title: Distributed Reconstruction of Noisy Pooled Data

abstract:
  In the pooled data problem we are given a set of $n$ agents, each of which
holds a hidden state bit, either $0$ or $1$. A querying procedure returns for a
query set the sum of the states of the queried agents. The goal is to
reconstruct the states using as few queries as possible. In this paper we
consider two noise models for the pooled data problem. In the noisy channel
model, the result for each agent flips with a certain probability. In the noisy
query model, each query result is subject to random Gaussian noise. Our results
are twofold. First, we present and analyze for both error models a simple and
efficient distributed algorithm that reconstructs the initial states in a
greedy fashion. Our novel analysis pins down the range of error probabilities
and distributions for which our algorithm reconstructs the exact initial states
with high probability. Secondly, we present simulation results of our algorithm
and compare its performance with approximate message passing (AMP) algorithms
that are conjectured to be optimal in a number of related problems.


title:
A Machine Learning Tutorial for Operational Meteorology, Part I:
  Traditional Machine Learning

abstract:
  Recently, the use of machine learning in meteorology has increased greatly.
While many machine learning methods are not new, university classes on machine
learning are largely unavailable to meteorology students and are not required
to become a meteorologist. The lack of formal instruction has contributed to
perception that machine learning methods are 'black boxes' and thus end-users
are hesitant to apply the machine learning methods in their every day workflow.
To reduce the opaqueness of machine learning methods and lower hesitancy
towards machine learning in meteorology, this paper provides a survey of some
of the most common machine learning methods. A familiar meteorological example
is used to contextualize the machine learning methods while also discussing
machine learning topics using plain language. The following machine learning
methods are demonstrated: linear regression; logistic regression; decision
trees; random forest; gradient boosted decision trees; naive Bayes; and support
vector machines. Beyond discussing the different methods, the paper also
contains discussions on the general machine learning process as well as best
practices to enable readers to apply machine learning to their own datasets.
Furthermore, all code (in the form of Jupyter notebooks and Google Colaboratory
notebooks) used to make the examples in the paper is provided in an effort to
catalyse the use of machine learning in meteorology.


title: Prescribed Mean Curvature Min-Max Theory in Some Non-Compact Manifolds

abstract:
  This paper develops a technique for applying one-parameter prescribed mean
curvature min-max theory in certain non-compact manifolds. We give two main
applications. First, fix a dimension $3\le n+1 \le 7$ and consider a smooth
function $h\colon \mathbb{R}^{n+1}\to \mathbb{R}$ which is asymptotic to a
positive constant near infinity. We show that, under certain additional
assumptions on $h$, there exists a closed hypersurface $\Sigma$ in
$\mathbb{R}^{n+1}$ with mean curvature prescribed by $h$. Second, let $(M^3,g)$
be an asymptotically flat 3-manifold and fix a constant $c > 0$. We show that,
under an additional assumption on $M$, it is possible to find a closed surface
$\Sigma$ of constant mean curvature $c$ in $M$.


title:
The major role of eccentricity in the evolution of colliding
  pulsar-stellar winds

abstract:
  Binary systems that host a massive star and a non-accreting pulsar can be
powerful non-thermal emitters. The relativistic pulsar wind and the
non-relativistic stellar outflows interact along the orbit, producing
ultrarelativistic particles that radiate from radio to gamma rays. To properly
characterize the physics of these sources, and better understand their emission
and impact on the environment, careful modelling of the outflow interactions,
spanning a broad range of spatial and temporal scales, is needed. Full
3-dimensional approaches are very computationally expensive, but simpler
approximate approaches, while still realistic at the semi-quantitative level,
are available. We present here the results of calculations done with a quasi
3-dimensional scheme to compute the evolution of the interacting flows in a
region spanning in size up to a thousand times the size of the binary. In
particular, we analyze for the first time the role of different eccentricities
in the large scale evolution of the shocked flows. We find that the higher the
eccentricity, the closer the flows behave like a one-side outflow, which
becomes rather collimated for eccentricity values $\gtrsim 0.75$. The
simulations also unveil that the pulsar and the stellar winds become fully
mixed within the grid for low eccentricity systems, presenting a more
stochastic behavior at large scales than in the highly eccentric systems.


title:
Analytical Model of Disk Evaporation and State Transitions in Accreting
  Black Holes

abstract:
  State transitions in black hole X-ray binaries are likely caused by gas
evaporation from a thin accretion disk into a hot corona. We present a
height-integrated version of this process which is suitable for analytical and
numerical studies. With radius $r$ scaled to Schwarzschild units and coronal
mass accretion rate $\dot{m}_c$ to Eddington units, the results of the model
are independent of black hole mass. State transitions should thus be similar in
X-ray binaries and AGN. The corona solution consists of two power-law segments
separated at a break radius $r_b \sim10^3 \,(\alpha/0.3)^{-2}$, where $\alpha$
is the viscosity parameter. Gas evaporates from the disk to the corona for
$r>r_b$, and condenses back for $r<r_b$. At $r_b$, $\dot{m}_c$ reaches its
maximum, $\dot{m}_{c,{\rm max}} \approx 0.02\, (\alpha/0.3)^3$. If at $r\gg
r_b$ the thin disk accretes with $\dot{m}_d < \dot{m}_{c,{\rm max}} $, then the
disk evaporates fully before reaching $r_b$, giving the hard state. Otherwise,
the disk survives at all radii, giving the thermal state. While the basic model
considers only bremsstrahlung cooling and viscous heating, we also discuss a
more realistic model which includes Compton cooling and direct coronal heating
by energy transport from the disk. Solutions are again independent of black
hole mass, and $r_b$ remains unchanged. This model predicts strong coronal
winds for $r>r_b$, and a $T\sim 6\times 10^8\,{\rm K}$ Compton-cooled corona
for $r < r_b$. Two-temperature effects are ignored, but may be important at
small radii.


title: Improving Passage Retrieval with Zero-Shot Question Generation

abstract:
  We propose a simple and effective re-ranking method for improving passage
retrieval in open question answering. The re-ranker re-scores retrieved
passages with a zero-shot question generation model, which uses a pre-trained
language model to compute the probability of the input question conditioned on
a retrieved passage. This approach can be applied on top of any retrieval
method (e.g. neural or keyword-based), does not require any domain- or
task-specific training (and therefore is expected to generalize better to data
distribution shifts), and provides rich cross-attention between query and
passage (i.e. it must explain every token in the question). When evaluated on a
number of open-domain retrieval datasets, our re-ranker improves strong
unsupervised retrieval models by 6%-18% absolute and strong supervised models
by up to 12% in terms of top-20 passage retrieval accuracy. We also obtain new
state-of-the-art results on full open-domain question answering by simply
adding the new re-ranker to existing models with no further changes.


title:
Helicity-conservative Physics-informed Neural Network Model for
  Navier-Stokes Equations

abstract:
  We design the helicity-conservative physics-informed neural network model for
the Navier-Stokes equation in the ideal case. The key is to provide an
appropriate PDE model as loss function so that its neural network solutions
produce helicity conservation. Physics-informed neural network model is based
on the strong form of PDE. We compare the proposed Physics-informed neural
network model and a relevant helicity-conservative finite element method. We
arrive at the conclusion that the strong form PDE is better suited for
conservation issues. We also present theoretical justifications for helicity
conservation as well as supporting numerical calculations.


