\psi \phi$ is the predominant decay channel, or a D-wave
tetraquark state if $X(4700)$ $\to J/\psi \phi$ is not the predominant one and
there exist other decays.


title:
Biometric identification by means of hand geometry and a neural net
  classifier

abstract:
  This Paper describes a hand geometry biometric identification system. We have
acquired a database of 22 people using a conventional document scanner. The
experimental section consists of a study about the discrimination capability of
different extracted features, and the identification rate using different
classifiers based on neural networks.


title:
Volcano effect in chemotactic aggregation and an extended Keller-Segel
  mode

abstract:
  Aggregation of chemotactic bacteria under a unimodal distribution of chemical
cues was investigated by Monte Carlo simulation and asymptotic analysis based
on a kinetic transport equation, which considers an internal adaptation
dynamics as well as a finite tumbling duration. It was found that there exist
two different regimes of the adaptation time, between which the effect of the
adaptation time on the aggregation behavior is reversed; that is, when the
adaptation time is as small as the running duration, the aggregation becomes
increasingly steeper as the adaptation time increases, while, when the
adaptation time is as large as the diffusion time of the population density,
the aggregation becomes more diffusive as the adaptation time increases.
Moreover, notably, the aggregation profile becomes bimodal (volcano) at the
large adaptation-time regime while it is always unimodal at the small
adaptation-time regime. The volcano effect occurs in such a way that the
population of tumbling cells considerably decreases in a diffusion layer which
is created near the peak of the external chemical cues due to the coupling of
diffusion and internal adaptation of the bacteria. Two different
continuum-limit models are derived by the asymptotic analysis according to the
scaling of the adaptation time; that is, at the small adaptation-time regime,
the Keller-Segel model while, at the large adaptation-time regime, an extension
of KS model, which involves both the internal variable and the tumbling
duration. Importantly, either of the models can accurately reproduce the MC
results at each adaptation-time regime, involving the volcano effect. Thus, we
conclude that the coupling of diffusion, adaptation, and finite tumbling
duration is crucial for the volcano effect.


title: On computing the symplectic $LL^T$ factorization

abstract:
  We analyze two algorithms for computing the symplectic $LL^T$ factorization
$A=LL^T$ of a given symmetric positive definite symplectic matrix $A$. The
first algorithm $W_1$ is an implementation of the $HH^T$ factorization from
[Dopico et al., 2009], see Theorem 5.2. The second one, algorithm $W_2$ uses
both Cholesky and Reverse Cholesky decompositions of symmetric positive
definite matrices. We presents a comparison of these algorithms and illustrate
their properties by numerical experiments in MATLAB. A particular emphasis is
given on simplecticity properties of the computed matrices in floating-point
arithmetic.


title:
Diverse Exotic Orders and Fermiology in Fe-based Superconductors: A
  Unified Mechanism for $B_{1g}/B_{2g}$ Nematicity in FeSe/(Cs,Rb)Fe$_2$As$_2$
  and Smectic Order in BaFe$_2$As$_2$

abstract:
  Rich nematic/smectic orders in Fe-based superconductors are important
unsolved problems in the strongly correlated electron systems. A unified
understanding of these rich orders has been investigated for the last decade.
In this article, we explain the $B_{1g}$ symmetry nematic transition in
FeSe$_{1-x}$Te$_x$, the $B_{2g}$ symmetry nematicity in AFe$_2$As$_2$ (A=Cs,
Rb), and the smectic state in BaFe$_2$As$_2$ based on the same framework. We
investigate the quantum interference mechanism between spin fluctuations by
developing the density wave equation. The observed rich variety of
nematic/smectic orders is naturally understood in this mechanism. The obtained
nematic/smectic orders depend on the characteristic shape and topology of the
Fermi surface (FS) of each compound. (i) In FeSe$_{1-x}$Te$_x$, each FS is very
small and the dxy-orbital hole pocket is below the Fermi level. In this case,
the small spin fluctuations on three dxz, dyz, and dxy orbitals cooperatively
lead to the $B_{1g}$ nematic order. The experimental Lifshitz transition below
the nematic transition temperature $(T_S)$ is naturally reproduced. (ii) In
BaFe$_2$As$_2$, the dxy-orbital hole pocket emerges around M point, and each FS
is relatively large. The strong spin fluctuations due to the dxy-orbital
nesting give rise to the $B_{1g}$ nematic order and the smectic order, and the
latter transition temperature ($T^*$) exceeds the former one $T_S$. (iii) In
heavily hole-doped AFe$_2$As$_2$, the large dxy-orbital hole pocket and the
four tiny Dirac pockets appear due to the hole-doping. The $B_{2g}$ nematic
bond order emerges on the dxy-orbital hole pocket due to the same interference
mechanism. The present spin-fluctuation interference mechanism provides a
unified explanation of why the nematic/smectic orders in Fe-based
superconductors are so rich, based on the well-established fermiology of
Fe-based superconductors.


title:
Deep Hyperspectral-Depth Reconstruction Using Single Color-Dot
  Projection

abstract:
  Depth reconstruction and hyperspectral reflectance reconstruction are two
active research topics in computer vision and image processing. Conventionally,
these two topics have been studied separately using independent imaging setups
and there is no existing method which can acquire depth and spectral
reflectance simultaneously in one shot without using special hardware. In this
paper, we propose a novel single-shot hyperspectral-depth reconstruction method
using an off-the-shelf RGB camera and projector. Our method is based on a
single color-dot projection, which simultaneously acts as structured light for
depth reconstruction and spatially-varying color illuminations for
hyperspectral reflectance reconstruction. To jointly reconstruct the depth and
the hyperspectral reflectance from a single color-dot image, we propose a novel
end-to-end network architecture that effectively incorporates a geometric
color-dot pattern loss and a photometric hyperspectral reflectance loss.
Through the experiments, we demonstrate that our hyperspectral-depth
reconstruction method outperforms the combination of an existing
state-of-the-art single-shot hyperspectral reflectance reconstruction method
and depth reconstruction method.


title:
From Rewriting to Remembering: Common Ground for Conversational QA
  Models

abstract:
  In conversational QA, models have to leverage information in previous turns
to answer upcoming questions. Current approaches, such as Question Rewriting,
struggle to extract relevant information as the conversation unwinds. We
introduce the Common Ground (CG), an approach to accumulate conversational
information as it emerges and select the relevant information at every turn. We
show that CG offers a more efficient and human-like way to exploit
conversational information compared to existing approaches, leading to
improvements on Open Domain Conversational QA.


