title: UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022

abstract:
  We present the UTokyo-SaruLab mean opinion score (MOS) prediction system
submitted to VoiceMOS Challenge 2022. The challenge is to predict the MOS
values of speech samples collected from previous Blizzard Challenges and Voice
Conversion Challenges for two tracks: a main track for in-domain prediction and
an out-of-domain (OOD) track for which there is less labeled data from
different listening tests. Our system is based on ensemble learning of strong
and weak learners. Strong learners incorporate several improvements to the
previous fine-tuning models of self-supervised learning (SSL) models, while
weak learners use basic machine-learning methods to predict scores from SSL
features. In the Challenge, our system had the highest score on several metrics
for both the main and OOD tracks. In addition, we conducted ablation studies to
investigate the effectiveness of our proposed methods.


title:
An analysis of the most distant catalogued open clusters -- Re-assessing
  fundamental parameters with Gaia EDR3 and $\texttt{ASteCA}$

abstract:
  Several studies have been presented in the last few years applying some kind
of automatic processing of data to estimate the fundamental parameters of open
clusters. These parameters are later on employed in larger scale analyses, for
example the structure of the Galaxy's spiral arms. The distance is one of the
more straightforward parameters to estimate, yet enormous differences can still
be found among published data. This is particularly true for open clusters
located more than a few kpc away. We cross-matched several published catalogues
and selected the twenty-five most distant open clusters ($>$9000 pc). We then
performed a detailed analysis of their fundamental parameters, with emphasis on
their distances, to determine the agreement between catalogues and our
estimates.} Photometric and astrometric data from the Gaia EDR3 survey was
employed. The data was processed with our own membership analysis code
(pyUPMASK), and our package for automatic fundamental cluster's parameters
estimation ($\texttt{ASteCA}$). We find differences in the estimated distances
of up to several kpc between our results and those catalogued, even for the
catalogues that show the best matches with $\texttt{ASteCA}$ values. Large
differences are also found for the age estimates. As a by-product of the
analysis we find that vd Bergh-Hagen 176 could be the open cluster with the
largest heliocentric distance catalogued to date. Caution is thus strongly
recommended when using catalogued parameters of open clusters to infer
large-scale properties of the Galaxy, particularly for those located more than
a few kpc away.


title: Simple dominance of fixed priority top trading cycles

abstract:
  We consider assignment problems where agents are to be assigned at most one
indivisible object and monetary transfers are not allowed. We study the
implementation of fixed priority top trading cycles (FPTTC) rules via simply
dominant mechanisms, and provide characterizations of all such FPTTC rules. We
further introduce the notion of simple strategy-proofness to resolve the issue
with agents being concerned about having time-inconsistent preferences, and
discuss its relation with simple dominance.


title: Detecting Anchors' Opinion in Hinghlish News Delivery

abstract:
  Humans like to express their opinions and crave the opinions of others.
Mining and detecting opinions from various sources are beneficial to
individuals, organisations, and even governments. One such organisation is news
media, where a general norm is not to showcase opinions from their side.
Anchors are the face of the digital media, and it is required for them not to
be opinionated. However, at times, they diverge from the accepted norm and
insert their opinions into otherwise straightforward news reports, either
purposefully or unintentionally. This is primarily seen in debates as it
requires the anchors to be spontaneous, thus making them vulnerable to add
their opinions. The consequence of such mishappening might lead to biased news
or even supporting a certain agenda at the worst. To this end, we propose a
novel task of anchors' opinion detection in debates. We curate code-mixed news
debates and develop the ODIN dataset. A total of 2054 anchors' utterances in
the dataset are marked as opinionated or non-opinionated. Lastly, we propose
DetONADe, an interactive attention-based framework for classifying anchors'
utterances and obtain the best weighted-F1 score of 0.703. A thorough analysis
and evaluation show many interesting patterns in the dataset and predictions.


title:
Universality-of-Clock-Rates Test using Atom Interferometry with $T^{3}$
  Scaling

abstract:
  We propose a competitive quantum test of the universality of clock rates that
depends on the proper time of a freely-falling particle, scaling cubic with the
laboratory time. In contrast to current tests with fountain clocks, our
proposed atom-interferometric scheme can be made robust against initial
conditions and recoil effects, making optical frequencies accessible even for
long interferometer durations. We study the influence of parasitic effects and
discuss implementations with strontium isotopes that may even outperform
current tests with fountain clocks.


title:
An integral condition involving $\overline\partial$-harmonic
  $(0,1)$-forms

abstract:
  We study compact almost complex manifolds admitting a Hermitian metric
satisfying an integral condition involving $\overline \partial$-harmonic
$(0,1)$-forms. We prove that this integral condition is automatically
satisfied, if the Hermitian metric on the compact almost complex manifold is
strongly Gauduchon. Under the further assumption that the almost complex
structure is integrable, we show that the integral condition for a Gauduchon
metric is equivalent to be strongly Gauduchon. In particular, a compact complex
surface with a Gauduchon metric satisfying the integral condition is
automatically K\"ahler. If we drop the integrability assumption on the complex
structure, we show that there exists a compact almost complex $4$-dimensional
manifold with a Hermitian metric satisfying the integral condition, but which
does not admit any compatible almost-K\"ahler metric.


title: Quantum Field Theory Anomalies in Condensed Matter Physics

abstract:
  We give a pedagogical introduction to quantum anomalies, how they are
calculated using various methods, and why they are important in condensed
matter theory. We discuss axial, chiral, and gravitational anomalies as well as
global anomalies. We illustrate the theory with examples such as quantum Hall
liquids, Fermi liquids, Weyl semi-metals, topological insulators and
topological superconductors. The required background is basic knowledge of
quantum field theory, including fermions and gauge fields, and some familiarity
with path integral and functional methods. Some knowledge of topological phases
of matter is helpful, but not necessary.


title: Systematic Unsupervised Recycled Field-Programmable Gate Array Detection

abstract:
  With the expansion of the semiconductor supply chain, the use of recycled
field-programmable gate arrays (FPGAs) has become a serious concern. Several
methods for detecting recycled FPGAs by analyzing the ring oscillator (RO)
frequencies have been proposed; however, most assume the known fresh FPGAs
(KFFs) as the training data in machine-learning-based classification. In this
study, we propose a novel recycled FPGA detection method based on an
unsupervised anomaly detection scheme when there are few or no KFFs available.
As the RO frequencies in the neighboring logic blocks on an FPGA are similar
because of systematic process variation, our method compares the RO frequencies
and does not require KFFs. The proposed method efficiently identifies recycled
FPGAs through outlier detection using direct density ratio estimation.
Experiments using Xilinx Artix-7 FPGAs demonstrate that the proposed method
successfully distinguishes recycled FPGAs from 35 fresh FPGAs. In contrast, a
conventional recycled FPGA detection method results in certain
misclassification.


title:
Some multifaceted aspects of mathematical physics, our common
  denominator with Elliott Lieb

abstract:
  Mathematical physics has many facets, of which we shall briefly give a (very
partial) description, centered around those of main interest for Elliott and us
(Moshe Flato and I). In our case these aspects had as a corollary a variety of
"parascientific activities", in particular the foundation of IAMP (the
International Association of Mathematical Physics) and of the journal LMP
(Letters in Mathematical Physics), both of which were strongly impacted by
Elliott, and Elliott's long insistence that publishers do not demand "copyright
transfer" as a precondition for publication but are satisfied with "consent to
publish", which is increasingly becoming standard. Since this article is
testimony to the huge scientific impact of Elliott, the latter intertwined
aspects constitute the core of the present contribution.

categories: math.HO math-ph math.MP physics.hist-ph physics.soc-ph quant-ph


title:
Upper bound on delta-crossing and tabulation of knots up to four
  delta-crossings

abstract:
  We strengthened the known upper bound on the delta-crossing number of knots
in therms of the triple-crossing number with additional assumptions. We also
prove that we can always find at least one tangle from the set of four tangles,
in any triple-crossing projections of any non-trivial knot or non-split link.
In the last section, we enumerate and generate tables of minimal delta-diagrams
for all prime knots up to the delta-crossing number equal to four.


title: Positive and Negative Critiquing for VAE-based Recommenders

abstract:
  Providing explanations for recommended items allows users to refine the
recommendations by critiquing parts of the explanations. As a result of
revisiting critiquing from the perspective of multimodal generative models,
recent work has proposed M&Ms-VAE, which achieves state-of-the-art performance
in terms of recommendation, explanation, and critiquing. M&Ms-VAE and similar
models allow users to negatively critique (i.e., explicitly disagree). However,
they share a significant drawback: users cannot positively critique (i.e.,
highlight a desired feature). We address this deficiency with M&Ms-VAE+, an
extension of M&Ms-VAE that enables positive and negative critiquing. In
addition to modeling users' interactions and keyphrase-usage preferences, we
model their keyphrase-usage dislikes. Moreover, we design a novel critiquing
module that is trained in a self-supervised fashion. Our experiments on two
datasets show that M&Ms-VAE+ matches or exceeds M&Ms-VAE in recommendation and
explanation performance. Furthermore, our results demonstrate that representing
positive and negative critiques differently enables M&Ms-VAE+ to significantly
outperform M&Ms-VAE and other models in positive and negative multi-step
critiquing.


title: Leveraging Equivariant Features for Absolute Pose Regression

abstract:
  While end-to-end approaches have achieved state-of-the-art performance in
many perception tasks, they are not yet able to compete with 3D geometry-based
methods in pose estimation. Moreover, absolute pose regression has been shown
to be more related to image retrieval. As a result, we hypothesize that the
statistical features learned by classical Convolutional Neural Networks do not
carry enough geometric information to reliably solve this inherently geometric
task. In this paper, we demonstrate how a translation and rotation equivariant
Convolutional Neural Network directly induces representations of camera motions
into the feature space. We then show that this geometric property allows for
implicitly augmenting the training data under a whole group of image
plane-preserving transformations. Therefore, we argue that directly learning
equivariant features is preferable than learning data-intensive intermediate
representations. Comprehensive experimental validation demonstrates that our
lightweight model outperforms existing ones on standard datasets.


