title: On the Dynamics of Inference and Learning

abstract:
  Statistical Inference is the process of determining a probability
distribution over the space of parameters of a model given a data set. As more
data becomes available this probability distribution becomes updated via the
application of Bayes' theorem. We present a treatment of this Bayesian updating
process as a continuous dynamical system. Statistical inference is then
governed by a first order differential equation describing a trajectory or flow
in the information geometry determined by a parametric family of models. We
solve this equation for some simple models and show that when the
Cram\'{e}r-Rao bound is saturated the learning rate is governed by a simple
$1/T$ power-law, with $T$ a time-like variable denoting the quantity of data.
The presence of hidden variables can be incorporated in this setting, leading
to an additional driving term in the resulting flow equation. We illustrate
this with both analytic and numerical examples based on Gaussians and Gaussian
Random Processes and inference of the coupling constant in the 1D Ising model.
Finally we compare the qualitative behaviour exhibited by Bayesian flows to the
training of various neural networks on benchmarked data sets such as MNIST and
CIFAR10 and show how that for networks exhibiting small final losses the simple
power-law is also satisfied.


title: Meshless method stencil evaluation with machine learning

abstract:
  Meshless methods are an active and modern branch of numerical analysis with
many intriguing benefits. One of the main open research questions related to
local meshless methods is how to select the best possible stencil - a
collection of neighbouring nodes - to base the calculation on. In this paper,
we describe the procedure for generating a labelled stencil dataset and use a
variation of pointNet - a deep learning network based on point clouds - to
create a classifier for the quality of the stencil. We exploit features of
pointNet to implement a model that can be used to classify differently sized
stencils and compare it against models dedicated to a single stencil size. The
model is particularly good at detecting the best and the worst stencils with a
respectable area under the curve (AUC) metric of around 0.90. There is much
potential for further improvement and direct application in the meshless
domain.


title: Unsupervised Learning of Unbiased Visual Representations

abstract:
  Deep neural networks are known for their inability to learn robust
representations when biases exist in the dataset. This results in a poor
generalization to unbiased datasets, as the predictions strongly rely on
peripheral and confounding factors, which are erroneously learned by the
network. Many existing works deal with this issue by either employing an
explicit supervision on the bias attributes, or assuming prior knowledge about
the bias. In this work we study this problem in a more difficult scenario, in
which no explicit annotation about the bias is available, and without any prior
knowledge about its nature. We propose a fully unsupervised debiasing
framework, consisting of three steps: first, we exploit the natural preference
for learning malignant biases, obtaining a bias-capturing model; then, we
perform a pseudo-labelling step to obtain bias labels; finally we employ
state-of-the-art supervised debiasing techniques to obtain an unbiased model.
We also propose a theoretical framework to assess the biasness of a model, and
provide a detailed analysis on how biases affect the training of neural
networks. We perform experiments on synthetic and real-world datasets, showing
that our method achieves state-of-the-art performance in a variety of settings,
sometimes even higher than fully supervised debiasing approaches.


title:
Improved performances of the NectarCAM, a Medium-Sized Telescope Camera
  for the Cherenkov Telescope Array

abstract:
  NectarCAM is a camera developed to detect Cherenkov light between 80 GeV and
30 TeV. It will equip the medium-sized telescopes (MST) of the Cherenkov
Telescope Array Observatory (CTAO). The camera comprises 265 modules, covering
a field of view of 8 degrees. Each module consists of 7 photomultiplier Tubes
(PMTs) equipped with light guides and a front-end board performing the data
capture. NectarCAM is based on the NECTAr chip, which combines a switch
capacitor array sampling at 1GHz and a 12-bit Analog to Digital Converter
(ADC). The NectarCAM camera is currently under integration in CEA Paris-Saclay
(France). In this contribution, I focus on the ongoing performance tests for
its characterization and calibration before deployment on the CTAO North site.


title:
Echoes through Time: Evolution of the Italian COVID-19 Vaccination
  Debate

abstract:
  Twitter is one of the most popular social media platforms in the country, but
pre-pandemic vaccination debate has been shown to be polarized and siloed into
echo chambers. It is thus imperative to understand the nature of this
discourse, with a specific focus on the vaccination hesitant individuals, whose
healthcare decisions may affect their communities and the country at large. In
this study we ask, how has the Italian discussion around vaccination changed
during the COVID-19 pandemic, and have the unprecedented events of 2020-2021
been able to break the echo chamber around this topic? We use a Twitter dataset
spanning September 2019 - November 2021 to examine the state of polarization
around vaccination. We propose a hierarchical clustering approach to find the
largest communities in the endorsement networks of different time periods, and
manually illustrate that it produces communities of users sharing a stance.
Examining the structure of these networks, as well as textual content of their
interactions, we find the stark division between supporters and hesitant
individuals to continue throughout the vaccination campaign. However, we find
an increasing commonality in the topical focus of the vaccine supporters and
vaccine hesitant, pointing to a possible common set of facts the two sides may
agree on. Still, we discover a series of concerns voiced by the hesitant
community, ranging from unfounded conspiracies (microchips in vaccines) to
public health policy discussion (vaccine passport limitations). We recommend an
ongoing surveillance of this debate, especially to uncover concerns around
vaccination before the public health decisions and official messaging are made
public.


title: Junction conditions of Palatini $f\left(\mathcal R,T\right)$ gravity

abstract:
  We work out the junction conditions for the Palatini $f(\mathcal{R},T)$
extension of General Relativity, where $f$ is an arbitrary function of the
curvature scalar $\mathcal{R}$ of an independent connection, and of the trace
$T$ of the stress-energy tensor of the matter fields. We find such conditions
on the allowed discontinuities of several geometrical and matter quantities,
some of which depart from their metric counterparts, and in turn extend their
Palatini $f(\mathcal{R})$ versions via some new $T$-dependent terms. Moreover,
we also identify some "exceptional cases" of $f(\mathcal{R},T)$ Lagrangians
such that some of these conditions can be discarded, thus allowing for further
discontinuities in $\mathcal{R}$ and $T$ and, in contrast with other theories
of gravity, they are shown to not give rise to extra components in the matter
sector e.g. momentum fluxes and double gravitational layers. We discuss how
these junction conditions, together with the non-conservation of the
stress-energy tensor ascribed to these theories, may induce non-trivial changes
in the shape of specific applications such as traversable thin-shell wormholes.


title:
Search for Higgs boson decays to a Z boson and a photon in proton-proton
  collisions at $\sqrt{s}$ = 13 TeV

abstract:
  Results are presented from a search for the Higgs boson decay
H$\to$Z$\gamma$, where Z$\to\ell^+\ell^-$ with $\ell$ = e or $\mu$. The search
is performed using a sample of proton-proton (pp) collision data at a
center-of-mass energy of 13 TeV, recorded by the CMS experiment at the LHC,
corresponding to an integrated luminosity of 138 fb$^{-1}$. Events are assigned
to mutually exclusive categories, which exploit differences in both event
topology and kinematics of distinct Higgs production mechanisms to enhance
signal sensitivity. The signal strength $\mu$, defined as the product of the
cross section and the branching fraction
[$\sigma($pp$\to$H$)\mathcal{B}($H$\to$Z$\gamma)$] relative to the standard
model prediction, is extracted from a simultaneous fit to the
$\ell^+\ell^-\gamma$ invariant mass distributions in all categories and is
found to be $\mu$=2.4$\pm$0.9 for a Higgs boson mass of 125.38 GeV. The
statistical significance of the observed excess of events is 2.7 standard
deviations. This measurement corresponds to
$\sigma($pp$\to$H$)\mathcal{B}($H$\to$Z$\gamma)$ = 0.21$\pm$0.08 pb. The
observed (expected) upper limit at 95% confidence level on $\mu$ is 4.1 (1.8).
The ratio of branching fractions
$\mathcal{B}($H$\to$Z$\gamma)/\mathcal{B}($H$\to\gamma\gamma)$ is measured to
be 1.5$^{+0.7}_{-0.6}$, which agrees with the standard model prediction of 0.69
$\pm$ 0.04 at the 1.5 standard deviation level.


title: The linear mode analysis and spin relaxation

abstract:
  In this paper, a detailed analysis on normal modes of the linearized Hermite
collision operator is presented, which follows from linearizing spin Boltzmann
equation for massive fermions proposed in \cite{Weickgenannt:2021cuo} with the
non-diagonal part of the transition rate neglected and approximating what we
got with a mutilated operator. With the assumption of total angular momentum
conservation, the collision term is proved to well describe the equilibrium
state and gives proper interpretation for collisional invariants, thus is
relevant for the research on local spin polarization. Following the familiar
fashion as used in quantum mechanics, we treat the problem of solving normal
modes as a degenerate perturbation problem and calculate the dispersion
relations for intriguing eleven zero modes, which form one-to-one
correspondence to all collisional invariants. We find that the results of
spinless modes appearing in ordinary hydrodynamics are consistent with
available conclusions in textbooks. As for spin-related modes, we obtain the
frequencies up to second order in wave vector and relate them with the
dissipation of spin density fluctuation. In addition, the ratio of two
relaxation time scales for spin and momentum is shown as a function of reduced
mass, which reads that based on present framework spin equilibration is almost
as slow as momentum equilibration as far as the strange quark spins in quark
gluon plasma (QGP) are concerned.


title: Edge-PRUNE: Flexible Distributed Deep Learning Inference

abstract:
  Collaborative deep learning inference between low-resource endpoint devices
and edge servers has received significant research interest in the last few
years. Such computation partitioning can help reducing endpoint device energy
consumption and improve latency, but equally importantly also contributes to
privacy-preserving of sensitive data. This paper describes Edge-PRUNE, a
flexible but light-weight computation framework for distributing machine
learning inference between edge servers and one or more client devices.
Compared to previous approaches, Edge-PRUNE is based on a formal dataflow
computing model, and is agnostic towards machine learning training frameworks,
offering at the same time wide support for leveraging deep learning
accelerators such as embedded GPUs. The experimental section of the paper
demonstrates the use and performance of Edge-PRUNE by image classification and
object tracking applications on two heterogeneous endpoint devices and an edge
server, over wireless and physical connections. Endpoint device inference time
for SSD-Mobilenet based object tracking, for example, is accelerated 5.8x by
collaborative inference.