title:
HINNPerf: Hierarchical Interaction Neural Network for Performance
  Prediction of Configurable Systems

abstract:
  Modern software systems are usually highly configurable, providing users with
customized functionality through various configuration options. Understanding
how system performance varies with different option combinations is important
to determine optimal configurations that meet specific requirements. Due to the
complex interactions among multiple options and the high cost of performance
measurement under a huge configuration space, it is challenging to study how
different configurations influence the system performance. To address these
challenges, we propose HINNPerf, a novel hierarchical interaction neural
network for performance prediction of configurable systems. HINNPerf employs
the embedding method and hierarchic network blocks to model the complicated
interplay between configuration options, which improves the prediction accuracy
of the method. Besides, we devise a hierarchical regularization strategy to
enhance the model robustness. Empirical results on 10 real-world configurable
systems show that our method statistically significantly outperforms
state-of-the-art approaches by achieving average 22.67% improvement in
prediction accuracy. In addition, combined with the Integrated Gradients
method, the designed hierarchical architecture provides some insights about the
interaction complexity and the significance of configuration options, which
might help users and developers better understand how the configurable system
works and efficiently identify significant options affecting the performance.


title:
A Complete Classification of Sub-Shocks in the Shock Structure of a
  Binary Mixture of Eulerian Gases with Different Degrees of Freedom

abstract:
  The shock structure in a binary mixture of polyatomic Eulerian gases with
different degrees of freedom of a molecule is studied based on the
multi-temperature model of rational extended thermodynamics. Since the system
of field equations is hyperbolic, the shock-structure solution is not always
regular, and discontinuous parts (sub-shocks) can be formed. For given values
of the mass ratio and the specific heats of the constituents, we identify the
possible sub-shocks as the Mach number $M_0$ of the shock wave and the
concentration $c$ of the constituents change. In the plane $(c,M_0)$, we
identify the possible regions for the sub-shock formation. The analysis is
obtained to verify when the velocity of the shock wave meets a characteristic
velocity in the unperturbed or perturbed equilibrium states that gives a
necessary condition for the sub-shock formation. The condition becomes
necessary and sufficient when the velocity of the shock becomes greater than
the maximum characteristic velocity in the unperturbed state. Namely, the
regions with no sub-shocks, a sub-shock for only one constituent, or sub-shocks
for both constituents are comprehensively classified. The most interesting case
is that the lighter molecule has more degrees of freedom than that of the heavy
one. In this situation, the topology of the various regions becomes different.
We also solve the system of the field equations numerically using the
parameters in the various regions and confirm whether the sub-shocks emerge or
not. Finally, the relationship between an acceleration wave in a constituent
and the sub-shock in the other constituent is explicitly derived.


title:
Estimation of Nitrogen-to-Iron Abundance Ratios From Low-Resolution
  Spectra

abstract:
  We present a method to determine nitrogen abundance ratios with respect to
iron ([N/Fe]) from molecular CN-band features observed in low-resolution ($R
\sim$ 2000) stellar spectra obtained by the Sloan Digital Sky Survey (SDSS) and
the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). Various
tests are carried out to check the systematic and random errors of our
technique, and the impact of signal-to-noise (S/N) ratios of stellar spectra on
the determined [N/Fe]. We find that the uncertainty of our derived [N/Fe] is
less than 0.3 dex for S/N ratios larger than 10 in the ranges $T_{eff}$ =
[4000, 6000] K, log g = [0.0, 3.5], [Fe/H] = [--3.0, 0.0], [C/Fe] = [--1.0,
+4.5], and [N/Fe] = [--1.0, +4.5], the parameter space that we are interested
in to identify N-enhanced stars in the Galactic halo. A star-by-star comparison
with a sample of stars with [N/Fe] estimates available from the Apache Point
Observatory Galactic Evolution Experiment (APOGEE) also suggests a similar
level of uncertainty in our measured [N/Fe], after removing its systematic
error. Based on these results, we conclude that our method is able to reproduce
[N/Fe] from low-resolution spectroscopic data, with an uncertainty sufficiently
small to discover N-rich stars that presumably originated from disrupted
Galactic globular clusters.


title: Does Robustness on ImageNet Transfer to Downstream Tasks?

abstract:
  As clean ImageNet accuracy nears its ceiling, the research community is
increasingly more concerned about robust accuracy under distributional shifts.
While a variety of methods have been proposed to robustify neural networks,
these techniques often target models trained on ImageNet classification. At the
same time, it is a common practice to use ImageNet pretrained backbones for
downstream tasks such as object detection, semantic segmentation, and image
classification from different domains. This raises a question: Can these robust
image classifiers transfer robustness to downstream tasks? For object detection
and semantic segmentation, we find that a vanilla Swin Transformer, a variant
of Vision Transformer tailored for dense prediction tasks, transfers robustness
better than Convolutional Neural Networks that are trained to be robust to the
corrupted version of ImageNet. For CIFAR10 classification, we find that models
that are robustified for ImageNet do not retain robustness when fully
fine-tuned. These findings suggest that current robustification techniques tend
to emphasize ImageNet evaluations. Moreover, network architecture is a strong
source of robustness when we consider transfer learning.


title:
Study of a committee of neural networks for biometric hand-geometry
  recognition

abstract:
  This Paper studies different committees of neural networks for biometric
pattern recognition. We use the neural nets as classifiers for identification
and verification purposes. We show that a committee of nets can improve the
recognition rates when compared with a multi-start initialization algo-rithm
that just picks up the neural net which offers the best performance. On the
other hand, we found that there is no strong correlation between
identifi-cation and verification applications using the same classifier.


title: Holomorphic H\"ormander-Type Functional Calculus on Sectors and Strips

abstract:
  In this paper, recent abstract multiplier theorems for $0$-sectorial and
$0$-strip type operators by Kriegler and Weis (2018) are refined and
generalized to arbitrary sectorial and strip-type operators. To this end,
holomorphic H\"ormander-type functions on sectors and strips are introduced
with a scale of smoothness being finer than the classical polynomial one.
Moreover, we establish alternative descriptions of these spaces involving
Schwartz and "holomorphic Schwartz" functions. Finally, the abstract results
are combined with a recent result by Carbonaro and Dragi\v{c}evi\'c (2017) to
obtain an improvement -- with respect to the smoothness condition -- of the
known H\"ormander-type multiplier theorem for general symmetric contraction
semigroups.