title:
Inner and outer smooth approximation of convex hypersurfaces. When is it
  possible?

abstract:
  Let $S$ be a convex hypersurface (the boundary of a closed convex set $V$
with nonempty interior) in $\mathbb{R}^n$. We prove that $S$ contains no lines
if and only if for every open set $U\supset S$ there exists a real-analytic
convex hypersurface $S_{U} \subset U\cap \textrm{int}(V) $. We also show that
$S$ contains no rays if and only if for every open set $U\supset S$ there
exists a real-analytic convex hypersurface $S_{U}\subset U\setminus V$.
Moreover, in both cases, $S_U$ can be taken strongly convex. We also establish
similar results for convex functions defined on open convex subsets of
$\mathbb{R}^n$, completely characterizing the class of convex functions that
can be approximated in the $C^0$-fine topology by smooth convex functions from
above or from below. We also provide similar results for $C^1$-fine
approximations


title:
Endomorphisms and derivations of the measure algebra of commutative
  hypergroups

abstract:
  Endomorphisms of the measure algebra of commutative hypergroups are
investigated. We focus on derivations and higher order derivations which are
closely related to moment function sequences of higher rank. We describe the
exact connection between those higher order derivations which are endomorphisms
of the measure algebra if it is considered as a module over the ring of
continuous functions.


title:
Existence results for nonexpansive multi-valued operators and nonlinear
  integral inclusions

abstract:
  In this paper, we establish some new variants of fixed point theorems for a
large class of countably nonexpansive multi-valued mappings. Some fixed point
theorems for the sum and the product of three multi-valued mappings defined on
nonempty, closed convex set of Banach algebras are also presented. These
results improve and complement a number of earlier works. As an application, we
prove existence results for a broad class of nonlinear functional integral
inclusions as well as nonlinear differential inclusions.


title:
Evaluating few shot and Contrastive learning Methods for Code Clone
  Detection

abstract:
  Context: Code Clone Detection (CCD) is a software engineering task that is
used for plagiarism detection, code search, and code comprehension. Recently,
deep learning-based models have achieved an F1 score (a metric used to assess
classifiers) of $\sim$95\% on the CodeXGLUE benchmark. These models require
many training data, mainly fine-tuned on Java or C++ datasets. However, no
previous study evaluates the generalizability of these models where a limited
amount of annotated data is available.
  Objective: The main objective of this research is to assess the ability of
the CCD models as well as few shot learning algorithms for unseen programming
problems and new languages (i.e., the model is not trained on these
problems/languages).
  Method: We assess the generalizability of the state of the art models for CCD
in few shot settings (i.e., only a few samples are available for fine-tuning)
by setting three scenarios: i) unseen problems, ii) unseen languages, iii)
combination of new languages and new problems. We choose three datasets of
BigCloneBench, POJ-104, and CodeNet and Java, C++, and Ruby languages. Then, we
employ Model Agnostic Meta-learning (MAML), where the model learns a
meta-learner capable of extracting transferable knowledge from the train set;
so that the model can be fine-tuned using a few samples. Finally, we combine
contrastive learning with MAML to further study whether it can improve the
results of MAML.


title:
Validating a Simulation Model for Laser-Induced Thermotherapy Using MR
  Thermometry

abstract:
  Laser-induced interstitial thermotherapy (LITT) is applied to ex-vivo porcine
livers. An artificial blood vessel is used to study the cooling effect of
larger blood vessels in proximity to the ablation zone. The same setting is
simulated using a model based on partial differential equations (PDEs) for
temperature, radiation, and tissue damage. The simulated temperature
distributions are compared to temperature data obtained from MR thermometry.
The study shows that the quality and resolution of the thermometry data is
sufficient to validate and improve modeling approaches. Furthermore, the data
can be used to identify missing model parameters as well as the exact placement
of the laser applicator in relation to the imaging plane.


title: Cryogenic Neuromorphic Hardware

abstract:
  The revolution in artificial intelligence (AI) brings up an enormous storage
and data processing requirement. Large power consumption and hardware overhead
have become the main challenges for building next-generation AI hardware.
Therefore, it is imperative to look for a new architecture capable of
circumventing these bottlenecks of conventional von Neumann architecture. Since
the human brain is the most compact and energy-efficient intelligent device
known, it was intuitive to attempt to build an architecture that could mimic
our brain, and so the chase for neuromorphic computing began. While relentless
research has been underway for years to minimize the power consumption in
neuromorphic hardware, we are still a long way off from reaching the energy
efficiency of the human brain. Besides, design complexity, process variation,
etc. hinder the large-scale implementation of current neuromorphic platforms.
Recently, the concept of implementing neuromorphic computing systems in
cryogenic temperature has garnered immense attention. Several cryogenic devices
can be engineered to work as neuromorphic primitives with ultra-low demand for
power. Cryogenic electronics has therefore become a promising exploratory
platform for an energy-efficient and bio-realistic neuromorphic system. Here we
provide a comprehensive overview of the reported cryogenic neuromorphic
hardware. We carefully classify the existing cryogenic neuromorphic hardware
into different categories and draw a comparative analysis based on several
performance metrics. Finally, we explore the future research prospects to
circumvent the challenges associated with the current technologies.


title:
Systematic review of development literature from Latin America between
  2010- 2021

abstract:
  The purpose of this systematic review is to identify and describe the state
of development literature published in Latin America, in Spanish and English,
since 2010. For this, we carried out a topographic review of 44 articles
available in the most important bibliographic indexes of Latin America,
published in journals of diverse disciplines. Our analysis focused on analyzing
the nature and composition of literature, finding a large proportion of
articles coming from Mexico and Colombia, as well as specialized in the
economic discipline. The most relevant articles reviewed show methodological
and thematic diversity, with special attention to the problem of growth in
Latin American development. An important limitation of this review is the
exclusion of articles published in Portuguese, as well as non-indexed
literature (such as theses and dissertations). This leads to various
recommendations for future reviews of the development literature produced in
Latin America.


title:
Asymptotics of solutions of differential equations with a spectral
  parameter

abstract:
  The main goal of this paper is to construct the so-called Birkhoff-type
solutions for linear ordinary differential equations with a spectral parameter.
Such solutions play an important role in direct and inverse problems of
spectral theory. In Section 1, we construct the Birkhoff-type solutions for
n-th order differential equations. Section 2 is devoted to first-order systems
of differential equations.


title:
Price and Payoff Autocorrelations in the Consumption-Based Asset Pricing
  Model

abstract:
  This paper considers common consumption-based asset pricing model and derives
approximations of the basic pricing equation that describes mutual dependence
of the mean price "to-day", mean payoff "next-day", price and payoff volatility
and impact of the price and payoff autocorrelations. The deep conjunction of
the consumption-based model with other modifications of asset pricing as ICAPM,
APM and etc. (Cochrane, 2001) causes that our results can be derived in other
versions of CAPM. We introduce the market-based price averaging and discuss the
origin of its distinctions from the common frequency-based price probability.
The market-based price statistical moments, price volatility and
autocorrelation are determined by statistical moments of the random market
trade value and volume. Distinctions between the frequency-based and the
market-based approaches to price averaging cause different assessment of the
price volatility and autocorrelation and result in different treatment of the
price-volume relations in particular. The market-based price averaging provides
direct dependence of the price statistical moments on the market trade value
and volume statistical moments. It establishes a unified ground for description
of financial markets but uncovers tough complexity. The usage of the
frequency-based or proposed market-based price averaging is completely
determined by agent's preferences, beliefs and habits. The collision between
'rational' market-based approach and "soulful" or "home-felt" frequency-based
approach to price averaging creates significant challenge for financial theory.


