title:
On the Performance of Co-existence between Public eMBB and Non-public
  URLLC Networks

abstract:
  To ensure the high level of automation required in today's industrial
applications, next-generation wireless networks must enable real-time control
and automation of dynamic processes with the requirements of extreme
low-latency and ultra-reliable communications. In this paper, we provide a
performance assessment for the co-existence of a public enhanced mobile
broadband (eMBB) and a local non-public factory (URLLC) network and evaluate
the network conditions under which the stringent latency and reliability
requirements of factory automation applications are met. The evaluations
consider both an unsynchronized and a synchronized time division duplexing
(TDD) deployment between the networks, as well as scenarios both with and
without any macro eMBB traffic located inside the factory. The results show
that an unsynchronized deployment is possible if the isolation between the
networks is sufficiently high, either as a result of a separation distance,
wall loss or the use of separate frequencies for the networks. A synchronized
deployment will avoid the cross-link interference, but it will not resolve the
problems related to the closed access and the near-far interference. If the
factory contains eMBB traffic served by the overlaid macro cells, the
performance of both networks will suffer due to a high level of cross-link and
near-far interference. The problems related to the cross-link interference can
be resolved by synchronizing the networks, while the level of the near-far
interference can be reduced by allowing the eMBB users to be connected to base
stations located inside the factory. Finally, if an unsynchronized deployment
is desired, the factory should be deployed on an isolated frequency.


title: Phase-Aware Deep Speech Enhancement: It's All About The Frame Length

abstract:
  While phase-aware speech processing has been receiving increasing attention
in recent years, most narrowband STFT approaches with frame lengths of about
32ms show a rather modest impact of phase on overall performance. At the same
time, modern deep neural network (DNN)-based approaches, like Conv-TasNet, that
implicitly modify both magnitude and phase yield great performance on very
short frames (2ms). Motivated by this observation, in this paper we
systematically investigate the role of phase and magnitude in DNN-based speech
enhancement for different frame lengths. The results show that a phase-aware
DNN can take advantage of what previous studies concerning reconstruction of
clean speech have shown: When using short frames, the phase spectrum becomes
more important while the importance of the magnitude spectrum decreases.
Furthermore, our experiments show that when both magnitude and phase are
estimated, shorter frames result in a considerably improved performance in a
DNN with explicit phase estimation. Contrarily, in the phase-blind case, where
only magnitudes are processed, 32ms frames lead to the best performance. We
conclude that DNN-based phase estimation benefits from the use of shorter
frames and recommend a frame length of about 4ms for future phase-aware deep
speech enhancement methods.


title: Hypergraphon Mean Field Games

abstract:
  We propose an approach to modelling large-scale multi-agent dynamical systems
allowing interactions among more than just pairs of agents using the theory of
mean-field games and the notion of hypergraphons, which are obtained as limits
of large hypergraphs. To the best of our knowledge, ours is the first work on
mean field games on hypergraphs. Together with an extension to a multi-layer
setup, we obtain limiting descriptions for large systems of non-linear,
weakly-interacting dynamical agents. On the theoretical side, we prove the
well-foundedness of the resulting hypergraphon mean field game, showing both
existence and approximate Nash properties. On the applied side, we extend
numerical and learning algorithms to compute the hypergraphon mean field
equilibria. To verify our approach empirically, we consider an epidemic control
problem and a social rumor spreading model, where we give agents intrinsic
motivation to spread rumors to unaware agents.


title: End to End Lip Synchronization with a Temporal AutoEncoder

abstract:
  We study the problem of syncing the lip movement in a video with the audio
stream. Our solution finds an optimal alignment using a dual-domain recurrent
neural network that is trained on synthetic data we generate by dropping and
duplicating video frames. Once the alignment is found, we modify the video in
order to sync the two sources. Our method is shown to greatly outperform the
literature methods on a variety of existing and new benchmarks. As an
application, we demonstrate our ability to robustly align text-to-speech
generated audio with an existing video stream. Our code and samples are
available at
https://github.com/itsyoavshalev/End-to-End-Lip-Synchronization-with-a-Temporal-AutoEncoder.


title:
Exploring ML testing in practice -- Lessons learned from an interactive
  rapid review with Axis Communications

abstract:
  There is a growing interest in industry and academia in machine learning (ML)
testing. We believe that industry and academia need to learn together to
produce rigorous and relevant knowledge. In this study, we initiate a
collaboration between stakeholders from one case company, one research
institute, and one university. To establish a common view of the problem
domain, we applied an interactive rapid review of the state of the art. Four
researchers from Lund University and RISE Research Institutes and four
practitioners from Axis Communications reviewed a set of 180 primary studies on
ML testing. We developed a taxonomy for the communication around ML testing
challenges and results and identified a list of 12 review questions relevant
for Axis Communications. The three most important questions (data testing,
metrics for assessment, and test generation) were mapped to the literature, and
an in-depth analysis of the 35 primary studies matching the most important
question (data testing) was made. A final set of the five best matches were
analysed and we reflect on the criteria for applicability and relevance for the
industry. The taxonomies are helpful for communication but not final.
Furthermore, there was no perfect match to the case company's investigated
review question (data testing). However, we extracted relevant approaches from
the five studies on a conceptual level to support later context-specific
improvements. We found the interactive rapid review approach useful for
triggering and aligning communication between the different stakeholders.


title:
Cellular automata and substitutions in topological spaces defined via
  edit distances

abstract:
  The Besicovitch pseudo-metric is a shift-invariant pseudo-metric on the set
of infinite sequences, that enjoys interesting properties and is suitable for
studying the dynamics of cellular automata. They correspond to the asymptotic
behavior of the Hamming distance on longer and longer prefixes. Though dynamics
of cellular automata were already studied in the literature, we propose the
first study of the dynamics of substitutions. We characterize those that yield
a well-defined dynamical system as essentially the uniform ones. We also
explore a variant of this pseudo-metric, the Feldman pseudo-metric, where the
Hamming distance is replaced by the Levenshtein distance. Like in the
Besicovitch space, cellular automata are Lipschitz in this space, but here also
all substitutions are Lipschitz. In both spaces, we discuss equicontinuity of
these systems, and give a number of examples, and generalize our results to the
class of the dill maps, that embed both cellular automata and substitutions.


title: Weak optimal transport with unnormalized kernels

abstract:
  We introduce a new variant of the weak optimal transport problem where mass
is distributed from one space to the other through unnormalized kernels. We
give sufficient conditions for primal attainment and prove a dual formula for
this transport problem. We also obtain dual attainment conditions for some
specific cost functions. As a byproduct we obtain a transport characterization
of the stochastic order defined by convex positively 1-homogenous functions, in
the spirit of Strassen theorem for convex domination.


title:
An Efficiency-Based Power Management Strategy for an Isolated Microgrid
  Project

abstract:
  The microgrids design for remote locations represents one of the most
important and critical applications of the microgrid concept. It requires the
correct sizing and the proper utilization of the different sources to guarantee
the economical feasibility and the reliability of the supply. This study
illustrates an efficiency-based power management strategy, designed for an
undergoing microgrid project, where the sizing process of the resources (diesel
generators, battery energy storage system, and PV plant) is obtained using a
mixed-integer optimization algorithm. The proposed power management strategy
guarantees the efficient exploitation of the power sources, which is one of the
key elements of the optimal sizing process, being naturally included in the
definition of the energy cost functions. The effectiveness of the power control
strategy is validated by means of quasi-dynamic simulations on the complete
microgrid model, where sources are defined by the optimal problem solution,
while the cabling (size and length) and the main switchboards location reflect
the expected system layout. Results obtained from the simulation of the
microgrid electrical system include losses, and allow to verify and to
highlight the desired quantities, such as the quality of supply at each busbar
(voltage magnitude), and the state of charge of the energy storage system.


title: Temporal dynamics of diode-pumped circulation-free liquid dye lasers

abstract:
  A highly stable diode-pumped circulation-free liquid dye laser in a vertical
external cavity is reported. The design is simple (no fabrication process step
required, no fluid circuitry), compact (~ cm sized), and cost-effective. An
optical efficiency of 18% with a M${}^2$ of 1 are reported, with an excellent
photostability-no efficiency drop was seen after 1.4 million pulses at 50 Hz, a
value comparable to flowing systems and much higher than values achievable with
organic solid-state lasers. We show that thermal effects are central in the
stability and also on the dynamics of this laser. The laser build-up and
shutdown dynamics are studied in detail for different pump pulse
durations/repetition rates; they reveal a pulse shortening with increasing pump
pulse duration and repetition rate that are shown to be due to thermal lensing
diffraction losses. This laser structure offers a very convenient and simple
platform for testing or harvesting solution-processable gain materials.


title:
Automatic generation of semantic corpora for improving intent estimation
  of taxonomy-driven search engines

abstract:
  With the increasing demand of intelligent systems capable of operating in
different user contexts (e.g. users on the move) the correct interpretation of
the user-need by such systems has become crucial to give a consistent answer to
the user query. The most effective techniques which are used to address such
task are in the fields of natural language processing and semantic expansion of
terms. Such systems are aimed at estimating the actual meaning of input
queries, addressing the concepts of the words which are expressed within the
user questions. The aim of this paper is to demonstrate which semantic relation
impacts the most in semantic expansion-based retrieval systems and to identify
the best tradeoff between accuracy and noise introduction when combining such
relations. The evaluations are made building a simple natural language
processing system capable of querying any taxonomy-driven domain, making use of
the combination of different semantic expansions as knowledge resources. The
proposed evaluation employs a wide and varied taxonomy as a use-case,
exploiting its labels as basis for the expansions. To build the knowledge
resources several corpora have been produced and integrated as gazetteers into
the NLP infrastructure with the purpose of estimating the pseudo-queries
corresponding to the taxonomy labels, considered as the possible intents.