title:
Joint Learning of Feature Extraction and Cost Aggregation for Semantic
  Correspondence

abstract:
  Establishing dense correspondences across semantically similar images is one
of the challenging tasks due to the significant intra-class variations and
background clutters. To solve these problems, numerous methods have been
proposed, focused on learning feature extractor or cost aggregation
independently, which yields sub-optimal performance. In this paper, we propose
a novel framework for jointly learning feature extraction and cost aggregation
for semantic correspondence. By exploiting the pseudo labels from each module,
the networks consisting of feature extraction and cost aggregation modules are
simultaneously learned in a boosting fashion. Moreover, to ignore unreliable
pseudo labels, we present a confidence-aware contrastive loss function for
learning the networks in a weakly-supervised manner. We demonstrate our
competitive results on standard benchmarks for semantic correspondence.


title:
SMORS: A soft multirotor UAV for multimodal locomotion and robust
  interaction

abstract:
  We present SMORS, the first Soft fully actuated MultirOtoR System for
multimodal locomotion. Unlike conventional hexarotors, SMORS is equipped with
three rigid and three continuously soft arms, with each arm hosting a
propeller. We create a bridge between the fields of soft and aerial robotics by
mechanically coupling the actuation of a fully actuated flying platform with
the actuation of a soft robotic manipulator. Each rotor is slightly tilted,
allowing for full actuation of the platform. The soft components combined with
the platform's full actuation allow for a robust interaction, in the form of
efficient multimodal locomotion. In this work, we present the dynamical model
of the platform, derive a closed-loop control, and present simulation results
fortifying the robustness of the platform under a jumping-flying maneuver. We
demonstrate in simulations that our multimodal locomotion approach can be more
energy-efficient than the flight with a hexarotor.


title:
Disentangled Speech Representation Learning Based on Factorized
  Hierarchical Variational Autoencoder with Self-Supervised Objective

abstract:
  Disentangled representation learning aims to extract explanatory features or
factors and retain salient information. Factorized hierarchical variational
autoencoder (FHVAE) presents a way to disentangle a speech signal into
sequential-level and segmental-level features, which represent speaker identity
and speech content information, respectively. As a self-supervised objective,
autoregressive predictive coding (APC), on the other hand, has been used in
extracting meaningful and transferable speech features for multiple downstream
tasks. Inspired by the success of these two representation learning methods,
this paper proposes to integrate the APC objective into the FHVAE framework
aiming at benefiting from the additional self-supervision target. The main
proposed method requires neither more training data nor more computational cost
at test time, but obtains improved meaningful representations while maintaining
disentanglement. The experiments were conducted on the TIMIT dataset. Results
demonstrate that FHVAE equipped with the additional self-supervised objective
is able to learn features providing superior performance for tasks including
speech recognition and speaker recognition. Furthermore, voice conversion, as
one application of disentangled representation learning, has been applied and
evaluated. The results show performance similar to baseline of the new
framework on voice conversion.


title:
Inverse uncertainty quantification of a mechanical model of arterial
  tissue with surrogate modeling

abstract:
  Disorders of coronary arteries lead to severe health problems such as
atherosclerosis, angina, heart attack and even death. Considering the clinical
significance of coronary arteries, an efficient computer model is a vital step
towards tissue engineering, enhancing the research of coronary diseases, and
developing medical treatment and interventional tools. In this work, we apply
inverse uncertainty quantification to a microscale agent-based arterial tissue
model, a component of the 3D multiscale model of in-stent restenosis (ISR3D).
IUQ provides calibration of the arterial tissue model to achieve realistic
mechanical behaviour in line with the experimental data measured from the
tissue's macroscopic behaviour. Bayesian calibration with bias term correction
is applied as an IUQ technique to reduce the uncertainty of unknown polynomial
coefficients of the attractive force function and achieve agreement with the
experimental data based on the uniaxial strain tests of arterial tissue. Due to
the high computational costs of the ISR3D model, the Gaussian process (GP)
regression surrogate model is introduced to ensure the feasibility of the IUQ
computations. The result is an IUQ methodology to calibrate a model with
uncertain parameters and a microscale agent-based model of arterial tissue,
which produces mechanical behaviour in line with the experimental data.

categories: physics.med-ph cs.CE physics.bio-ph stat.AP


title: A direct proof of the irrationality of $\tan^2(r \pi)$

abstract:
  Given a rational number $r$ such that $2r$ is not an integer, we prove that
$\tan^2(r\pi)$ is irrational unless it is equal to $0$, $1$, $3$ or
$\frac{1}{3}$, using only basic trigonometry and the Rational Root Theorem.
Moreover, we deduce that $\tan(r\pi)$, $\cos^2(r\pi)$ ans $\cos(r\pi)$ are
irrational numbers except in usual cases.


title: Hybrid Predictive Coding: Inferring, Fast and Slow

abstract:
  Predictive coding is an influential model of cortical neural activity. It
proposes that perceptual beliefs are furnished by sequentially minimising
"prediction errors" - the differences between predicted and observed data.
Implicit in this proposal is the idea that perception requires multiple cycles
of neural activity. This is at odds with evidence that several aspects of
visual perception - including complex forms of object recognition - arise from
an initial "feedforward sweep" that occurs on fast timescales which preclude
substantial recurrent activity. Here, we propose that the feedforward sweep can
be understood as performing amortized inference and recurrent processing can be
understood as performing iterative inference. We propose a hybrid predictive
coding network that combines both iterative and amortized inference in a
principled manner by describing both in terms of a dual optimization of a
single objective function. We show that the resulting scheme can be implemented
in a biologically plausible neural architecture that approximates Bayesian
inference utilising local Hebbian update rules. We demonstrate that our hybrid
predictive coding model combines the benefits of both amortized and iterative
inference -- obtaining rapid and computationally cheap perceptual inference for
familiar data while maintaining the context-sensitivity, precision, and sample
efficiency of iterative inference schemes. Moreover, we show how our model is
inherently sensitive to its uncertainty and adaptively balances iterative and
amortized inference to obtain accurate beliefs using minimum computational
expense. Hybrid predictive coding offers a new perspective on the functional
relevance of the feedforward and recurrent activity observed during visual
perception and offers novel insights into distinct aspects of visual
phenomenology.


title: Semiparametric Approach to Estimation of Marginal and Quantile Effects

abstract:
  We consider a semiparametric generalized linear model and study estimation of
both marginal and quantile effects in this model. We propose an approximate
maximum likelihood estimator, and rigorously establish the consistency, the
asymptotic normality, and the semiparametric efficiency of our method in both
the marginal effect and the quantile effect estimation. Simulation studies are
conducted to illustrate the finite sample performance, and we apply the new
tool to analyze a Swiss non-labor income data and discover a new interesting
predictor.


title:
Overall Complexity Certification of a Standard Branch and Bound Method
  for Mixed-Integer Quadratic Programming

abstract:
  This paper presents a method to certify the computational complexity of a
standard Branch and Bound method for solving Mixed-Integer Quadratic
Programming (MIQP) problems defined as instances of a multi-parametric MIQP.
Beyond previous work, not only the size of the binary search tree is
considered, but also the exact complexity of solving the relaxations in the
nodes by using recent result from exact complexity certification of active-set
QP methods. With the algorithm proposed in this paper, a total worst-case
number of QP iterations to be performed in order to solve the MIQP problem can
be determined as a function of the parameter in the problem. An important
application of the proposed method is Model Predictive Control for hybrid
systems, that can be formulated as an MIQP that has to be solved in real-time.
The usefulness of the proposed method is successfully illustrated in numerical
examples.


title:
AILTTS: Adversarial Learning of Intermediate Acoustic Feature for
  End-to-End Lightweight Text-to-Speech

abstract:
  The quality of end-to-end neural text-to-speech (TTS) systems highly depends
on the reliable estimation of intermediate acoustic features from text inputs.
To reduce the complexity of the speech generation process, several
non-autoregressive TTS systems directly find a mapping relationship between
text and waveforms. However, the generation quality of these system is
unsatisfactory due to the difficulty in modeling the dynamic nature of prosodic
information. In this paper, we propose an effective prosody predictor that
successfully replicates the characteristics of prosodic features extracted from
mel-spectrograms. Specifically, we introduce a generative model-based
conditional discriminator to enable the estimated embeddings have highly
informative prosodic features, which significantly enhances the expressiveness
of generated speech. Since the estimated embeddings obtained by the proposed
method are highly correlated with acoustic features, the time-alignment of
input texts and intermediate features is greatly simplified, which results in
faster convergence. Our proposed model outperforms several publicly available
models based on various objective and subjective evaluation metrics, even using
a relatively small number of parameters.


title:
Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture
  for Complex NER

abstract:
  We investigate the task of complex NER for the English language. The task is
non-trivial due to the semantic ambiguity of the textual structure and the
rarity of occurrence of such entities in the prevalent literature. Using
pre-trained language models such as BERT, we obtain a competitive performance
on this task. We qualitatively analyze the performance of multiple
architectures for this task. All our models are able to outperform the baseline
by a significant margin. Our best performing model beats the baseline F1-score
by over 9%.


title: Multi-View Transformer for 3D Visual Grounding

abstract:
  The 3D visual grounding task aims to ground a natural language description to
the targeted object in a 3D scene, which is usually represented in 3D point
clouds. Previous works studied visual grounding under specific views. The
vision-language correspondence learned by this way can easily fail once the
view changes. In this paper, we propose a Multi-View Transformer (MVT) for 3D
visual grounding. We project the 3D scene to a multi-view space, in which the
position information of the 3D scene under different views are modeled
simultaneously and aggregated together. The multi-view space enables the
network to learn a more robust multi-modal representation for 3D visual
grounding and eliminates the dependence on specific views. Extensive
experiments show that our approach significantly outperforms all
state-of-the-art methods. Specifically, on Nr3D and Sr3D datasets, our method
outperforms the best competitor by 11.2% and 7.1% and even surpasses recent
work with extra 2D assistance by 5.9% and 6.6%. Our code is available at
https://github.com/sega-hsj/MVT-3DVG.


title: A gate-tunable graphene Josephson parametric amplifier

abstract:
  With a large portfolio of elemental quantum components, superconducting
quantum circuits have contributed to dramatic advances in microwave quantum
optics. Of these elements, quantum-limited parametric amplifiers have proven to
be essential for low noise readout of quantum systems whose energy range is
intrinsically low (tens of $\mu$eV ). They are also used to generate non
classical states of light that can be a resource for quantum enhanced
detection. Superconducting parametric amplifiers, like quantum bits, typically
utilize a Josephson junction as a source of magnetically tunable and
dissipation-free nonlinearity. In recent years, efforts have been made to
introduce semiconductor weak links as electrically tunable nonlinear elements,
with demonstrations of microwave resonators and quantum bits using
semiconductor nanowires, a two dimensional electron gas, carbon nanotubes and
graphene. However, given the challenge of balancing nonlinearity, dissipation,
participation, and energy scale, parametric amplifiers have not yet been
implemented with a semiconductor weak link. Here we demonstrate a parametric
amplifier leveraging a graphene Josephson junction and show that its working
frequency is widely tunable with a gate voltage. We report gain exceeding 20 dB
and noise performance close to the standard quantum limit. Our results complete
the toolset for electrically tunable superconducting quantum circuits and offer
new opportunities for the development of quantum technologies such as quantum
computing, quantum sensing and fundamental science.