title: Revisiting the general cubic: A simplification of Cardano's solution

abstract:
  Motivated by the recent work of William Y.C. Chen, in which he presents a way
to solve cubic equations by considering the identity of Sylvester, we
investigate the solutions obtained in this way. It leads us to a unified
expression of the solutions different from Cardano's formula. In some cases,
the expression can be simplified further, which compared to Cardano's formula
has certain computational advantages. Plenty of examples are given in this
paper, also some interesting identities are obtained as byproducts.


title:
Initial analysis of the impact of the Ukrainian power grid
  synchronization with Continental Europe

abstract:
  When Russia invaded Ukraine on the 24th of February 2022, this lead to many
acts of solidarity with Ukraine, including support for its electricity system.
Just 20 days after the invasion started, the Ukrainian and Moldovan power grids
were synchronized to the Continental European power grid to provide stability
to these grids. Here, we present an initial analysis of how this
synchronization affected the statistics of the power grid frequency and
cross-border flows of electric power within Continental Europe. We observe
mostly small changes in the system, such as an increase in fluctuations, kept
in check by an increase in control. We also observe changes in cross-border
flows in and out of Ukraine and surrounding countries and the effect of the
synchronization with Continental Europe.


title: Do anthropic arguments really work?

abstract:
  The anthropic explanation for the peculiarly small observed value of the
cosmological constant $\Lambda_{\rm obs}$ argues that this value promotes the
formation of stars, planets, and ultimately of observers such as ourselves. I
show that a recent analytic model of cosmic star formation predicts that
although $\Lambda_{\rm obs}$ maximises the overall efficiency of star formation
in the universe, the probability of generating observers peaks at $\sim400-500
\, \Lambda_{\rm obs}$. These preliminary results suggest that an immediate
connection between star formation efficiency and observers' generation is not
straightforward, and highlight the subtleties involved with the application of
anthropic reasoning.


title: Parton distributions: Functional complexity and Lorentz parametrization

abstract:
  In the paper we focus on the study of the functional complexity of the
Lorentz parametrizing functions in connection with the time-reversal
transformations. We argue that the interactions encoded in the corresponding
correlators of non-local quark(-gluon) operators generate additional sources of
functional complexity for the parametrizing functions which are not discussed
in the literature. We also revisit the Lorentz parametrization of different
correlators given by the hadron matrix elements of the non-local operators. The
evidences for the new parametrizing function existence have been presented.


title:
Implication of the $W$ boson mass anomaly at CDF II in the Higgs triplet
  model with a mass difference

abstract:
  A new report for the measurement of the W boson mass has been provided by the
CDF II experiment. The measured value of the W boson mass is given to be
$m_W^{\text{CDF II}} = 80.4335 \pm 0.0094$ GeV which shows $7\sigma$ deviation
from the standard model prediction, while the other groups of high energy
experiments, e.g., ATLAS and D0 II, show consistent results with the latter.
Assuming that the measurement at CDF II is correct, we discuss the possibility
to explain the anomaly in the Higgs triplet model. We find that the anomaly can
be explained under the constraints from the electroweak rho parameter and
current direct searches at LHC when non-zero mass differences among the
triplet-like Higgs bosons and the vacuum expectation value of the triplet Higgs
field are taken to be ${\cal O}(100)$ GeV and ${\cal O}(1)$ GeV, respectively.


title: Near and far-field hydrodynamic interaction of two chiral squirmers

abstract:
  Hydrodynamic interaction strongly influences the collective behavior of the
microswimmers. With this work, we study the behavior of two hydrodynamically
interacting self-propelled chiral swimmers in the low Reynolds number regime,
considering both the near and far-field interactions. We use the chiral
squirmer model, a spherically shaped body with non-axisymmetric surface slip
velocity, which generalizes the well-known squirmer model. We calculate the
lubrication force between the swimmers when they are very close to each other.
By varying the slip coefficients and the initial configuration of the swimmers,
we investigate their hydrodynamic behavior. In the presence of lubrication
force, the swimmers either repel each other or exhibit bounded motion where the
distance between the swimmers alters periodically. The lubrication force favors
the bounded motion in some parameter regime. This study is helpful to
understand the collective behavior of dense suspension of ciliated
microorganisms and artificial swimmers.


title: Synthesizing Informative Training Samples with GAN

abstract:
  Remarkable progress has been achieved in synthesizing photo-realistic images
with generative adversarial neural networks (GANs). Recently, GANs are utilized
as the training sample generator when obtaining or storing real training data
is expensive even infeasible. However, traditional GANs generated images are
not as informative as the real training samples when being used to train deep
neural networks. In this paper, we propose a novel method to synthesize
Informative Training samples with GAN (IT-GAN). Specifically, we freeze a
pre-trained GAN model and learn the informative latent vectors that corresponds
to informative training samples. The synthesized images are required to
preserve information for training deep neural networks rather than visual
reality or fidelity. Experiments verify that the deep neural networks can learn
faster and achieve better performance when being trained with our IT-GAN
generated images. We also show that our method is a promising solution to
dataset condensation problem.


title: Magneto-optical trap of a Group III atom

abstract:
  We realize the first magneto-optical trap of an atom in main group III of the
Periodic Table. Our atom of choice (indium) does not have a transition out of
its ground state suitable for laser cooling; therefore, laser cooling is
performed on the $\lvert 5P_{3/2},F=6 \rangle \rightarrow \lvert 5D_{5/2},F=7
\rangle$ transition, where $\lvert 5P_{3/2},F=6 \rangle$ is a long-lived
metastable state. Optimization of our trap parameters results in atoms numbers
as large as $5\times10^8$ atoms with temperatures of order 1 mK. Additionally,
through trap decay measurements, we infer a one-body trap lifetime of 12.3 s.
This lifetime is consistent with background gas collisions and indicates that
our repumpers have closed all leakage pathways. We also infer a two-body loss
rate of $1.6\times 10^{-11}\ \mathrm{cm^3/s}$, which is comparable to those
measured in alkali atoms. The techniques demonstrated in this work can be
straightforwardly applied to other group III atoms, and our results pave the
way for realizing quantum degenerate gases of these particles.


title:
A possible solution for the faint young Sun paradox: Clues from the
  exoplanetary data

abstract:
  Faint young Sun paradox (FYSP) is one of the unsolved problem in solar
physics. The present study aims to get a possible solution for the FYSP through
sun-like G stars and their exoplanetary systems. Using physical properties of
exoplanetary data, an empirical relationship between the rate of mass loss
($\frac{dM}{dt}$) with stellar mass (M$_{\star}$) and age ({\em t}) is
obtained. We found mass loss rate varies with stellar mass as $\propto$
$(M_{\star}/M_\odot)^{-3.788}$ and proportional to the age as $\propto$
t$^{-1.25}$, which indicates rate of mass loss is higher during early
evolutionary stages. Then we applied mass loss corrections to stellar masses of
G-type stars with planets and obtained their initial masses at the early
evolutionary stages. Subsequently, we applied these relationships to calculate
the mass loss rate and mass of Sun at the early evolutionary stage, which is
found to be $\sim$ 10$^{-11}$ solar mass per year and $\sim$ (1.061$\pm$0.006)
solar mass respectively. The higher solar mass can probably alleviate the
problem of the faint young Sun paradox. Then the estimated initial stellar
masses of the host stars are used to obtain a best power law relationship with
the planetary masses that supports the hypothesis that the {\em massive stars
harbour massive planets.} Finally, by using the same empirical power law,
planetary mass in the vicinity of Sun is estimated to be $\sim$ (0.84$\pm$0.19)
Jupiter mass, which is much higher compared to the present solar terrestrial
planetary mass. Hence, this study also suggests that there is a missing
planetary mass in the vicinity of the Sun, which can solve the FYSP problem.