title:
Domain Knowledge-Infused Deep Learning for Automated
  Analog/Radio-Frequency Circuit Parameter Optimization

abstract:
  The design automation of analog circuits is a longstanding challenge. This
paper presents a reinforcement learning method enhanced by graph learning to
automate the analog circuit parameter optimization at the pre-layout stage,
i.e., finding device parameters to fulfill desired circuit specifications.
Unlike all prior methods, our approach is inspired by human experts who rely on
domain knowledge of analog circuit design (e.g., circuit topology and couplings
between circuit specifications) to tackle the problem. By originally
incorporating such key domain knowledge into policy training with a multimodal
network, the method best learns the complex relations between circuit
parameters and design targets, enabling optimal decisions in the optimization
process. Experimental results on exemplary circuits show it achieves
human-level design accuracy (99%) 1.5X efficiency of existing best-performing
methods. Our method also shows better generalization ability to unseen
specifications and optimality in circuit performance optimization. Moreover, it
applies to design radio-frequency circuits on emerging semiconductor
technologies, breaking the limitations of prior learning methods in designing
conventional analog circuits.


title: Shadows of boson and Proca stars with thin accretion disks

abstract:
  In this work, we obtain the shadow images of spherically symmetric scalar
boson and Proca stars using analytical fittings of numerical solutions, when
illuminated by a geometrically thin accretion disk. We chose a sample of four
boson and four Proca stars with radii ranging from more compact configurations
with $R\sim 9M$ to more dilute configurations with $R\sim 20M$, where $M$ is
the total mass of the bosonic star. In these configurations, the absence of the
photon sphere (the locus of unstable bound geodesics) makes the optical
appearance of these stars to be dominated by a single luminous ring enclosing a
central brightness depression, and no further light rings are available. We
show that if one considers face-on observations and a disk model whose emission
is truncated at some finite radius at which the luminosity attains its maximum
value, both the size of the shadow, as well as the luminosity and depth of the
bright region, are heavily influenced by the emission profile, with the choice
of the type and parameters of the bosonic stars in our samples having a
sub-dominant influence. These differences are nonetheless significantly
magnified when one allows the accretion disk to extend close enough to the
center of the star. Our results point out that even though bosonic stars are
horizonless and do not have a photon sphere, some of them may be able to
produce conventional black hole shadow-like images provided that their
compactness is large enough, thus being potentially consistent with current and
future observations.


title: MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

abstract:
  Neural Architecture Search (NAS) has enabled automatic discovery of more
efficient neural network architectures, especially for mobile and embedded
vision applications. Although recent research has proposed ways of quickly
estimating latency on unseen hardware devices with just a few samples, little
focus has been given to the challenges of estimating latency on runtimes using
optimized graphs, such as TensorRT and specifically for edge devices. In this
work, we propose MAPLE-Edge, an edge device-oriented extension of MAPLE, the
state-of-the-art latency predictor for general purpose hardware, where we train
a regression network on architecture-latency pairs in conjunction with a
hardware-runtime descriptor to effectively estimate latency on a diverse pool
of edge devices. Compared to MAPLE, MAPLE-Edge can describe the runtime and
target device platform using a much smaller set of CPU performance counters
that are widely available on all Linux kernels, while still achieving up to
+49.6% accuracy gains against previous state-of-the-art baseline methods on
optimized edge device runtimes, using just 10 measurements from an unseen
target device. We also demonstrate that unlike MAPLE which performs best when
trained on a pool of devices sharing a common runtime, MAPLE-Edge can
effectively generalize across runtimes by applying a trick of normalizing
performance counters by the operator latency, in the measured hardware-runtime
descriptor. Lastly, we show that for runtimes exhibiting lower than desired
accuracy, performance can be boosted by collecting additional samples from the
target device, with an extra 90 samples translating to gains of nearly +40%.


title: An End-to-End Dialogue Summarization System for Sales Calls

abstract:
  Summarizing sales calls is a routine task performed manually by salespeople.
We present a production system which combines generative models fine-tuned for
customer-agent setting, with a human-in-the-loop user experience for an
interactive summary curation process. We address challenging aspects of
dialogue summarization task in a real-world setting including long input
dialogues, content validation, lack of labeled data and quality evaluation. We
show how GPT-3 can be leveraged as an offline data labeler to handle training
data scarcity and accommodate privacy constraints in an industrial setting.
Experiments show significant improvements by our models in tackling the
summarization and content validation tasks on public datasets.


title:
Candidate eclipsing binary systems with a Delta Scuti star in Northern
  TESS field

abstract:
  Existence of pulsating stars in eclipsing binaries has been known for
decades. These types of objects are extremely valuable systems for astronomical
studies as they exhibit both eclipsing and pulsation variations. The eclipsing
binaries are the only way to directly measure the mass and radius of stars with
a good accuracy ($\leq$1\%), while the pulsations are a unique way to probe the
stellar interior via oscillation frequencies. There are different types of
pulsating stars existing in eclipsing binaries. One of them is the Delta Scuti
variables. Currently, the known number of Delta Scuti stars in eclipsing
binaries is around 90 according to the latest catalog of these variables. An
increasing number of these kinds of variables is important to understand the
stellar structure, evolution and the effect of binarity on the pulsations.
Therefore, in this study, we focus on discovering new eclipsing binaries with
Delta Scuti component(s). We searched for the northern TESS field with a visual
inspection by following some criteria such as light curve shape, the existence
of pulsation like variations in the out-of-eclipse light curve and the Teff
values of the targets. As a result of these criteria, we determined some
targets. The TESS light curves of the selected targets first were removed from
the binarity and frequency analysis was performed on the residuals. The
luminosity, absolute and bolometric magnitudes of the targets were calculated
as well. To find how much of these parameters represent the primary binary
component (more luminous) we also computed the flux density ratio of the
systems by utilizing the area of the eclipses. In addition, the positions of
the systems in the H-R diagram were examined considering the flux density
ratios. As a consequence of the investigation, we defined 38 candidates Delta
Scuti and also one Maia variable in eclipsing binary systems.


title: Market Integration of Excess Heat

abstract:
  Excess heat will be an important heat source in future carbon-neutral
district heating systems. A barrier to excess heat integration is the lack of
appropriate scheduling and pricing systems for these producers, which generally
have small capacity and limited flexibility. In this work, we formulate and
analyze two methods for scheduling and pricing excess heat producers:
self-scheduling and market participation. In the former, a price signal is sent
to excess heat producers, based on which they determine their optimal schedule.
The latter approach allows excess heat producers to participate in a market
clearing. In a realistic case study of the Copenhagen district heating system,
we investigate market outcomes for the two excess heat integration paradigms
under increasing excess heat penetration. An important conclusion is that in
systems of high excess heat penetration, simple price signal methods will not
suffice, and more sophisticated price signals or coordinated dispatch become a
necessity.


title: Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming

abstract:
  Short video streaming applications have recently gained substantial traction,
but the non-linear video presentation they afford swiping users fundamentally
changes the problem of maximizing user quality of experience in the face of the
vagaries of network throughput and user swipe timing. This paper describes the
design and implementation of Dashlet, a system tailored for high quality of
experience in short video streaming applications. With the insights we glean
from an in-the-wild TikTok performance study and a user study focused on swipe
patterns, Dashlet proposes a novel out-of-order video chunk pre-buffering
mechanism that leverages a simple, non machine learning-based model of users'
swipe statistics to determine the pre-buffering order and bitrate. The net
result is a system that achieves 77-99% of an oracle system's QoE and
outperforms TikTok by 43.9-45.1x, while also reducing by 30% the number of
bytes wasted on downloaded video that is never watched.


title: Theoretical Study of Kr Hollow Atoms under X-ray Free-Electron Laser

abstract:
  By calculating the correlation between the total photoionization
cross-section of the ground state of the Kr atom and photon energy, three
particular photon energies close to the near inner orbital energy of 1.75 keV,
1.90 keV, and 14.30 keV are determined in this work. The dynamical simulation
under 17.50 keV photon energy in the experimental conditions is achieved by
implementing the Monte Carlo method and optimizing the photon flux modeling
parameters. As a result, our calculated data are more consistent with the
experimental phenomena. The light saturation phenomenon of Kr at 1.75 keV, 1.90
keV, 14.30 keV, and 17.50 keV energies is further calculated and researched
using the optimized photon flux model theory. We statistically compare the main
ionization paths under those four specific photon energies and calculate the
population changes of various hollow atoms. The results demonstrate that the
ratio of hollow atoms produced at the critical ionization photon energy is
high. Furthermore, the change of position is smooth, showing the significant
difference between the generation mode of ions with low photon energy and those
with high photon energy, which has important reference significance for
studying hollow atoms with medium and high charge states.


title:
Towards assessing agricultural land suitability with causal machine
  learning

abstract:
  Understanding the suitability of agricultural land for applying specific
management practices is of great importance for sustainable and resilient
agriculture against climate change. Recent developments in the field of causal
machine learning enable the estimation of intervention impacts on an outcome of
interest, for samples described by a set of observed characteristics. We
introduce an extensible data-driven framework that leverages earth observations
and frames agricultural land suitability as a geospatial impact assessment
problem, where the estimated effects of agricultural practices on
agroecosystems serve as a land suitability score and guide decision making. We
formulate this as a causal machine learning task and discuss how this approach
can be used for agricultural planning in a changing climate. Specifically, we
extract the agricultural management practices of "crop rotation" and "landscape
crop diversity" from crop type maps, account for climate and land use data, and
use double machine learning to estimate their heterogeneous effect on Net
Primary Productivity (NPP), within the Flanders region of Belgium from 2010 to
2020. We find that the effect of crop rotation was insignificant, while
landscape crop diversity had a small negative effect on NPP. Finally, we
observe considerable effect heterogeneity in space for both practices and
analyze it.