title:
Highly photo-stable, kHz-repetition-rate, diode pumped circulation-free
  liquid dye laser with thermal lens management

abstract:
  Liquid dye lasers have long been considered as ideal tunable laser sources in
the visible range but are bulky, expensive, and require a complex system for
dye circulation. Here, we present a system that relies on a low-cost blue laser
diode as the pump source and a sealed dye cell with no flowing circuitry,
resulting in a device that combines the convenience and size of a solid-state
device with the stability of a liquid organic laser. A very high
photo-stability is obtained (up to 1.2 x 10$^9$ pulses or 12 days at 1 kHz),
which is five orders of magnitude higher than a solid-state dye laser operated
in similar conditions. The number of pulses obtainable at low repetition rates
is found to be limited by molecular self-diffusion and, hence, related to the
total cuvette volume. In contrast, the repetition rate is limited to a few kHz,
which suggests that thermal effects play a bigger role than triplet population
effects. Thermal effects participate in the suppression of lasing through the
buildup of a strong negative thermal lens; correcting the non-aberrant part of
this thermal lens by resonator design enables the repetition rate to be pushed
up to 14 kHz with possible further optimization. This work shows a route for
building off-the-shelf, compact, low-cost, and convenient tunable pulsed lasers
in the visible range that have superior stability over organic solid-state
lasers.


title: Massey products in Galois cohomology and the Elementary Type Conjecture

abstract:
  Let $p$ be a prime. We prove that a positive solution to Efrat's Elementary
Type Conjecture implies a positive solution to the strengthened version of
Minac-T\^an's Massey Vanishing Conjecture. Consequently, the maximal pro-$p$
Galois group of a field $K$ containing a root of 1 of order $p$ satisfies the
strong $n$-Massey vanishing property -- a.k.a. cup-defining $n$-Massey product
property -- for every $n>2$ in several relevant cases.


title: Robust signal dimension estimation via SURE

abstract:
  The estimation of signal dimension under heavy-tailed latent factor models is
studied. As a primary contribution, robust extensions of an earlier estimator
based on Gaussian Stein's unbiased risk estimation are proposed. These novel
extensions are based on the framework of elliptical distributions and robust
scatter matrices. Extensive simulation studies are conducted in order to
compare the novel methods with several well-known competitors in both
estimation accuracy and computational speed. The novel methods are applied to a
financial asset return data set.


title: An analytic viewpoint on the Hasse principle

abstract:
  Working on Berkovich analytic curves, we propose a geometric approach to the
study of the Hasse principle over function fields of curves defined over a
complete discretely valued field. Using it, we show the Hasse principle to be
verified for certain families of projective homogeneous spaces. As a
consequence, we also obtain said principle in the already known cases of
quadratic forms and homogeneous varieties over unitary groups.


title:
Comparative studies on unconventional superconductivity in Cr$_3$Ru
  compounds with bcc and A15 structures

abstract:
  Chromium (Cr) is a transition metal element with 3$d$ orbital electrons. In
most compounds containing Cr, due to the correlation effect, twofold features
of localization and itinerancy are expected. The localization gives rise to a
magnetic moment, while the latter exhibits as the effective coherent weight for
conductivity. Here we report the physical properties of Cr$_3$Ru compounds with
bcc or A15 phases by using multiple experimental tools. The resistivity
measurements show sharp superconducting transitions at $T_{\rm c}$ = 2.77 K and
$T_{\rm c}$ = 3.37 K for the bcc and A15 structures. A high residual
resistivity exists for both samples with the mean-free-path about 2 nm.
Magnetization measurements also show rather narrow transitions, with a clear
hump structure at high temperatures around 150 K, which may be ascribed to the
remaining antiferromagnetic spin fluctuations. The specific heat coefficient
reveals a clear jump at $T_{\rm c}$. We find that s-wave gaps can be adopted to
fit the low temperature specific heat data yielding ratios of about
$2\Delta/k_{\scriptscriptstyle B}T_{\rm c}$ $\approx$ 3.6, indicating a
moderate pairing strength. Interestingly, the Wilson ratios are 3.81 and 3.62
for the bcc and A15 phases, suggesting a moderate correlation effect of
conducting electron in the normal state. Besides, for samples with A15
structure, another specific heat anomaly occurs at about 0.85 K and is
sensitive to magnetic fields. By applying high pressures, both system exhibit
an enhancement of $T_{\rm c}$ with a rate of about 0.019 K/GPa and 0.013 K/GPa
for the bcc and A15 phases. We also conduct tunneling spectrum measurements and
found that the coherence peaks are strongly smeared out. The strong suppression
to the coherence peaks may be ascribed to the strong scattering. Our
combinatory results point to an unconventional superconductivity in these Cr
based compounds.


title:
Massive compact dust disk with a gap around CW Tau revealed by ALMA
  multi-band observations

abstract:
  Compact protoplanetary disks with a radius of $\lesssim$ 50 au are common
around young low-mass stars. We report high resolution ALMA dust continuum
observations toward a compact disk around CW Tau at Band 4 ($\lambda=2.2$ mm),
6 (1.3 mm), 7 (0.89 mm) and 8 (0.75 mm). The SED shows the spectral slope of
$2.0\pm0.24$ between 0.75 and 1.3 mm, while it is $3.7\pm0.29$ between 2.17 and
3.56 mm. The steep slope between 2.17 and 3.56 mm is consistent with that of
optically thin emission from small grains ($\lesssim$ 350 ${\rm \mu m}$). We
perform parametric fitting of the ALMA data to characterize the dust disk.
Interestingly, if the dust-to-gas mass ratio is 0.01, the Toomre's Q parameter
reaches $\sim$ 1-3, suggesting that the CW Tau disk might be marginally
gravitationally unstable. The total dust mass is estimated as
$\sim250M_{\oplus}$ for the maximum dust size of 140 ${\rm \mu m}$ that is
inferred from the previous Band 7 polarimetric observation and at least
$80M_{\oplus}$ even for larger grain sizes. This result shows that the CW Tau
disk is quite massive in spite of its smallness. Furthermore, we clearly
identify a gap structure located at $\sim20$ au, which might be induced by a
giant planet. In spite of these interesting characteristics, the CW Tau disk
has normal disk luminosity, size and spectral index at ALMA Band 6, which could
be a clue to the mass budget problem in Class II disks.


title: On the Regret of $\mathcal{H}_{\infty}$ control

abstract:
  The $\mathcal{H}_{\infty}$ synthesis approach is a cornerstone robust control
design technique, but is known to be conservative in some cases. The objective
of this work is to quantify the additional cost the controller incurs planning
for the worst-case scenario, by adopting an approach inspired by regret from
online learning. We define the \textit{disturbance-reality gap} as the
difference between the predicted worst-case disturbance signal and the observed
one. The regret is then shown to scale with the norm of this error, thus
achieving an upper bound that is similar in terms of structure to that of the
certainty equivalent controller with inaccurate predictions.


title: A disintegration of the Christoffel function

abstract:
  We show that the Christoffel function (CF) factorizes (or can be
disintegrated) as the product of two Christoffel functions, one associated with
the marginal and the another related to the conditional distribution, in the
spirit of "the CF of the disintegration is the disintegration of the CFs". In
the proof one uses an apparently overlooked property (but interesting in its
own) which states that any sum-of-squares polynomial is the Christoffel
function of some linear form (with a representing measure in the univariate
case). The same is true for the convex cone of polynomials that are positive on
a basic semi-algebraic set. This interpretation of the CF establishes another
bridge between polynomials optimization and orthogonal polynomials.


title: Saturated total-population dependent branching process and viral markets

abstract:
  The universal prevalence of online social networks (OSNs) is unquestionable
today; the interesting posts/contents are forwarded repeatedly by the users of
the OSN. Such propagation leads to the re-forwarding of the post to a subset of
previous recipients, which increases as the post gets viral (reaches a large
number of the users). Consequently, the effective forwards (after deleting the
re-forwards) reduce, eventually leading to the saturation of the total number
of copies. We study such a process using a new variant of the branching process
and named it as `Saturated total-population-dependent branching process'.
  We analyse the new variant using the stochastic approximation technique and
derive an ODE, dependent on the expected effective forwards. Finally, we obtain
deterministic trajectories which approximate the total and unread copies of the
post `asymptotically and almost surely' over any finite time window; this
trajectory is entirely described by four parameters related to the network
characteristics. Further, we provide theoretical expressions for the peak
unread copies, maximum outreach and the life span of the post. We again observe
well-known exponential growth, however, with time-varying rates. At last, we
validate our theory through detailed simulations on the SNAP Twitter dataset,
which illustrate a good fit.


title:
Tin (Sn) at high pressure: review, X-ray diffraction, DFT calculations,
  and Gibbs energy modeling

abstract:
  An assessment of the Sn unary system is presented. First, the literature on
phase equilibria, the thermodynamic properties, the volume and related
properties, and shock compression of tin is thoroughly reviewed. Second, the Sn
system is investigated by means of synchrotron x-ray diffraction in a
diamond-anvil cell up to pressures and temperatures of 57 GPa and 730 K. New
information is obtained on the thermal stability and thermal expansion
coefficient of the gamma (I4/mmm) and gamma" (Im-3m) phases. Third, density
functional theory calculations are conducted on the six allotropic phases of
tin observed in experiments using both a local density approximation (LDA) and
a generalized gradient approximation (GGA) functional. This combined
experimental and theoretical investigation provides further insights on the
pronounced metastable nature of Sn in the 30 - 70 GPa range. Last, a Gibbs
energy modeling is conducted using the recently proposed Joubert-Lu-Grover
model which is compatible with the CALPHAD method. Special emphasis is placed
on discussing extrapolations to high pressures and temperatures of the volume
and of the thermodynamic properties. While the description of the heat capacity
is approximate, all available data are closely reproduced up to 2500 K, which
is 5 times higher than the atmospheric pressure melting point of tin, and 150
GPa, which is almost 3 times the standard bulk modulus of beta-Sn. Therefore,
this approach appears promising to model multi-component phase diagrams at high
pressure.