title: On the Idempotent Conjecture for Sidki Doubles

abstract:
  Let $G$ be a group and $X(G)$ its Sidki Double. The idempotent conjecture
says that there should be no non-trivial idempotent in the complex group ring
of a torsion-free group. We investigate this conjecture for the Sidki double of
a torsion-free group, and obtain a partial result.


title:
A note on adiabatic time evolution and quasi-static processes in
  translation-invariant quantum systems

abstract:
  We study the slowly varying, non-autonomous quantum dynamics of a translation
invariant spin or fermion system on the lattice $\mathbb Z^d$. This system is
assumed to be initially in thermal equilibrium, and we consider realizations of
quasi-static processes in the adiabatic limit. By combining the Gibbs
variational principle with the notion of quantum weak Gibbs states introduced
in [Jak\v{s}i\'c, Pillet, Tauber, arXiv:2204.00440], we establish a number of
general structural results regarding such realizations. In particular, we show
that such a quasi-static process is incompatible with the property of approach
to equilibrium studied in this previous work.

categories: math-ph cond-mat.stat-mech math.MP quant-ph


title: The Hilbert reciprocity law on 3-manifolds

abstract:
  Based on our homological idelic class field theory, we formulate an analogue
of the Hilbert reciprocity law for a rational homology 3-sphere endowed with an
infinite link, in the spirit of arithmetic topology; We regard the intersection
form on the unitary normal bundle of each knot as an analogue of the Hilbert
symbol at each prime ideal to formulate the Hilbert reciprocity law, ensuring
that cyclic covers of links are analogues of Kummer extensions.


title:
Towards Power-Efficient Design of Myoelectric Controller based on
  Evolutionary Computation

abstract:
  Myoelectric pattern recognition is one of the important aspects in the design
of the control strategy for various applications including upper-limb
prostheses and bio-robotic hand movement systems. The current work has proposed
an approach to design an energy-efficient EMG-based controller by considering a
supervised learning framework using a kernelized SVM classifier for decoding
the information of surface electromyography (sEMG) signals to infer the
underlying muscle movements. In order to achieve the optimized performance of
the EMG-based controller, our main strategy of classifier design is to reduce
the false movements of the overall system (when the EMG-based controller is at
the `Rest' position). To this end, unlike the traditional single training
objective of soft margin kernelized SVM, we have formulated the training
algorithm of the proposed supervised learning system as a general constrained
multi-objective optimization problem. An elitist multi-objective evolutionary
algorithm $-$ the non-dominated sorting genetic algorithm II (NSGA-II) has been
used for the tuning of SVM hyperparameters. We have presented the experimental
results by performing the experiments on a dataset consisting of the sEMG
signals collected from eleven subjects at five different upper limb positions.
It is evident from the presented result that the proposed approach provides
much more flexibility to the designer in selecting the parameters of the
classifier to optimize the energy efficiency of the EMG-based controller.


title:
A New Correction to the Rytov Approximation for Strongly Scattering
  Lossy Media

abstract:
  We propose a correction to the conventional Rytov approximation (RA) and
investigate its performance for predicting wave scattering under strong
scattering conditions. An important motivation for the correction and
investigation is to help in the development of better models for inverse
scattering. The correction is based upon incorporating the high frequency
theory of inhomogeneous wave propagation for lossy media into the RA
formulation. We denote the technique as the extended Rytov approximation for
lossy media (xRA-LM). xRA-LM significantly improves upon existing non-iterative
linear scattering approximations such as RA and the Born approximation (BA) by
providing a validity range for the permittivity of the objects of up to 50
times greater than RA. We demonstrate the technique by providing results for
predicting wave scattering from piece-wise homogeneous scatterers in a
two-dimensional (2D) region. Numerical investigation of the performance of
xRA-LM for solving direct problem show that xRA-LM can accurately predict wave
scattering by electrically large, low-loss scatterers with high complex
permittivity ($\epsilon_r> 50+5j$). To the best of our knowledge, this is the
first non-iterative, linear approximate wave scattering model which has a large
validity range in terms of both permittivity and electrical size.

categories: physics.comp-ph eess.SP physics.app-ph physics.optics


title:
Vision Transformer Equipped with Neural Resizer on Facial Expression
  Recognition Task

abstract:
  When it comes to wild conditions, Facial Expression Recognition is often
challenged with low-quality data and imbalanced, ambiguous labels. This field
has much benefited from CNN based approaches; however, CNN models have
structural limitation to see the facial regions in distant. As a remedy,
Transformer has been introduced to vision fields with global receptive field,
but requires adjusting input spatial size to the pretrained models to enjoy
their strong inductive bias at hands. We herein raise a question whether using
the deterministic interpolation method is enough to feed low-resolution data to
Transformer. In this work, we propose a novel training framework, Neural
Resizer, to support Transformer by compensating information and downscaling in
a data-driven manner trained with loss function balancing the noisiness and
imbalance. Experiments show our Neural Resizer with F-PDLS loss function
improves the performance with Transformer variants in general and nearly
achieves the state-of-the-art performance.


title:
Periodic solutions of the non-chiral intermediate Heisenberg ferromagnet
  equation described by elliptic spin Calogero-Moser dynamics

abstract:
  We present a class of periodic solutions of the non-chiral intermediate
Heisenberg ferromagnet (ncIHF) equation, which was recently introduced by the
authors together with Langmann as a classical, continuum limit of an
Inozemtsev-type spin chain. These exact analytic solutions are constructed via
a spin-pole ansatz written in terms of certain elliptic functions. The
dynamical parameters in our solutions solve an elliptic spin Calogero-Moser
(CM) system subject to certain constraints. In the course of our construction,
we establish a novel B\"acklund transformation for this constrained elliptic
spin CM system.


title: Optimising Communication Overhead in Federated Learning Using NSGA-II

abstract:
  Federated learning is a training paradigm according to which a server-based
model is cooperatively trained using local models running on edge devices and
ensuring data privacy. These devices exchange information that induces a
substantial communication load, which jeopardises the functioning efficiency.
The difficulty of reducing this overhead stands in achieving this without
decreasing the model's efficiency (contradictory relation). To do so, many
works investigated the compression of the pre/mid/post-trained models and the
communication rounds, separately, although they jointly contribute to the
communication overload. Our work aims at optimising communication overhead in
federated learning by (I) modelling it as a multi-objective problem and (II)
applying a multi-objective optimization algorithm (NSGA-II) to solve it. To the
best of the author's knowledge, this is the first work that \texttt{(I)}
explores the add-in that evolutionary computation could bring for solving such
a problem, and \texttt{(II)} considers both the neuron and devices features
together. We perform the experimentation by simulating a server/client
architecture with 4 slaves. We investigate both convolutional and
fully-connected neural networks with 12 and 3 layers, 887,530 and 33,400
weights, respectively. We conducted the validation on the \texttt{MNIST}
dataset containing 70,000 images. The experiments have shown that our proposal
could reduce communication by 99% and maintain an accuracy equal to the one
obtained by the FedAvg Algorithm that uses 100% of communications.


title: A Non Relativistic Argument Against Continuous Trajectories of Particles

abstract:
  A thought experiment is described and the probability of a particular type of
results is predicted according to the quantum formalism. Then, the assumption
is made that there exists a particle that travels from the source to one of the
detectors, along a continuous trajectory. A contradiction appears: for agreeing
with the quantum prediction, the particle has to land at once on two space
separated detectors. Therefore, the trajectory of the particle, if it exists,
cannot be continuous.


title:
Smoothing and differentiation of data by Tikhonov and fractional
  derivative tools, applied to surface-enhanced Raman scattering (SERS) spectra
  of crystal violet dye

abstract:
  All signals obtained as instrumental response of analytical apparatus are
affected by noise, as in Raman spectroscopy. Whereas Raman scattering is an
inherently weak process, the noise background can lead to misinterpretations.
Although surface amplification of the Raman signal using metallic nanoparticles
has been a strategy employed to partially solve the signal-to-noise problem,
the pre-processing of Raman spectral data through the use of mathematical
filters has become an integral part of Raman spectroscopy analysis. In this
paper, a Tikhonov modified method to remove random noise in experimental data
is presented. In order to refine and improve the Tikhonov method as filter, the
proposed method includes Euclidean norm of the fractional-order derivative of
the solution as an additional criterion in Tikhonov function. In the strategy
used here, the solution depends on the regularization parameter, $\lambda$, and
on the fractional derivative order, $\alpha$. As will be demonstrated, with the
algorithm presented here, it is possible to obtain a noise free spectrum
without affecting the fidelity of the molecular signal. In this alternative,
the fractional derivative works as a fine control parameter for the usual
Tikhonov method. The proposed method was applied to simulated data and to
surface-enhanced Raman scattering (SERS) spectra of crystal violet dye in Ag
nanoparticles colloidal dispersion.


title:
Model Selection using Baryon Acoustic Oscillations in the Final SDSS-IV
  Release

abstract:
  The baryon acoustic oscillation (BAO) peak, seen in the cosmic matter
distribution at redshifts up to ~3.5, reflects the continued expansion of the
sonic horizon first identified in temperature anisotropies of the cosmic
microwave background. The BAO peak position can now be measured to better than
~1% accuracy using galaxies, and ~1.4-1.6% precision with Ly-alpha forrests and
the clustering of quasars. In conjunction with the Alcock-Paczy\'nski (AP)
effect, which arises from the changing ratio of angular to spatial/redshift
size of (presumed) spherically-symmetric source distributions with distance,
the BAO measurement is viewed as one of the most powerful tools to use in
assessing the geometry of the Universe. In this paper, we employ five BAO peak
measurements from the final release of the Sloan Digital Sky Survey IV, at
average redshifts <z>=0.38, 0.51, 0.70, 1.48 and 2.33, to carry out a direct
head-to-head comparison of the standard model, Lambda-CDM, and one of its
principal competitors, known as the R_h=ct universe. For completeness, we
complement the AP diagnostic with a volume-averaged distance probe that assumes
a constant comoving distance scale r_d. Both probes are free of uncertain
parameters, such as the Hubble constant, and are therefore ideally suited for
this kind of model selection. We find that R_h=ct is favored by these
measurements over the standard model based solely on the AP effect, with a
likelihood ~75% versus ~25%, while Planck-Lambda-CDM is favored over R_h=ct
based solely on the volume-averaged distance probe, with a likelihood ~80%
versus ~20%. A joint analysis using both probes produces an inconclusive
outcome, yielding comparable likelihoods to both models. We are therefore not
able to confirm with this work that the BAO data, on their own, support an
accelerating Universe.