title:
Measurements of Higgs boson production in the decay channel with a pair
  of $\tau$ leptons in proton-proton collisions at $\sqrt{s}$ = 13 TeV

abstract:
  Measurements of Higgs boson production, where the Higgs boson decays into a
pair of $\tau$ leptons, are presented, using a sample of proton-proton
collisions collected with the CMS experiment at a center-of-mass energy of 13
TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. Three analyses
are presented. Two are targeting Higgs boson production via gluon fusion and
vector boson fusion: a neural network based analysis and an analysis based on
an event categorization optimized on the ratio of signal over background
events. These are complemented by an analysis targeting vector boson associated
Higgs boson production. Results are presented in the form of signal strengths
relative to the standard model predictions and products of cross sections and
branching fraction to $\tau$ leptons, in up to 16 different kinematic regions.
For the simultaneous measurements of the neural network based analysis and the
analysis targeting vector boson associated Higgs boson production signal
strengths are found to be 0.82$\pm$0.11 for inclusive Higgs boson production,
0.67$\pm$0.19 (0.81$\pm$0.17) for the production mainly via gluon fusion
(vector boson fusion), and 1.79$\pm$0.45 for vector boson associated Higgs
boson production.


title:
Mean oscillation gradient estimates for elliptic systems in divergence
  form with VMO coefficients

abstract:
  We consider gradient estimates for $H^1$ solutions of linear elliptic systems
in divergence form $\partial_\alpha(A_{ij}^{\alpha\beta} \partial_\beta u^j) =
0$. It is known that the Dini continuity of coefficient matrix $A =
(A_{ij}^{\alpha\beta}) $ is essential for the differentiability of solutions.
We prove the following results:
  (a) If $A$ satisfies a condition slightly weaker than Dini continuity but
stronger than belonging to VMO, namely that the $L^2$ mean oscillation
$\omega_{A,2}$ of $A$ satisfies \[ X_{A,2} := \limsup_{r\rightarrow 0} r
\int_r^2 \frac{\omega_{A,2}(t)}{t^2} \exp\Big(C_* \int_{t}^R
\frac{\omega_{A,2}(s)}{s}\,ds\Big)\,dt < \infty, \] where $C_*$ is a positive
constant depending only on the dimensions and the ellipticity, then $\nabla u
\in BMO$.
  (b) If $X_{A,2} = 0$, then $\nabla u \in VMO$.
  (c) If $A \in VMO$ and if $\nabla u \in L^\infty$, then $\nabla u \in VMO$.
  (d) Finally, examples satisfying $X_{A,2} = 0$ are given showing that it is
not possible to prove the boundedness of $\nabla u$ in statement (b), nor the
continuity of $\nabla u$ in statement (c).


title:
Low Frequency Marsquakes and Where to Find Them: Back Azimuth
  Determination Using a Polarization Analysis Approach

abstract:
  NASA's InSight mission on Mars continues to record seismic data over 3 years
after landing, and to date, over a thousand marsquakes have been identified.
With only a single seismic station, the determination of the epicentral
location is far more challenging than on Earth. The Marsquake Service (MQS)
produces seismicity catalogues from data collected by InSight and provides
distance and back azimuth estimates when these can be reliably determined -
when both are available these are combined to provide a location. Currently,
MQS do not assign a back azimuth to the vast majority of marsquakes. In this
work we develop and apply a polarization analysis method to determine the back
azimuth of seismic events from the polarization of observed P and S-wave
arrivals. The method is first applied to synthetic marsquakes, and then
calibrated using a set of well-located earthquakes that have been recorded in
Tennant Creek, Australia. We find that the back azimuth is estimated reliably
using our polarization method. The same approach is then used for a set of high
quality marsquakes recorded up to October 2021. We are able to estimate back
azimuths for 24 marsquakes, 16 of these without MQS back azimuths. We locate
most events to the east of InSight, in the general region of Cerberus Fossae.


title: SXP 15.6 -- an accreting pulsar close to spin equilibrium?

abstract:
  SXP 15.6 is a recently established Be star X-ray binary system (BeXRB) in the
Small Magellanic Cloud (SMC). Like many such systems the variable X-ray
emission is driven by the underlying behaviour of the mass donor Be star. It is
shown here that the neutron star in this system is exceptionally close to spin
equilibrium averaged over several years, with the angular momentum gain from
mass transfer being almost exactly balanced by radiative losses. This makes SXP
15.6 exceptional compared to all other known members of its class in the SMC,
all of whom exhibit much higher spin period changes. In this paper we report on
X-ray observations of the brightest known outburst from this system. These
observations are supported by contemporaneous optical and radio observations,
as well as several years of historical data.


title:
Electronic band structure and surface states in Dirac semimetal
  LaAgSb$_{2}$

abstract:
  LaAgSb$_{2}$ is a Dirac semimetal showing charge density wave (CDW) order.
Previous ARPES results suggest the existence of the Dirac-cone-like structure
in the vicinity of the Fermi level along the $\Gamma$-M direction [X. Shi et
al., Phys. Rev. B 93, 081105(R) (2016)]. This paper is devoted to a complex
analysis of the electronic band structure of LaAgSb$_{2}$ by means of
angle-resolved photoemission spectroscopy (ARPES) and theoretical calculations
within the direct ab initio method as well as tight binding model formulation.
To investigate the possible surface states we performed the direct DFT slab
calculation and the surface Green function calculation for the (001) surface.
The appearance of the surface states, which depends strongly on surface, points
to the conclusion that LaSb termination is realized in the cleaved crystals.
Moreover, the surface states predicted by our calculations at the $\Gamma$ and
$X$ points are found by ARPES. Nodal lines, which exist along X--R and M-A path
due to crystal symmetry, are also observed experimentally. The calculations
reveal another nodal lines, which originate from vanishing of spin-orbit
coupling and are located at X-M-A-R plane at the Brillouin zone boundary. In
addition, we analyze band structure along the $\Gamma$-M path to verify,
whether Dirac surface states can be expected. Their appearance in this region
is not confirmed.


title: A categorical characterization of strong Steiner $\omega$-categories

abstract:
  Strong Steiner $\omega$-categories are a class of $\omega$-categories that
admit algebraic models in the form of chain complexes, whose formalism allows
for several explicit computations. The conditions defining strong Steiner
$\omega$-categories are traditionally expressed in terms of the associated
chain complex, making them somewhat disconnected from the $\omega$-categorical
intuition. The purpose of this paper is to characterize this class as the class
of polygraphs that satisfy a loop-freeness condition that does not make
explicit use of the associated chain complex and instead relies on the
categorical features of $\omega$-categories.


title:
Complementarity Reformulations for the Optimal Design of Distributed
  Energy Systems with Multiphase Optimal Power Flow

abstract:
  The design of grid-connected distributed energy systems (DES) has been
investigated extensively as an optimisation problem in the past, but most
studies do not include nonlinear constraints associated with unbalanced
alternating current (AC) power flow in distribution networks. Previous studies
that do consider AC power flow use either less complex balanced formulations
applicable to transmission networks, or iterative linearisations derived from
local power flow solutions and prior knowledge of the design. To address these
limitations, this study proposes a new algorithm for obtaining DES design
decisions subject to nonlinear power flow models. The use of regularised
complementarity reformulations for operational constraints that contain binary
variables is proposed. This allows the use of large-scale nonlinear solvers
that can find locally optimal solutions, eliminating the need for
linearisations and prior knowledge while improving accuracy. DES design models
with either multiphase optimal power flow (MOPF), which captures inherent phase
imbalances present in distribution networks, or balanced optimal power flow
formulations (OPF) are tested using a modified version of the unbalanced IEEE
EU low-voltage network. Results are compared with a popular linear DES design
framework, which proposes an infeasible operational schedule when tested with
MOPF, while the fixed design alone produces the highest annualised costs.
Despite the increased complexity, DES with MOPF obtains the best solution,
enabling a greater integration of solar capacity and reducing total annualised
cost when compared to DES with OPF. The new algorithm achieves a 19%
improvement when compared with solving a bi-level model for DES with OPF, where
the entire binary topology in the nonlinear model is fixed. The study therefore
enables the acquisition of DES designs that can work symbiotically within
distribution networks.


title:
Stability in affine optimal control problems constrained by semilinear
  elliptic partial differential equations

abstract:
  This paper investigates stability properties of affine optimal control
problems constrained by semilinear elliptic partial differential equations.
This is done by studying the so called metric subregularity of the set-valued
mapping associated with the system of first order necessary optimality
conditions. Preliminary results concerning the differentiability of the
functions involved are established, especially the so-called switching
function. Using this ansatz, more general nonlinear perturbations are
encompassed, and under weaker assumptions, than the ones previously considered
in the literature on control constrained elliptic problems. Finally, the
applicability of the results is illustrated with some error estimates for the
Tikhonov regularization.


title: Scalable particle-based alternatives to EM

abstract:
  Building on (Neal and Hinton, 1998), where the problem tackled by EM is
recast as the optimization of a free energy functional on an
infinite-dimensional space, we obtain three practical particle-based
alternatives to EM applicable to broad classes of models. All three are derived
through straightforward discretizations of gradient flows associated with the
functional. The novel algorithms scale well to high-dimensional settings and
outperform existing state-of-the-art methods in numerical experiments.


title: Neural-Network Quantum States: A Systematic Review

abstract:
  The so-called contemporary AI revolution has reached every corner of the
social, human and natural sciences -- physics included. In the context of
quantum many-body physics, its intersection with machine learning has
configured a high-impact interdisciplinary field of study; with the arise of
recent seminal contributions that have derived in a large number of
publications. One particular research line of such field of study is the
so-called Neural-Network Quantum States, a powerful variational computational
methodology for the solution of quantum many-body systems that has proven to
compete with well-established, traditional formalisms. Here, a systematic
review of literature regarding Neural-Network Quantum States is presented.


title:
Refining magnetic interactions from the magnetic field dependence of
  spin-wave excitations in magnetoelectric LiFePO$_4$

abstract:
  We investigated the spin excitations of magnetoelectric $\text{LiFePO}_4$ by
THz absorption spectroscopy in magnetic fields up to 33$\,$T. By studying their
selection rules, we found not only magnetic-dipole, but also electric-dipole
active (electromagnons) and magnetoelectric resonances. The magnetic field
dependence of four strong low-energy modes is reproduced well by our
four-sublattice spin model for fields applied along the three orthorhombic
axes. From the fit, we refined the exchange couplings, single-ion anisotropies,
and the Dzyaloshinskii-Moriya interaction parameters. Additional spin
excitations not described by the mean-field model are observed at higher
frequencies. Some of them shows a strong shift with magnetic field, up to
4$\,\text{cm}^{-1}/\text{T}$, when the field is applied along the easy axis.
Based on this field dependence, we attribute these high frequency resonances to
excitation of higher spin multipoles and of two magnons, which become
THz-active due to the low symmetry of the magnetically ordered state.