title: Biclustering Algorithms Based on Metaheuristics: A Review

abstract:
  Biclustering is an unsupervised machine learning technique that
simultaneously clusters rows and columns in a data matrix. Biclustering has
emerged as an important approach and plays an essential role in various
applications such as bioinformatics, text mining, and pattern recognition.
However, finding significant biclusters is an NP-hard problem that can be
formulated as an optimization problem. Therefore, different metaheuristics have
been applied to biclustering problems because of their exploratory capability
of solving complex optimization problems in reasonable computation time.
Although various surveys on biclustering have been proposed, there is a lack of
a comprehensive survey on the biclustering problem using metaheuristics. This
chapter will present a survey of metaheuristics approaches to address the
biclustering problem. The review focuses on the underlying optimization methods
and their main search components: representation, objective function, and
variation operators. A specific discussion on single versus multi-objective
approaches is presented. Finally, some emerging research directions are
presented.


title:
Symmetry and monotonicity of singular solutions to $p$-Laplacian systems
  involving a first order term

abstract:
  We consider positive singular solutions (i.e. with a non-removable
singularity) of a system of PDEs driven by $p$-Laplacian operators and with the
additional presence of a nonlinear first order term. By a careful use of a
rather new version of the moving plane method, we prove the symmetry of the
solutions. The result is already new in the scalar case.


title: Theory of Critical Phenomena with Memory

abstract:
  Memory is a ubiquitous characteristic of complex systems and critical
phenomena are one of the most intriguing phenomena in nature. Here, we develop
a theory of critical phenomena with memory for complex systems and discovered a
series of surprising novel results. We show that a naive theory of a usual
Hamiltonian with a direct inclusion of a temporally power-law decaying
long-range interaction violates radically a hyperscaling law for all spatial
dimensions even at and below the upper critical dimension. This entails both
indispensable consideration of the Hamiltonian for dynamics, rather than the
usual practice of just focusing on the corresponding dynamic Lagrangian alone,
and transformations that result in a correct theory in which space and time are
inextricably interwoven into an effective spatial dimension that repairs the
hyperscaling law. The theory gives rise to a set of novel mean-field critical
exponents, which are different from the usual Landau ones, as well as new
universality classes. These exponents are verified by numerical simulations of
an Ising model with memory in two and three spatial dimensions.


title: CycDA: Unsupervised Cycle Domain Adaptation from Image to Video

abstract:
  Although action recognition has achieved impressive results over recent
years, both collection and annotation of video training data are still
time-consuming and cost intensive. Therefore, image-to-video adaptation has
been proposed to exploit labeling-free web image source for adapting on
unlabeled target videos. This poses two major challenges: (1) spatial domain
shift between web images and video frames; (2) modality gap between image and
video data. To address these challenges, we propose Cycle Domain Adaptation
(CycDA), a cycle-based approach for unsupervised image-to-video domain
adaptation by leveraging the joint spatial information in images and videos on
the one hand and, on the other hand, training an independent spatio-temporal
model to bridge the modality gap. We alternate between the spatial and
spatio-temporal learning with knowledge transfer between the two in each cycle.
We evaluate our approach on benchmark datasets for image-to-video as well as
for mixed-source domain adaptation achieving state-of-the-art results and
demonstrating the benefits of our cyclic adaptation.


title:
Effective-Dimension Theory of Critical Phenomena above Upper Critical
  Dimensions

abstract:
  Phase transitions and critical phenomena are among the most intriguing
phenomena in nature and their renormalization-group theory is one of the
greatest achievements of theoretical physics. However, the predictions of the
theory above an upper critical dimension $d_c$ seriously disagree with reality.
In addition to its fundamental significance, the problem is also of practical
importance because both complex systems with spatial or temporal long-range
interactions and quantum phase transitions can substantially lower $d_c$. The
extant scenarios built on a dangerous irrelevant variable (DIV) to resolve the
problem introduce two sets of critical exponents and even two sets of scaling
laws whose origin is obscure. Here, we consider the DIV from a different
perspective and clearly unveil the origin of the two sets of exponents and
hence the intrinsic inconsistency in those scenarios. We then develop an
effective-dimension theory in which critical fluctuations and system volume are
fixed at an effective dimension by the DIV. This enables us to account for all
the extant results consistently. A novel asymptotic finite-size scaling
behavior for a correlation function together with a new anomalous dimension and
its associated scaling law is also derived.


abstract:
  Nowadays, detecting anomalous communities in networks is an essential task in
research, as it helps discover insights into community-structured networks.
Most of the existing methods leverage either information regarding attributes
of vertices or the topological structure of communities. In this study, we
introduce the Co-Membership-based Generic Anomalous Communities Detection
Algorithm (referred as to CMMAC), a novel and generic method that utilizes the
information of vertices co-membership in multiple communities. CMMAC is
domain-free and almost unaffected by communities' sizes and densities.
Specifically, we train a classifier to predict the probability of each vertex
in a community being a member of the community. We then rank the communities by
the aggregated membership probabilities of each community's vertices. The
lowest-ranked communities are considered to be anomalous. Furthermore, we
present an algorithm for generating a community-structured random network
enabling the infusion of anomalous communities to facilitate research in the
field. We utilized it to generate two datasets, composed of thousands of
labeled anomaly-infused networks, and published them. We experimented
extensively on thousands of simulated, and real-world networks, infused with
artificial anomalies. CMMAC outperformed other existing methods in a range of
settings. Additionally, we demonstrated that CMMAC can identify abnormal
communities in real-world unlabeled networks in different domains, such as
Reddit and Wikipedia.


title:
Bounds on anomalous quartic $WWZ\gamma$ couplings in $e^-p$ collisions
  at the FCC-he

abstract:
  Non-Abelian structure of the Standard Model predicts the self-interactions of
gauge bosons triple gauge couplings (TGC) and quartic gauge couplings (QGC). On
the other hand, it is also important to determine the deviations from Standard
Model (SM) via anomalous triple gauge couplings (aTGC) and anomalous quartic
gauge couplings (aQGC) to test the nature of the Standard Model (SM) and to see
the effects of new physics arising from the beyond standard model (BSM). In
this study, we focus on the $e^-p \to j Z\gamma \nu_e \to j (l^{+} l^{-})\gamma
\nu_e$ process to examine the $WWZ\gamma$ anomalous quartic gauge couplings
(aQGC) at the Future Circular Collider-hadron electron (FCC-he) with
center-of-mass energy $\sqrt{s}=5.29$ TeV using with a model-independent way in
the effective theory approach. A Cut-based method are applied to analyse the
signal and relevant SM background. Using with the total cross-sections, we
constrained on the anomalous $ f_ {T,i=0,1,2,5,6,7}/\Lambda^4$ couplings
arising from dimension-8 operators at $95\%$ Confidence Level (C.L.) for the
dilepton decay of the $Z$-boson. The results are composed with integrated
luminosities of ${\cal L}=1,2$ and $3$ $\rm ab^{-1}$ under the systematic
uncertainties of $\%0$, $\%5$ and $\%10$. In the calculations, we use the form
factor to eleminate the effects of the violation of unitarity. With this, we
obtained the limits for the energy scale of the form factor $\Lambda_{FF}=1$
TeV together with $\Lambda_{FF}=\infty$. In addition to all this, we use the
adventages of $ep$ collisions for analysing the process with much cleaner
environment and lower background effects comparing with the $pp$ collisions.
The obtained sensitivities on $ f_ {T,i=0,1,2,5,6,7}/\Lambda^4$ are comparable
with the experimental results and related phenomenological studies in the
literature.


title: InstaFormer: Instance-Aware Image-to-Image Translation with Transformer

abstract:
  We present a novel Transformer-based network architecture for instance-aware
image-to-image translation, dubbed InstaFormer, to effectively integrate
global- and instance-level information. By considering extracted content
features from an image as tokens, our networks discover global consensus of
content features by considering context information through a self-attention
module in Transformers. By augmenting such tokens with an instance-level
feature extracted from the content feature with respect to bounding box
information, our framework is capable of learning an interaction between object
instances and the global image, thus boosting the instance-awareness. We
replace layer normalization (LayerNorm) in standard Transformers with adaptive
instance normalization (AdaIN) to enable a multi-modal translation with style
codes. In addition, to improve the instance-awareness and translation quality
at object regions, we present an instance-level content contrastive loss
defined between input and translated image. We conduct experiments to
demonstrate the effectiveness of our InstaFormer over the latest methods and
provide extensive ablation studies.


abstract:
  Comment on "Scalings of Rayleigh-Taylor Instability at Large Viscosity
Contrasts in Porous Media" (N. Sabet, H. Hassanzadeh, A. De Wit, and J. Abedi
(2021) Phys. Rev. Lett. 126:094501).


title: PP-YOLOE: An evolved version of YOLO

abstract:
  In this report, we present PP-YOLOE, an industrial state-of-the-art object
detector with high performance and friendly deployment. We optimize on the
basis of the previous PP-YOLOv2, using anchor-free paradigm, more powerful
backbone and neck equipped with CSPRepResStage, ET-head and dynamic label
assignment algorithm TAL. We provide s/m/l/x models for different practice
scenarios. As a result, PP-YOLOE-l achieves 51.4 mAP on COCO test-dev and 78.1
FPS on Tesla V100, yielding a remarkable improvement of (+1.9 AP, +13.35% speed
up) and (+1.3 AP, +24.96% speed up), compared to the previous state-of-the-art
industrial models PP-YOLOv2 and YOLOX respectively. Further, PP-YOLOE inference
speed achieves 149.2 FPS with TensorRT and FP16-precision. We also conduct
extensive experiments to verify the effectiveness of our designs. Source code
and pre-trained models are available at
https://github.com/PaddlePaddle/PaddleDetection.


title:
Robust Generation Dispatch with Strategic Renewable Power Curtailment
  and Decision-Dependent Uncertainty

abstract:
  As renewable energy sources replace traditional power sources (such as
thermal generators), uncertainty grows while there are fewer controllable
units. To reduce operational risks and avoid frequent real-time emergency
controls, a preparatory schedule of renewable generation curtailment is
required. This paper proposes a novel two-stage robust generation dispatch
(RGD) model, where the curtailment strategy is optimized in the pre-dispatch
stage. The curtailment strategy will then influence the real-time renewable
power output, resulting in a decision-dependent uncertainty (DDU) set. In the
re-dispatch stage, the controllable units adjust their outputs within the
reserve capacities to maintain power balancing. To overcome the difficulty in
solving the RGD with DDU, an adaptive column-and-constraint generation (AC&CG)
algorithm is developed. We prove that the proposed algorithm can generate the
optimal strategy in finite iterations. Numerical examples validate the
practicability and scalability of the proposed model and algorithm.