title:
Revealing Josephson vortex dynamics in proximity junctions below
  critical current

abstract:
  Made of a thin non-superconducting metal (N) sandwiched by two
superconductors (S), SNS Josephson junctions enable novel quantum
functionalities by mixing up the intrinsic electronic properties of N with the
superconducting correlations induced from S by proximity. Electronic properties
of these devices are governed by Andreev quasiparticles [1] which are absent in
conventional SIS junctions whose insulating barrier (I) between the two S
electrodes owns no electronic states. Here we focus on the Josephson vortex
(JV) motion inside Nb-Cu-Nb proximity junctions subject to electric currents
and magnetic fields. The results of local (Magnetic Force Microscopy) and
global (transport) experiments provided simultaneously are compared with our
numerical model, revealing the existence of several distinct dynamic regimes of
the JV motion. One of them, identified as a fast hysteretic entry/escape below
the critical value of Josephson current, is analyzed and suggested for
low-dissipative logic and memory elements.


title: On Distinctive Image Captioning via Comparing and Reweighting

abstract:
  Recent image captioning models are achieving impressive results based on
popular metrics, i.e., BLEU, CIDEr, and SPICE. However, focusing on the most
popular metrics that only consider the overlap between the generated captions
and human annotation could result in using common words and phrases, which
lacks distinctiveness, i.e., many similar images have the same caption. In this
paper, we aim to improve the distinctiveness of image captions via comparing
and reweighting with a set of similar images. First, we propose a
distinctiveness metric -- between-set CIDEr (CIDErBtw) to evaluate the
distinctiveness of a caption with respect to those of similar images. Our
metric reveals that the human annotations of each image in the MSCOCO dataset
are not equivalent based on distinctiveness; however, previous works normally
treat the human annotations equally during training, which could be a reason
for generating less distinctive captions. In contrast, we reweight each
ground-truth caption according to its distinctiveness during training. We
further integrate a long-tailed weight strategy to highlight the rare words
that contain more information, and captions from the similar image set are
sampled as negative examples to encourage the generated sentence to be unique.
Finally, extensive experiments are conducted, showing that our proposed
approach significantly improves both distinctiveness (as measured by CIDErBtw
and retrieval metrics) and accuracy (e.g., as measured by CIDEr) for a wide
variety of image captioning baselines. These results are further confirmed
through a user study.


title: GigaST: A 10,000-hour Pseudo Speech Translation Corpus

abstract:
  This paper introduces GigaST, a large-scale pseudo speech translation (ST)
corpus. We create the corpus by translating the text in GigaSpeech, an English
ASR corpus, into German and Chinese. The training set is translated by a strong
machine translation system and the test set is translated by human. ST models
trained with an addition of our corpus obtain new state-of-the-art results on
the MuST-C English-German benchmark test set. We provide a detailed description
of the translation process and verify its quality. We make the translated text
data public and hope to facilitate research in speech translation.
Additionally, we also release the training scripts on NeurST to make it easy to
replicate our systems. GigaST dataset is available at
https://st-benchmark.github.io/resources/GigaST.


title:
IgA-BEM for 3D Helmholtz problems on multi-patch domains using B-spline
  tailored numerical integration

abstract:
  An Isogeometric Boundary Element Method (IgA-BEM) is considered for the
numerical solution of Helmholtz problems on 3D domains admitting a smooth
conformal multi-patch representation of the boundary surface. The
discretization space is formed by C^0 inter-patch continuous basis functions
whose restriction to a patch simplifies to the span of tensor product B-splines
composed with the given patch parameterization. To profit by efficient
function-by-function matrix assembly, the proposed model utilizes a spline
quasi-interpolation numerical integration procedure for both singular and
regular integrals defined on the support of each B-spline basis function in the
discretization space. In particular, in the singular case the scheme combines a
singularity extraction technique with a B-spline recursion. Numerical examples
on relevant benchmarks show that the expected convergence orders are achieved
with a small number of quadrature nodes.


title: A novel concept for a fully digital particle detector

abstract:
  Silicon sensors are the most diffuse position sensitive device in particle
physics 8 experiments and in countless applications in science and technology.
They had a spectacular progress in performance over almost 40 years since their
first introduction, but their evolution is now slowing down. The position
resolution for single particle hits is larger than a few microns in the most
advanced sensors. This value was reached already over 30 years ago [1]. The
minimum ionising path length a sensor can detect is several tens of microns.
There are fundamental reasons why these limits will not be substantially
improved by further refinements of the current technology. This makes silicon
sensors unsuitable to applications where the physics signature is the short
path of a recoiling atom and constrains the layout of physics experiments where
they represent by far the best option like high energy physics collider
experiments. In perspective, the availability of sensors with sub-micron
spatial resolution, in the order of a few tens of nanometres, would be a
disruptive change for the sensor technology with a foreseeable huge impact on
experiment layout and various applications of these devices. For providing such
a leap in resolution, we propose a novel design based on a purely digital
circuit. This disruptive concept potentially enables pixel sizes much smaller
than 1{\mu}m2 and a number of advantages in terms of power consumption, readout
speed and reduced thickness (for low mass sensors).


title: WG3 Summary -- Rare $B$, $D$ and $K$ decays

abstract:
  We summarize the presentations made within Working Group 3 of the CKM2021
workshop. This working group is devoted to rare $B$, $D$ and $K$ decays,
radiative and electroweak-penguin decays, including constraints on $V_{\rm
td}/V_{\rm ts}$ and $\epsilon^\prime / \epsilon$. The working group has thus a
very broad scope, and includes very topical subjects such as the coherent array
of discrepancies in semi-leptonic $B$ decays. Each contribution is here
summarized very succinctly with the aim of providing an overview of the main
results. The reader interested in fuller details is referred to the individual
contributions.