title:
Accelerating Materials-Space Exploration by Mapping Materials Properties
  via Artificial Intelligence: The Case of the Lattice Thermal Conductivity

abstract:
  Accurate artificial-intelligence models are key to accelerate the discovery
of new functional materials with optimal properties for various applications.
Examples include superconductivity, catalysis, and thermoelectricity.
Advancements in this field are often hindered by the scarcity and quality of
available data and the significant effort required to acquire new data. For
such applications, reliable surrogate models that help guide materials space
exploration using easily accessible materials properties are urgently needed.
Here, we present a general, data-driven framework that provides quantitative
predictions as well as qualitative rules for steering data creation for all
datasets via a combination of symbolic regression and sensitivity analysis. We
demonstrate the power of the framework by generating an accurate analytic model
for the lattice thermal conductivity using only 75 experimentally measured
values. By extracting the most influential material properties from this model,
we are then able to hierarchically screen 732 materials and find 80
ultra-insulating materials.


title:
Dependence on the thermodynamic state of self-diffusion of pseudo
  hard-spheres

abstract:
  Self-diffusion, $D$, in a system of particles that interact with a pseudo
hard sphere potential is analyzed. Coupling with a solvent is represented by a
Langevin thermostat, characterized by the damping time $t_d$. The hypotheses
that $D=D_0 \varphi$ is proposed, where $D_0$ is the small concentration
diffusivity and $\varphi$ is a thermodynamic function that represents the
effects of interactions as concentration is increased. Molecular dynamics
simulations show that different values of the noise intensity modify $D_0$ but
do not modify $\varphi$. This result is consistent with the assumption that
$\varphi$ is a thermodynamic function, since the thermodynamic state is not
modified by the presence of damping and noise.


title:
Blending Data and Physics Against False Data Injection Attack: An
  Event-Triggered Moving Target Defence Approach

abstract:
  Recently, data-driven detectors and physics-based Moving Target Defences
(MTD) have been proposed to detect false data injection (FDI) attacks on power
system state estimation. However, the uncontrollable false positive rate of the
data-driven detector and the extra cost of frequent MTD usage limit their wide
applications. Few works have explored the overlap between these two areas to
collaboratively detect FDI attacks. To fill the gap, this paper proposes
blending data-driven and physics-based approaches to enhance the detection of
FDI attacks. To start, a physics-informed data-driven attack detection and
identification algorithm is proposed. Following this, an MTD protocol is
triggered by a positive signal from the data-driven detector and is formulated
as a bilevel optimisation to robustly guarantee the effectiveness of MTD
against the worst-case attack around the identified attack vector while
maximising the hiddenness of MTD. To guarantee the feasibility and convergence,
the bilevel nonconvex optimisation is separated into two stages, and for each
stage, a semidefinite programming is derived through duality and linear matrix
inequality. The simulation results verify that by blending data and physics, it
can significantly enhance the detection performance while simultaneously
reducing the false positive rate of the data-driven detector and the usage of
MTD.


title:
Ab initio studies of double Gamow-Teller transition and its correlation
  with neutrinoless double beta decay

abstract:
  We use chiral interactions and several {\em ab initio} methods to compute the
nuclear matrix elements (NMEs) for ground-state to ground-state double
Gamow-Teller transitions in a range of isotopes, and explore the correlation of
these NMEs with those for neutrinoless double beta decay produced by the
exchange of a light Majorana neutrino. When all the NMEs of both
isospin-conserving and isospin-changing transitions from the {\em ab initio}
calculations are considered, the correlation is strong. For the experimentally
relevant isospin-changing transitions by themselves, however, the correlation
is weaker and may not be helpful for reducing the uncertainty in the NMEs for
neutrinoless double-beta decay.


title:
Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying
  Non-Autonomous Dynamical Systems

abstract:
  While trade-offs between modeling effort and model accuracy remain a major
concern with system identification, resorting to data-driven methods often
leads to a complete disregard for physical plausibility. To address this issue,
we propose a physics-guided hybrid approach for modeling non-autonomous systems
under control. Starting from a traditional physics-based model, this is
extended by a recurrent neural network and trained using a sophisticated
multi-objective strategy yielding physically plausible models. While purely
data-driven methods fail to produce satisfying results, experiments conducted
on real data reveal substantial accuracy improvements by our approach compared
to a physics-based model.


title: How much gallium do we need for a p-type Cu(In,Ga)Se2?

abstract:
  Doping in the chalcopyrite Cu(In,Ga)Se2 is determined by intrinsic point
defects. In the ternary CuInSe2, both N-type and P-type conductivity can be
obtained depending on the growth conditions and stoichiometry: N-type is
obtained when grown Cu-poor, Se-poor and alkali-free. CuGaSe2, on the other
hand, is found to be always a P-type semiconductor that seems to resist all
kinds of N-type doping no matter whether it comes from native defects or
extrinsic impurities. In this contribution, we study the N-to-P transition in
Cu-poor Cu(In,Ga)Se2 single crystals in dependence of the gallium content. Our
results show that Cu(In,Ga)Se2 can still be grown as an N-type semiconductor
until the gallium content reaches the critical concentration of 15-19%, where
the N-to-P transition occurs. Furthermore, trends in the Seebeck coefficient
and activation energies extracted from temperature-dependent conductivity
measurements, demonstrate that the carrier concentration drops by around two
orders of magnitude near the transition concentration. Our proposed model
explains the N-to-P transition based on the differences in formation energies
of donor and acceptor defects caused by the addition of gallium.


title: CapOnImage: Context-driven Dense-Captioning on Image

abstract:
  Existing image captioning systems are dedicated to generating narrative
captions for images, which are spatially detached from the image in
presentation. However, texts can also be used as decorations on the image to
highlight the key points and increase the attractiveness of images. In this
work, we introduce a new task called captioning on image (CapOnImage), which
aims to generate dense captions at different locations of the image based on
contextual information. To fully exploit the surrounding visual context to
generate the most suitable caption for each location, we propose a multi-modal
pre-training model with multi-level pre-training tasks that progressively learn
the correspondence between texts and image locations from easy to difficult.
Since the model may generate redundant captions for nearby locations, we
further enhance the location embedding with neighbor locations as context. For
this new task, we also introduce a large-scale benchmark called CapOnImage2M,
which contains 2.1 million product images, each with an average of 4.8
spatially localized captions. Compared with other image captioning model
variants, our model achieves the best results in both captioning accuracy and
diversity aspects. We will make code and datasets public to facilitate future
research.


title:
Euler top and freedom in supersymmetrization of one-dimensional
  mechanics

abstract:
  Recently A.Galajinsky has suggested the N=1 supersymmetric extension of Euler
top and made a few interesting observations on its properties [arXiv:2111.06083
[hep-th]]. In this paper we use the formulation of the Euler top as a system on
complex projective plane, playing the role of phase space, i.e. as a
one-dimensional mechanical system.
  Then we suggest the supersymmetrization scheme of the generic one-dimensional
systems with positive Hamiltonian which yields a priori integrable family of
N=2k supersymmetric Hamiltonians parameterized by N/2 arbitrary real functions.


title:
Computing the Lyapunov operator \varphi-functions, with an application
  to matrix-valued exponential integrators

abstract:
  In this paper, we develop efficient and accurate evaluation for the Lyapunov
operator function $\varphi_l(\mathcal{L}_A)[Q],$ where $\varphi_l(\cdot)$ is
the function related to the exponential, $\mathcal{L}_A$ is a Lyapunov operator
and $Q$ is a symmetric and full-rank matrix. An important application of the
algorithm is to the matrix-valued exponential integrators for matrix
differential equations such as differential Lyapunov equations and differential
Riccati equations. The method is exploited by using the modified scaling and
squaring procedure combined with the truncated Taylor series. A quasi-backward
error analysis is presented to determine the value of the scaling parameter and
the degree of the Taylor approximation. Numerical experiments show that the
algorithm performs well in both accuracy and efficiency.


title:
Discovering gravitationally lensed gravitational waves: predicted rates,
  candidate selection, and localization with the Vera Rubin Observatory

abstract:
  The first secure detection of a gravitationally lensed gravitational (GW)
will be a watershed moment, as it will bring together these two pillars of
General Relativity for the first time. Accurate selection and interpretation of
candidate lensed GWs is challenging for numerous reasons, including large sky
localization uncertainties for most GW detections, the broad range of
gravitational lenses spanning galaxy/group/cluster-scales in the dark matter
halo mass function, and uncertainty in the intrinsic mass function of compact
object remnants of stellar evolution. We introduce a new magnification-based
approach to predicting the rates of lensed GWs that is agnostic to the mass and
structure of the lenses and combine it with expressions for arrival time
difference for representative lenses and their catastrophes to delineate the
range of expected arrival time differences. We also predict the lightcurves of
lensed kilonova counterparts to lensed binary neutron star (NS-NS) mergers and
assess the feasibility of detection with the Vera Rubin Observatory. Our main
conclusions are: (1) selection of candidate lensed NS-NS mergers from the mass
gap between NS and black holes in low latency is an efficient approach with a
rate approaching one per year in the mid-2020s, (2) the arrival time
differences of lensed NS-NS/kilonovae are typically $\lesssim1\,$year, and thus
well-matched to the operations of GW detectors and optical telescopes, and (3)
detection of lensed kilonovae is feasible with the Vera Rubin Observatory.
Whilst our predictions are motivated by lensing, they provide a physically
well-understood approach to exploring the mass gap electromagnetically as the
number of detections in this exciting region of parameter space grows.