title: Kerr-Schild Tetrads and the Nijenhuis Tensor

abstract:
  We write the Kerr-Schild tetrads in terms of the flat space-time tetrads and
of a (1,1) tensor $S^\lambda_\mu$. This tensor can be considered as a
projection operator, since it transforms (i) flat space-time tetrads into
non-flat tetrads, and vice-versa, and (ii) the Minkowski space-time metric
tensor into a non-flat metric tensor, and vice-versa. The $S^\lambda_\mu$
tensor and its inverse are constructed in terms of the standard null vector
field $l_\mu$ that defines the Kerr-Schild form of the metric tensor in general
relativity, and that yields black holes and non-linear gravitational waves as
solutions of the vacuum Einstein's field equations. We show that the condition
for the vanishing of the Ricci tensor obtained by Kerr and Schild, in empty
space-time, is also a condition for the vanishing of the Nijenhuis tensor
constructed out of $S^\lambda_\mu$. Thus, a theory based on the Nijenhuis
tensor yields an important class of solutions of the Einstein's field
equations, namely, black holes and non-linear gravitational waves.


title: Detection of Gaussianity using envelopes

abstract:
  Here we introduce a new solution for Gaussianity testing using the envelope
of a signal. The coefficient of variation ($\frac{std}{mean}$) of the envelope
(CVE) of zero-mean Gaussian noise is a universal constant equal to $m =
\sqrt{(4-\pi)/\pi}\approx 0.523$. Thus, for any signal the result CVE = $m$ can
be used as a fingerprint to assess Gaussianity. Interestingly, the CVE is also
unique for uniform noise time series or low density Filtered Poisson Processes.
Here we summarize the mathematics and computer methods behind using the CVE for
Gaussianity testing in time series. In particular, we describe how to perform
Gaussianity testing in a step-by-step fashion for experimental data using the
Hilbert transform, showing that the sampling rate as well as the duration and
the filtering of the data stream affect the analysis. Additionally, through the
use of the Fourier transform phase randomization, we reveal the
interconnections among CVE, Gaussianity, and temporal modulation profiles.
Furthermore, we use the CVE to assess the degree of synchronization in Kuramoto
and Matthews-Mirollo-Strogatz models and show that CVE is relevant to the study
of coupled oscillators systems. CVE Gaussianity testing provides a new tool for
signal classification.


title:
Hebbian learning of higher-order interactions facilitates abrupt
  anti-phase synchronization

abstract:
  This Letter investigates the transition to synchronization of oscillator
ensembles encoded by simplicial complexes in which pairwise and higher-order
coupling weights adapt with time through the Hebbian learning mechanism. These
concurrently evolving disparate adaptive coupling weights lead to a novel
phenomenon in that the in-phase synchronization is completely obliterated;
instead, the anti-phase synchronization is originated. Besides this, the onsets
of anti-phase synchronization and desynchronization are manageable through both
dyadic and triadic Hebbian learning rates. The theoretical validation of these
numerical assessments is delineated thoroughly by employing Ott-Antonsen
dimensionality reduction. The framework and results of the Letter would help
understand the underlying synchronization behavior of a range of real-world
systems, such as the brain functions and social systems where interactions
evolve with time.


title: Physics Community Needs, Tools, and Resources for Machine Learning

abstract:
  Machine learning (ML) is becoming an increasingly important component of
cutting-edge physics research, but its computational requirements present
significant challenges. In this white paper, we discuss the needs of the
physics community regarding ML across latency and throughput regimes, the tools
and resources that offer the possibility of addressing these needs, and how
these can be best utilized and accessed in the coming years.


title:
Research topic trend prediction of scientific papers based on spatial
  enhancement and dynamic graph convolution network

abstract:
  In recent years, with the increase of social investment in scientific
research, the number of research results in various fields has increased
significantly. Accurately and effectively predicting the trends of future
research topics can help researchers discover future research hotspots.
However, due to the increasingly close correlation between various research
themes, there is a certain dependency relationship between a large number of
research themes. Viewing a single research theme in isolation and using
traditional sequence problem processing methods cannot effectively explore the
spatial dependencies between these research themes. To simultaneously capture
the spatial dependencies and temporal changes between research topics, we
propose a deep neural network-based research topic hotness prediction
algorithm, a spatiotemporal convolutional network model. Our model combines a
graph convolutional neural network (GCN) and Temporal Convolutional Network
(TCN), specifically, GCNs are used to learn the spatial dependencies of
research topics a and use space dependence to strengthen spatial
characteristics. TCN is used to learn the dynamics of research topics' trends.
Optimization is based on the calculation of weighted losses based on time
distance. Compared with the current mainstream sequence prediction models and
similar spatiotemporal models on the paper datasets, experiments show that, in
research topic prediction tasks, our model can effectively capture
spatiotemporal relationships and the predictions outperform state-of-art
baselines.


title: On the motive of O'Grady's six dimensional hyper-K\"{a}hler varieties

abstract:
  We prove that the rational Chow motive of a six-dimensional hyper-K\"{a}hler
variety obtained as symplectic resolution of a singular moduli space of
semistable sheaves on an abelian surface $A$ belongs to the tensor category of
motives generated by the motive of $A$. We in fact give a formula for the
rational Chow motive of such a variety in terms of that of the surface. As a
consequence, the conjectures of Hodge and Tate hold for many varieties of
OG6-type.


title: Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data

abstract:
  Segmenting or detecting objects in sparse Lidar point clouds are two
important tasks in autonomous driving to allow a vehicle to act safely in its
3D environment. The best performing methods in 3D semantic segmentation or
object detection rely on a large amount of annotated data. Yet annotating 3D
Lidar data for these tasks is tedious and costly. In this context, we propose a
self-supervised pre-training method for 3D perception models that is tailored
to autonomous driving data. Specifically, we leverage the availability of
synchronized and calibrated image and Lidar sensors in autonomous driving
setups for distilling self-supervised pre-trained image representations into 3D
models. Hence, our method does not require any point cloud nor image
annotations. The key ingredient of our method is the use of superpixels which
are used to pool 3D point features and 2D pixel features in visually similar
regions. We then train a 3D network on the self-supervised task of matching
these pooled point features with the corresponding pooled image pixel features.
The advantages of contrasting regions obtained by superpixels are that: (1)
grouping together pixels and points of visually coherent regions leads to a
more meaningful contrastive task that produces features well adapted to 3D
semantic segmentation and 3D object detection; (2) all the different regions
have the same weight in the contrastive loss regardless of the number of 3D
points sampled in these regions; (3) it mitigates the noise produced by
incorrect matching of points and pixels due to occlusions between the different
sensors. Extensive experiments on autonomous driving datasets demonstrate the
ability of our image-to-Lidar distillation strategy to produce 3D
representations that transfer well on semantic segmentation and object
detection tasks.


title:
A hybrid inventory policy for non-stationary lot-sizing problem with
  lateral transshipment

abstract:
  This paper addresses the two-stocking locations single item non-stationary
stochastic lot-sizing problem. The inventory level at each location is reviewed
periodically. Items can be reordered and received from a common central
warehouse and can also be transshipped laterally from the other location.
Lateral transshipment is assumed to be proactive to re-distribute the stock
between two stocking locations. Therefore, the order of action in each period
is: transshipping (if necessary), reordering (if necessary) and satisfying the
demand at each location and each installation. The costs are imposed on
transshipping, ordering, holding, and back-ordering. The key issue in such
systems is to determine the quantity of the lateral transshipment between
depots and the order quantities from the warehouse to both locations. We
formulate the problem via stochastic dynamic programming to minimise the
expected total cost. Since the number of actions increases exponentially as the
feasible quantities of transshipment and replenishment grow, we develop
two-stage dynamic programming to improve the computation efficiency. A
near-optimal policy against this two-stage formulation is introduced based on a
mixed integer linear programming and receding-horizon approach. numerical
experiments are implemented to demonstrate the performance of the two-stage
model and the heuristic algorithm


title: Giant planet formation in Palatini gravity

abstract:
  Some parts of the accretion model of the jovian planets' formation are
studied in the context of Palatini gravity. We mainly focus on the critical
core mass, that is, a mass for which there is no hydrostatic equilibrium
solution for the planet's envelope, which is a starting point of the runaway
accretion. We also discuss the conditions needed to be satisfied by a planet
such that it can posses a massive gaseous envelope around a solid core.


title:
Packaging, containerization, and virtualization of computational omics
  methods: Advances, challenges, and opportunities

abstract:
  Omics software tools have reshaped the landscape of modern biology and become
an essential component of biomedical research. The increasing dependence of
biomedical scientists on these powerful tools creates a need for easier
installation and greater usability. Packaging, virtualization, and
containerization are different approaches to satisfy this need by wrapping
omics tools in additional software that makes the omics tools easier to install
and use. Here, we systematically review practices across prominent packaging,
virtualization, and containerization platforms. We outline the challenges,
advantages, and limitations of each approach and some of the most widely used
platforms from the perspectives of users, software developers, and system
administrators. We also propose principles to make packaging, virtualization,
and containerization of omics software more sustainable and robust to increase
the reproducibility of biomedical and life science research.


title:
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified
  Understanding with Self-supervised Contrastive Learning

abstract:
  To avoid collapse in self-supervised learning (SSL), a contrastive loss is
widely used but often requires a large number of negative samples. Without
negative samples yet achieving competitive performance, a recent work has
attracted significant attention for providing a minimalist simple Siamese
(SimSiam) method to avoid collapse. However, the reason for how it avoids
collapse without negative samples remains not fully clear and our investigation
starts by revisiting the explanatory claims in the original SimSiam. After
refuting their claims, we introduce vector decomposition for analyzing the
collapse based on the gradient analysis of the $l_2$-normalized representation
vector. This yields a unified perspective on how negative samples and SimSiam
alleviate collapse. Such a unified perspective comes timely for understanding
the recent progress in SSL.


title: Does Audio Deepfake Detection Generalize?