title: Electric Charge Mutation by Four Vector Bosons

abstract:
  A general theory of electric charge is proposed. It is based on two
phenomenologies. Electric charge mutation and conservation law. Three charges
$\{ +, - ,0\}$ transformations physics succeeds. Quantum field theory underlies
corresponding creations and annihilation. A potential field's quadruplet is
ruled. Microscopic electromagnetism is processed by four vectors bosons
intermediations. The electromagnetism closure is accomplished. The quadruplet
$A_{\mu I} \equiv \{ A_\mu, U_\mu, V_\mu^\pm\}$ completeness introduces the
most generic EM energy flux between electric charges. Charge mutation includes
that besides usual photon, EM phenomena is enlarged by massive and charged
photons. Charge conservation associates these four vector fields. Electric
charge symmetry, extends EM for an abelian symmetry $U_{Q} \equiv U(1) \times
SO(2)_{global}$. A new EM Lagrangian beyond Maxwell results. A symmetry
equation for electric charge is established through Noether theorem. The
electric charge transfer physics extends the EM phenomenon. Nonlinear
Electromagnetic fields modified electric charge symmetry, new EM regimes.
Potential fields become a physical entity producing conglomerates, collective
fields, mass, sources, charges, monopoles, forces. EM features ruled from an
extended electric charge abelian symmetry. Systemic, nonlinear, neutral,
spintronics, photonics, electroweak EM regimes are constituted.


title:
The study of conformal geometry and its exact solution of the geodesic
  deviation equation

abstract:
  In this paper, the geometric properties of the conformal metric are studied
and its exact solution of the geodesic deviation equation is presented. We also
find out the stress-energy tensor of this geometry and compare it with the
usual prefect-fluid case, obtaining an equation of state as $P =
-\frac{1}{3}\rho$ in 4D space-time dimension. Finally, the low-energy regime of
the metric is studied, in which we obtain the stress-energy tensor proportional
to the projection tensor.


title: Automating Staged Rollout with Reinforcement Learning

abstract:
  Staged rollout is a strategy of incrementally releasing software updates to
portions of the user population in order to accelerate defect discovery without
incurring catastrophic outcomes such as system wide outages. Some past studies
have examined how to quantify and automate staged rollout, but stop short of
simultaneously considering multiple product or process metrics explicitly. This
paper demonstrates the potential to automate staged rollout with
multi-objective reinforcement learning in order to dynamically balance
stakeholder needs such as time to deliver new features and downtime incurred by
failures due to latent defects.


title:
Healing the cosmological constant problem during inflation through a
  unified quasi-quintessence matter field

abstract:
  We heal the cosmological constant problem by means of a \emph{cancellation
mechanism} that adopts a phase transition during which quantum fluctuations are
eliminated. To this purpose, we propose that a generalized scalar (dark) matter
field with a non-vanishing pressure term can remove the vacuum energy
contribution, if its corresponding thermodynamics is written in terms of a
\emph{quasi-quintessence} representation. In such a picture, pressure differs
from quintessence as it shows a zero kinetic contribution. Using this field, we
investigate a metastable transition phase, in which the universe naturally
passes through an inflationary phase. To reach this target, we single out a
double exponential potential, describing the metastable inflationary dynamics
by considering suitable boundaries and thermodynamic conditions. We analyze
stability investigating saddle, stable and unstable points and we thus predict
a chaotic inflation that mimics the Starobinsky exponential potential.
Consequently, the role of the proposed dark matter field is investigated
throughout the overall universe evolution. To do so, we provide a physical
explanation on unifying the dark sector with inflation by healing the
cosmological constant problem.


title: LES/DNS of flow past T106 LPT cascade using a higher-order LB model

abstract:
  The main objective of the present work is to assess higher-order Entropic
Lattice Boltzmann Method (ELBM) for separated and transitional flows without
the use of any explicit turbulence model. For this, we chose to simulate two
cases of T106 Low-Pressure Turbine (LPT) cascade -- T106A and T106C --
representing incompressible and compressible flow regimes respectively. These
results are obtained using our company's in-house higher-order ELBM transonic
solver. We have carried out two sets of simulations for both the test cases.
One with a clean inlet and the other with an inlet disturbance given by white
Gaussian noise superimposed on the inlet velocity. For the clean inlet case,
the flow remains laminar on the entire blade surface for both the test cases.
It undergoes transition on the suction side for the inlet disturbance case. The
pressure coefficient for the T106A and the isentropic Mach number on the blade
surface for the T106C matches well with the experimental results. Also, the
qualitative comparison of flow features in terms of the Q-criterion is in good
agreement with the earlier computational results reported in literature.


title:
On the Hill's Spherical Vortex in Fluid and Plasma, its Generalization,
  and Stability

abstract:
  In 1894 M.J.M. Hill published an article describing a spherical vortex moving
through a stationary fluid. Using cylindrical coordinates and assuming the
azimuthal velocity component zero, Hill found a simple solution that described
this flow. A similar modern problem in the MHD framework was put forth in 1987
by A. A. Bobnev and in 1995 by R. Kaiser and D. Lortz who applied the setup to
model a ball lighting. We present a much simpler derivation of Hill's spherical
vortex using the Bragg-Hawthorne equation. In particular, by using the moving
frame of reference, the Euler equations reduce to equilibrium flow which are
equivalent to the static equilibrium MHD equations up to relabelling. A new
generalized version of Hill's spherical vortex with a nonzero azimuthal
component is derived. A physical solution to the static equilibrium MHD
equations is computed by looking at a separated solution to the Grad-Shafranov
equation in spherical coordinates. Finally, the stability of Hill's spherical
vortex is examined by performing an axisymmetric perturbation described; it is
shown that the Hill's spherical vortex is linearly unstable with respect to
certain kinds of small perturbations.


title: Data-driven stochastic Lie transport modelling of the 2D Euler equations

abstract:
  Stochastic modelling of coarse-grid SPDEs of the two-dimensional Euler
equations, in the framework of Stochastic Advection by Lie Transport (SALT)
[Cotter et al, 2019], is considered. We propose and assess a number of models
as stochastic forcing. The latter is decomposed in terms of a deterministic
basis (empirical orthogonal functions) multiplied by temporal traces, here
regarded as stochastic processes. In particular, we construct the stochastic
forcing from the probability density functions (pdfs) and the correlation times
obtained from a fine-grid data set. We perform uncertainty quantification tests
to compare the different stochastic models. In particular, comparison to
Gaussian noise, in terms of ensemble mean and ensemble spread, is conducted.
Reduced uncertainty is observed for the developed models. On short timescales,
such as those used for data assimilation [Cotter et al, 2020a], the former
models show a reduced ensemble mean error and a reduced spread. Estimating the
pdfs yielded stochastic ensembles which rarely failed to capture the reference
solution on short timescales, as is demonstrated by rank histograms. Overall,
introducing correlation into the stochastic models improves the quality of the
coarse-grid predictions with respect to white noise.


title: New proofs of some theorems for binomial transform and Fibonacci powers

abstract:
  Our aim in writing this paper is to answer to both V. E. Hoggatt, JR
\cite{hogg} and Wessner\cite{wess} on the next question: find
$\sum_{k=0}^n\binom{n}{k}F_{[k]}^p$, for the case $p\equiv 1\, mod\, 4$ and
$p\equiv 3\, mod\, 4$. \par The case $p\equiv 0\, mod \,4$ and $p\equiv 2\,
mod\, 4$, Wessner has given an answer. In particular, we give another
presentation, another proof of the paper of Wessner. Our method use,
essentially, the paper of Boyadzhiev\cite{boy}


title: Complex Recurrent Variational Autoencoder for Speech Enhancement

abstract:
  Commonly-used methods in speech enhancement are based on short-time fourier
transform (STFT) representation, in particular on the magnitude of the STFT.
This is because phase is naturally unstructured and intractable, and magnitude
has shown more importance in speech enhancement. Nevertheless, phase has shown
its significance in some research and cannot be ignored. Complex neural
networks, with their inherent advantage, provide a solution for complex
spectrogram processing. Complex variational autoencoder (VAE), as an extension
of vanilla \acrshort{vae}, has shown positive results in complex spectrogram
representation. However, the existing work on complex \acrshort{vae} only uses
linear layers and merely applies the model on direct spectra representation.
This paper extends the linear complex \acrshort{vae} to a non-linear one.
Furthermore, on account of the temporal property of speech signals, a complex
recurrent \acrshort{vae} is proposed. The proposed model has been applied on
speech enhancement. As far as we know, it is the first time that a complex
generative model is applied to speech enhancement. Experiments are based on the
TIMIT dataset, while speech intelligibility and speech quality have been
evaluated. The results show that, for speech enhancement, the proposed method
has better performance on speech intelligibility and comparable performance on
speech quality.


title:
Relative Rota-Baxter operators of nonzero weights on $3$-Lie algebras
  and 3-post-Lie algebras

abstract:
  In this paper, first we introduce the notion of a relative Rota-Baxter
operator of nonzero weights on a $3$-Lie algebra with respect to an action on
another 3-Lie algebra, which can be characterized by graphs of the semidirect
product 3-Lie algebra constructed from the action. %We show that a relative
Rota-Baxter operator of weight $\lambda$ induces a new $3$-Lie algebra
structure, which is called the descendent 3-Lie algebra. Then we introduce a
new algebraic structure, which is called a $3$-post-Lie algebra. A 3-post-Lie
algebra consists of a 3-Lie algebra structure and a ternary operation such that
some compatibility conditions are satisfied. We show that a relative
Rota-Baxter operator of nonzero weights induces a $3$-post-Lie algebra
naturally. Thus $3$-post-Lie algebras can be viewed as the underlying algebraic
structures of relative Rota-Baxter operators of nonzero weights on $3$-Lie
algebras. Moreover, a $3$-post-Lie algebra also gives rise to a new 3-Lie
algebra, which is called the subadjacent 3-Lie algebra, and an action on the
original 3-Lie algebra. Next we construct an $L_\infty$-algebra from an action
of 3-Lie algebras whose Maurer-Cartan elements are relative Rota-Baxter
operators of nonzero weights. Consequently, we obtain the twisted
$L_\infty$-algebra that controls deformations of a given relative Rota-Baxter
operator of nonzero weights on 3-Lie algebras. Finally, we construct a
cohomology theory for a relative Rota-Baxter operator of nonzero weights on
$3$-Lie algebras and use the second cohomology group to classify infinitesimal
deformations.