title: Substitutions on compact alphabets

abstract:
  We develop a general theory of continuous substitutions on compact Hausdorff
alphabets. Focussing on implications of primitivity, we provide a
self-contained introduction to the topological dynamics of their subshifts. We
then reframe questions from ergodic theory in terms of spectral properties of
the corresponding substitution operator. The standard Perron--Frobenius theory
in finite dimensions is no longer applicable. To overcome this, we exploit the
theory of positive operators on Banach lattices. As an application, we identify
computable criteria that guarantee quasi-compactness of the substitution
operator and hence unique ergodicity of the associated subshift.


title:
Comets in context: Comparing comet compositions with protosolar nebula
  models

abstract:
  Comets provide a valuable window into the chemical and physical conditions at
the time of their formation in the young solar system. We seek insights into
where and when these objects formed by comparing the range of abundances
observed for nine molecules and their average values across a sample of 29
comets to the predicted midplane ice abundances from models of the protosolar
nebula. Our fiducial model, where ices are inherited from the interstellar
medium, can account for the observed mixing ratio ranges of each molecule
considered, but no single location or time reproduces the abundances of all
molecules simultaneously. This suggests that each comet consists of material
processed under a range of conditions. In contrast, a model where the initial
composition of disk material is `reset', wiping out any previous chemical
history, cannot account for the complete range of abundances observed in
comets.
  Using toy models that combine material processed under different thermal
conditions we find that a combination of warm (CO-poor) and cold (CO-rich)
material is required to account for both the average properties of the
Jupiter-family and Oort cloud comets, and the individual comets we consider.
This could occur by the transport (either radial or vertical) of ice-coated
dust grains in the early solar system.
  Comparison of the models to the average Jupiter-family and Oort cloud comet
compositions suggest the two families formed in overlapping regions of the
disk, in agreement with the findings of A'Hearn et al. (2012) and with the
predictions of the Nice model (Gomes et al. 2005, Tsiganis et al. 2005).


title: Random Access in Distributed Source Coding

abstract:
  The lossless compression of a single source $X^n$ was recently shown to be
achievable with a notion of strong locality; any $X_i$ can be decoded from a
{\emph{constant}} number of compressed bits, with a vanishing in $n$
probability of error. In contrast with the single source setup, we show that
for two separately encoded sources $(X^n,Y^n)$, lossless compression and strong
locality is generally not possible. More precisely, we show that for the class
of "confusable" sources strong locality cannot be achieved whenever one of the
sources is compressed below its entropy. In this case, irrespectively of $n$,
the probability of error of decoding any $(X_i,Y_i)$ is lower bounded by
$2^{-O(d_{\mathrm{loc}})}$, where $d_{\mathrm{loc}}$ denotes the number of
compressed bits accessed by the local decoder. Conversely, if the source is not
confusable, strong locality is possible even if one of the sources is
compressed below its entropy. Results extend to any number of sources.


title:
An Introductory Review of Spiking Neural Network and Artificial Neural
  Network: From Biological Intelligence to Artificial Intelligence

abstract:
  Recently, stemming from the rapid development of artificial intelligence,
which has gained expansive success in pattern recognition, robotics, and
bioinformatics, neuroscience is also gaining tremendous progress. A kind of
spiking neural network with biological interpretability is gradually receiving
wide attention, and this kind of neural network is also regarded as one of the
directions toward general artificial intelligence. This review introduces the
following sections, the biological background of spiking neurons and the
theoretical basis, different neuronal models, the connectivity of neural
circuits, the mainstream neural network learning mechanisms and network
architectures, etc. This review hopes to attract different researchers and
advance the development of brain-inspired intelligence and artificial
intelligence.


title:
Resource-Aware Distributed Submodular Maximization: A Paradigm for
  Multi-Robot Decision-Making

abstract:
  We introduce the first algorithm for distributed decision-making that
provably balances the trade-off of centralization, for global near-optimality,
vs. decentralization, for near-minimal on-board computation, communication, and
memory resources. We are motivated by the future of autonomy that involves
heterogeneous robots collaborating in complex~tasks, such as image covering,
target tracking, and area monitoring. Current algorithms, such as consensus
algorithms, are insufficient to fulfill this future: they achieve distributed
communication only, at the expense of high communication, computation, and
memory overloads. A shift to resource-aware algorithms is needed, that can
account for each robot's on-board resources, independently. We provide the
first resource-aware algorithm, Resource-Aware distributed Greedy (RAG). We
focus on maximization problems involving monotone and "doubly" submodular
functions, a diminishing returns property. RAG has near-minimal on-board
resource requirements. Each agent can afford to run the algorithm by adjusting
the size of its neighborhood, even if that means selecting actions in complete
isolation. RAG has provable approximation performance, where each agent can
independently determine its contribution. All in all, RAG is the first
algorithm to quantify the trade-off of centralization, for global
near-optimality, vs. decentralization, for near-minimal on-board resource
requirements. To capture the trade-off, we introduce the notion of
Centralization Of Information among non-Neighbors (COIN). We validate RAG in
simulated scenarios of image covering with mobile robots.


title: On locally finite orthomodular lattices

abstract:
  Let us denote by LF the class of all orthomodular lattices (OMLs) that are
locally finite (i.e., L in LF provided each finite subset of L generates in L a
finite subOML). We first show in this note how one can obtain new locally
finite OMLs from the initial ones and enlarge thus the class LF . We find LF
considerably large though, obviously, not all OMLs belong to LF . We then study
states on the OMLs of LF . We show that local finiteness may to a certain
extent make up for distributivity. We for instance show that if L in LF and if
for any finite subOML K there is a state s : K to [0, 1] on K, then there is a
state on the entire L. We also consider further algebraic and state properties
of LF relevant to quantum logic theory.


title:
Caging-Pnictogen-Induced Superconductivity in Skutterudites IrX3 (X =
  As, P)

abstract:
  Here we report on a new kind of compound, X{\delta}Ir4X12-{\delta} (X = P,
As), the first hole-doped skutterudites superconductor. We provide atomic
resolution images of the caging As atoms using scanning transmission electron
microscopy (STEM). By inserting As atoms into the caged structure under a high
pressure, superconductivity emerges with a maximum transition temperature (Tc)
of 4.4 K (4.8 K) in IrAs3 (IrP3). In contrast to all of the electron-doped
skutterudites, the electronic states around the Fermi level in
X{\delta}Ir4X12-{\delta} are dominated by the caged X atom, which can be
described by a simple body-centered tight-binding model, implying a distinct
paring mechanism. Our density functional theory (DFT) calculations reveal an
intimate relationship between the pressure-dependent local-phonon mode and the
enhancement of Tc. The discovery of X{\delta}Ir4X12-{\delta} provides an arena
to investigate the uncharted territory of hole-doped skutterudites, and the
method proposed here represents a new strategy of carrier doping in caged
structures, without introducing extra elements.