abstract:
  Current text-to-speech algorithms produce realistic fakes of human voices,
making deepfake detection a much-needed area of research. While researchers
have presented various techniques for detecting audio spoofs, it is often
unclear exactly why these architectures are successful: Preprocessing steps,
hyperparameter settings, and the degree of fine-tuning are not consistent
across related work. Which factors contribute to success, and which are
accidental? In this work, we address this problem: We systematize audio
spoofing detection by re-implementing and uniformly evaluating architectures
from related work. We identify overarching features for successful audio
deepfake detection, such as using cqtspec or logspec features instead of
melspec features, which improves performance by 37% EER on average, all other
factors constant. Additionally, we evaluate generalization capabilities: We
collect and publish a new dataset consisting of 37.9 hours of found audio
recordings of celebrities and politicians, of which 17.2 hours are deepfakes.
We find that related work performs poorly on such real-world data (performance
degradation of up to one thousand percent). This may suggest that the community
has tailored its solutions too closely to the prevailing ASVSpoof benchmark and
that deepfakes are much harder to detect outside the lab than previously
thought.


title: Optimally Tracking Labels on an Evolving Tree

abstract:
  Motivated by the problem of maintaining data structures for a large sets of
points that are evolving over the course of time, we consider the problem of
maintaining a set of labels assigned to the vertices of a tree, where the
locations of these labels change over time. We study the problem in the
evolving data framework, where labels change over time due to the action of an
agent called the evolver. The algorithm can only track these changes by
explicitly probing individual nodes of the tree. This framework captures the
tradeoff between the complexity of maintaining an up-to-date view of the
structure and the quality of results computed with the available view. Our
results allow for both randomized and adversarial evolution of the data,
subject to allowing different speedup factors between the algorithm and the
evolver. We show that in the limit, our algorithm maintains labels to within an
average distance of $O(1)$ of their actual locations. We also present nearly
matching lower bounds.


title: SeqTR: A Simple yet Universal Network for Visual Grounding

abstract:
  In this paper, we propose a simple yet universal network termed SeqTR for
visual grounding tasks, e.g., phrase localization, referring expression
comprehension (REC) and segmentation (RES). The canonical paradigms for visual
grounding often require substantial expertise in designing network
architectures and loss functions, making them hard to generalize across tasks.
To simplify and unify the modeling, we cast visual grounding as a point
prediction problem conditioned on image and text inputs, where either the
bounding box or binary mask is represented as a sequence of discrete coordinate
tokens. Under this paradigm, visual grounding tasks are unified in our SeqTR
network without task-specific branches or heads, e.g., the convolutional mask
decoder for RES, which greatly reduces the complexity of multi-task modeling.
In addition, SeqTR also shares the same optimization objective for all tasks
with a simple cross-entropy loss, further reducing the complexity of deploying
hand-crafted loss functions. Experiments on five benchmark datasets demonstrate
that the proposed SeqTR outperforms (or is on par with) the existing
state-of-the-arts, proving that a simple yet universal approach for visual
grounding is indeed feasible.


title: Non-autoregressive Translation with Dependency-Aware Decoder

abstract:
  Non-autoregressive translation (NAT) models suffer from inferior translation
quality due to removal of dependency on previous target tokens from inputs to
the decoder. In this paper, we propose a novel and general approach to enhance
the target dependency within the NAT decoder from two perspectives: decoder
input and decoder self-attention. First, we transform the initial decoder input
from the source language space to the target language space through a novel
attentive transformation process. The transformation reassembles the decoder
input based on target token embeddings and conditions the final output on the
target-side information. Second, before NAT training, we introduce an effective
forward-backward pre-training phase, implemented with different triangle
attention masks. This pre-training phase enables the model to gradually learn
bidirectional dependencies for the final NAT decoding process. Experimental
results demonstrate that the proposed approaches consistently improve highly
competitive NAT models on four WMT translation directions by up to 1.88 BLEU
score, while overall maintaining inference latency comparable to other fully
NAT models.


title:
Online Monitoring of Dynamic Systems for Signal Temporal Logic
  Specifications with Model Information

abstract:
  Online monitoring aims to evaluate or to predict, at runtime, whether or not
the behaviors of a system satisfy some desired specification. It plays a key
role in safety-critical cyber-physical systems. In this work, we propose a new
model-based approach for online monitoring for specifications described by
Signal Temporal Logic (STL) formulae. Specifically, we assume that the observed
state traces are generated by an underlying dynamic system whose model is
known. The main idea is to consider the dynamic of the system when evaluating
the satisfaction of the STL formulae. To this end, effective approaches for the
computation of feasible sets for STL formulae are provided. We show that, by
explicitly utilizing the model information of the dynamic system, the proposed
online monitoring algorithm can falsify or certify of the specification in
advance compared with existing algorithms, where no model information is used.
We also demonstrate the proposed monitoring algorithm by case studies.


title:
Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object
  Tracking

abstract:
  Multiple object tracking (MOT) is a significant task in achieving autonomous
driving. Traditional works attempt to complete this task, either based on point
clouds (PC) collected by LiDAR, or based on images captured from cameras.
However, relying on one single sensor is not robust enough, because it might
fail during the tracking process. On the other hand, feature fusion from
multiple modalities contributes to the improvement of accuracy. As a result,
new techniques based on different sensors integrating features from multiple
modalities are being developed. Texture information from RGB cameras and 3D
structure information from Lidar have respective advantages under different
circumstances. However, it's not easy to achieve effective feature fusion
because of completely distinct information modalities. Previous fusion methods
usually fuse the top-level features after the backbones extract the features
from different modalities. In this paper, we first introduce PointNet++ to
obtain multi-scale deep representations of point cloud to make it adaptive to
our proposed Interactive Feature Fusion between multi-scale features of images
and point clouds. Specifically, through multi-scale interactive query and
fusion between pixel-level and point-level features, our method, can obtain
more distinguishing features to improve the performance of multiple object
tracking. Besides, we explore the effectiveness of pre-training on each single
modality and fine-tuning on the fusion-based model. The experimental results
demonstrate that our method can achieve good performance on the KITTI benchmark
and outperform other approaches without using multi-scale feature fusion.
Moreover, the ablation studies indicates the effectiveness of multi-scale
feature fusion and pre-training on single modality.


title:
Experimental activation of strong local passive states with quantum
  information

abstract:
  Strong local passivity is a property of multipartite quantum systems from
which it is impossible to locally extract energy. A popular example is the
vacuum state of a quantum field theory from which it is impossible to extract
zero-point energy. However, adding classical communication between different
partitions of the system through so-called 'quantum energy teleportation'
protocols makes it possible to activate local energy of strong local passive
states, including the ground state of quantum systems if this state displays
entanglement. Here we report the first experimental realization of both the
activation of a strong local passive state and the demonstration of a quantum
energy teleportation protocol by using nuclear magnetic resonance on a
bipartite quantum system.


title: Contact process in an evolving random environment

abstract:
  In this paper we introduce a contact process in an evolving random
environment (CPERE) on a connected and transitive graph with bounded degree,
where we assume that this environment is described through an ergodic spin
systems with finite range. We show that under a certain growth condition the
phase transition of survival is independent of the initial configuration of the
process. We study the invariant laws of the CPERE and show that under
aforementioned growth condition the phase transition for survival coincides
with the phase transition of non-triviality of the upper invariant law.
Furthermore, we prove continuity properties for the survival probability and
derive equivalent conditions for complete convergence, in an analogous way as
for the classical contact process. We then focus on the special case, where the
evolving random environment is described through a dynamical percolation. We
show that the contact process on a dynamical percolation on the $d$-dimensional
integer dies out a.s. at criticality and complete convergence holds for all
parameter choices. In the end we derive some comparison results between a
dynamical percolation and ergodic spin systems with finite range such that we
get bounds on the survival probability of a contact process in an evolving
random environment and we determine in this case that complete convergence
holds in a certain parameter regime.


title:
Alternating direction method of multipliers for convex programming: a
  lift-and-permute scheme

abstract:
  A lift-and-permute scheme of alternating direction method of multipliers
(ADMM) is proposed for linearly constrained convex programming. It contains not
only the newly developed balanced augmented Lagrangian method and its
dual-primal variation, but also the proximal ADMM and Douglas-Rachford
splitting algorithm. It helps to propose accelerated algorithms with worst-case
$O(1/k^2)$ convergence rates in the case that the objective function to be
minimized is strongly convex.


title: On a geometrical notion of dimension for partially ordered sets

abstract:
  The well-known notion of dimension for partial orders by Dushnik and Miller
allows to quantify the degree of incomparability and, thus, is regarded as a
measure of complexity for partial orders. However, despite its usefulness, its
definition is somewhat disconnected from the geometrical idea of dimension,
where, essentially, the number of dimensions indicates how many real lines are
required to represent the underlying partially ordered set.
  Here, we introduce a new notion of dimension for partial orders called Debreu
dimension, a variation of the Dushnik-Miller dimension that is closer to
geometry. Instead of arbitrary linear extensions as considered by the
Dushnik-Miller dimension, we consider specifically Debreu separable linear
extensions. Our main result is that the Debreu dimension is countable if and
only if countable multi-utilities exist. Importantly, unlike the classical
results concerning linear extensions like the Szpilrajn extension theorem, we
avoid using the axiom of choice. Instead, we rely on countability restrictions
to sequentially construct extensions which, in the limit, yield Debreu
separable linear extensions.


title: Interpretable Vertebral Fracture Diagnosis

abstract:
  Do black-box neural network models learn clinically relevant features for
fracture diagnosis? The answer not only establishes reliability quenches
scientific curiosity but also leads to explainable and verbose findings that
can assist the radiologists in the final and increase trust. This work
identifies the concepts networks use for vertebral fracture diagnosis in CT
images. This is achieved by associating concepts to neurons highly correlated
with a specific diagnosis in the dataset. The concepts are either associated
with neurons by radiologists pre-hoc or are visualized during a specific
prediction and left for the user's interpretation. We evaluate which concepts
lead to correct diagnosis and which concepts lead to false positives. The
proposed frameworks and analysis pave the way for reliable and explainable
vertebral fracture diagnosis.