title:
Tensor approximation of the self-diffusion matrix of tagged particle
  processes

abstract:
  The objective of this paper is to investigate a new numerical method for the
approximation of the self-diffusion matrix of a tagged particle process defined
on a grid. While standard numerical methods make use of long-time averages of
empirical means of deviations of some stochastic processes, and are thus
subject to statistical noise, we propose here a tensor method in order to
compute an approximation of the solution of a high-dimensional quadratic
optimization problem, which enables to obtain a numerical approximation of the
self-diffusion matrix. The tensor method we use here relies on an iterative
scheme which builds low-rank approximations of the quantity of interest and on
a carefully tuned variance reduction method so as to evaluate the various terms
arising in the functional to minimize. In particular, we numerically observe
here that it is much less subject to statistical noise than classical
approaches.


title:
Channel model for end-to-end learning of communications systems: A
  survey

abstract:
  The traditional communication model based on chain of multiple independent
processing blocks is constraint to efficiency and introduces artificial
barriers. Thus, each individually optimized block does not guarantee end-to-end
performance of the system. Recently, end-to-end learning of communications
systems through machine learning (ML) have been proposed to optimize the system
metrics jointly over all components. These methods show performance
improvements but has a limitation that it requires a differentiable channel
model. In this study, we have summarized the existing approaches that
alleviates this problem. We believe that this study will provide better
understanding of the topic and an insight into future research in this field.


title:
The XXL Survey: XLVIII; X-ray follow-up of distant XXL clusters: Masses,
  scaling relations and AGN contamination

abstract:
  We use deep follow-up XMM-Newton observations of 6 clusters discovered in the
XXL Survey at $z>1$ to gain robust measurements of their X-ray properties and
to investigate the extent to which scaling relations at low redshift are valid
at $z>1$. This sample is unique as it has been investigated for AGN
contamination, which ensures measurements are not undermined by systematic
uncertainties, and pushes to lower mass at higher redshift than is usually
possible, for example with Sunyaev-Zel'dovich (SZ) selected clusters. We
determine the flux contribution of point sources to the XXL cluster flux in
order to test for the presence of AGN in other high-redshift cluster
candidates, and find 3XLSS J231626.8-533822 to be a point source misclassified
as a cluster and 3XLSS J232737.3-541618 to be a genuine cluster. We present the
first attempt to measure the hydrostatic masses in a bright subsample of $z>1$
X-ray selected galaxy clusters with a known selection function. Periods of high
particle background significantly reduced the effective exposure times of
observations (losing >50% in some cases) limiting the power of this study. When
combined with complementary SZ selected cluster samples at higher masses, the
data appear broadly consistent with the self-similar evolution of the low
redshift scaling relations between ICM properties and cluster mass, suggesting
that properties such as the X-ray temperature, gas mass and SZ signal remain
reliable mass proxies even at high redshift.


title: Probabilistic Representations for Video Contrastive Learning

abstract:
  This paper presents Probabilistic Video Contrastive Learning, a
self-supervised representation learning method that bridges contrastive
learning with probabilistic representation. We hypothesize that the clips
composing the video have different distributions in short-term duration, but
can represent the complicated and sophisticated video distribution through
combination in a common embedding space. Thus, the proposed method represents
video clips as normal distributions and combines them into a Mixture of
Gaussians to model the whole video distribution. By sampling embeddings from
the whole video distribution, we can circumvent the careful sampling strategy
or transformations to generate augmented views of the clips, unlike previous
deterministic methods that have mainly focused on such sample generation
strategies for contrastive learning. We further propose a stochastic
contrastive loss to learn proper video distributions and handle the inherent
uncertainty from the nature of the raw video. Experimental results verify that
our probabilistic embedding stands as a state-of-the-art video representation
learning for action recognition and video retrieval on the most popular
benchmarks, including UCF101 and HMDB51.


title:
Lensless coherent diffraction imaging based on spatial light modulator
  with unknown modulation curve

abstract:
  Lensless imaging is a popular research field for the advantages of small
size, wide field-of-view and low aberration in recent years. However, some
traditional lensless imaging methods suffer from slow convergence, mechanical
errors and conjugate solution interference, which limit its further application
and development. In this work, we proposed a lensless imaging method based on
spatial light modulator (SLM) with unknown modulation curve. In our imaging
system, we use SLM to modulate the wavefront of object, and introduce the
ptychographic scanning algorithm that is able to recover the complex amplitude
information even the SLM modulation curve is inaccurate or unknown. In
addition, we also design a split-beam interference experiment to calibrate the
modulation curve of SLM, and using the calibrated modulation function as the
initial value of the expended ptychography iterative engine (ePIE) algorithm
can improve the convergence speed. We further analyze the effect of modulation
function, algorithm parameters and the characteristics of the coherent light
source on the quality of reconstructed image. The simulated and real
experiments show that the proposed method is superior to traditional mechanical
scanning methods in terms of recovering speed and accuracy, with the recovering
resolution up to 14 um.


title: The Psychology of Mineral Wealth: Empirical Evidence from Kazakhstan

abstract:
  Despite rapidly-expanding academic and policy interest in the links between
natural resource wealth and development failures (commonly referred to as the
resource curse) little attention has been devoted to the psychology behind the
phenomenon. Rent-seeking and excessive reliance on mineral revenues can be
attributed largely to social psychology. Mineral booms (whether due to the
discovery of mineral reserves or to the drastic rise in commodity prices) start
as positive income shocks that can subsequently evolve into influential and
expectation-changing public and media narratives; these lead consecutively to
unrealistic demands that favor immediate consumption of accrued mineral
revenues and to the postponement of productive investment. To our knowledge,
this paper is the first empirical analysis that tests hypotheses regarding the
psychological underpinnings of resource mismanagement in mineral-rich states.
Our study relies on an extensive personal survey (of 1977 respondents) carried
out in Almaty, Kazakhstan, between May and August 2018. We find empirical
support for a positive link between exposure to news and inflated expectations
regarding mineral availability, as well as evidence that the latter can
generate preferences for excessive consumption, and hence, rent-seeking.