title:
Non-Invertible Symmetries from Discrete Gauging and Completeness of the
  Spectrum

abstract:
  We study global 1- and $(d-2)$-form symmetries for gauge theories based on
disconnected gauge groups which include charge conjugation. For pure gauge
theories, the 1-form symmetries are shown to be non-invertible. In addition,
being the gauge groups disconnected, the theories automatically have a
$\mathbb{Z}_2$ global $(d-2)$-form symmetry. We propose String Theory
embeddings for gauge theories based on these groups. Remarkably, they all
automatically come with twist vortices which break the $(d-2)$-form global
symmetry. This is consistent with the conjectured absence of global symmetries
in Quantum Gravity.


title: Neural Structured Prediction for Inductive Node Classification

abstract:
  This paper studies node classification in the inductive setting, i.e., aiming
to learn a model on labeled training graphs and generalize it to infer node
labels on unlabeled test graphs. This problem has been extensively studied with
graph neural networks (GNNs) by learning effective node representations, as
well as traditional structured prediction methods for modeling the structured
output of node labels, e.g., conditional random fields (CRFs). In this paper,
we present a new approach called the Structured Proxy Network (SPN), which
combines the advantages of both worlds. SPN defines flexible potential
functions of CRFs with GNNs. However, learning such a model is nontrivial as it
involves optimizing a maximin game with high-cost inference. Inspired by the
underlying connection between joint and marginal distributions defined by
Markov networks, we propose to solve an approximate version of the optimization
problem as a proxy, which yields a near-optimal solution, making learning more
efficient. Extensive experiments on two settings show that our approach
outperforms many competitive baselines.


title: A Solution to Galileo's Enigma "Mostro Son Io"

abstract:
  Galileo Galilei was a skilled writer and explored several genres, from the
well-known scientific writings (often in the form of dialogs) to theater and
poetry. His last published poem, "Mostro son io" (A Monster am I), is a riddle
written in the form of a sonnet. We suggest that the solution to Galileo's
riddle is the Zodiac.


title:
Statistical-Computational Trade-offs in Tensor PCA and Related Problems
  via Communication Complexity

abstract:
  Tensor PCA is a stylized statistical inference problem introduced by
Montanari and Richard to study the computational difficulty of estimating an
unknown parameter from higher-order moment tensors. Unlike its matrix
counterpart, Tensor PCA exhibits a statistical-computational gap, i.e., a
sample size regime where the problem is information-theoretically solvable but
conjectured to be computationally hard. This paper derives computational lower
bounds on the run-time of memory bounded algorithms for Tensor PCA using
communication complexity. These lower bounds specify a trade-off among the
number of passes through the data sample, the sample size, and the memory
required by any algorithm that successfully solves Tensor PCA. While the lower
bounds do not rule out polynomial-time algorithms, they do imply that many
commonly-used algorithms, such as gradient descent and power method, must have
a higher iteration count when the sample size is not large enough. Similar
lower bounds are obtained for Non-Gaussian Component Analysis, a family of
statistical estimation problems in which low-order moment tensors carry no
information about the unknown parameter. Finally, stronger lower bounds are
obtained for an asymmetric variant of Tensor PCA and related statistical
estimation problems. These results explain why many estimators for these
problems use a memory state that is significantly larger than the effective
dimensionality of the parameter of interest.

categories: math.ST cs.IT cs.LG math.IT stat.ML stat.TH


title:
Local well-posedness of a three-dimensional phase-field model for
  thrombus and blood flow

abstract:
  In this article we consider a fluid-structure interactions model on a three
dimensional bounded domain, that describes the mechanical interaction between
blood flow and a thrombus with Hookean elasticity. The interface between the
two phases is given by a smooth transition layer, diffuse with a finite
thickness. We derive various a priori estimates and prove local well-posedness
results using the Faedo-Galerkin method.


title:
Inherent-State Melting and the Onset of Glassy Dynamics in
  Two-Dimensional Supercooled Liquids

abstract:
  Below the onset temperature $T_\text{o}$, the equilibrium relaxation time of
most glass-forming liquids exhibits glassy dynamics characterized by
super-Arrhenius temperature dependence. In this supercooled regime, the
relaxation dynamics also proceeds through localized elastic excitations
corresponding to hopping events between inherent states, i.e., potential-energy
minimizing configurations of the liquid. Despite its importance in
distinguishing the supercooled regime from the high-temperature regime, the
microscopic origin of $T_\text{o}$ is not yet known. Here, we construct a
theory for the onset temperature in two dimensions and find that inherent-state
melting transition, described by the binding-unbinding transition of dipolar
elastic excitations, delineates the supercooled regime from the
high-temperature regime. The corresponding melting transition temperature is in
good agreement with the onset temperature found in various two-dimensional
atomistic models of glass formers. We discuss the predictions of our theory on
the displacement and density correlations of two-dimensional supercooled
liquids, which are consistent with observations of the Mermin-Wagner
fluctuations in recent experiments and molecular simulations.


title: Lyapunov-Type Inequalities for Third Order Nonlinear Equations

abstract:
  We derive Lyapunov-type inequalities for general third order nonlinear
equations involving multiple $\psi$-Laplacian operators of the form
\begin{equation*} (\psi_{2}((\psi_{1}(u'))'))' + q(x)f(u) = 0, \end{equation*}
where $\psi_{2}$ and $\psi_{1}$ are odd, increasing functions, $\psi_{2}$ is
super-multiplicative, $\psi_{1}$ is sub-multiplicative, and
$\frac{1}{\psi_{1}}$ is convex, and $f$ is a continuous function which
satisfies a sign condition. Our results utilize $q_{+}$ and $q_{-}$, as opposed
to $|q|$ which appears in most results in the literature. Additionally, these
new inequalities generalize previously obtained results, and the proofs utilize
a different technique than most other works in the literature. Furthermore,
using the obtained inequalities, we obtain a constraint on the location of the
maximum of a solution, properties of oscillatory solutions, and an upper bound
for the number of zeroes.


title: Retrieving Hierarchies

abstract:
  Several real-world and abstract structures and systems are characterized by
marked hierarchy to the point of being expressed as trees. Because the study of
these entities often involves sampling (or discovering) the tree nodes in a
specific order that may not correspond to that originally shaping the tree,
reconstruction errors can be obtained. The present work addresses this
important problem based on two main resources: (i) the adoption of a simple
model of trees, involving a single parameter; and (ii) the use of the
coincidence similarity as the means to quantify the errors by comparing the
original and reconstructed structures considering diverse sampling error
probability and extent. Several interesting results are described and
discussed, including the fact that the average and standard deviation values of
the reconstruction errors depend only moderately on the extent of the errors as
well as on the types of trees. At the same time, it is identified that the
relative reconstruction accuracy substantially decreases markedly with the
error probability, with larger reconstructions accuracy relative variations
being observed for the smallest values of that probability.