title:
Characterizing YouTube and BitChute Content and Mobilizers During U.S.
  Election Fraud Discussions on Twitter

abstract:
  In this study, we characterize the cross-platform mobilization of YouTube and
BitChute videos on Twitter during the 2020 U.S. Election fraud discussions.
Specifically, we extend the VoterFraud2020 dataset to describe the prevalence
of content supplied by both platforms, the mobilizers of that content, the
suppliers of that content, and the content itself. We find that while BitChute
videos promoting election fraud claims were linked to and engaged with in the
Twitter discussion, they played a relatively small role compared to YouTube
videos promoting fraud claims. This core finding points to the continued need
for proactive, consistent, and collaborative content moderation solutions
rather than the reactive and inconsistent solutions currently being used.
Additionally, we find that cross-platform disinformation spread from video
platforms was not prominently from bot accounts or political elites, but rather
average Twitter users. This finding supports past work arguing that research on
disinformation should move beyond a focus on bots and trolls to a focus on
participatory disinformation spread.


title: Reinforcement Learning Guided by Provable Normative Compliance

abstract:
  Reinforcement learning (RL) has shown promise as a tool for engineering safe,
ethical, or legal behaviour in autonomous agents. Its use typically relies on
assigning punishments to state-action pairs that constitute unsafe or unethical
choices. Despite this assignment being a crucial step in this approach,
however, there has been limited discussion on generalizing the process of
selecting punishments and deciding where to apply them. In this paper, we adopt
an approach that leverages an existing framework -- the normative supervisor of
(Neufeld et al., 2021) -- during training. This normative supervisor is used to
dynamically translate states and the applicable normative system into
defeasible deontic logic theories, feed these theories to a theorem prover, and
use the conclusions derived to decide whether or not to assign a punishment to
the agent. We use multi-objective RL (MORL) to balance the ethical objective of
avoiding violations with a non-ethical objective; we will demonstrate that our
approach works for a multiplicity of MORL techniques, and show that it is
effective regardless of the magnitude of the punishment we assign.


title:
Questioning Planck-selected star-forming high-redshift galaxy
  protoclusters and their fate

abstract:
  About 2100 star-forming galaxy protocluster candidates at z=1-4 were
identified at submillimeter (sub-mm) wavelengths in the Planck all sky survey.
Follow-up spectroscopic observations of a few candidates have confirmed the
presence of galaxy overdensities with large star-formation rates. In this work,
we use state-of-the-art hydrodynamical simulations to investigate whether the
Planck high-z sub-mm sources (PHz) are progenitors of massive clusters at z=0.
To match the PHz sources with simulated halos, we select the most star-forming
(SF) halos from z=3 to z=1.3 in the IllustrisTNG300 simulation. At each
redshift, the total star formation rate (SFR) of the simulated protocluster
candidates is computed from the SFR of all the galaxies within an aperture
corresponding to the Planck beam size, including those along the line-of-sight.
The simulations reproduce the Planck derived SFRs as the sum of both, the SFR
of at least one of the most SF high-z halo, and the average contribution from
SF sources along the line-of-sight. Focusing on the spectroscopically confirmed
PHz protoclusters, we compare the observed properties of their galaxy members
with those in the most SF simulated halos. We find a good agreement in the
stellar mass and SFR distributions, and in the galaxy number counts, but the
SFR-stellar mass relation of the simulated galaxies tend to be shifted to lower
SFRs with respect to the observed one. Based on the estimated final masses of
the simulated halos, we infer that between 63% and 72% of the Planck selected
protoclusters will evolve into massive galaxy clusters by z=0. We confirm the
efficiency of Planck to select star-forming protoclusters at Cosmic Noon with
the simulations, and provide a new criterion for selecting the most massive
cluster progenitors at high-z, using observables like the number of galaxy
members and their SFR distribution.


title: Universal Aspects of Barrier Crossing Under Bias

abstract:
  The thermal activation process by which a system passes from one local energy
minimum to another is a recurring motif in physics, chemistry, and biology. For
instance, biopolymer chains are typically modeled in terms of energy
landscapes, with folded and unfolded conformations represented by two distinct
wells separated by a barrier. The rate of transfer between wells depends
primarily on the height of the barrier, but it also depends on the details of
the shape of the landscape along the trajectory. We consider the case of bias
due to an external force, analogous to the pulling force applied in optical
tweezer experiments on biopolymers. Away from the Arrhenius-law limit and well
out of equilibrium, somewhat idiosyncratic behavior might be expected. Instead,
we identify universal behavior of the biased activated-barrier-crossing process
and demonstrate that data collapse onto a universal curve can be achieved for
simulated data over a wide variety of energy landscapes having barriers of
different height and shape and for loading rates spanning many orders of
magnitude.


title:
Does Science need Intersubjectivity? The Problem of Confirmation in
  Orthodox Interpretations of Quantum Mechanics

abstract:
  Any successful interpretation of quantum mechanics must explain how our
empirical evidence allows us to come to know about quantum mechanics. In this
article, we argue that this vital criterion is not met by the class of
'orthodox interpretations,' which includes QBism, neo-Copenhagen
interpretations, and some versions of relational quantum mechanics. We
demonstrate that intersubjectivity fails in radical ways in these approaches,
and we explain why intersubjectivity matters for empirical confirmation. We
take a detailed look at the way in which belief-updating might work in the kind
of universe postulated by an orthodox interpretation, and argue that observers
in such a universe are unable to escape their own perspective in order to learn
about the structure of the set of perspectives that is supposed to make up
reality according to these interpretations. We also argue that in some versions
of these interpretations it is not even possible to use one's own relative
frequencies for empirical confirmation. Ultimately we conclude that it cannot
be rational to believe these sorts of interpretations unless they are
supplemented with some observer-independent structure which underwrites
intersubjective agreement in at least certain sorts of cases.


title: Neural Pipeline for Zero-Shot Data-to-Text Generation

abstract:
  In data-to-text (D2T) generation, training on in-domain data leads to
overfitting to the data representation and repeating training data noise. We
examine how to avoid finetuning pretrained language models (PLMs) on D2T
generation datasets while still taking advantage of surface realization
capabilities of PLMs. Inspired by pipeline approaches, we propose to generate
text by transforming single-item descriptions with a sequence of modules
trained on general-domain text-based operations: ordering, aggregation, and
paragraph compression. We train PLMs for performing these operations on a
synthetic corpus WikiFluent which we build from English Wikipedia. Our
experiments on two major triple-to-text datasets -- WebNLG and E2E -- show that
our approach enables D2T generation from RDF triples in zero-shot settings.


title: CMMD: Cross-Metric Multi-Dimensional Root Cause Analysis

abstract:
  In large-scale online services, crucial metrics, a.k.a., key performance
indicators (KPIs), are monitored periodically to check their running statuses.
Generally, KPIs are aggregated along multiple dimensions and derived by complex
calculations among fundamental metrics from the raw data. Once abnormal KPI
values are observed, root cause analysis (RCA) can be applied to identify the
reasons for anomalies, so that we can troubleshoot quickly. Recently, several
automatic RCA techniques were proposed to localize the related dimensions (or a
combination of dimensions) to explain the anomalies. However, their analyses
are limited to the data on the abnormal metric and ignore the data of other
metrics which may be also related to the anomalies, leading to imprecise or
even incorrect root causes. To this end, we propose a cross-metric
multi-dimensional root cause analysis method, named CMMD, which consists of two
key components: 1) relationship modeling, which utilizes graph neural network
(GNN) to model the unknown complex calculation among metrics and aggregation
function among dimensions from historical data; 2) root cause localization,
which adopts the genetic algorithm to efficiently and effectively dive into the
raw data and localize the abnormal dimension(s) once the KPI anomalies are
detected. Experiments on synthetic datasets, public datasets and online
production environment demonstrate the superiority of our proposed CMMD method
compared with baselines. Currently, CMMD is running as an online service in
Microsoft Azure.


title:
A Novel First-Order Autoregressive Moving Average Model to Analyze
  Discrete-Time Series Irregularly Observed

abstract:
  A novel first-order autoregressive moving average model for analyzing
discrete-time series observed at irregularly spaced times is introduced. Under
Gaussianity, it is established that the model is strictly stationary and
ergodic. In the general case, it is shown that the model is weakly stationary.
The lowest dimension of the state-space representation is given along with the
one-step linear predictors and their mean squared errors. The maximum
likelihood estimation procedure is discussed, and their finite-sample behavior
is assessed through Monte Carlo experiments. These experiments show that bias,
root mean squared error, and coefficient of variation are smaller when the
length of the series increases. Further, the method provides good estimations
for the standard errors, even with relatively small sample sizes. Also, the
irregularly spaced times seem to increase the estimation variability. The
application of the proposed model is made through two real-life examples. The
first is concerned with medical data, whereas the second describes an
astronomical data set analysis.


title: The Weak Supervision Landscape

abstract:
  Many ways of annotating a dataset for machine learning classification tasks
that go beyond the usual class labels exist in practice. These are of interest
as they can simplify or facilitate the collection of annotations, while not
greatly affecting the resulting machine learning model. Many of these fall
under the umbrella term of weak labels or annotations. However, it is not
always clear how different alternatives are related. In this paper we propose a
framework for categorising weak supervision settings with the aim of: (1)
helping the dataset owner or annotator navigate through the available options
within weak supervision when prescribing an annotation process, and (2)
describing existing annotations for a dataset to machine learning practitioners
so that we allow them to understand the implications for the learning process.
To this end, we identify the key elements that characterise weak supervision
and devise a series of dimensions that categorise most of the existing
approaches. We show how common settings in the literature fit within the
framework and discuss its possible uses in practice.


title: Uniform solvability for families of linear systems on time scales

abstract:
  We give explicit criteria of solvability for families of linear systems on
time scales. We introduce a new method of embedding a time scale into a
non-autonomous system of ODEs. This will be the first step to implementing the
structural stability result obtained by one of the co-authors together with V.
A. Pliss to time scale dynamics.