title: Penalised FTRL With Time-Varying Constraints

abstract:
  In this paper we extend the classical Follow-The-Regularized-Leader (FTRL)
algorithm to encompass time-varying constraints, through adaptive penalization.
We establish sufficient conditions for the proposed Penalized FTRL algorithm to
achieve $O(\sqrt{t})$ regret and violation with respect to strong benchmark
$\hat{X}^{max}_t$. Lacking prior knowledge of the constraints, this is probably
the largest benchmark set that we can reasonably hope for. Our sufficient
conditions are necessary in the sense that when they are violated there exist
examples where $O(\sqrt{t})$ regret and violation is not achieved. Compared to
the best existing primal-dual algorithms, Penalized FTRL substantially extends
the class of problems for which $O(\sqrt{t})$ regret and violation performance
is achievable.


title: IR Constraints on Gyromagnetic Ratios

abstract:
  In Bosonic, Heterotic and Type II theories, by employing soft theorems and
string techniques, we analyze the gyromagnetic ratios of massive high spin
particles coupled to Abelian gauge fields arising from the compactification of
the metric and Kalb-Ramond fields. We derive a universal form for such
couplings showing that mixed symmetry states of the leading Regge-trajectory,
described by a Young-tableau with two rows, have a gyromagnetic ratio
associated with each row.


title:
On an ecumenical natural deduction with stoup -- Part I: The
  propositional case

abstract:
  Natural deduction systems, as proposed by Gentzen and further studied by
Prawitz, is one of the most well known proof-theoretical frameworks. Part of
its success is based on the fact that natural deduction rules present a simple
characterization of logical constants, especially in the case of intuitionistic
logic. However, there has been a lot of criticism on extensions of the
intuitionistic set of rules in order to deal with classical logic. Indeed, most
of such extensions add, to the usual introduction and elimination rules, extra
rules governing negation. As a consequence, several meta-logical properties,
the most prominent one being harmony, are lost. Dag Prawitz proposed a natural
deduction ecumenical system, where classical logic and intuitionistic logic are
codified in the same system. In this system, the classical logician and the
intuitionistic logician would share the universal quantifier, conjunction,
negation and the constant for the absurd, but they would each have their own
existential quantifier, disjunction and implication, with different meanings.
Prawitz' main idea is that these different meanings are given by a semantical
framework that can be accepted by both parties. In this paper, we propose a
different approach adapting, to the natural deduction framework, Girard's
mechanism of stoup. This will allow the definition of a pure harmonic natural
deduction system for the propositional fragment of Prawitz' ecumenical logic.


title:
Shear flow over a surface containing a groove covered by an
  incompressible surfactant phase

abstract:
  We study shear-driven liquid flow over a planar surface with an embedded
gas-filled groove, with the gas-liquid interface protruding slightly above or
below the planar surface. The flow direction is along the groove, taken to be
much longer than wide, and the gas-liquid interface is assumed to be covered by
an incompressible surface fluid, representing a surfactant phase. Using the
incompressiblity condition for the surface fluid, the equations of motion and
corresponding boundary conditions for the liquid phase are obtained by
minimizing the dissipation rate. Assuming a moderate deformation of the
interface, a domain perturbation technique with the maximal deformation as
small parameter is employed. The Stokes equation in the liquid phase under
corresponding boundary conditions is solved to second order in the deformation
using the Keldysh-Sedov formalism. The obtained analytical results are compared
with numerical calculations of the same problem, allowing an assessment of the
limits of validity of the expansion. While on a planar gas-liquid interface no
flow is induced, a recirculating flow is observed on an interface protruding
slightly above or below the planar surface. The study sheds light onto the
mobility of curved gas-liquid interfaces in the presence of surfactants acting
as an incompressible surface fluid.


title: On the size distribution of Levenshtein balls with radius one

abstract:
  The fixed length Levenshtein (FLL) distance between two words $\mathbf{x,y}
\in \mathbb{Z}_m^n$ is the smallest integer $t$ such that $\mathbf{x}$ can be
transformed to $\mathbf{y}$ by $t$ insertions and $t$ deletions. The size of a
ball in FLL metric is a fundamental but challenging problem. Very recently,
Bar-Lev, Etzion, and Yaakobi explicitly determined the minimum, maximum and
average sizes of the FLL balls with radius one. In this paper, based on these
results, we further prove that the size of the FLL balls with radius one is
highly concentrated around its mean by Azuma's inequality.


title: A photonic integrated circuit based erbium-doped amplifier

abstract:
  Erbium-doped fiber amplifiers have revolutionized long-haul optical
communications and laser technology. Erbium ions could equally provide a basis
for efficient optical amplification in photonic integrated circuits. However,
this approach has thus far remained impractical due to insufficient output
power. Here, we demonstrate a photonic integrated circuit based erbium
amplifier reaching 145 mW output power and more than 30 dB small-signal gain --
on par with commercial fiber amplifiers and beyond state-of-the-art III-V
heterogeneously integrated semiconductor amplifiers. We achieve this by
applying ion implantation to recently emerged ultralow-loss Si3N4 photonic
integrated circuits with meter-scale-length waveguides. We utilize the device
to increase by 100-fold the output power of soliton microcombs, required for
low-noise photonic microwave generation or as a source for wavelength-division
multiplexed optical communications. Endowing Si3N4 photonic integrated circuits
with gain enables the miniaturization of a wide range of fiber-based devices
such as high-pulse-energy femtosecond mode-locked lasers.


title:
On the new and old physics in the interaction of a radiating electron
  with the extreme electromagnetic field

abstract:
  We show that an all-optical configuration of the laser-electron collision in
the $\lambda^{3}$ configuration based on 10~PW-class lasers presents a viable
platform for reaching the range of parameters where a perturbative QED in
strong external electromagnetic field breaks. This case is contingently
referred to as a case of the nonperturbative QED; and this range of parameters
is the intriguing goal from an experimental point of view because of a possible
manifestation of a new physics of the interaction of a highly radiating
particle with a strong electromagnetic field. We show that the strong field
region can be reached by the electrons having the initial energy higher than 50
GeV. Our theoretical considerations are in agreement with three-dimensional
particle-in-cell simulations. While increasing of the electron energy raises
the number of electrons experiencing the strong field region, the observable
signature of photon emission radiative correction in the strong field is
expected to fade out when the electron energy surpasses the optimal value. This
threshold of electron energy is identified and the parameters for achieving the
nonperturbative limit of QED are provided.


title: Finite rigid sets in sphere complexes

abstract:
  A subcomplex $X\leq \mathcal{C}$ of a simplicial complex is rigid if every
locally injective, simplicial map $X\to\mathcal{C}$ is the restriction of an
automorphism of $\mathcal{C}$. Aramayona and the second author proved that the
curve complex of an orientable surface can be exhausted by finite rigid sets.
The Hatcher sphere complex is an analog of the curve complex for isotopy
classes of essential spheres in a connect sum of $n$ copies of $S^1\times S^2$.
We show that there is an exhaustion of the sphere complex by finite rigid sets
for all $n\ge 3$ and that when $n=2$ the sphere complex does not have finite
rigid sets.


title:
The interplay between AGN feedback and precipitation of the intracluster
  medium in simulations of galaxy groups and clusters

abstract:
  Using high-resolution hydrodynamical simulations of galaxy clusters, we study
the interaction between the brightest cluster galaxy, its supermassive black
hole (BH) and the intracluster medium (ICM). We create initial conditions for
which the ICM is in hydrostatic equilibrium within the gravitational potential
from the galaxy and an NFW dark matter halo. Two free parameters associated
with the thermodynamic profiles determine the cluster gas fraction and the
central temperature, where the latter can be used to create cool-core or
non-cool-core systems. Our simulations include radiative cooling, star
formation, BH accretion, and stellar and active galactic nucleus (AGN)
feedback. Even though the energy of AGN feedback is injected thermally and
isotropically, it leads to anisotropic outflows and buoyantly rising bubbles.
We find that the BH accretion rate (BHAR) is highly variable and only
correlates strongly with the star formation rate (SFR) and the ICM when it is
averaged over more than $1~\rm Myr$. We generally find good agreement with the
theoretical precipitation framework. In $10^{13}~\rm M_\odot$ haloes, AGN
feedback quenches the central galaxy and converts cool-core systems into
non-cool-core systems. In contrast, higher-mass, cool-core clusters evolve
cyclically. Episodes of high BHAR raise the entropy of the ICM out to the
radius where the ratio of the cooling time and the local dynamical time $t_{\rm
cool}/t_{\rm dyn} > 10$, thus suppressing condensation and, after a delay, the
BHAR. The corresponding reduction in AGN feedback allows the ICM to cool and
become unstable to precipitation, thus initiating a new episode of high SFR and
BHAR.


title: Hot 2DHG states in tellurium

abstract:
  Element semiconductor Te is very popular in both fundamental electronic
structure study, and device fabrication research area due to its unique band
structure. Specifically, in low temperatures, Te possesses strong quantum
oscillations with magnetic field applied in basal plane, either following
Shubnikov-de Haas (SdH) oscillation rule or following log-periodic oscillation
rule. With magnetic field applied along the [001] direction, the SdH
oscillations are attributed to the two-dimensional hole gas (2DHG) surface
states. Here we reported an interesting SdH oscillation in Te-based single
crystals, with the magnetic field applied along the [001] direction of the
crystals, showing the maximum oscillation intensity at ~ 75 K, and still
traceable at 200 K, which indicates a rather hot 2DHG state. The nontrivial
Berry phase can be also obtained from the oscillations, implying the
contribution from topological states. More importantly, the high temperature
SdH oscillation phenomena are observed in different Te single crystals samples,
and Te single crystals with nonmagnetic/magnetic dopants, showing robustness to
bulk defects. Therefore, the oscillation may be contributed by the bulk
symmetry protected hot 2DHG states, which will offer a new platform for
high-temperature quantum transport studies.