title: Understanding Game-Playing Agents with Natural Language Annotations

abstract:
  We present a new dataset containing 10K human-annotated games of Go and show
how these natural language annotations can be used as a tool for model
interpretability. Given a board state and its associated comment, our approach
uses linear probing to predict mentions of domain-specific terms (e.g., ko,
atari) from the intermediate state representations of game-playing agents like
AlphaGo Zero. We find these game concepts are nontrivially encoded in two
distinct policy networks, one trained via imitation learning and another
trained via reinforcement learning. Furthermore, mentions of domain-specific
terms are most easily predicted from the later layers of both models,
suggesting that these policy networks encode high-level abstractions similar to
those used in the natural language annotations.


title: Accurate ADMET Prediction with XGBoost

abstract:
  The absorption, distribution, metabolism, excretion, and toxicity (ADMET)
properties are important in drug discovery as they define efficacy and safety.
Here, we apply an ensemble of features, including fingerprints and descriptors,
and a tree-based machine learning model, extreme gradient boosting, for
accurate ADMET prediction. Our model performs well in the Therapeutics Data
Commons ADMET benchmark group. For 22 tasks, our model is ranked first in 10
tasks and top 3 in 18 tasks.


title:
Driven-dissipative time crystalline phases in a two-mode bosonic system
  with Kerr nonlinearity

abstract:
  For the driven-dissipative system of two coupled bosonic modes in a nonlinear
cavity resonator we demonstrate a sequence of phase transitions from a trivial
steady state to two distinct dissipative time crystalline phases. These effects
are already anticipated at the level of the semi-classical analysis of the
Lindblad equation and further supported by the full quantum (numerical)
treatment. The system is predicted to exhibit different dynamical phases
characterized by an oscillating non-equilibrium steady state with subharmonic
periodicity, which is a hallmark of time crystals. We expect that these phases
can be directly probed in various cavity QED experiments.


title:
Ultra-fast Spectroscopy for High-Throughput and Interactive Quantum
  Chemistry

abstract:
  We present ultra-fast quantum chemical methods for the calculation of
infrared and ultraviolet-visible spectra designed to provide fingerprint
information during autonomous and interactive explorations of molecular
structures. Characteristic spectral signals can serve as diagnostic probes for
the identification and characterization of molecular structures. These features
often do not require ultimate accuracy with respect to peak position and
intensity, which alleviates the accuracy-time dilemma in ultra-fast electronic
structure methods. If approximate ultra-fast algorithms are supplemented with
an uncertainty quantification scheme for the detection of potentially large
prediction errors in signal position and intensity, an offline refinement will
always be possible to confirm or discard the predictions of the ultra-fast
approach. Here, we present ultra-fast electronic structure methods for such a
protocol in order to obtain ground- and excited-state electronic energies,
dipole moments, and their derivatives for real-time applications in vibrational
spectroscopy and photophysics. As part of this endeavor, we devise an
information-inheritance partial Hessian approach for vibrational spectroscopy,
a tailored subspace diagonalization approach and a determinant-selection scheme
for excited-state calculations.


title: The classical obstacle problem with H\"older continuous coefficients

abstract:
  Weiss' and Monneau's type quasi-monotonicity formulas are established for
quadratic energies having matrix of coefficients which are Dini, double-Dini
continuity, respectively. Free boundary regularity for the corresponding
classical obstacle problems under H\"older continuity assumptions is then
deduced.


title:
Non-local temporal interferometry for highly resilient free-space
  quantum communication

abstract:
  Entanglement distribution via photons over long distances enables many
applications, including quantum key distribution (QKD), which provides
unprecedented privacy. The inevitable degradation of entanglement through noise
accumulated over long distances remains one of the key challenges in this area.
Exploiting the potential of higher-dimensional entangled photons promises to
address this challenge, but poses extreme demands on the experimental
implementation. Here, we present an interstate free-space quantum link,
distributing hyper-entanglement over $10.2\,$km with flexible dimensionality of
encoding by deploying a phase-stable non-local Franson interferometer. With
this distribution of multidimensional energy-time entangled photons, we analyse
the achievable key rate in a dimensionally-adaptive QKD protocol that can be
optimized with respect to any environmental noise conditions. Our approach
enables and emphasises the power of high-dimensional entanglement for quantum
communication, yielding a positive asymptotic key rate well into the dawn of
the day.


title: Unconditional Image-Text Pair Generation with Multimodal Cross Quantizer

abstract:
  Though deep generative models have gained a lot of attention, most of the
existing works are designed for the unimodal generation task. In this paper, we
explore a new method for unconditional image-text pair generation. We propose
MXQ-VAE, a vector quantization method for multimodal image-text representation.
MXQ-VAE accepts a paired image and text as input, and learns a joint quantized
representation space, so that the image-text pair can be converted to a
sequence of unified indices. Then we can use autoregressive generative models
to model the joint image-text representation, and even perform unconditional
image-text pair generation. Extensive experimental results demonstrate that our
approach effectively generates semantically consistent image-text pair and also
enhances meaningful alignment between image and text.


title:
Deterministic preparation of supersinglets with collective spin
  projections

abstract:
  We introduce a procedure to generate supersinglets, the multipartite
generalization of angular momentum singlet states. A supersinglet is defined as
a total spin zero state consisting of $ N $ spin-$ j $ particles. They are
highly entangled and have zero spin variance in any direction, and as such are
potentially useful for quantum metrology. Our scheme is based on projective
measurements that measure the collective spin of the whole spin ensemble. A
local unitary rotation is applied conditionally on the measurement outcome,
such as to maximize the probability of obtaining spin zero on the subsequent
measurement. The sequence is repeated in the $ z $- and $ x $-basis until
convergence is obtained towards the supersinglet state. Our sequence works
regardless of the initial state, and no postselection is required. Due to the
use of strong projective measurements, very fast convergence towards zero spin
variance is obtained. We discuss an example implementation using quantum
nondemolition measurements in atomic ensembles, and perform numerical
simulations to demonstrate the procedure.


title:
Stability and Robustness of a Hybrid Control Law for the Half-bridge
  Inverter

abstract:
  Hybrid systems combine both discrete and continuous state dynamics. Power
electronic inverters are inherently hybrid systems: they are controlled via
discrete-valued switching inputs which determine the evolution of the
continuous-valued current and voltage state dynamics.
  Hybrid systems analysis could prove increasingly useful as large numbers of
renewable energy sources are incorporated to the grid with inverters as their
interface. In this work, we explore a hybrid systems approach for the stability
analysis of power and power electronic systems. We provide an analytical proof
showing that the use of a hybrid model for the half-bridge inverter allows the
derivation of a control law that drives the system states to desired sinusoidal
voltage and current references. We derive an analytical expression for a global
Lyapunov function for the dynamical system in terms of the system parameters,
which proves uniform, global, and asymptotic stability of the origin in error
coordinates. Moreover, we demonstrate robustness to parameter changes through
this Lyapunov function. We validate these results via simulation.
  Finally, we show empirically the incorporation of droop control with this
hybrid systems approach. In the low-inertia grid community, the juxtaposition
of droop control with the hybrid switching control can be considered a
grid-forming control strategy using a switched inverter model.


title:
Gravitational waves and monopoles dark matter from first-order phase
  transition

abstract:
  We study the possibility of monopoles serving as dark matter when they are
produced during the first-order phase transition in the dark sector. Our study
shows that dark monopoles can contribute only a small piece of dark matter
relic density within parameter spaces where strong gravitational waves can be
probed by ET and CE, and the monopoles can contribute a sizable component of
the observed dark matter relic density for fast phase transitions with short
duration.