title:
On the gravitational lensing interpretation of three gravitational wave
  detections in the mass gap by LIGO and Virgo

abstract:
  We search for gravitational wave (GW) events from LIGO-Virgo's third run that
may have been affected by gravitational lensing. Gravitational lensing delays
the arrival of GWs, and alters their amplitude -- thus biasing the inferred
progenitor masses. This would provide a physically well-understood
interpretation of GW detections in the "mass gap" between neutron stars and
black holes, as gravitationally lensed binary neutron star (BNS) mergers. We
selected three GW detections in LIGO-Virgo's third run for which the
probability of at least one of the constituent compact objects being in the
mass gap was reported as high with low latency -- i.e. candidate lensed BNS
mergers. Our observations of powerful strong lensing clusters located adjacent
to the peak of their sky localisation error maps reached a sensitivity $\rm
AB\simeq25.5$ in the $z'$-band with the GMOS instruments on the Gemini
telescopes, and detected no candidate lensed optical counterparts. We combine
recent kilonova lightcurve models with recent predictions of the lensed BNS
population and the properties of the objects that we followed up to show that
realistic optical counterparts were detectable in our observations. Further
detailed analysis of two of the candidates suggests that they are a plausible
pair of images of the same low-mass binary black hole merger, lensed by a local
galaxy or small group of galaxies. This further underlines that access to
accurate mass information with low latency would improve the efficiency of
candidate lensed NS-NS selection.


title:
Heat equilibration of integer and fractional quantum Hall edge modes in
  graphene

abstract:
  Hole-conjugate states of the fractional quantum Hall effect hosts
counter-propagating edge channels which are thought to exchange charge and
energy. These exchanges have been the subject of extensive theoretical and
experimental works; in particular, it is yet unclear if the presence of integer
quantum Hall edge channels stemming from fully filled Landau levels affects
heat equilibration along the edge. In this letter, we present heat transport
measurements in quantum Hall states of graphene demonstrating that the integer
channels can strongly equilibrate with the fractional ones, leading to markedly
different regimes of quantized heat transport that depend on edge
electrostatics. Our results allow for a better comprehension of the complex
edge physics in the fractional quantum Hall regime.


title: Switchback Deflections Beyond the Early Parker Solar Probe Encounters

abstract:
  Switchbacks are Aflv\'enic fluctuations in the solar wind, which exhibit
large rotations in the magnetic field direction. Observations from Parker Solar
Probe's (PSP's) first two solar encounters have formed the basis for many of
the described switchback properties and generation mechanisms. However, this
early data may not be representative of the typical near-Sun solar wind,
biasing our current understanding of these phenomena. One defining switchback
property is the direction in which the magnetic fields deflects as the
spacecraft samples them. During the first solar encounter, this was primarily
in the tangential direction for the longest switchbacks, which has since been
discussed as evidence, and a testable prediction, of several switchback
generation methods. In this study, we re-examine the deflection direction of
switchbacks during the first eight PSP encounters to confirm the existence of a
systematic deflection direction. We first identify switchbacks exceeding a
threshold deflection in the magnetic field and confirm a previous finding that
they are arc-polarized. In agreement with earlier results from PSP's first
encounter, we find that groups of longer switchbacks tend to deflect in the
same direction for several hours. However, in contrast to earlier studies, we
find that there is no unique direction for these deflections, although several
solar encounters showed a non-uniform distribution in deflection direction with
a slight preference for the tangential direction. This result suggests a
systematic magnetic configuration for switchback generation, which is
consistent with interchange reconnection as a source mechanism, although this
new evidence does not rule out other mechanisms, such as the expansion of wave
modes.


title: The Wentzell Laplacian via Forms and the Approximative Trace

abstract:
  We use form methods to define suitable realisations of the Laplacian on a
domain $\Omega$ with Wentzell boundary conditions, i.e. such that $\partial_\nu
u + \beta u + \Delta u = 0$ holds in a suitable sense on the boundary of
$\Omega$. For those realisations, we study their semigroup generation
properties. Using the approximative trace, we give a unified treatment that in
part allows domains less regular than Lipschitz. Moreover, we admit $\beta$ to
be merely essentially bounded and complex-valued.


title: On the role of population heterogeneity in emergent communication

abstract:
  Populations have often been perceived as a structuring component for language
to emerge and evolve: the larger the population, the more structured the
language. While this observation is widespread in the sociolinguistic
literature, it has not been consistently reproduced in computer simulations
with neural agents. In this paper, we thus aim to clarify this apparent
contradiction. We explore emergent language properties by varying agent
population size in the speaker-listener Lewis Game. After reproducing the
experimental difference, we challenge the simulation assumption that the agent
community is homogeneous. We first investigate how speaker-listener asymmetry
alters language structure to examine two potential diversity factors: training
speed and network capacity. We find out that emergent language properties are
only altered by the relative difference of learning speeds between speaker and
listener, and not by their absolute values. From then, we leverage this
observation to control population heterogeneity without introducing confounding
factors. We finally show that introducing such training speed heterogeneities
naturally sort out the initial contradiction: larger simulated communities
start developing more stable and structured languages.


title:
Macaulay Matrix for Feynman Integrals: Linear Relations and Intersection
  Numbers

abstract:
  We elaborate on the connection between Gel'fand-Kapranov-Zelevinsky systems,
de Rham theory for twisted cohomology groups, and Pfaffian equations for
Feynman integrals. We propose a novel, more efficient algorithm to compute
Macaulay matrices, which are used to derive Pfaffian systems of differential
equations. The Pfaffian matrices are then employed to obtain linear relations
for ${\cal A}$-hypergeometric (Euler) integrals and Feynman integrals, through
recurrence relations and through projections by intersection numbers.


title:
Enabling discovery of gravitationally lensed explosive transients: a new
  method to build an all-sky watch-list of groups and clusters of galaxies

abstract:
  Cross-referencing a watchlist of galaxy groups and clusters with transient
detections from real-time streams of wide-field survey data is a promising
method for discovering gravitationally lensed explosive transients including
supernovae, kilonovae, gravitational waves and gamma-ray bursts in the next ten
years. However, currently there exists no catalogue of lenses with both
sufficient angular extent and depth to adequately perform such a search. In
this study, we develop a method capable of creating an all-sky list of galaxy
group- and cluster-scale objects out to $z\simeq1$ based on their lens-plane
properties and using only existing data from wide-field infrared surveys.
Importantly, the data exists ready to implement our methods in advance of when
the Vera Rubin Observatory's (Rubin's) Legacy Survey of Space and Time (LSST)
commences survey operations. In testing this method, we recover 91 per cent of
a sample containing known and candidate lensing objects with Einstein radii of
$\theta_E \geq 5\arcsec$. We also search the surrounding regions of this test
sample for other groups and clusters using our method and verify the existence
of any significant findings by visual inspection, deriving estimates of the
false positive rate that are as low as 6 per cent. The method is also tested on
simulated Rubin data from their DP0 programme, which yields complementary
results of a good recovery rate and low false positives.


title: Quantum Linear Optics via String Diagrams

abstract:
  We establish a formal bridge between qubit-based and photonic quantum
computing. We do this by defining a functor from the ZX calculus to linear
optical circuits. In the process we provide a compositional theory of quantum
linear optics which allows to reason about events involving multiple photons
such as those required to perform linear-optical and fusion-based quantum
computing.


title: Studies on Electrical Properties of Resistive Plate Chamber (RPC)

abstract:
  A numerical model based on finite element method has been developed to
simulate the electrical properties of RPC utilizing the law of current
conservation. It has been used to systematically investigate the effect of the
electrical as well as the geometrical parameters of the device components on
the potential distribution and field configuration, which are responsible
factors for governing performance of the RPC. The numerical model has been
validated by comparing the dark current and electric field with those produced
by another mathematical model based on surface currents. The efficacy of the
present model has been demonstrated by comparing its results to the
experimental measurements done with a glass RPC. The measured and estimated
values of these observables have been found to be in good agreement.


title: Absorption and Fixed Points for Semigroups of Quantum Channels

abstract:
  In the present work we review and refine some results about fixed points of
semigroups of quantum channels. Noncommutative potential theory enables us to
show that the set of fixed points of a recurrent semigroup is a W*-algebra;
aside from the intrinsic interest of this result, it brings an improvement in
the study of fixed points by means of absorption operators (a noncommutative
generalization of absorption probabilities): under the assumption of absorbing
recurrent space (hence allowing non-trivial transient space) we can provide a
description of the fixed points set and a probabilistic characterization of
when it is a W*-algebra in terms of absorption operators. Moreover we are able
to exhibit an example of a recurrent semigroup which does not admit a
decomposition of the Hilbert space into orthogonal minimal invariant domains
(contrarily to the case of classical Markov chains and positive recurrent
semigroups of quantum channels).


title:
Energy-Efficient Dynamic Edge Computing with Electromagnetic Field
  Exposure Constraints

abstract:
  We present a dynamic resource allocation strategy for energy-efficient and
Electromagnetic Field (EMF) exposure aware computation offloading at the
wireless network edge. The goal is to maximize the overall system sum-rate of
offloaded data, under stability (i.e. finite end-to-end delay), EMF exposure
and system power constraints. The latter comprises end devices for uplink
transmission and a Mobile Edge Host (MEH) for computation. Our proposed method,
based on Lyapunov stochastic optimization, is able to achieve this goal with
theoretical guarantees on asymptotic optimality, without any prior knowledge of
wireless channel statistics. Although a complex long-term optimization problem
is formulated, a per-slot optimization based on instantaneous realizations is
derived. Moreover, the solution of the instantaneous problem is provided with
closed form expressions and fast iterative procedures. Besides the theoretical
analysis, numerical results assess the performance of the proposed strategy in
striking the best trade-off between offloading sum-rate, power consumption, EMF
exposure, and E2E delay. To the best of our knowledge, this is the first work
addressing the problem of energy and exposure aware computation offloading.


title: Parametric control of Meissner screening in light-driven superconductors

abstract:
  We investigate the Meissner effect in a parametrically driven superconductor
using a semiclassical $U(1)$ lattice gauge theory. Specifically, we
periodically drive the $z$-axis tunneling, which leads to an enhancement of the
imaginary part of the $z$-axis conductivity at low frequencies if the driving
frequency is blue-detuned from the plasma frequency. This has been proposed as
a possible mechanism for light-enhanced interlayer transport in
YBa$_2$C$_3$O$_{7-\delta}$ (YBCO). In contrast to this enhancement of the
conductivity, we find that the screening of magnetic fields is less effective
than in equilibrium for blue-detuned driving, while it displays a tendency to
be enhanced for red-detuned driving.