title: Modular bootstrap for D4-D2-D0 indices on compact Calabi-Yau threefolds

abstract:
  We investigate the modularity constraints on the generating series
$h_r(\tau)$ of BPS indices counting D4-D2-D0 bound states with fixed D4-brane
charge $r$ in type IIA string theory compactified on complete Intersection
Calabi-Yau threefolds with $b_2 = 1$. For unit D4-brane, $h_1$ transforms as a
(vector-valued) modular form under the action of $SL(2,Z)$ and thus is
completely determined by its polar terms. We propose an Ansatz for these terms
in terms of rank 1 Donaldson-Thomas invariants, which incorporates
contributions from a single D6-anti-D6 pair. Using an explicit overcomplete
basis of the relevant space of weakly holomorphic modular forms (valid for any
$r$), we find that for 10 of the 13 allowed threefolds, the Ansatz leads to a
solution for $h_1$ with integer Fourier coefficients, thereby predicting an
infinite series of DT invariants.For $r > 1$, $h_r$ is mock modular and
determined by its polar part together with its shadow. Restricting to $r = 2$,
we use the generating series of Hurwitz class numbers to construct a series
$h^{\rm an}_2$ with exactly the same modular anomaly as $h_2$, so that the
difference $h_{2}-h^{\rm an}_2$ is an ordinary modular form fixed by its polar
terms. For lack of a satisfactory Ansatz, we leave the determination of these
polar terms as an open problem.


title:
Abstractive summarization of hospitalisation histories with transformer
  networks

abstract:
  In this paper we present a novel approach to abstractive summarization of
patient hospitalisation histories. We applied an encoder-decoder framework with
Longformer neural network as an encoder and BERT as a decoder. Our experiments
show improved quality on some summarization tasks compared with
pointer-generator networks. We also conducted a study with experienced
physicians evaluating the results of our model in comparison with PGN baseline
and human-generated abstracts, which showed the effectiveness of our model.


title: Robust multipartite entanglement in dirty topological wires

abstract:
  Identifying and characterizing quantum phases of matter in the presence of
long range correlations and/or spatial disorder is, generally, a challenging
and relevant task. Here, we study a generalization of the Kiteav chain with
variable-range pairing and different site-dependence of the chemical potential,
addressing commensurable and incommensurable modulations as well as Anderson
disorder. In particular, we analyze multipartite entanglement (ME) in the
ground state of the dirty topological wires by studying the scaling of the
quantum Fisher information (QFI) with the system's size. For nearest-neighbour
pairing the Heisenberg scaling of the QFI is found in one-to-one correspondence
with topological phases hosting Majorana modes. For finite-range pairing, we
recognize long-range phases by the super-extensive scaling of the QFI and
characterize complex lobe-structured phase diagrams. Overall, we observe that
ME is robust against finite strengths of spatial inhomogeneity. This work
contributes to establish ME as a central quantity to study intriguing aspects
of topological systems.


title: Model Based Meta Learning of Critics for Policy Gradients

abstract:
  Being able to seamlessly generalize across different tasks is fundamental for
robots to act in our world. However, learning representations that generalize
quickly to new scenarios is still an open research problem in reinforcement
learning. In this paper we present a framework to meta-learn the critic for
gradient-based policy learning. Concretely, we propose a model-based bi-level
optimization algorithm that updates the critics parameters such that the policy
that is learned with the updated critic gets closer to solving the
meta-training tasks. We illustrate that our algorithm leads to learned critics
that resemble the ground truth Q function for a given task. Finally, after
meta-training, the learned critic can be used to learn new policies for new
unseen task and environment settings via model-free policy gradient
optimization, without requiring a model. We present results that show the
generalization capabilities of our learned critic to new tasks and dynamics
when used to learn a new policy in a new scenario.


title: Elucidation of 'Cosmic Coincidence'

abstract:
  In the standard cosmological model the dark energy (DE) and nonrelativistic
(NR) matter densities are determined to be comparable at the present time, in
spite of their greatly different evolution histories. This `cosmic coincidence'
enigma could be explained as a non-anthropic observational selection effect: We
show that in a suitably chosen frame the Universe is at its most probable epoch
when the Hubble radius attains a maximum, at the epoch when the energy
densities of DE and NR matter are comparable.


title: How Should Voice Assistants Deal With Users' Emotions?

abstract:
  There is a growing body of research in HCI on detecting the users' emotions.
Once it is possible to detect users' emotions reliably, the next question is
how an emotion-aware interface should react to the detected emotion. In a first
step, we tried to find out how humans deal with the negative emotions of an
avatar. The hope behind this approach was to identify human strategies, which
we can then mimic in an emotion-aware voice assistant. We present a user study
in which participants were confronted with an angry, sad, or frightened avatar.
Their task was to make the avatar happy by talking to it. We recorded the voice
signal and analyzed it. The results show that users predominantly reacted with
neutral emotion. However, we also found gender differences, which opens a range
of questions.


title: EntSUM: A Data Set for Entity-Centric Summarization

abstract:
  Controllable summarization aims to provide summaries that take into account
user-specified aspects and preferences to better assist them with their
information need, as opposed to the standard summarization setup which build a
single generic summary of a document. We introduce a human-annotated data set
EntSUM for controllable summarization with a focus on named entities as the
aspects to control. We conduct an extensive quantitative analysis to motivate
the task of entity-centric summarization and show that existing methods for
controllable summarization fail to generate entity-centric summaries. We
propose extensions to state-of-the-art summarization approaches that achieve
substantially better results on our data set. Our analysis and results show the
challenging nature of this task and of the proposed data set.


title: A stable pair liquid phase in fermionic systems

abstract:
  We predict the existence of a pair-liquid phase in lattice fermion systems
with finite-range attractive interactions. This exotic state competes on one
side with a normal Fermi liquid of unpaired fermions and on the other side with
a phase-separated state where all fermions are coupled into macroscopic
clusters. We show that such a phase is absent in bosonic systems and therefore
is protected by the exclusion principle. In contrast with zero-range attractive
systems where clustering of more than two fermions is directly prohibited, here
quantum statistics acts dynamically and in a more subtle way. Since a
many-fermion wave function must have nodes, the cluster formation threshold is
larger than the pair formation threshold. By directly solving a four-body
Schr\"odinger equation on one- and two-dimensional lattices, we map the
boundaries of pair stability. The pair liquid phase should be observable in
cold atom systems. We also discuss implications for the preformed-pair
mechanism of high-temperature superconductivity.


title:
Accurate benchmark results of Blasius boundary layer problem using a
  leaping Taylors series that converges for all real values

abstract:
  Blasius boundary layer solution is a Maclaurin series expansion of the
function \(f(\eta)\), which has convergence problems when evaluating for higher
values of \(\eta\) due to a singularity present at \(\eta\approx-5.69\). In
this paper we are introducing an accurate solution to \(f(\eta)\) using
Taylor's series expansions with progressively shifted centers of
expansion(Leaping centers). Each series is solved as an IVP with the three
initial values computed from solution of the previous series, so the gap
between the centers of two consecutive expansions is selected from within the
convergence disc of the first series. The last series is formed such that it is
convergent for a reasonable high \(\eta\) value needed for implementing the
boundary condition at infinity. The present methodology uses Newton-Raphson
method to compute the value of the unknown initial condition viz. \(f''(0)\) in
an iterative manner. Benchmark accurate results of different parameters of flat
plate boundary layer with no slip and slip boundary conditions have been
reported in this paper.


title:
Innovating at Speed and at Scale: A Next Generation Infrastructure for
  Accelerating Semiconductor Technologies

abstract:
  Semiconductor innovation drives improvements to technologies that are
critical to modern society. The country that successfully accelerates
semiconductor innovation is positioned to lead future semiconductor-driven
industries and benefit from the resulting economic growth. It is our view that
a next generation infrastructure is necessary to accelerate and enhance
semiconductor innovation in the U.S. In this paper, we propose such an advanced
infrastructure composed of a national network of facilities with enhancements
in technology and business models. These enhancements enable application-driven
and challenge-based research and development, and ensure that facilities are
accessible and sustainable. The main tenets are: a challenge-driven operational
model, a next-generation infrastructure to serve that operational model,
technology innovations needed for advanced facilities to speed up learning
cycles, and innovative cost-effective business models for sustainability.
Ultimately, the expected outcomes of such a participatory, scalable, and
sustainable nation-level advanced infrastructure will have tremendous impact on
government, industry, and academia alike.


title: Generators and relations for 2-qubit Clifford+T operators

abstract:
  We give a presentation by generators and relations of the group of Clifford+T
operators on two qubits. The proof relies on an application of the
Reidemeister-Schreier theorem to an earlier result of Greylyn, and has been
formally verified in the proof assistant Agda.


title:
Local and global polarization of $\Lambda$ hyperons across RHIC-BES
  energies: the roles of spin hall effect, initial condition and baryon
  diffusion

abstract:
  We perform a systematic study on the local and global spin polarization of
$\Lambda$ and $\overline{\Lambda}$ hyperons in relativistic heavy-ion
collisions at beam energy scan energies via the (3+1)-dimensional CLVisc
hydrodynamics model with AMPT and SMASH initial conditions. Following the
quantum kinetic theory, we decompose the polarization vector as the parts
induced by thermal vorticity, shear tensor and the spin Hall effect (SHE). We
find that the polarization induced by SHE and the total polarization strongly
depends on the initial conditions. At $7.7$GeV, SHE gives a sizeable
contribution and even flips the sign of the local polarization along the beam
direction for AMPT initial condition, which is not observed for SMASH initial
condition. Meanwhile, the local polarization along the out-of-plane direction
induced by SHE with AMPT initial condition does not always increase with
decreasing collision energies. Next, we find that the polarization along the
beam direction is sensitive to the baryon diffusion coefficient, but the local
polarization along the out-of-plane direction is not. Our results for the
global polarization of $\Lambda$ and $\overline{\Lambda}$ agree well with the
STAR data. Interestingly, the global polarization of $\overline{\Lambda}$ is
not always larger than that of $\Lambda$ due to various competing effects. Our
findings are helpful for understanding the polarization phenomenon and the
detailed structure of quark-gluon plasma in relativistic heavy-ion collisions.