title:
DUSTiER (DUST in the Epoch of Reionization): dusty galaxies in
  cosmological radiation-hydrodynamical simulations of the Epoch of
  Reionization with RAMSES-CUDATON

abstract:
  In recent years, interstellar dust has become a crucial topic in the study of
the high and very high redshift Universe. Evidence points to the existence of
high dust masses in massive star forming galaxies already during the Epoch of
Reionization, potentially affecting the escape of ionising photons into the
intergalactic medium. Moreover, correctly estimating dust extinction at UV
wavelengths is essential for precise ultra-violet luminosity function (UVLF)
prediction and interpretation. In this paper, we investigate the impact of dust
on the observed properties of high redshift galaxies, and cosmic reionization.
To this end, we couple a physical model for dust production to the fully
coupled radiation-hydrodynamics cosmological simulation code RAMSES-CUDATON,
and perform a $16^3$, $2048^3$, simulation, that we call DUSTiER for DUST in
the Epoch of Reionization. It yields galaxies with dust masses and UV slopes
compatible with constraints at z $\geq 5$. We find that extinction has a
dramatic impact on the bright end of the UVLF, even as early as $\rm z=8$, and
our dusty UVLFs are in better agreement with observations than dust-less UVLFs.
The fraction of obscured star formation rises up to 55% at $\rm z=5$, in
agreement with some of the latest results from ALMA. Finally, we find that dust
reduces the escape of ionising photons from galaxies more massive than $10^{10}
M_\odot$ (brighter than $\approx -18$ MAB1600) by >10%, and possibly up to
80-90% for our most massive galaxies. Nevertheless, we find that the ionising
escape fraction is first and foremost set by neutral Hydrogen in galaxies, as
the latter produces transmissions up to 100 times smaller than through dust
alone.


title: On the robustness of the in vivo cyanobacterial circadian clock

abstract:
  We propose a revisited version of the in vivo model of the cyanobacterial
circadian clock. Our aim is to address the lack of robustness predicted for the
mutant cyanobacteria without transcriptional regulation of the original model.
For this, we rely on an in vitro model of the clock describing explicitly the
hexameric structure of the core protein of the clock. Our model is able to
reproduce oscillatory behavior for the mutant, as observed experimentally,
without finely tuned parameters.


title:
RuBioRoBERTa: a pre-trained biomedical language model for Russian
  language biomedical text mining

abstract:
  This paper presents several BERT-based models for Russian language biomedical
text mining (RuBioBERT, RuBioRoBERTa). The models are pre-trained on a corpus
of freely available texts in the Russian biomedical domain. With this
pre-training, our models demonstrate state-of-the-art results on RuMedBench -
Russian medical language understanding benchmark that covers a diverse set of
tasks, including text classification, question answering, natural language
inference, and named entity recognition.


title:
Proposal to repeat the Abraham force experiment using giant permittivity
  materials

abstract:
  In the more than 100 years-old Abraham-Minkowski problem in macroscopic
electrodynamics, the issue of how to observe the so-called Abraham term ${\bf
f}^{\rm Aterm} = [(\varepsilon\mu-1)/c^2] \partial/\partial t ({\bf E}\times
{\bf H})$ has been a main point. Recent years have seen a number of beautiful
experiments in radiation optics, but these experiments usually give no
information about the Abraham term as this term simply fluctuates out. So one
is left with somewhat indirect verifications of this force, as in the radiation
pressure of Jones {et al.} in the 1950's, testing the radiation pressure on a
mirror immersed in a dielectric liquid. Now, there is a different way to test
the existence of ${\bf f}^{ \rm Aterm}$, namely to work with low
(quasi-stationary) frequencies enabling one to observe the sinusoidal variation
of the force directly. These kind of experiments were actually done by Walker
{\it et al.} in 1he 1970's, using BaTiO$_3$ as a high-frequency dielectric
(permittivity $\varepsilon \sim 3600$). Now, in recent years there have
appeared dielectric materials with giant permittivities, of order $10^5$ or
even higher. It is therefore natural to consider the idea of Walker {\it et
al.} anew, in order to test if this demanding experiment can be facilitated and
give better accuracy. That is the main topic of the present paper. The positive
outcome of these kinds of experiments clearly supports the Abraham
energy-momentum tensor at low frequencies. The Minkowski tensor is unable to
predict a torque at all. The low-frequency and the high-frequency regimes are
in this way highly contrasted, as it is obvious that in optical experiments the
Minkowski tensor is by far the simplest and most convenient one to use. We end
this note by commenting upon the use of the Einstein-Laub tensor (1908) in
explaining this experiment, and discuss also the influence from air friction.


title:
RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying
  misogynous MEMEs

abstract:
  This work presents an ensemble system based on various uni-modal and bi-modal
model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia
Automatic Misogyny Identification. The challenge organizers provide an English
meme dataset to develop and train systems for identifying and classifying
misogynous memes. More precisely, the competition is separated into two
sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses
misogyny, while sub-task B is to classify misogynous memes into the potentially
overlapping sub-categories of stereotype, shaming, objectification, and
violence. For our submission, we implement a new model fusion network and
employ an ensemble learning approach for better performance. With this
structure, we achieve a 0.755 macroaverage F1-score (11th) in sub-task A and a
0.709 weighted-average F1-score (10th) in sub-task B.


title:
Bag-of-Words vs. Sequence vs. Graph vs. Hierarchy for Single- and
  Multi-Label Text Classification

abstract:
  Graph neural networks have triggered a resurgence of graph-based text
classification methods, defining today's state of the art. We show that a
simple multi-layer perceptron (MLP) using a Bag of Words (BoW) outperforms the
recent graph-based models TextGCN and HeteGCN in an inductive text
classification setting and is comparable with HyperGAT in single-label
classification. We also run our own experiments on multi-label classification,
where the simple MLP outperforms the recent sequential-based gMLP and aMLP
models. Moreover, we fine-tune a sequence-based BERT and a lightweight
DistilBERT model, which both outperform all models on both single-label and
multi-label settings in most datasets. These results question the importance of
synthetic graphs used in modern text classifiers. In terms of parameters,
DistilBERT is still twice as large as our BoW-based wide MLP, while graph-based
models like TextGCN require setting up an $\mathcal{O}(N^2)$ graph, where $N$
is the vocabulary plus corpus size.


title:
Optimizing Coordinative Schedules for Tanker Terminals: An Intelligent
  Large Spatial-Temporal Data-Driven Approach -- Part 2

abstract:
  In this study, a novel coordinative scheduling optimization approach is
proposed to enhance port efficiency by reducing weighted average turnaround
time. The proposed approach is developed as a heuristic algorithm applied and
investigated through different observation windows with weekly rolling horizon
paradigm method. The experimental results show that the proposed approach is
effective and promising on mitigating the turnaround time of vessels. The
results demonstrate that largest potential savings of turnaround time (weighted
average) are around 17 hours (28%) reduction on baseline of 1-week observation,
45 hours (37%) reduction on baseline of 2-week observation and 70 hours (40%)
reduction on baseline of 3-week observation. Even though the experimental
results are based on historical datasets, the results potentially present
significant benefits if real-time applications were applied under a quadratic
computational complexity.