title:
Selecting Continuous Life-Like Cellular Automata for Halting
  Unpredictability: Evolving for Abiogenesis

abstract:
  Substantial efforts have been applied to engineer CA with desired emergent
properties, such as supporting gliders. Recent work in continuous CA has
generated a wide variety of compelling bioreminescent patterns, and the
expansion of CA research into continuous numbers, multiple channels, and higher
dimensions complicates their study. In this work we devise a strategy for
evolving CA and CA patterns in two steps, based on the simple idea that CA are
likely to be complex and computationally capable if they support patterns that
grow indefinitely as well as patterns that vanish completely, and are difficult
to predict the difference in advance. The second part of our strategy evolves
patterns by selecting for mobility and conservation of mean cell value. We
validate our pattern evolution method by re-discovering gliders in 17 of 17
Lenia CA, and also report 5 new evolved CA that support evolved glider
patterns, differing from previously reported Lenia patterns. The CA reported
here share neighborhood kernels with previously described Lenia CA, but exhibit
a wider range of typical dynamics than their Lenia counterparts. Code for
evolving continuous CA is made available under an MIT License.


title: AdS Virasoro-Shapiro from dispersive sum rules

abstract:
  We consider the four-point correlator of the stress-energy tensor in ${\cal
N}=4$ SYM, to leading order in inverse powers of the central charge, but
including all order corrections in $1/\lambda$. This corresponds to the AdS
version of the Virasoro-Shapiro amplitude to all orders in the small
$\alpha'$/low energy expansion. Using dispersion relations in Mellin space, we
derive an infinite set of sum rules. These sum rules strongly constrain the
form of the amplitude, and determine all coefficients in the low energy
expansion in terms of the CFT data for heavy string operators, in principle
available from integrability. For the first set of corrections to the flat
space amplitude we find a unique solution consistent with the results from
integrability and localisation.


title: CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection

abstract:
  Single-particle cryo-electron microscopy (cryo-EM) has become one of the
mainstream structural biology techniques because of its ability to determine
high-resolution structures of dynamic bio-molecules. However, cryo-EM data
acquisition remains expensive and labor-intensive, requiring substantial
expertise. Structural biologists need a more efficient and objective method to
collect the best data in a limited time frame. We formulate the cryo-EM data
collection task as an optimization problem in this work. The goal is to
maximize the total number of good images taken within a specified period. We
show that reinforcement learning offers an effective way to plan cryo-EM data
collection, successfully navigating heterogenous cryo-EM grids. The approach we
developed, cryoRL, demonstrates better performance than average users for data
collection under similar settings.


title: Gamma-Minimax Wavelet Shrinkage with Three-Point Priors

abstract:
  In this paper we propose a method for wavelet denoising of signals
contaminated with Gaussian noise when prior information about the $L^2$-energy
of the signal is available. Assuming the independence model, according to which
the wavelet coefficients are treated individually, we propose a simple, level
dependent shrinkage rules that turn out to be
  $\Gamma$-minimax for a suitable class of priors.
  The proposed methodology is particularly well suited in denoising tasks when
the signal-to-noise ratio is low, which is illustrated by simulations on the
battery of standard test functions. Comparison to some standardly used wavelet
shrinkage methods is provided.


title:
Dynamic phase diagram of plastically deformed amorphous solids at finite
  temperature

abstract:
  The yielding transition that occurs in amorphous solids under athermal
quasistatic deformation has been the subject of many theoretical and
computational studies. Here, we extend this analysis to include thermal effects
at finite shear rate, focusing on how temperature alters avalanches. We derive
a nonequilibrium phase diagram capturing how temperature and strain rate
effects compete, when avalanches overlap, and whether finite-size effects
dominate over temperature effects. The predictions are tested through
simulations of an elastoplastic model in two dimensions and in a mean-field
approximation. We find a new scaling for temperature-dependent softening in the
low-strain rate regime when avalanches do not overlap, and a
temperature-dependent Herschel-Bulkley exponent in the high strain rate regime
when avalanches do overlap.


title:
Semi-supervised atmospheric component learning in low-light image
  problem

abstract:
  Ambient lighting conditions play a crucial role in determining the perceptual
quality of images from photographic devices. In general, inadequate
transmission light and undesired atmospheric conditions jointly degrade the
image quality. If we know the desired ambient factors associated with the given
low-light image, we can recover the enhanced image easily \cite{b1}. Typical
deep networks perform enhancement mappings without investigating the light
distribution and color formulation properties. This leads to a lack of image
instance-adaptive performance in practice. On the other hand, physical
model-driven schemes suffer from the need for inherent decompositions and
multiple objective minimizations. Moreover, the above approaches are rarely
data efficient or free of postprediction tuning. Influenced by the above
issues, this study presents a semisupervised training method using no-reference
image quality metrics for low-light image restoration. We incorporate the
classical haze distribution model \cite{b2} to explore the physical properties
of the given image in order to learn the effect of atmospheric components and
minimize a single objective for restoration. We validate the performance of our
network for six widely used low-light datasets. The experiments show that the
proposed study achieves state-of-the-art or comparable performance.


title: A Hitchhiker`s Guide through the Bio-image Analysis Software Universe

abstract:
  Modern research in the life sciences is unthinkable without computational
methods for extracting, quantifying and visualizing information derived from
biological microscopy imaging data. In the past decade, we observed a dramatic
increase in available software packages for these purposes. As it is
increasingly difficult to keep track of the number of available image analysis
platforms, tool collections, components and emerging technologies, we provide a
conservative overview of software we use in daily routine and give insights
into emerging new tools. We give guidance on which aspects to consider when
choosing the right platform, including aspects such as image data type, skills
of the team, infrastructure and community at the institute and availability of
time and budget.


title:
Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic
  Segmentation

abstract:
  Recent works on 3D semantic segmentation propose to exploit the synergy
between images and point clouds by processing each modality with a dedicated
network and projecting learned 2D features onto 3D points. Merging large-scale
point clouds and images raises several challenges, such as constructing a
mapping between points and pixels, and aggregating features between multiple
views. Current methods require mesh reconstruction or specialized sensors to
recover occlusions, and use heuristics to select and aggregate available
images. In contrast, we propose an end-to-end trainable multi-view aggregation
model leveraging the viewing conditions of 3D points to merge features from
images taken at arbitrary positions. Our method can combine standard 2D and 3D
networks and outperforms both 3D models operating on colorized point clouds and
hybrid 2D/3D networks without requiring colorization, meshing, or true depth
maps. We set a new state-of-the-art for large-scale indoor/outdoor semantic
segmentation on S3DIS (74.7 mIoU 6-Fold) and on KITTI-360 (58.3 mIoU). Our full
pipeline is accessible at https://github.com/drprojects/DeepViewAgg, and only
requires raw 3D scans and a set of images and poses.


title:
Human Judgement as a Compass to Navigate Automatic Metrics for Formality
  Transfer

abstract:
  Although text style transfer has witnessed rapid development in recent years,
there is as yet no established standard for evaluation, which is performed
using several automatic metrics, lacking the possibility of always resorting to
human judgement. We focus on the task of formality transfer, and on the three
aspects that are usually evaluated: style strength, content preservation, and
fluency. To cast light on how such aspects are assessed by common and new
metrics, we run a human-based evaluation and perform a rich correlation
analysis. We are then able to offer some recommendations on the use of such
metrics in formality transfer, also with an eye to their generalisability (or
not) to related tasks.