title: On the Dirac spectrum of homogeneous 3-spheres

abstract:
  We show that any two left-invariant metrics on $S^3\cong\operatorname{SU}(2)$
which are isospectral for the associated classical Dirac operator $D$ must be
isometric. In the case of left-invariant metrics of positive scalar curvature,
we compute and use the smallest eigenvalue of $D^2$. We show analogous results
for left-invariant metrics on $\operatorname{SO}(3)=S^3/\{\pm1\}$ for each of
its two spin structures.


title:
Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid
  Massive MIMO Receive Array

abstract:
  For a sub-connected hybrid multiple-input multiple-output (MIMO) receiver
with $K$ subarrays and $N$ antennas, there exists a challenging problem of how
to rapidly remove phase ambiguity in only single time-slot. First, a DOA
estimator of maximizing received power (Max-RP) is proposed to find the maximum
value of $K$-subarray output powers, where each subarray is in charge of one
sector, and the center angle of the sector corresponding to the maximum output
is the estimated true DOA. To make an enhancement on precision, Max-RP plus
quadratic interpolation (Max-RP-QI) method is designed. In the proposed
Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three
DOA values corresponding to the largest three receive powers of Max-RP.
Finally, to achieve the CRLB, a Root-MUSIC plus Max-RP-QI scheme is developed.
Simulation results show that the proposed three methods eliminate the phase
ambiguity during one time-slot and also show low-computational-complexities. In
particular, the proposed Root-MUSIC plus Max-RP-QI scheme can reach the CRLB,
and the proposed Max-RP and Max-RP-QI are still some performance losses
$2dB\thicksim4dB$ compared to the CRLB.


title:
Estimation of Recursive Route Choice Models with Incomplete Trip
  Observations

abstract:
  This work concerns the estimation of recursive route choice models in the
situation that the trip observations are incomplete, i.e., there are
unconnected links (or nodes) in the observations. A direct approach to handle
this issue would be intractable because enumerating all paths between
unconnected links (or nodes) in a real network is typically not possible. We
exploit an expectation-maximization (EM) method that allows to deal with the
missing-data issue by alternatively performing two steps of sampling the
missing segments in the observations and solving maximum likelihood estimation
problems. Moreover, observing that the EM method would be expensive, we propose
a new estimation method based on the idea that the choice probabilities of
unconnected link observations can be exactly computed by solving systems of
linear equations. We further design a new algorithm, called as
decomposition-composition (DC), that helps reduce the number of systems of
linear equations to be solved and speed up the estimation. We compare our
proposed algorithms with some standard baselines using a dataset from a real
network and show that the DC algorithm outperforms the other approaches in
recovering missing information in the observations. Our methods work with most
of the recursive route choice models proposed in the literature, including the
recursive logit, nested recursive logit, or discounted recursive models.


title:
First do no harm: counterfactual objective functions for safe & ethical
  AI

abstract:
  To act safely and ethically in the real world, agents must be able to reason
about harm and avoid harmful actions. In this paper we develop the first
statistical definition of harm and a framework for factoring harm into
algorithmic decisions. We argue that harm is fundamentally a counterfactual
quantity, and show that standard machine learning algorithms are guaranteed to
pursue harmful policies in certain environments. To resolve this, we derive a
family of counterfactual objective functions that robustly mitigate for harm.
We demonstrate our approach with a statistical model for identifying optimal
drug doses. While identifying optimal doses using the causal treatment effect
results in harmful treatment decisions, our counterfactual algorithm identifies
doses that are far less harmful without sacrificing efficacy. Our results show
that counterfactual reasoning is a key ingredient for safe and ethical AI.


title:
Bridging microscopic cell dynamics to nematohydrodynamics of cell
  monolayers

abstract:
  It is increasingly being realized that liquid-crystalline features can play
an important role in the properties and dynamics of cell monolayers. Here, we
present a cell-based model of cell layers, based on the phase-field
formulation, that connects mechanical properties at the single cell level to
large-scale nematic and hydrodynamic properties of the tissue. In particular,
we present a minimal formulation that reproduces the well-known bend and splay
hydrodynamic instabilities of the continuum nemato-hydrodynamic formulation of
active matter, together with an analytical description of the instability
threshold in terms of activity and elasticity of the cells. Furthermore, we
provide a quantitative characterisation and comparison of flows and topological
defects for extensile and contractile stress generation mechanisms, and
demonstrate activity-induced heterogeneity and spontaneous formation of gaps
within a confluent monolayer. Together, these results contribute to bridging
the gap between micro-scale cell dynamics and tissue-scale collective cellular
organisation.


title:
Magnetochiral Properties of Spin Waves Existing in Nanotubes with Axial
  and Circumferential Magnetization

abstract:
  We report experimental studies of spin-wave excitations in individual 22 nm
thick Ni80Fe20 nanotubes with diameters of about 150 nm by means of Brillouin
light-scattering (BLS) spectroscopy. Irradiated by microwaves we resolve sets
of discrete resonances in the center of nanotubes ranging from 2.5 to 12.5 GHz.
Comparing to a recent theoretical work and micromagnetic simulations, we
identify different characteristic eigenmodes depending on the axial, mixed or
vortex configuration. The mixed and vortex states give rise to modes with
helical phase profiles substantiating an unusual nature of modes attributed to
non-reciprocal spin waves. Our findings provide microscopic insight into
tubular spin-wave nanocavities and magnetochiral effects for 3D nanomagnonics.


title:
Magnetic Drag and 3-D Effects in Theoretical High-Resolution Emission
  Spectra of Ultrahot Jupiters: the Case of WASP-76b

abstract:
  Ultrahot Jupiters are ideal candidates to explore with high-resolution
emission spectra. Detailed theoretical studies are necessary to investigate the
range of spectra we can expect to see from these objects throughout their
orbit, because of the extreme temperature and chemical longitudinal gradients
that exist across day and nightside regions. Using previously published 3D GCM
models of WASP-76b with different treatments of magnetic drag, we post-process
the 3D atmospheres to generate high-resolution emission spectra for two
wavelength ranges and throughout the planet's orbit. We find that the
high-resolution emission spectra vary strongly as a function of phase, at times
showing emission features, absorption features, or both, which are a direct
result of the 3D structure of the planet. At phases exhibiting both emission
and absorption features, the Doppler shift differs in direction between the two
spectral features, making them differentiable instead of canceling each other
out. Through the use of cross-correlation, we find different patterns in net
Doppler shift for models with different treatments of drag: the nightside
spectra show opposite signs in their Doppler shift, while the dayside phases
have a reversal in the trend of net shift with phase. Finally, we caution
researchers from using a single spectral template throughout the planet's
orbit; this can bias the corresponding net Doppler shift returned, as it can
pick up on a bright region on the edge of the planet disk that is highly red-
or blue-shifted.


title:
DearKD: Data-Efficient Early Knowledge Distillation for Vision
  Transformers

abstract:
  Transformers are successfully applied to computer vision due to their
powerful modeling capacity with self-attention. However, the excellent
performance of transformers heavily depends on enormous training images. Thus,
a data-efficient transformer solution is urgently needed. In this work, we
propose an early knowledge distillation framework, which is termed as DearKD,
to improve the data efficiency required by transformers. Our DearKD is a
two-stage framework that first distills the inductive biases from the early
intermediate layers of a CNN and then gives the transformer full play by
training without distillation. Further, our DearKD can be readily applied to
the extreme data-free case where no real images are available. In this case, we
propose a boundary-preserving intra-divergence loss based on DeepInversion to
further close the performance gap against the full-data counterpart. Extensive
experiments on ImageNet, partial ImageNet, data-free setting and other
downstream tasks prove the superiority of DearKD over its baselines and
state-of-the-art methods.


title: Towards a Green Quotient for Software Projects

abstract:
  As sustainability takes center stage across businesses, green and
energy-efficient choices are more crucial than ever. While it is becoming
increasingly evident that software and the software industry are substantial
and rapidly evolving contributors to carbon emissions, there is a dearth of
approaches to create actionable awareness about this during the software
development lifecycle (SDLC). Can software teams comprehend how green are their
projects? Here we provide an industry perspective on why this is a challenging
and worthy problem that needs to be addressed. We also outline an approach to
quickly gauge the "greenness" of a software project based on the choices made
across different SDLC dimensions and present the initial encouraging feedback
this approach has received.


title:
Velocity circulation intermittency in finite-temperature turbulent
  superfluid helium

abstract:
  We study intermittency of circulation moments in turbulent superfluid helium
by using experimental grid turbulence and numerical simulations of the
Hall-Vinen-Bekarevich-Khalatnikov model. More precisely, we compute the
velocity circulation $\Gamma_r$ in loops of size $r$ laying in the inertial
range. For both, experimental and numerical data, the circulation variance
shows a clear Kolmogorov scaling $\langle \Gamma_r^2 \rangle \sim r^{8/3}$ in
the inertial range, independently of the temperature. Scaling exponents of
high-order moments are comparable, within error bars, to previously reported
anomalous circulation exponents in classical turbulence and low-temperature
quantum turbulence numerical simulations.


title: Topological sequence entropy and topological dynamics of tree maps

abstract:
  We prove that a zero topological entropy continuous tree map always displays
zero topological sequence entropy when it is restricted to its non-wandering
and chain recurrent sets. In addition, we show that a similar result is not
possible when the phase space is a dendrite even when we consider only the
restriction on the set of periodic points.


title: Relevance-based Margin for Contrastively-trained Video Retrieval Models

abstract:
  Video retrieval using natural language queries has attracted increasing
interest due to its relevance in real-world applications, from intelligent
access in private media galleries to web-scale video search. Learning the
cross-similarity of video and text in a joint embedding space is the dominant
approach. To do so, a contrastive loss is usually employed because it organizes
the embedding space by putting similar items close and dissimilar items far.
This framework leads to competitive recall rates, as they solely focus on the
rank of the groundtruth items. Yet, assessing the quality of the ranking list
is of utmost importance when considering intelligent retrieval systems, since
multiple items may share similar semantics, hence a high relevance. Moreover,
the aforementioned framework uses a fixed margin to separate similar and
dissimilar items, treating all non-groundtruth items as equally irrelevant. In
this paper we propose to use a variable margin: we argue that varying the
margin used during training based on how much relevant an item is to a given
query, i.e. a relevance-based margin, easily improves the quality of the
ranking lists measured through nDCG and mAP. We demonstrate the advantages of
our technique using different models on EPIC-Kitchens-100 and YouCook2. We show
that even if we carefully tuned the fixed margin, our technique (which does not
have the margin as a hyper-parameter) would still achieve better performance.
Finally, extensive ablation studies and qualitative analysis support the
robustness of our approach. Code will be released at
\url{https://github.com/aranciokov/RelevanceMargin-ICMR22}.