title:
High carrier lifetimes in UMG multicrystalline wafers after P- diffusion
  compatible with high-efficiency cell structures

abstract:
  High-quality multicrystalline Upgraded Metallurgical Grade Silicon (UMG-Si)
offers significant advantages over conventional polysilicon-based PV
technology, associated to lower cost, lower energy budget and lower carbon
footprint. The aim of this study is twofold: on the one hand, to ascertain the
efficiency potential of solar cells based on this material in terms of carrier
lifetime; and on the other hand, to explore, as a result of that, the adoption
of high-efficiency cell architectures by establishing an effective rear-side
passivation scheme for the implementation of passivated emitter rear contact
(PERC) devices. The carrier lifetime and the surface passivation efficacy are
investigated for different passivating layer configurations after single and
double P-diffusion gettering processes. Layer stacks consisting of Al2O3,
SiOxNy and a-SiNx:H capping overlayers have been optimized, on industrial size,
saw-damage-etched UMG wafers and results compared to those obtained using
reference iodine-ethanol (IE) passivation. Diagnosis based on minority carrier
lifetime and implied Voc (iVoc) measurements helped monitor the impact of
parameter optimization on wafer quality, particularly after firing processes.
Carrier lifetimes over 600 us at 10^15 cm-3 injection level as well as up to
790 us locally have been measured in UMG-Si wafers passivated with IE after a
Phosphorus Diffusion Gettering (PDG), demonstrating the suitability of the
material for high-efficiency cell architectures. Values higher than 300 us have
been obtained with Al2O3-based passivation layers for gettered UMG wafers, with
implied Voc values up to 710 mV. These record-breaking lifetimes and iVoc
figures obtained with p-type multicrystalline UMG-Si material demonstrate a
significant upgrading of its electronic quality by means of industry-scalable
technical processes.


title:
Points to Patches: Enabling the Use of Self-Attention for 3D Shape
  Recognition

abstract:
  While the Transformer architecture has become ubiquitous in the machine
learning field, its adaptation to 3D shape recognition is non-trivial. Due to
its quadratic computational complexity, the self-attention operator quickly
becomes inefficient as the set of input points grows larger. Furthermore, we
find that the attention mechanism struggles to find useful connections between
individual points on a global scale. In order to alleviate these problems, we
propose a two-stage Point Transformer-in-Transformer (Point-TnT) approach which
combines local and global attention mechanisms, enabling both individual points
and patches of points to attend to each other effectively. Experiments on shape
classification show that such an approach provides more useful features for
downstream tasks than the baseline Transformer, while also being more
computationally efficient. In addition, we also extend our method to feature
matching for scene reconstruction, showing that it can be used in conjunction
with existing scene reconstruction pipelines.


title:
Enhance Incomplete Utterance Restoration by Joint Learning Token
  Extraction and Text Generation

abstract:
  This paper introduces a model for incomplete utterance restoration (IUR).
Different from prior studies that only work on extraction or abstraction
datasets, we design a simple but effective model, working for both scenarios of
IUR. Our design simulates the nature of IUR, where omitted tokens from the
context contribute to restoration. From this, we construct a Picker that
identifies the omitted tokens. To support the picker, we design two label
creation methods (soft and hard labels), which can work in cases of no
annotation of the omitted tokens. The restoration is done by using a Generator
with the help of the Picker on joint learning. Promising results on four
benchmark datasets in extraction and abstraction scenarios show that our model
is better than the pretrained T5 and non-generative language model methods in
both rich and limited training data settings. The code will be also available
(https://github.com/shumpei19/jointiur).


title: Blockchain as an Enabler for Transfer Learning in Smart Environments

abstract:
  The knowledge, embodied in machine learning models for intelligent systems,
is commonly associated with time-consuming and costly processes such as
large-scale data collection, data labelling, network training, and fine-tuning
of models. Sharing and reuse of these elaborated models between intelligent
systems deployed in a different environment, which is known as transfer
learning, would facilitate the adoption of services for the users and
accelerates the uptake of intelligent systems in environments such as smart
building and smart city applications. In this context, the communication and
knowledge exchange between AI-enabled environments depend on a complicated
networks of systems, system of systems, digital assets, and their chain of
dependencies that hardly follows the centralized schema of traditional
information systems. Rather, it requires an adaptive decentralized system
architecture that is empowered by features such as data provenance, workflow
transparency, and validation of process participants. In this research, we
propose a decentralized and adaptive software framework based on blockchain and
knowledge graph technologies that supports the knowledge exchange and
interoperability between IoT-enabled environments, in a transparent and
trustworthy way.


title: Abundance of Second Order Topology in Two-dimensional Insulators

abstract:
  We have screened 71 two-dimensional (2D) materials with $C_3$ symmetry for
non-trivial second order topological order and find that 28 compounds exhibit
an obstructed atomic limit (OAL). In the case of $C_3$ symmetry, the second
order topology can be calculated from bulk symmetry indicator invariants, which
predict the value of fractional corner charges in symmetry conserving
nanoflakes. The procedure is exemplified by MoS$_2$ in the H-phase, which
constitutes a generic example of a 2D OAL material and the predicted fractional
corner charges is verified by direct calculations of nanoflakes with armchair
edges. We also determine the bulk topological polarization, which always lead
to gapless states at zigzag edges and thus deteriorates the concept of
fractional corner charges in nanoflakes with zigzag edges that are typically
more stable that armchair flakes. We then consider the case of TiCl$_2$, which
has vanishing polarization as well as an OAL and we verify that the edge states
of nanoflakes with zigzag edges may indeed by passivated such that the edges
remain insulating and the corner charges are well defined. For the 28 OAL
materials we find that 16 have vanishing polarization and these materials thus
constitute a promising starting point for experimental verification of second
order topology in a 2D material.