title:
HDSDF: Hybrid Directional and Signed Distance Functions for Fast Inverse
  Rendering

abstract:
  Implicit neural representations of 3D shapes form strong priors that are
useful for various applications, such as single and multiple view 3D
reconstruction. A downside of existing neural representations is that they
require multiple network evaluations for rendering, which leads to high
computational costs. This limitation forms a bottleneck particularly in the
context of inverse problems, such as image-based 3D reconstruction. To address
this issue, in this paper (i) we propose a novel hybrid 3D object
representation based on a signed distance function (SDF) that we augment with a
directional distance function (DDF), so that we can predict distances to the
object surface from any point on a sphere enclosing the object. Moreover, (ii)
using the proposed hybrid representation we address the multi-view consistency
problem common in existing DDF representations. We evaluate our novel hybrid
representation on the task of single-view depth reconstruction and show that
our method is several times faster compared to competing methods, while at the
same time achieving better reconstruction accuracy.


title:
A fast test to assess the impact of marginalization in Monte Carlo
  analyses, and its application to cosmology

abstract:
  Monte Carlo (MC) algorithms are commonly employed to explore high-dimensional
parameter spaces constrained by data. All the statistical information obtained
in the output of these analyses is contained in the Markov chains, which one
needs to process and interpret. The marginalization technique allows us to
digest these chains and compute the posterior distributions for the parameter
subsets of interest. In particular, it lets us draw confidence regions in
two-dimensional planes, and get the constraints for the individual parameters.
It is very well known, though, that the marginalized results can suffer from
volume effects, which can introduce a non-negligible bias into our conclusions.
The impact of these effects are barely studied in the literature. In this paper
we first illustrate the problem through a very clear and simple example in two
dimensions, and suggest the use of the profile distributions (PDs) as a
complementary tool to detect marginalization biases directly from the MC
chains. We apply our method to four cosmological models: the standard
$\Lambda$CDM, early dark energy, coupled dark energy and the Brans-Dicke model
with a cosmological constant. We discuss the impact of the volume effects on
each model and the cosmological tensions, using the full Planck 2018
likelihood, the Pantheon compilation of supernovae of Type Ia and data on
baryon acoustic oscillations. Our test is very efficient and can be easily
applied to any MC study. It allows us to estimate the PDs at a derisory
computational cost not only for the main cosmological parameters, but also for
the nuisance and derived ones, and to assess the need to perform a more
in-depth analysis with the exact computation of the PDs.


title:
Nouvelle Vague: a New Avenue to Accurate Analytical Waveforms and Fluxes
  for Eccentric Compact Binaries

abstract:
  We introduce a new paradigm for constructing accurate analytic waveforms (and
fluxes) for eccentric compact binaries. Our recipe builds on the standard
Post-Newtonian (PN) approach but (i) retains implicit time-derivatives of the
phase space variables in the instantaneous part of the noncircular waveform,
and then (ii) suitably factorizes and resums this partly PN-implicit waveform
using effective-one-body (EOB) procedures. We test our prescription against the
exact results obtained by solving the Teukolsky equation with a test-mass
source orbiting a Kerr black hole, and compare the use of the exact vs PN
equations of motion for the time derivatives computation. Focusing only on the
quadrupole contribution, we find that the use of the exact equations of motion
yields an analytical/numerical agreement of the (averaged) angular momentum
fluxes that is improved by at least $45\%$ with respect to previous work, with
$4\%$ fractional difference for eccentricity $e=0.9$ and black hole
dimensionless spin $-0.9\leq \hat{a}\leq +0.9$. We also find a remarkable
convergence trend between Newtonian, 1PN and 2PN results. Our approach carries
over to the comparable mass case using the resummed EOB equations of motion and
paves the way to faithful EOB-based waveform model for long-inspiral eccentric
binaries for current and future gravitational wave detectors.


title: Benchmarking distance-based partitioning methods for mixed-type data

abstract:
  Clustering mixed-type data, that is, observation by variable data that
consist of both continuous and categorical variables poses novel challenges.
Foremost among these challenges is the choice of the most appropriate
clustering method for the data. This paper presents a benchmarking study
comparing six distance-based partitioning methods for mixed-type data in terms
of cluster recovery performance. A series of simulations carried out by a full
factorial design are presented that examined the effect of a variety of factors
on cluster recovery. The amount of cluster overlap had the largest effect on
cluster recovery and in most of the tested scenarios. Modha-Spangler K-Means,
K-Prototypes and a sequential Factor Analysis and K-Means clustering typically
performed better than other methods. The study can be a useful reference for
practitioners in the choice of the most appropriate method.


title:
Region of Interest focused MRI to Synthetic CT Translation using
  Regression and Classification Multi-task Network

abstract:
  In this work, we present a method for synthetic CT (sCT) generation from
zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the
image, with a particular focus on the accurate bone density value prediction.
We propose a loss function that favors a spatially sparse region in the image.
We harness the ability of a multi-task network to produce correlated outputs as
a framework to enable localisation of region of interest (RoI) via
classification, emphasize regression of values within RoI and still retain the
overall accuracy via global regression. The network is optimized by a composite
loss function that combines a dedicated loss from each task. We demonstrate how
the multi-task network with RoI focused loss offers an advantage over other
configurations of the network to achieve higher accuracy of performance. This
is relevant to sCT where failure to accurately estimate high Hounsfield Unit
values of bone could lead to impaired accuracy in clinical applications. We
compare the dose calculation maps from the proposed sCT and the real CT in a
radiation therapy treatment planning setup.


title:
One-Step Two-Critic Deep Reinforcement Learning for Inverter-based
  Volt-Var Control in Active Distribution Networks

abstract:
  A one-step two-critic deep reinforcement learning (OSTC-DRL) approach for
inverter-based volt-var control (IB-VVC) in active distribution networks is
proposed in this paper. Firstly, considering IB-VVC can be formulated as a
single-period optimization problem, we formulate the IB-VVC as a one-step
Markov decision process rather than the standard Markov decision process, which
simplifies the DRL learning task. Then we design the one-step actor-critic DRL
scheme which is a simplified version of recent DRL algorithms, and it avoids
the issue of Q value overestimation successfully. Furthermore, considering two
objectives of VVC: minimizing power loss and eliminating voltage violation, we
utilize two critics to approximate the rewards of two objectives separately. It
simplifies the approximation tasks of each critic, and avoids the interaction
effect between two objectives in the learning process of critic. The OSTC-DRL
approach integrates the one-step actor-critic DRL scheme and the two-critic
technology. Based on the OSTC-DRL, we design two centralized DRL algorithms.
Further, we extend the OSTC-DRL to multi-agent OSTC-DRL for decentralized
IB-VVC and design two multi-agent DRL algorithms. Simulations demonstrate that
the proposed OSTC-DRL has a faster convergence rate and a better control
performance, and the multi-agent OSTC-DRL works well for decentralized IB-VVC
problems.


title:
An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX
  models

abstract:
  This paper deals with the design of nonlinear MPC controllers that provide
offset-free setpoint tracking for models described by Neural Nonlinear
AutoRegressive eXogenous (NNARX) networks. The NNARX model is identified from
input-output data collected from the plant, and can be given a state-space
representation with known measurable states made by past input and output
variables, so that a state observer is not required. In the training phase, the
Incremental Input-to-State Stability ({\delta}ISS) property can be forced when
consistent with the behavior of the plant. The {\delta}ISS property is then
leveraged to augment the model with an explicit integral action on the output
tracking error, which allows to achieve offset-free tracking capabilities to
the designed control scheme. The proposed control architecture is numerically
tested on a water heating system and the achieved results are compared to those
scored by another popular offset-free MPC method, showing that the proposed
scheme attains remarkable performances even in presence of disturbances acting
on the plant.


title:
AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe
  Domain Shift

abstract:
  We introduce AmsterTime: a challenging dataset to benchmark visual place
recognition (VPR) in presence of a severe domain shift. AmsterTime offers a
collection of 2,500 well-curated images matching the same scene from a street
view matched to historical archival image data from Amsterdam city. The image
pairs capture the same place with different cameras, viewpoints, and
appearances. Unlike existing benchmark datasets, AmsterTime is directly
crowdsourced in a GIS navigation platform (Mapillary). We evaluate various
baselines, including non-learning, supervised and self-supervised methods,
pre-trained on different relevant datasets, for both verification and retrieval
tasks. Our result credits the best accuracy to the ResNet-101 model pre-trained
on the Landmarks dataset for both verification and retrieval tasks by 84% and
24%, respectively. Additionally, a subset of Amsterdam landmarks is collected
for feature evaluation in a classification task. Classification labels are
further used to extract the visual explanations using Grad-CAM for inspection
of the learned similar visuals in a deep metric learning models.


title:
Put-Call Parities, absence of arbitrage opportunities and non-linear
  pricing rules

abstract:
  If prices of assets traded in a financial market are determined by non-linear
pricing rules, different versions of the Call-Put Parity have been considered.
We show that, under monotonicity, parities between call and put options and
discount certificates characterize ambiguity-sensitive (Choquet and/or Sipos)
pricing rules, i.e., pricing rules that can be represented via discounted
expectations with respect to non-additive probability measures. We analyze how
non-additivity relates to arbitrage opportunities and we give necessary and
sufficient conditions for Choquet and Sipos pricing rules to be arbitrage-free.
Finally, we identify violations of the Call-Put Parity with the presence of
bid-ask spreads.


title:
Core and stability notions in many-to-one matching markets with
  indifferences

abstract:
  In a many-to-one matchingmodel with responsive preferences in which
indifferences are allowed, we study three notions of core, three notions of
stability, and their relationships. We show that (i) the core contains the
stable set, (ii) the strong core coincides with the strongly stable set, and
(iii) the super core coincides with the super stable set. We also show how the
core and the strong core in markets with indifferences relate to the stable
matchings of their associated tie-breaking strict markets.


title: Context-aware Automatic Music Transcription

abstract:
  This paper presents an Automatic Music Transcription system that incorporates
context-related information. Motivated by the state-of-art psychological
research, we propose a methodology boosting the accuracy of AMT systems by
modeling the adaptations that performers apply to successfully convey their
interpretation in any acoustical context. In this work, we show that exploiting
the knowledge of the source acoustical context allows reducing the error
related to the inference of MIDI velocity. The proposed model structure first
extracts the interpretation features and then applies the modeled performer
adaptations. Interestingly, such a methodology is extensible in a
straightforward way since only slight efforts are required to train completely
context-aware AMT models.