title: SNUG: Self-Supervised Neural Dynamic Garments

abstract:
  We present a self-supervised method to learn dynamic 3D deformations of
garments worn by parametric human bodies. State-of-the-art data-driven
approaches to model 3D garment deformations are trained using supervised
strategies that require large datasets, usually obtained by expensive
physics-based simulation methods or professional multi-camera capture setups.
In contrast, we propose a new training scheme that removes the need for
ground-truth samples, enabling self-supervised training of dynamic 3D garment
deformations. Our key contribution is to realize that physics-based deformation
models, traditionally solved in a frame-by-frame basis by implicit integrators,
can be recasted as an optimization problem. We leverage such optimization-based
scheme to formulate a set of physics-based loss terms that can be used to train
neural networks without precomputing ground-truth data. This allows us to learn
models for interactive garments, including dynamic deformations and fine
wrinkles, with two orders of magnitude speed up in training time compared to
state-of-the-art supervised methods


title:
H-theorem bypassing by 2D Brownian particle under discontinuous magnetic
  field

abstract:
  In the present paper, we theoretically study the kinetic properties of 2D
charged particles under a discontinuous magnetic field. It is shown that
certain conditions could cause their bypassing of the H-theorem. We use the
classical kinetic equation to derive the diffusion equation for the case. The
essential postulates are made for modeling: (i) the motion of the particles is
restricted by a rectangular region; (ii) the mobility of the particles goes to
zero while they came closer to borders; (iii) the Coulomb interaction between
particles is absent; (iv)the interaction between charged particles and the
medium does not change the total momentum of the medium. The achievability of
the conditions is discussed.


title:
Desynchronization of clocks in rigid acceleration and gravitational
  redshift

abstract:
  In this paper, we examine the prediction of the theory of relativity for the
desynchronization of accelerating clocks separated by a proper distance l. We
adopt the approach of Larmor-Lorentz-Poincare-Bell to relativity and derive
clock desynchronization as a result of an acceleration procedure based on two
basic assumptions. Such an approach exhausts the freedom of Einstein's approach
that allows for different clock synchronizations. We show that contrary to
expectations, as a result of acceleration, the rear clock actually shifted
backwards with respect to the front clock according to an inertial observer.
However, due to Einstein's equivalence principle the accelerating observer
feels a gravitational field and observes that the rear clock undergoes a
gravitational redshift relative to the front clock. This gravitational time
shift is larger than the time shift for the acceleration and the difference is
exactly equal to the special relativistic time shift. Eventually, we arrive at
the conclusion that Einstein's equivalence principle and gravitational redshift
is necessary to explain special relativistic clock desynchronization.


title: Algebraic threefolds of general type with small volume

abstract:
  It is known that the optimal Noether inequality $\mathrm{vol}(X) \ge
\frac{4}{3}p_g(X) - \frac{10}{3}$ holds for every $3$-fold $X$ of general type
with $p_g(X) \ge 11$. In this paper, we give a complete classification of
$3$-folds $X$ of general type with $p_g(X) \ge 11$ satisfying the above
equality by giving the explicit structure of a relative canonical model of $X$.
This model coincides with the canonical model of $X$ when $p_g(X) \ge 23$. We
also establish the second and third optimal Noether inequalities for $3$-folds
$X$ of general type with $p_g(X) \ge 11$. These results answer two open
questions raised by J. Chen, M. Chen and C. Jiang, and in dimension three an
open question raised by J. Chen and C. Lai. A novel phenomenon shows that there
is a one-to-one correspondence between the three Noether inequalities and three
possible residues of $p_g(X)$ modulo $3$.


title:
Operator-valued $(L^{p},L^{q})$ Fourier multipliers and stability theory
  for evolution equations

abstract:
  We give an overview of some recent results on operator-valued $(L^{p},L^{q})$
Fourier multipliers and stability theory for evolution equations. The aim is to
provide a relatively nontechnical introduction to the underlying ideas,
emphasizing the connection between the two areas. We also indicate how
operator-valued $(L^{p},L^{q})$ Fourier multipliers can be applied to
functional calculus theory.


title: Neural Computing with Coherent Laser Networks

abstract:
  We show that a coherent network of lasers exhibits emergent neural computing
capabilities. The proposed scheme is built on harnessing the collective
behavior of laser networks for storing a number of phase patterns as stable
fixed points of the governing dynamical equations and retrieving such patterns
through proper excitation conditions, thus exhibiting an associative memory
property. The associative memory functionality is first discussed in the strong
pumping regime of a network of passive dissipatively coupled lasers which
simulate the classical XY model. It is discussed that despite the large storage
capacity of the network, the large overlap between fixed-point patterns
effectively limits pattern retrieval to only two images. Next, we show that
this restriction can be uplifted by using nonreciprocal coupling between lasers
and this allows for utilizing a large storage capacity. This work opens new
possibilities for neural computation with coherent laser networks as novel
analog processors. In addition, the underlying dynamical model discussed here
suggests a novel energy-based recurrent neural network that handles continuous
data as opposed to Hopfield networks and Boltzmann machines which are
intrinsically binary systems.

categories: physics.optics cs.LG cs.NE nlin.PS physics.comp-ph


title: Is Space-time Really Doomed?

abstract:
  For many years now it has become conventional for theorists to argue that
"space-time is doomed", with the difficulties in finding a quantum theory of
gravity implying the necessity of basing a fundamental theory on something
quite different than usual notions of space-time geometry. But what is this
space-time geometry that is doomed? In this essay we'll explore how our
understanding of four-dimensional geometry has evolved since Einstein, leading
to new ideas about such geometry which may not be doomed at all.


title: Two-channel charge-Kondo physics in graphene quantum dots

abstract:
  Nanoelectronic quantum dot devices exploiting the charge-Kondo paradigm have
been established as versatile and accurate analog quantum simulators of
fundamental quantum impurity models. In particular, hybrid metal-semiconductor
dots connected to two metallic leads realize the two-channel Kondo (2CK) model,
in which Kondo screening of the dot charge pseudospin is frustrated. Here, we
consider theoretically a two-channel charge-Kondo device made instead from
graphene components, realizing a pseudogapped version of the 2CK model. We
solve the model using Wilson's Numerical Renormalization Group method, and
uncover a rich phase diagram as a function of dot-lead coupling strength,
channel asymmetry, and potential scattering. The complex physics of this system
is explored through its thermodynamic properties, scattering T-matrix, and
experimentally measurable conductance. We find that the strong coupling
pseudogap Kondo phase persists in the channel-asymmetric two-channel context,
while in the channel-symmetric case frustration results in a novel quantum
phase transition. Remarkably, despite the vanishing density of states in the
graphene leads at low energies, we find a finite linear conductance at zero
temperature at the frustrated critical point, which is of non-Fermi liquid
type. Our results suggest that the graphene charge-Kondo platform offers a
unique possibility to access multichannel pseudogap Kondo physics.


title: When Sparsity Meets Dynamic Convolution

abstract:
  Dynamic convolution achieves a substantial performance boost for efficient
CNNs at a cost of increased convolutional weights. Contrastively, mask-based
unstructured pruning obtains a lightweight network by removing redundancy in
the heavy network at risk of performance drop. In this paper, we propose a new
framework to coherently integrate these two paths so that they can complement
each other compensate for the disadvantages. We first design a binary mask
derived from a learnable threshold to prune static kernels, significantly
reducing the parameters and computational cost but achieving higher performance
in Imagenet-1K(0.6\% increase in top-1 accuracy with 0.67G fewer FLOPs). Based
on this learnable mask, we further propose a novel dynamic sparse network
incorporating the dynamic routine mechanism, which exerts much higher accuracy
than baselines ($2.63\%$ increase in top-1 accuracy for MobileNetV1 with $90\%$
sparsity). As a result, our method demonstrates a more efficient dynamic
convolution with sparsity.


title:
3D tomographic analysis of the order-disorder interplay in the
  Pachyrhynchus congestus mirabilis weevil

abstract:
  The bright colors of Pachyrhynchus weevils originate from complex dielectric
nanostructures within their elytral scales. In contrast to previous work
exhibiting highly ordered single-network diamond-type photonic crystals, we
here show by combining optical microscopy and spectroscopy measurements with 3D
FIB tomography that the blue scales of P. congestus mirabilis differ from that
of an ordered diamond structure. Through the use of FIB tomography on elytral
scales filled with Pt by electron beam-assisted deposition, we reveal that the
red scales of this weevil possess a periodic diamond structure, while the
network morphology of the blue scales exhibit diamond morphology only on the
single scattering unit level with disorder on longer length scales. Full wave
simulations performed on the reconstructed volumes indicate that this local
order is sufficient to open a partial photonic bandgap even at low dielectric
constant contrast between chitin and air in the absence of long-range or
translational order. The observation of disordered and ordered photonic
crystals within a single organism opens up interesting questions on the
cellular origin of coloration and studies on bio-inspired replication of
angle-independent colors.


title: Measuring weak lensing masses on individual clusters

abstract:
  We present weak lensing mass estimates for a sample of 458 galaxy clusters
from the redMaPPer Sloan Digital Sky Survey DR8 catalogue using Hyper
Suprime-Cam weak lensing data. We develop a method to quickly estimate cluster
masses from weak lensing shear and use this method to estimate weak lensing
masses for each of the galaxy clusters in our sample. Subsequently, we
constrain the mass-richness relation as well as the intrinsic scatter between
the cluster richness and the measured weak lensing masses. When calculating the
mass-richness relation for all clusters with a richness $\lambda>20$, we find a
tension in the slope of the mass-richness relation with the Dark Energy Survey
Year 1 stacked weak lensing analysis. For a reduced sample of clusters with a
richness $\lambda>40$, our results are consistent with the Dark Energy Survey
Year 1 analysis.


title:
Distant trans-Neptunian object candidates from NASA's TESS mission
  scrutinized: fainter than predicted or false positives?

abstract:
  NASA's Transiting Exoplanet Survey Satellite (TESS) is performing a
homogeneous survey of the sky from space in search of transiting exoplanets.
The collected data are also being used for detecting passing Solar system
objects, including 17 new outer Solar system body candidates located at
geocentric distances in the range 80-200 au, that need follow-up observations
with ground-based telescope resources for confirmation. Here, we present
results of a proof-of-concept mini-survey aimed at recovering two of these
candidates that was carried out with the 4.2 m William Herschel Telescope and a
QHY600L CMOS camera mounted at its prime focus. For each candidate attempted,
we surveyed a patch of sky of over one square degree around its expected
coordinates in Sloan r'. The same location was revisited in five consecutive or
nearly consecutive nights, reaching S/N > 4 at r' < 23 mag. We focused on the
areas of sky around the circumpolar TESS candidates located at equatorial
coordinates (07:00:15, +86:55:19), 202.8 au from Earth, and (06:39:47,
+83:43:54) at 162.1 au, but we could not recover either of them at r' < 23 mag.
Based on the detailed analysis of the acquired images, we confirm that either
both candidates are much fainter than predicted or that they are false
positives.