title:
Effects of isospin violation in the $e^+e^- \rightarrow B^{(*)}\bar
  B^{(*)}$ cross sections

abstract:
  Model estimates are obtained for the influence of Coulomb effects on the
ratio of the cross sections for the production of charged and neutral $B\bar B$
and $B^*\bar B^*$ pairs in $e^+e^-$ annihilation. It is shown that the
difference between the masses of charged and neutral mesons obtained under the
assumption that this ratio is constant on a scale of the order of the beam
energy spread can differ from the true one by $\delta M \sim 0.03\,\mbox{MeV}$
at the energy of $\Upsilon (5S)$ and by $\delta M \sim 0.4\,\mbox{MeV}$ at the
energy of $\Upsilon (4S)$. Thus, the errors given in the PDG for the difference
between the masses of charged and neutral $B$ mesons, based on the results
obtained at the energy of $\Upsilon (4S)$, are strongly underestimated. Similar
measurements at the energy of $\Upsilon (5S)$ will have an order of magnitude
smaller systematic shift for the mass difference. This circumstance should be
taken into account when planning future experiments at the $B$ factory in KEK.


title:
Multi-functional metasurface architecture for amplitude, polarization
  and wavefront control

abstract:
  Metasurfaces (MSs) have been utilized to manipulate different properties of
electromagnetic waves. By combining local control over the wave amplitude,
phase, and polarization into a single tunable structure, a multi-functional and
reconfigurable metasurface can be realized, capable of full control over
incident radiation. Here, we experimentally validate a multi-functional
metasurface architecture for the microwave regime, where in principle variable
loads are connected behind the backplane to reconfigurably shape the complex
surface impedance. As a proof-of-concept step, we fabricate several metasurface
instances with static loads in different configurations (surface mount
capacitors and resistors of different values in different connection
topologies) to validate the approach and showcase the different achievable
functionalities. Specifically, we show perfect absorption for oblique incidence
(both polarizations), broadband linear polarization conversion, and beam
splitting, demonstrating control over the amplitude, polarization state, and
wavefront, respectively. Measurements are performed in the 4-18 GHz range
inside an anechoic chamber and show good agreement with
theoretically-anticipated results. Our results clearly demonstrate the
practical potential of the proposed architecture for reconfigurable
electromagnetic wave manipulation.


title:
Mixing and flow transition in an optimized electrokinetic turbulent
  micromixer

abstract:
  Micromixer is a key element in lab on a chip for broad applications in the
analysis and measurement of chemistry and engineering. Previous investigations
reported electrokinetic (EK) turbulence could be realized in a Y-type
micromixer with a cross-sectional dimension of 100 ${\mu}$m order. Although the
ultrafast turbulent mixing can be generated at a bulk flow Reynolds number of
O(1), the micromixer has not been optimized. In this investigation, we
systematically investigated the influence of electric field intensity, AC
frequency, electric conductivity ratio, and channel width at the entrance on
the mixing effect and transition electric Rayleigh number in the "Y" type
electrokinetic micromixer. It is found the optimal mixing is realized in a 350
${\mu}$m wide micromixer, under 100 kHz and 1.14*10^5 V/m AC electric field,
with an electric conductivity ratio of 1:3000. Under the conditions, a maximum
degree of mixedness of 0.93 can be achieved at 84 ${\mu}$m from the entrance
and 100 ms. A further investigation of the critical electric field and the
critical electric Rayleigh number indicates the most unstable condition of EK
flow instability is inconsistent with that of the optimal mixing in EK
turbulence. To predict the evolution of EK flow under high $Ra_{e}$, it is
necessary to apply a computational turbulence model, instead of linear
instability analysis.


title: Threshold for Steiner triple systems

abstract:
  We prove that with high probability $\mathbb{G}^{(3)}(n,n^{-1+o(1)})$
contains a spanning Steiner triple system for $n\equiv 1,3\pmod{6}$,
establishing the exponent for the threshold probability for existence of a
Steiner triple system. We also prove the analogous theorem for Latin squares.
Our result follows from a novel bootstrapping scheme that utilizes iterative
absorption as well as the connection between thresholds and fractional
expectation-thresholds established by Frankston, Kahn, Narayanan, and Park.


title:
Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or
  PLDA?

abstract:
  The emergence of large-margin softmax cross-entropy losses in training deep
speaker embedding neural networks has triggered a gradual shift from parametric
back-ends to a simpler cosine similarity measure for speaker verification.
Popular parametric back-ends include the probabilistic linear discriminant
analysis (PLDA) and its variants. This paper investigates the properties of
margin-based cross-entropy losses leading to such a shift and aims to find
scoring back-ends best suited for speaker verification. In addition, we revisit
the pre-processing techniques which have been widely used in the past and
assess their effectiveness on large-margin embeddings. Experiments on the
state-of-the-art ECAPA-TDNN networks trained with various large-margin softmax
cross-entropy losses show a substantial increment in intra-speaker compactness
making the conventional PLDA superfluous. In this regard, we found that
constraining the within-speaker covariance matrix could improve the performance
of the PLDA. It is demonstrated through a series of experiments on the
VoxCeleb-1 and SITW core-core test sets with 40.8% equal error rate (EER)
reduction and 35.1% minimum detection cost (minDCF) reduction. It also
outperforms cosine scoring consistently with reductions in EER and minDCF by
10.9% and 4.9%, respectively.


title:
Local and non-local properties of the entanglement Hamiltonian for two
  disjoint intervals

abstract:
  We consider free-fermion chains in the ground state and the entanglement
Hamiltonian for a subsystem consisting of two separated intervals. In this
case, one has a peculiar long-range hopping between the intervals in addition
to the well-known and dominant short-range hopping. We show how the continuum
expressions can be recovered from the lattice results for general filling and
arbitrary intervals. We also discuss the closely related case of a single
interval located at a certain distance from the end of a semi-infinite chain
and the continuum limit for this problem. Finally, we show that for the double
interval in the continuum a commuting operator exists which can be used to find
the eigenstates.


title: The Sillwood Technologies System for the VoiceMOS Challenge 2022

abstract:
  In this paper we describe our entry for the VoiceMOS Challenge 2022 for both
the main and out-of-domain (OOD) track of the competition. Our system is based
on finetuning pre-trained self-supervised waveform prediction models, while
improving its generalisation ability through stochastic weight averaging.
Further, we use influence functions to identity possible low-quality data
within the training set to further increase our model's performance for the OOD
track. Our system ranked 5th and joint 7th for the main track and OOD track,
respectively.