title:
Crystal-field states and defect levels in candidate quantum spin ice
  Ce$_{2}$Hf$_{2}$O$_{7}$

abstract:
  We report the synthesis of powder and single-crystal samples of the cerium
pyrohafnate and their characterization using neutron diffraction,
thermogravimetry and X-ray absorption spectroscopy. We evaluate the amount of
non-magnetic Ce$^{4+}$ defects and use this result to interpret the spectrum of
crystal-electric field transitions observed using inelastic neutron scattering.
The analysis of these single-ion transitions indicates the dipole-octupole
nature of the ground state doublet and a significant degree of spin-lattice
coupling. The single-ion properties calculated from the crystal-electric field
parameters obtained spectroscopically are in good agreement with bulk magnetic
susceptibility data down to about 1 K. Below this temperature, the behavior of
the magnetic susceptibility indicates a correlated regime without showing any
sign of magnetic long-range order or freezing down to 0.08 K. We conclude that
Ce$_2$Hf$_2$O$_{7}$ is another candidate to investigate exotic correlated
states of quantum matter such as the octupolar quantum spin ice recently argued
to exist in the isostructural compounds Ce$_2$Sn$_2$O$_7$ and
Ce$_2$Zr$_2$O$_7$.


title:
Precise electronic structures of amorphous solids: unraveling the color
  origin and photocatalysis of black titania

abstract:
  Water splitting through efficient catalysts represents an ultimate solution
for carbon neutrality within 40 years. To achieve this goal, amorphous
photocatalysts are noted for their promising performances. Among them, the best
known is black titania (amorphous TiOx, x < 2). However, despite the large
number of studies on black titania, its color origin, structure-property
relationship, and photocatalytic mechanism remain a topic of hot debate,
largely due to the difficulty to calculate its precise electronic structure.
Here, using ab initio molecular dynamics simulations, we report the precise
electronic structures of black titania and further reveal the generic evolution
pattern of the electronic structures of covalent compounds upon amorphization
and reduction. Moreover, surface adsorption of the co-catalyst atoms (e.g., Pt)
on amorphous substances is simulated for the first time: the disordered surface
enables easy accommodation of the co-catalyst atoms, while the extended
electronic states facilitate the separation of photo-induced electrons and
holes, beneficial for hydrogen evolution. This study elucidates the workings of
amorphous catalysts and offers practical guidance for enhancing their
performances.


title: Forecasting from LiDAR via Future Object Detection

abstract:
  Object detection and forecasting are fundamental components of embodied
perception. These two problems, however, are largely studied in isolation by
the community. In this paper, we propose an end-to-end approach for detection
and motion forecasting based on raw sensor measurement as opposed to ground
truth tracks. Instead of predicting the current frame locations and forecasting
forward in time, we directly predict future object locations and backcast to
determine where each trajectory began. Our approach not only improves overall
accuracy compared to other modular or end-to-end baselines, it also prompts us
to rethink the role of explicit tracking for embodied perception. Additionally,
by linking future and current locations in a many-to-one manner, our approach
is able to reason about multiple futures, a capability that was previously
considered difficult for end-to-end approaches. We conduct extensive
experiments on the popular nuScenes dataset and demonstrate the empirical
effectiveness of our approach. In addition, we investigate the appropriateness
of reusing standard forecasting metrics for an end-to-end setup, and find a
number of limitations which allow us to build simple baselines to game these
metrics. We address this issue with a novel set of joint forecasting and
detection metrics that extend the commonly used AP metrics from the detection
community to measuring forecasting accuracy. Our code is available at
https://github.com/neeharperi/FutureDet


title: Algebraic properties of the first-order part of a problem

abstract:
  In this paper we study the notion of first-order part of a computational
problem, first introduced by Dzhafarov, Solomon, and Yokoyama, which captures
the "strongest computational problem with codomain $\mathbb{N}$ that is
Weihrauch reducible to $f$". This operator is very useful to prove separation
results, especially at the higher levels of the Weihrauch lattice. We explore
the first-order part in relation with several other operators already known in
the literature. We also introduce a new operator, called unbounded finite
parallelization, which plays an important role in characterizing the
first-order part of parallelizable problems. We show how the obtained results
can be used to explicitly characterize the first-order part of several known
problems.


title: Analytical theory of enhanced Bose-Einstein condensation in thin films

abstract:
  We present an analytically solvable theory of Bose-Einstein condensation in
thin film geometries. Analytical closed-form expressions for the critical
temperature are obtained in both the low-to-moderate confinement regime (where
the film thickness $L$ is in the order of microns) as well as in the strong
confinement regime where the thickness is in the order of few nanometers or
lower. The possibility of high-temperature BEC is predicted in the strong
confinement limit, with a square-root divergence of the critical temperature
$T_{c} \sim L^{-1/2}$. For cold Bose gases, this implies an enhancement up to
two orders of magnitude in $T_{c}$ for films on the nanometer scale. Analytical
predictions are also obtained for the heat capacity and the condensate
fraction. A new law for the heat capacity of the condensate, i.e. $C \sim
T^{2}$, is predicted for nano-scale films, which implies a different $\lambda$
point behaviour with respect to bulk systems, while the condensate fraction is
predicted to follow a $[1- (T/T_{c})^{2}]$ law.

categories: cond-mat.quant-gas cond-mat.mes-hall cond-mat.stat-mech cond-mat.supr-con physics.app-ph


title:
Analog cross coupled controller for oscillations: modeling and design
  via dominant system theory

abstract:
  We propose a new analog feedback controller based on the classical cross
coupled electronic oscillator. The goal is to drive a linear passive plant into
oscillations. We model the circuit as Lur'e system and we derive a new
graphical condition to certify oscillations (inverse circle criterion for
dominance theory). These conditions are then specialized to minimal control
architectures like RLC and RC networks, and are illustrated with an example
based on a DC motor model.


title:
PEGG-Net: Background Agnostic Pixel-Wise Efficient Grasp Generation
  Under Closed-Loop Conditions

abstract:
  Performing closed-loop grasping at close proximity to an object requires a
large field of view. However, such images will inevitably bring large amounts
of unnecessary background information, especially when the camera is far away
from the target object at the initial stage, resulting in performance
degradation of the grasping network. To address this problem, we design a novel
PEGG-Net, a real-time, pixel-wise, robotic grasp generation network. The
proposed lightweight network is inherently able to learn to remove background
noise that can reduce grasping accuracy. Our proposed PEGG-Net achieves
improved state-of-the-art performance on both Cornell dataset (98.9%) and
Jacquard dataset (93.8%). In the real-world tests, PEGG-Net can support
closed-loop grasping at up to 50Hz using an image size of 480x480 in dynamic
environments. The trained model also generalizes to previously unseen objects
with complex geometrical shapes, household objects and workshop tools and
achieved an overall grasp success rate of 91.2% in our real-world grasping
experiments.


title: High fidelity two-qubit gates on fluxoniums using a tunable coupler

abstract:
  Superconducting fluxonium qubits provide a promising alternative to transmons
on the path toward large-scale superconductor-based quantum computing due to
their better coherence and larger anharmonicity. A major challenge for
multi-qubit fluxonium devices is the experimental demonstration of a scalable
crosstalk-free multi-qubit architecture with high fidelity single-qubit and
two-qubit gates, single-shot readout and state initialization. Here, we present
a two-qubit fluxonium-based quantum processor with a tunable coupler element
following our theoretical proposal [DOI: 10.1063/5.0064800]. We experimentally
demonstrate fSim-type and controlled-Z gates with $99.55\%$ and $99.23\%$
fidelities, respectively. The residual ZZ interaction is suppressed down to the
few kHz level. Using a galvanically coupled flux control line, we implement
high fidelity single-qubit gates and ground state initialization with a single
arbitrary waveform generator channel per qubit.


title:
Tunable magnetically induced transparency spectra in magnon-magnon
  coupled Y3Fe5O12/permalloy bilayers

abstract:
  Hybrid magnonic systems host a variety of characteristic quantum phenomena
such as the magnetically-induced transparency (MIT) and Purcell effect, which
are considered useful for future coherent quantum information processing. In
this work, we experimentally demonstrate a tunable MIT effect in the
Y3Fe5O12(YIG)/Permalloy(Py) magnon-magnon coupled system via changing the
magnetic field orientations. By probing the magneto-optic effects of Py and
YIG, we identify clear features of MIT spectra induced by the mode
hybridization between the uniform mode of Py and the perpendicular standing
spin-wave modes of YIG. By changing the external magnetic field orientations,
we observe a tunable coupling strength between the YIG's spin-wave modes and
the Py's uniform mode, upon the application of an out-of-plane magnetic field.
This observation is theoretically interpreted by a geometrical consideration of
the Py and YIG magnetization under the oblique magnetic field even at a
constant interfacial exchange coupling. Our findings show high promise for
investigating tunable coherent phenomena with hybrid magnonic platforms.


title:
Path integrals of perturbative strings on curved backgrounds from string
  geometry theory

abstract:
  String geometry theory is one of the candidates of the non-perturbative
formulation of string theory. In this paper, from the closed bosonic sector of
string geometry theory, we derive path integrals of all order perturbative
strings on all the string backgrounds, $G_{\mu\nu}(x)$, $B_{\mu\nu}(x)$, and
$\Phi (x)$, by considering fluctuations around the string background
configurations, which are parametrized by the string backgrounds.


title: Equivalent constructions of nilpotent quadratic Lie algebras

abstract:
  The double extension and the T*-extension are classical methods for
constructing finite dimensional quadratic Lie algebras. The first one gives an
inductive classification in characteristic zero, while the latest produces
quadratic non-associative algebras (not only Lie) out of arbitrary ones in
characteristic different from 2. The classification of quadratic nilpotent Lie
algebras can also be reduced to the study of free nilpotent Lie algebras and
their invariant forms. In this work we will establish an equivalent
characterization among these three construction methods. This equivalence
reduces the classification of quadratic 2-step nilpotent to that of trivectors
in a natural way. In addition, theoretical results will provide simple rules
for switching among them.