title: Causal inference: critical developments, past and future

abstract:
  Causality is a subject of philosophical debate and a central scientific issue
with a long history. In the statistical domain, the study of cause and effect
based on the notion of `fairness' in comparisons dates back several hundred
years, and yet statistical concepts and developments that form the area of
causal inference are only decades old. In this paper, we review core tenets and
methods of causal inference and key developments in the history of the field.
We highlight connections with traditional `associational' statistical methods,
including estimating equations and semiparametric theory, and point to current
topics of active research in this crucial area of our field.


title:
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from
  Photometric Images

abstract:
  We propose a neural inverse rendering pipeline called IRON that operates on
photometric images and outputs high-quality 3D content in the format of
triangle meshes and material textures readily deployable in existing graphics
pipelines. Our method adopts neural representations for geometry as signed
distance fields (SDFs) and materials during optimization to enjoy their
flexibility and compactness, and features a hybrid optimization scheme for
neural SDFs: first, optimize using a volumetric radiance field approach to
recover correct topology, then optimize further using edgeaware physics-based
surface rendering for geometry refinement and disentanglement of materials and
lighting. In the second stage, we also draw inspiration from mesh-based
differentiable rendering, and design a novel edge sampling algorithm for neural
SDFs to further improve performance. We show that our IRON achieves
significantly better inverse rendering quality compared to prior works. Our
project page is here: https://kai-46.github.io/IRON-website/


title:
A Molecular-Continuum Multiscale Model for Inviscid Liquid-Vapor Flow
  with Sharp Interfaces

abstract:
  The dynamics of compressible liquid-vapor flow depends sensitively on the
microscale behavior at the phase boundary. We consider a sharp-interface
approach, and propose a multiscale model to describe liquid-vapor flow
accurately, without imposing ad-hoc closure relations on the continuum scale.
The multiscale model combines the Euler equations on the continuum scale with
molecular-scale particle simulations that govern the interface motion. We rely
on an interface-preserving moving mesh finite volume method to discretize the
continuum-scale sharp-interface flow in a conservative manner. Computational
efficiency, while preserving physical properties, is achieved by a surrogate
solver for the interface dynamics based on constraint-aware neural networks.
The multiscale model is presented in its general form, and applied to regimes
of temperature-dependent liquid-vapor flow which have not been accessible
before.

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


title:
Experimental Investigation of the Hyperfine Structure of Tm I with
  Fourier Transform Spectroscopy, Part A: in the visible wavelength range 400
  nm 700 nm

abstract:
  The spectra emitted by a thulium hollow-cathode discharge lamp with argon or
neon as inert gases have been recorded with a Bruker IFS 125 HR Fourier
Transform spectrometer in the visible wavelength region from 400 nm to 700 nm.
The paper presents the investigation of the hyperfine-structure splitting of 50
spectral lines of neutral thulium. As a result, the magnetic dipole hyperfine
structure constant A for 20 fine structure levels of Tm I have been determined
for the first time.


title:
At the Locus of Performance: A Case Study in Enhancing CPUs with Copious
  3D-Stacked Cache

abstract:
  Over the last three decades, innovations in the memory subsystem were
primarily targeted at overcoming the data movement bottleneck. In this paper,
we focus on a specific market trend in memory technology: 3D-stacked memory and
caches. We investigate the impact of extending the on-chip memory capabilities
in future HPC-focused processors, particularly by 3D-stacked SRAM. First, we
propose a method oblivious to the memory subsystem to gauge the upper-bound in
performance improvements when data movement costs are eliminated. Then, using
the gem5 simulator, we model two variants of LARC, a processor fabricated in
1.5 nm and enriched with high-capacity 3D-stacked cache. With a volume of
experiments involving a board set of proxy-applications and benchmarks, we aim
to reveal where HPC CPU performance could be circa 2028, and conclude an
average boost of 9.77x for cache-sensitive HPC applications, on a per-chip
basis. Additionally, we exhaustively document our methodological exploration to
motivate HPC centers to drive their own technological agenda through enhanced
co-design.


title:
Designing Interference-Immune Doppler-TolerantWaveforms for Automotive
  Radar Applications

abstract:
  Dynamic target detection using FMCW waveform is challenging in the presence
of interference for different radar applications. Degradation in SNR is
irreparable and interference is difficult to mitigate in time and frequency
domain. In this paper, a waveform design problem is addressed using the
Majorization-Minimization (MM) framework by considering PSL/ISL cost functions,
resulting in a code sequence with Doppler-tolerance characteristics of an FMCW
waveform and interference immune characteristics of a tailored PMCW waveform
(unique phase code + minimal ISL/PSL). The optimal design sequences possess
polynomial phase behavior of degree Q amongst its sub-sequences and obtain
optimal ISL and PSL solutions with guaranteed convergence. By tuning the
optimization parameters such as degree Q of the polynomial phase behavior,
sub-sequence length M and the total number of sub-sequences L, the optimized
sequences can be as Doppler tolerant as FMCW waveform in one end, and they can
possess small cross-correlation values similar to random-phase sequences in
PMCW waveform on the other end. If required in the event of acute interference,
new codes can be generated in the runtime which have low cross-correlation with
the interferers. The performance analysis indicates that the proposed method
outperforms the state-of-the-art counterparts.


title: Designing a Future Worth Wanting: Applying Virtue Ethics to HCI

abstract:
  Out of the three major approaches to ethics, virtue ethics is uniquely well
suited as a moral guide in the digital age, given the pace of sociotechnical
change and the complexity of society. Virtue ethics focuses on the traits,
situations and actions of moral agents, rather than on rules (as in deontology)
or outcomes (consequentialism). Even as interest in ethics has grown within
HCI, there has been little engagement with virtue ethics. To address this
lacuna and demonstrate further opportunities for ethical design, this paper
provides an overview of virtue ethics for application in HCI. It reviews
existing HCI work engaging with virtue ethics, provides a primer on virtue
ethics to correct widespread misapprehensions within HCI, and presents a
deductive literature review illustrating how existing lines of HCI research
resonate with the practices of virtue cultivation, paving the way for further
work in virtue-oriented design.


title: Capture of Electroweak Multiplet Dark Matter in Neutron Stars

abstract:
  If dark matter has a sizable scattering cross section with nucleons, it can
efficiently be captured by a neutron star. Its energy is then transferred to
the neutron star as heat through the scattering and annihilation inside the
star. This heating effect may be detectable via dedicated temperature
observations of nearby old pulsars, providing an alternative method for dark
matter searches. In this paper, we show that for electroweak multiplet dark
matter this search strategy can probe the parameter region which is out of
reach of future dark matter direct detection experiments. To see this
systematically, we classify such dark matter candidates in terms of their
electroweak charges and investigate the effect of ultraviolet physics by means
of higher-dimensional effective operators. We then show that if the effect of
ultraviolet physics is sizable, the dark matter-nucleon elastic scattering
cross section becomes sufficiently large, whilst if it is suppressed, then the
mass splittings among the components of the DM multiplet get small enough so
that the inelastic scattering processes are operative. In any case, the
electroweak multiplet dark matter particles are efficiently captured in neutron
stars, making the search strategy with the temperature observation of old
neutron stars promising.


title:
Experimental Investigation of the Hyperfine Structure of Tm I with
  Fourier Transform Spectroscopy Part B: in the NIR wavelength range from 700
  nm to 2250 nm

abstract:
  In this study, we investigated the hyperfine structure of 43 spectral lines
of atomic thulium. We analyzed Fourier-transform spectra in the wavelength
range from 700 nm to 2250 nm, which corresponds to the wavenumber range from
14300 cm-1 to 4440 cm-1, respectively. The excited thulium atoms were generated
in a hollow-cathode lamp. As a result of this investigation, the
magnetic-dipole hyperfine constant A of 17 fine structure levels have been
determined experimentally, 14 of them for the first time. The magnetic-dipole
hyperfine constant values of the three remaining levels, reported in the
literature, differed significantly from the results of our determination.


title: Strongly-interacting bosons at 2D-1D Dimensional Crossover

abstract:
  We study a two dimensional (2D) system of interacting quantum bosons,
subjected to a periodic potential in one direction. Such system exhibits a
dimensional crossover between a canonical 2D behavior with
Berezinski-Kosterlitz Thouless (BKT) type of superfluid order and a
one-dimensional (1D) behavior when the potential is large and splits the system
in essentially independent tubes. The later is in the universality class of
Tomonaga-Luttinger liquids (TLL). Using a continuous quantum Monte Carlo
method, we investigate this dimensional crossover by computing longitudinal and
transverse superfluid fraction as well as the superfluid correlation as a
function of temperature, interactions and potential. We discuss how the
consequences of the dimensional crossover can be investigated in cold atomic
gases experiments.


title:
A Set Membership Approach to Discovering Feature Relevance and
  Explaining Neural Classifier Decisions

abstract:
  Neural classifiers are non linear systems providing decisions on the classes
of patterns, for a given problem they have learned. The output computed by a
classifier for each pattern constitutes an approximation of the output of some
unknown function, mapping pattern data to their respective classes. The lack of
knowledge of such a function along with the complexity of neural classifiers,
especially when these are deep learning architectures, do not permit to obtain
information on how specific predictions have been made. Hence, these powerful
learning systems are considered as black boxes and in critical applications
their use tends to be considered inappropriate. Gaining insight on such a black
box operation constitutes a one way approach in interpreting operation of
neural classifiers and assessing the validity of their decisions. In this paper
we tackle this problem introducing a novel methodology for discovering which
features are considered relevant by a trained neural classifier and how they
affect the classifier's output, thus obtaining an explanation on its decision.
Although, feature relevance has received much attention in the machine learning
literature here we reconsider it in terms of nonlinear parameter estimation
targeted by a set membership approach which is based on interval analysis.
Hence, the proposed methodology builds on sound mathematical approaches and the
results obtained constitute a reliable estimation of the classifier's decision
premises.