title: The level-1 trigger for the SuperCDMS experiment at SNOLAB

abstract:
  The SuperCDMS SNOLAB dark matter search experiment aims to be sensitive to
energy depositions down to O(1 eV). This imposes requirements on the
resolution, signal efficiency, and noise rejection of the trigger system. To
accomplish this, the SuperCDMS level-1 trigger system is implemented in an FPGA
on a custom PCB. A time-domain optimal filter algorithm realized as a finite
impulse response filter provides a baseline resolution of 0.38 times the
standard deviation of the noise, $\sigma_n$, and a 99.9% trigger efficiency for
signal amplitudes of 1.1 $\sigma_n$ in typical noise conditions. Embedded in a
modular architecture, flexible trigger logic enables reliable triggering and
vetoing in a dead-time-free manner for a variety of purposes and run
conditions. The trigger architecture and performance are detailed in this
article.


title:
MovieMat: Context-aware Movie Recommendation with Matrix Factorization
  by Matrix Fitting

abstract:
  Movie Recommender System is widely applied in commercial environments such as
NetFlix and Tubi. Classic recommender models utilize technologies such as
collaborative filtering, learning to rank, matrix factorization and deep
learning models to achieve lower marketing expenses and higher revenues.
However, audience of movies have different ratings of the same movie in
different contexts. Important movie watching contexts include audience mood,
location, weather, etc. Tobe able to take advantage of contextual information
is of great benefit to recommender builders. However, popular techniques such
as tensor factorization consumes an impractical amount of storage, which
greatly reduces its feasibility in real world environment. In this paper, we
take advantage of the MatMat framework, which factorizes matrices by matrix
fitting to build a context-aware movie recommender system that is superior to
classic matrix factorization and comparable in the fairness metric.


title:
Defending Against Person Hiding Adversarial Patch Attack with a
  Universal White Frame

abstract:
  Object detection has attracted great attention in the computer vision area
and has emerged as an indispensable component in many vision systems. In the
era of deep learning, many high-performance object detection networks have been
proposed. Although these detection networks show high performance, they are
vulnerable to adversarial patch attacks. Changing the pixels in a restricted
region can easily fool the detection network in the physical world. In
particular, person-hiding attacks are emerging as a serious problem in many
safety-critical applications such as autonomous driving and surveillance
systems. Although it is necessary to defend against an adversarial patch
attack, very few efforts have been dedicated to defending against person-hiding
attacks. To tackle the problem, in this paper, we propose a novel defense
strategy that mitigates a person-hiding attack by optimizing defense patterns,
while previous methods optimize the model. In the proposed method, a
frame-shaped pattern called a 'universal white frame' (UWF) is optimized and
placed on the outside of the image. To defend against adversarial patch
attacks, UWF should have three properties (i) suppressing the effect of the
adversarial patch, (ii) maintaining its original prediction, and (iii)
applicable regardless of images. To satisfy the aforementioned properties, we
propose a novel pattern optimization algorithm that can defend against the
adversarial patch. Through comprehensive experiments, we demonstrate that the
proposed method effectively defends against the adversarial patch attack.


title:
The Robin and Neumann problems for the nonlinear Schr\"odinger equation
  on the half-plane

abstract:
  This work studies the initial-boundary value problem of the two-dimensional
nonlinear Schr\"odinger equation on the half-plane with initial data in Sobolev
spaces and Neumann or Robin boundary data in appropriate Bourgain spaces. It
establishes well-posedness in the sense of Hadamard by utilizing the explicit
solution formula for the forced linear initial-boundary value problem obtained
via Fokas's unified transform, and a contraction mapping argument.


title: Ollivier-Ricci Curvature For Head Pose Estimation From a Single Image

abstract:
  Head pose estimation is a crucial challenge for many real-world applications,
such as attention and human behavior analysis. This paper aims to estimate head
pose from a single image by applying notions of network curvature. In the real
world, many complex networks have groups of nodes that are well connected to
each other with significant functional roles. Similarly, the interactions of
facial landmarks can be represented as complex dynamic systems modeled by
weighted graphs. The functionalities of such systems are therefore
intrinsically linked to the topology and geometry of the underlying graph. In
this work, using the geometric notion of Ollivier-Ricci curvature (ORC) on
weighted graphs as input to the XGBoost regression model, we show that the
intrinsic geometric basis of ORC offers a natural approach to discovering
underlying common structure within a pool of poses. Experiments on the BIWI,
AFLW2000 and Pointing'04 datasets show that the ORC_XGB method performs well
compared to state-of-the-art methods, both landmark-based and image-only.


title: Identification of inertial modes in the solar convection zone

abstract:
  The observation of global acoustic waves (p modes) in the Sun has been key to
unveil its internal structure and dynamics. A different kind of waves, known as
sectoral Rossby modes, have been observed and identified relatively recently,
which potentially opens the door to probe internal processes that are
inaccessible through p mode helioseismology. Yet another set of waves,
appearing as retrograde-propagating, equatorially antisymmetric vorticity
waves, have been observed very recently but their identification remained
elusive. Here, through a numerical model implemented as an eigenvalue problem,
we provide evidence supporting the identification of those waves as inertial
eigenmodes, of a class distinct from Rossby modes, with substantial amplitudes
deep in the solar convective zone. We also suggest that the signature of
tesseral Rossby modes might be present in the recent observational data.


title: Quantum mappings and designs

abstract:
  In order to use quantum devices for computations, it is necessary to
understand the intricacies of the theoretical description. To this end, we
provide several novel constructions useful for the comprehension of quantum
mechanics from the perspective of mappings and designs. The unistochasticity
problem, which relates the classical and the quantum domain, is solved in
specific cases, e.g. for all matrices of dimension 4. Furthermore, we provide
an explicit formula for entangling power in the multipartite case. Most
importantly, the thesis presents and elaborates on the path that lead to the
recent construction of absolutely maximally entangled state of four subsystems
six levels each. Finally, we study cardinality as a measure of "quantumness" of
quantum Latin squares and quantum Sudoku (SudoQ). Arrays of the highest
cardinality yield families of quantum measurements of special properties. A
connection between SudoQ designs and mutually unbiased bases is demonstrated.


title:
Extremal GloVe: Theoretically Accurate Distributed Word Embedding by
  Tail Inference

abstract:
  Distributed word embeddings such as Word2Vec and GloVe have been widely
adopted in industrial context settings. Major technical applications of GloVe
include recommender systems and natural language processing. The fundamental
theory behind GloVe relies on the selection of a weighting function in the
weighted least squres formulation that computes the powered ratio of word
occurrence count and the maximum word count in the corpus. However, the initial
formulation of GloVe is not theoretically sound in two aspects, namely the
selection of the weighting function and its power exponent is ad-hoc. In this
paper, we utilize the theory of extreme value analysis and propose a
theoretically accurate version of GloVe. By reformulating the weighted least
squares loss function as the expected loss function and accurately choosing the
power exponent, we create a theoretically accurate version of GloVe. We
demonstrate the competitiveness of our algorithm and show that the initial
formulation of GloVe with the suggested optimal parameter can be viewed as a
special case of our paradigm.


title:
Infrared nanoplasmonic properties of hyperdoped embedded Si nanocrystals
  in the few electrons regime

abstract:
  Using Localized Surface Plasmon Resonance (LSPR) as an optical probe we
demonstrate the presence of free carriers in phosphorus doped silicon
nanocrystals (SiNCs) embedded in a silica matrix. In small SiNCs, with radius
ranging from 2.6 to 5.5 nm, the infrared spectroscopy study coupled to
numerical simulations allows us to determine the number of electrically active
phosphorus atoms with a precision of a few atoms. We demonstrate that LSP
resonances can be supported with only about 10 free electrons per nanocrystal,
confirming theoretical predictions and probing the limit of the collective
nature of plasmons. We reveal a phenomenon, unique to embedded nanocrystals,
with the appearance of an avoided crossing behavior linked to the hybridization
between the localized surface plasmon in the doped nanocrystals and the silica
matrix phonon modes. Finally, a careful analysis of the scattering time
dependence versus carrier density in the small size regime allows us to detect
the appearance of a new scattering process at high dopant concentration.


title: Simplified feedback control system for Scanning Tunneling Microscopy

abstract:
  A Scanning Tunneling Microscope (STM) is one of the most important scanning
probe tools available to study and manipulate matter at the nanoscale. In a
STM, a tip is scanned on top of a surface with a separation of a few \AA.
Often, the tunneling current between tip and sample is maintained constant by
modifying the distance between the tip apex and the surface through a feedback
mechanism acting on a piezoelectric transducer. This produces very detailed
images of the electronic properties of the surface. The feedback mechanism is
nearly always made using a digital processing circuit separate from the user
computer. Here we discuss another approach, using a computer and data
acquisition through the USB port. We find that it allows succesful ultra low
noise studies of surfaces at cryogenic temperatures. We show results on
different compounds, a type II Weyl semimetal (WTe$_2$), a quasi
two-dimensional dichalcogenide superconductor (2H-NbSe$_2$), a magnetic Weyl
semimetal (Co$_3$Sn$_2$S$_2$) and an iron pnictide superconductor (FeSe).


title: Besov regularity in non-linear generalized functions

abstract:
  We introduce and study new modules and spaces of generalized functions that
are related to the classical Besov spaces and might be useful in the analysis
of non-linear problems. Various Schwartz distribution spaces are naturally
embedded into our new generalized function spaces. We obtain precise criteria
for detecting Besov regularity of distributions.