title: Machine Learning architectures for price formation models

abstract:
  Here, we study machine learning (ML) architectures to solve a mean-field
games (MFGs) system arising in price formation models. We formulate a training
process that relies on a min-max characterization of the optimal control and
price variables. Our main theoretical contribution is the development of a
posteriori estimates as a tool to evaluate the convergence of the training
process. We illustrate our results with numerical experiments for a
linear-quadratic model.


title:
Disability prediction in multiple sclerosis using performance outcome
  measures and demographic data

abstract:
  Literature on machine learning for multiple sclerosis has primarily focused
on the use of neuroimaging data such as magnetic resonance imaging and clinical
laboratory tests for disease identification. However, studies have shown that
these modalities are not consistent with disease activity such as symptoms or
disease progression. Furthermore, the cost of collecting data from these
modalities is high, leading to scarce evaluations. In this work, we used
multi-dimensional, affordable, physical and smartphone-based performance
outcome measures (POM) in conjunction with demographic data to predict multiple
sclerosis disease progression. We performed a rigorous benchmarking exercise on
two datasets and present results across 13 clinically actionable prediction
endpoints and 6 machine learning models. To the best of our knowledge, our
results are the first to show that it is possible to predict disease
progression using POMs and demographic data in the context of both clinical
trials and smartphone-base studies by using two datasets. Moreover, we
investigate our models to understand the impact of different POMs and
demographics on model performance through feature ablation studies. We also
show that model performance is similar across different demographic subgroups
(based on age and sex). To enable this work, we developed an end-to-end
reusable pre-processing and machine learning framework which allows quicker
experimentation over disparate MS datasets.


title:
SOI-based micro-mechanical terahertz detector operating at
  room-temperature

abstract:
  We present a micro-mechanical terahertz (THz) detector fabricated on a
silicon on insulator (SOI) substrate and operating at room-temperature. The
device is based on a U-shaped cantilever of micrometric size, on top of which
two aluminum half-wave dipole antennas are deposited. This produces an
absorption extending over the $\sim 2-3.5$THz frequency range. Due to the
different thermal expansion coefficients of silicon and aluminum, the absorbed
radiation induces a deformation of the cantilever, which is read out optically
using a $1.5\mu$m laser diode. By illuminating the detector with an amplitude
modulated, 2.5 THz quantum cascade laser, we obtain, at room-temperature and
atmospheric pressure, a responsivity of $\sim 1.5 \times 10^{8}$pm/W for the
fundamental mechanical bending mode of the cantilever. This yields an
noise-equivalent-power of 20 nW/Hz$^{1/2}$ at 2.5THz. Finally, the low
mechanical quality factor of the mode grants a broad frequency response of
approximately 150kHz bandwidth, with a response time of $\sim 2.5\mu$s.


title: No eleventh conditional Ingleton inequality

abstract:
  A rational probability distribution on four binary random variables $X, Y, Z,
U$ is constructed which satisfies the conditional independence relations $[X
\mathrel{\text{$\perp\mkern-10mu\perp$}} Y]$, $[X
\mathrel{\text{$\perp\mkern-10mu\perp$}} Z \mid U]$, $[Y
\mathrel{\text{$\perp\mkern-10mu\perp$}} U \mid Z]$ and $[Z
\mathrel{\text{$\perp\mkern-10mu\perp$}} U \mid XY]$ but whose entropy vector
violates the Ingleton inequality. This settles a recent question of Studen\'y
(IEEE Trans. Inf. Theory vol. 67, no. 11) and shows that there are, up to
symmetry, precisely ten inclusion-minimal sets of conditional independence
assumptions on four discrete random variables which make the Ingleton
inequality hold.


title: FashionCLIP: Connecting Language and Images for Product Representations

abstract:
  The steady rise of online shopping goes hand in hand with the development of
increasingly complex ML and NLP models. While most use cases are cast as
specialized supervised learning problems, we argue that practitioners would
greatly benefit from more transferable representations of products. In this
work, we build on recent developments in contrastive learning to train
FashionCLIP, a CLIP-like model for the fashion industry. We showcase its
capabilities for retrieval, classification and grounding, and release our model
and code to the community.


title: Gradient dynamics in reinforcement learning

abstract:
  Despite the success achieved by the analysis of supervised learning
algorithms in the framework of statistical mechanics, reinforcement learning
has remained largely untouched. Here we move towards closing the gap by
analyzing the dynamics of the policy gradient algorithm. For a convex problem,
we show that it obeys a drift-diffusion motion with coeffcients tuned by
learning rate. Furthermore, we propose a mapping between a non-convex
reinforcement learning problem and a disordered system. This mapping enables us
to show how the learning rate acts as an effective temperature and thus is
capable of smoothing rough landscapes, corroborating what is displayed by the
drift-diffusive description and paving the way for physics-inspired algorithmic
optimization based on annealing procedures in disordered systems.


title:
A General Framework for Hierarchical Redundancy Resolution Under
  Arbitrary Constraints

abstract:
  The increasing interest in autonomous robots with a high number of degrees of
freedom for industrial applications and service robotics demands control
algorithms to handle multiple tasks as well as hard constraints efficiently.
This paper presents a general framework in which both kinematic (velocity- or
acceleration-based) and dynamic (torque-based) control of redundant robots are
handled in a unified fashion. The framework allows for the specification of
redundancy resolution problems featuring a hierarchy of arbitrary (equality and
inequality) constraints, arbitrary weighting of the control effort in the cost
function and an additional input used to optimize possibly remaining
redundancy. To solve such problems, a generalization of the Saturation in the
Null Space (SNS) algorithm is introduced, which extends the original method
according to the features required by our general control framework. Variants
of the developed algorithm are presented, which ensure both efficient
computation and optimality of the solution. Experiments on a KUKA LBRiiwa
robotic arm, as well as simulations with a highly redundant mobile manipulator
are reported.


title: Measurement and characterization of DNS over HTTPS traffic

abstract:
  Domain name system communication may provide sensitive information on users'
Internet activity. DNS-over-TLS and DNS-over-HTTPS are proposals aiming at
increasing the privacy of Internet end users. In this paper we present an
overview of the current state in the deployment of DNS-over-HTTPS (DoH)
implementations complemented by measurements of DoH traffic in terms of the
incurred overhead and the possibility of the DoH detection based on the
inherent characteristics of the communication patterns.