title:
Statistically Consistent Inverse Optimal Control for Linear-Quadratic
  Tracking with Random Time Horizon

abstract:
  The goal of Inverse Optimal Control (IOC) is to identify the underlying
objective function based on observed optimal trajectories. It provides a
powerful framework to model expert's behavior, and a data-driven way to design
an objective function so that the induced optimal control is adapted to a
contextual environment. In this paper, we design an IOC algorithm for
linear-quadratic tracking problems with random time horizon, and prove the
statistical consistency of the algorithm. More specifically, the proposed
estimator is the solution to a convex optimization problem, which means that
the estimator does not suffer from local minima. This enables the proven
statistical consistency to actually be achieved in practice. The algorithm is
also verified on simulated data as well as data from a real world experiment,
both in the setting of identifying the objective function of human tracking
locomotion. The statistical consistency is illustrated on the synthetic data
set, and the experimental results on the real data shows that we can get a good
prediction on human tracking locomotion based on estimating the objective
function. It shows that the theory and the model have a good performance in
real practice. Moreover, the identified model can be used as a control target
in personalized rehabilitation robot controller design, since the identified
objective function describes personal habit and preferences.


title:
A semi-analytical solution to the forward-reverse shock hydrodynamics of
  the gamma-ray burst afterglow

abstract:
  We extend the standard model of forward-reverse shock (FS-RS) for gamma-ray
burst (GRB) afterglow to more general cases. On one hand, we derive the
analytical solution to the hydrodynamics of the shocks in two limiting cases,
i.e., an ultra-relativistic reverse shock case and a Newtonian reverse shock
case. Based on the asymptotic solutions in these two limiting cases, we
constitute a semi-analytical solution for the hydrodynamics of the shocks in
the generic case, covering the mildly-relativistic reverse shock case. On the
other hand, we derive the evolution of the system taking into account the
condition of energy conservation which is not satisfied in the standard FS-RS
model. A generic solution of semi-analytical expressions is also given. In both
the extended standard FS-RS model (satisfying pressure balance condition) and
the model satisfying energy conservation, we find that the results in the
ultra-relativistic reverse shock case and in the early stage of the Newtonian
reverse shock case are different from those in the standard FS-RS model by only
a factor that close to one while the same initial conditions adopted. However,
the asymptotic solutions in the limiting cases are not good approximations to
those in the intermediate case. Our semi-analytical results agree well with the
numerical results for a large range of model parameters, and hence can be
easily employed to diagnose the physical quantities of the GRB shell and
circumburst environment.


title:
Refined nuclear magnetic dipole moment of rhenium: $^{185}$Re and
  $^{187}$Re

abstract:
  The refined values of the magnetic dipole moments of $^{185}$Re and
$^{187}$Re nuclei are obtained. For this, we perform a combined relativistic
coupled cluster and density functional theory calculation of the shielding
constant for the ReO$_4^-$ anion. In this calculation, we explicitly include
the effect of the finite nuclear magnetization distribution in the
single-particle nuclear model using the Woods-Saxon potential for the valence
nucleon. By combining the obtained value of the shielding constant
$\sigma=4142(389)$ ppm with the available experimental nuclear magnetic
resonance data we obtain the values: $\mu(^{185}{\rm Re})=3.1564(3)(12) \mu_N,
\mu(^{187}{\rm Re})=3.1887(3)(12) \mu_N$, where the first uncertainty is the
experimental one and second is due to theory. The refined values of magnetic
moments are in disagreement with the tabulated values, $\mu(^{185}{\rm
Re})=3.1871(3) \mu_N, \mu(^{187}{\rm Re})=3.2197(3) \mu_N$, which were obtained
using the shielding constant value calculated for the atomic cation Re$^{7+}$
rather than the molecular anion. The updated values of the nuclear magnetic
moments resolve the disagreement between theoretical predictions of the
hyperfine structure of H-like rhenium ions which were based on the tabulated
magnetic moment values and available experimental measurements. Using these
experimental data we also extract the value of the parameter of nuclear
magnetization distribution introduced in [J. Chem. Phys. \textbf{153}, 114114
(2020)], which is required to predict hyperfine structure constants of rhenium
compounds.


title:
RankMat : Matrix Factorization with Calibrated Distributed Embedding and
  Fairness Enhancement

abstract:
  Matrix Factorization is a widely adopted technique in the field of
recommender system. Matrix Factorization techniques range from SVD, LDA, pLSA,
SVD++, MatRec, Zipf Matrix Factorization and Item2Vec. In recent years,
distributed word embeddings have inspired innovation in the area of recommender
systems. Word2vec and GloVe have been especially emphasized in many industrial
application scenario such as Xiaomi's recommender system. In this paper, we
propose a new matrix factorization inspired by the theory of power law and
GloVe. Instead of the exponential nature of GloVe model, we take advantage of
Pareto Distribution to model our loss function. Our method is explainable in
theory and easy-to-implement in practice. In the experiment section, we prove
our approach is superior to vanilla matrix factorization technique and
comparable with GloVe-based model in both accuracy and fairness metrics.


title:
Quantitative inverse problem in visco-acoustic media under attenuation
  model uncertainty

abstract:
  We consider the inverse problem of quantitative reconstruction of properties
(e.g., bulk modulus, density) of visco-acoustic materials based on measurements
of responding waves after stimulation of the medium. Numerical reconstruction
is performed by an iterative minimization algorithm. Firstly, we investigate
the robustness of the algorithm with respect to attenuation model uncertainty,
that is, when different attenuation models are used to simulate synthetic
observation data and for the inversion, respectively. Secondly, to handle
data-sets with multiple reflections generated by wall boundaries around the
domain, we perform inversion using complex frequencies, and show that it offers
a robust framework that alleviates the difficulties of multiple reflections. To
illustrate the efficiency of the algorithm, we perform numerical simulations of
ultrasound imaging experiments to reconstruct a synthetic breast sample that
contains an inclusion of sharp contrast, in two and three dimensions, where the
latter also serves to demonstrate the numerical feasibility in a large-scale
configuration.


title:
New invariants of Gromov-Hausdorff limits of Riemannian surfaces with
  curvature bounded below

abstract:
  Let $\{X_i\}$ be a sequence of compact $n$-dimensional Alexandrov spaces
(e.g. Riemannian manifolds) with curvature uniformly bounded below which
converges in the Gromov-Hausdorff sense to a compact Alexandrov space $X$. In
an earlier paper by the first author there was described (without a proof) a
construction of an integer valued function on $X$; this function carries
additional geometric information on the sequence such as the limit of intrinsic
volumes of $X_i$'s. In this paper we consider sequences of closed 2-surfaces
and (1) prove the existence of such a function in this situation; and (2)
classify the functions which may arise from the construction.


title: Fair and Efficient Allocation with Quotas

abstract:
  In many settings, such as the rationing of medical care and supplies,
university admissions, and the assignment of public housing, the decision of
who receives an allocation can be justified by various normative criteria
(ethical, financial, legal, etc.). These criteria can influence priorities and
restrict the number of units available to particular demographics. We consider
a setting wherein a set of identical items must be distributed among
unit-demand applicants. The items are divided across a set of categories (each
with an allocation quota), and each category has a priority ordering over its
eligible applicants. Building on previous work, we define a set of natural
desiderata for allocations. We use techniques from linear and integer
programming to give a polynomial-time procedure for finding Pareto efficient
allocations satisfying these desiderata. The clean formulation of our algorithm
allows us to more easily derive known results in this space and provides the
flexibility to consider alternate objectives and enforce various notions of
equity between categories.


title:
Isotope velocimetry: Experimental and theoretical demonstration of the
  potential importance of gas flow for isotope fractionation during evaporation
  of protoplanetary material

abstract:
  We use new experiments and a theoretical analysis of the results to show that
the isotopic fractionation associated with laser-heating aerodynamic levitation
experiments is consistent with the velocity of flowing gas as the primary
control on the fractionation. The new Fe and Mg isotope data are well explained
where the gas is treated as a low-viscosity fluid that flows around the molten
spheres with high Reynolds numbers and minimal drag. A relationship between the
ratio of headwind velocity to thermal velocity and saturation is obtained on
the basis of this analysis. The recognition that it is the ratio of flow
velocity to thermal velocity that controls fractionation allows for
extrapolation to other environments in which molten rock encounters gas with
appreciable headwinds. In this way, in some circumstances, the degree of
isotope fractionation attending evaporation is as much a velocimeter as it is a
barometer.

categories: astro-ph.EP astro-ph.IM physics.flu-dyn physics.geo-ph


title:
NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural
  Language Understanding in Task-Oriented Dialogue

abstract:
  We present NLU++, a novel dataset for natural language understanding (NLU) in
task-oriented dialogue (ToD) systems, with the aim to provide a much more
challenging evaluation environment for dialogue NLU models, up to date with the
current application and industry requirements. NLU++ is divided into two
domains (BANKING and HOTELS) and brings several crucial improvements over
current commonly used NLU datasets. 1) NLU++ provides fine-grained domain
ontologies with a large set of challenging multi-intent sentences, introducing
and validating the idea of intent modules that can be combined into complex
intents that convey complex user goals, combined with finer-grained and thus
more challenging slot sets. 2) The ontology is divided into domain-specific and
generic (i.e., domain-universal) intent modules that overlap across domains,
promoting cross-domain reusability of annotated examples. 3) The dataset design
has been inspired by the problems observed in industrial ToD systems, and 4) it
has been collected, filtered and carefully annotated by dialogue NLU experts,
yielding high-quality annotated data. Finally, we benchmark a series of current
state-of-the-art NLU models on NLU++; the results demonstrate the challenging
nature of the dataset, especially in low-data regimes, the validity of `intent
modularisation', and call for further research on ToD NLU.


title: Binding Actions to Objects in World Models

abstract:
  We study the problem of binding actions to objects in object-factored world
models using action-attention mechanisms. We propose two attention mechanisms
for binding actions to objects, soft attention and hard attention, which we
evaluate in the context of structured world models for five environments. Our
experiments show that hard attention helps contrastively-trained structured
world models to learn to separate individual objects in an object-based
grid-world environment. Further, we show that soft attention increases
performance of factored world models trained on a robotic manipulation task.
The learned action attention weights can be used to interpret the factored
world model as the attention focuses on the manipulated object in the
environment.