title: Cryptographic Strain-Dependent Light Pattern Generators

abstract:
  Refractive freeform components are becoming increasingly relevant for
generating controlled patterns of light, because of their capability to
spatially-modulate optical signals with high efficiency and low background.
However, the use of these devices is still limited by difficulties in
manufacturing macroscopic elements with complex, 3-dimensional (3D) surface
reliefs. Here, 3D-printed and stretchable magic windows generating light
patterns by refraction are introduced. The shape and, consequently, the light
texture achieved can be changed through controlled device strain. Cryptographic
magic windows are demonstrated through exemplary light patterns, including
micro-QR-codes, that are correctly projected and recognized upon strain gating
while remaining cryptic for as-produced devices. The light pattern of
micro-QR-codes can also be projected by two coupled magic windows, with one of
them acting as the decryption key. Such novel, freeform elements with 3D shape
and tailored functionalities is relevant for applications in illumination
design, smart labels, anti-counterfeiting systems, and cryptographic
communication.


title: Summarization with Graphical Elements

abstract:
  Automatic text summarization has experienced substantial progress in recent
years. With this progress, the question has arisen whether the types of
summaries that are typically generated by automatic summarization models align
with users' needs. Ter Hoeve et al (2020) answer this question negatively.
Amongst others, they recommend focusing on generating summaries with more
graphical elements. This is in line with what we know from the
psycholinguistics literature about how humans process text. Motivated from
these two angles, we propose a new task: summarization with graphical elements,
and we verify that these summaries are helpful for a critical mass of people.
We collect a high quality human labeled dataset to support research into the
task. We present a number of baseline methods that show that the task is
interesting and challenging. Hence, with this work we hope to inspire a new
line of research within the automatic summarization community.


title: Local available quantum correlations of X states: The asymmetric case

abstract:
  Local available quantum correlations (LAQC), as defined by Mundarain et al.,
are analyzed for non-symmetric 2-qubit X states, that is, X-states that are not
invariant under the exchange of subsystems and therefore have local Bloch
vectors whose norms are different. A simple analytic expression for their LAQC
quantifier is obtained. As an example, we analyze the local application of the
amplitude damping channel for Werner states and general canonical X states.
Although this local quantum channel can create quantum discord in some cases,
no such outcome is possible for LAQC, which hints toward their monotonicity
under LOCC operations. This work, along with our previous result for so-called
symmetric and anti-symmetric X states, gives a complete characterization and
analytical expressions for the LAQC quantifier for the whole family of
canonical 2-qubit X states.


title: Improving Rare Word Recognition with LM-aware MWER Training

abstract:
  Language models (LMs) significantly improve the recognition accuracy of
end-to-end (E2E) models on words rarely seen during training, when used in
either the shallow fusion or the rescoring setups. In this work, we introduce
LMs in the learning of hybrid autoregressive transducer (HAT) models in the
discriminative training framework, to mitigate the training versus inference
gap regarding the use of LMs. For the shallow fusion setup, we use LMs during
both hypotheses generation and loss computation, and the LM-aware MWER-trained
model achieves 10\% relative improvement over the model trained with standard
MWER on voice search test sets containing rare words. For the rescoring setup,
we learn a small neural module to generate per-token fusion weights in a
data-dependent manner. This model achieves the same rescoring WER as regular
MWER-trained model, but without the need for sweeping fusion weights.


title: Efficient Architecture Search for Diverse Tasks

abstract:
  While neural architecture search (NAS) has enabled automated machine learning
(AutoML) for well-researched areas, its application to tasks beyond computer
vision is still under-explored. As less-studied domains are precisely those
where we expect AutoML to have the greatest impact, in this work we study NAS
for efficiently solving diverse problems. Seeking an approach that is fast,
simple, and broadly applicable, we fix a standard convolutional network (CNN)
topology and propose to search for the right kernel sizes and dilations its
operations should take on. This dramatically expands the model's capacity to
extract features at multiple resolutions for different types of data while only
requiring search over the operation space. To overcome the efficiency
challenges of naive weight-sharing in this search space, we introduce DASH, a
differentiable NAS algorithm that computes the mixture-of-operations using the
Fourier diagonalization of convolution, achieving both a better asymptotic
complexity and an up-to-10x search time speedup in practice. We evaluate DASH
on NAS-Bench-360, a suite of ten tasks designed for benchmarking NAS in diverse
domains. DASH outperforms state-of-the-art methods in aggregate, attaining the
best-known automated performance on seven tasks. Meanwhile, on six of the ten
tasks, the combined search and retraining time is less than 2x slower than
simply training a CNN backbone that is far less accurate.


title: Chinese Idiom Paraphrasing

abstract:
  Idioms, are a kind of idiomatic expression in Chinese, most of which consist
of four Chinese characters. Due to the properties of non-compositionality and
metaphorical meaning, Chinese Idioms are hard to be understood by children and
non-native speakers. This study proposes a novel task, denoted as Chinese Idiom
Paraphrasing (CIP). CIP aims to rephrase idioms-included sentences to
non-idiomatic ones under the premise of preserving the original sentence's
meaning. Since the sentences without idioms are easier handled by Chinese NLP
systems, CIP can be used to pre-process Chinese datasets, thereby facilitating
and improving the performance of Chinese NLP tasks, e.g., machine translation
system, Chinese idiom cloze, and Chinese idiom embeddings. In this study, CIP
task is treated as a special paraphrase generation task. To circumvent
difficulties in acquiring annotations, we first establish a large-scale CIP
dataset based on human and machine collaboration, which consists of 115,530
sentence pairs. We further deploy three baselines and two novel CIP approaches
to deal with CIP problems. The results show that the proposed methods have
better performances than the baselines based on the established CIP dataset.


title: Streaming Align-Refine for Non-autoregressive Deliberation

abstract:
  We propose a streaming non-autoregressive (non-AR) decoding algorithm to
deliberate the hypothesis alignment of a streaming RNN-T model. Our algorithm
facilitates a simple greedy decoding procedure, and at the same time is capable
of producing the decoding result at each frame with limited right context, thus
enjoying both high efficiency and low latency. These advantages are achieved by
converting the offline Align-Refine algorithm to be streaming-compatible, with
a novel transformer decoder architecture that performs local self-attentions
for both text and audio, and a time-aligned cross-attention at each layer.
Furthermore, we perform discriminative training of our model with the minimum
word error rate (MWER) criterion, which has not been done in the non-AR
decoding literature. Experiments on voice search datasets and Librispeech show
that with reasonable right context, our streaming model performs as well as the
offline counterpart, and discriminative training leads to further WER gain when
the first-pass model has small capacity.


title: Neutral-Current Electroweak Physics and SMEFT Studies at the EIC

abstract:
  We study the potential for precision electroweak (EW) measurements and
beyond-the-Standard Model (BSM) searches using cross-section asymmetries in
neutral-current (NC) deep inelastic scattering at the electron-ion collider
(EIC). Our analysis uses a complete and realistic accounting of systematic
errors from both theory and experiment and considers the potential of both
proton and deuteron beams for a wide range of energies and luminosities. We
also consider what can be learned from a possible future positron beam and a
potential ten-fold luminosity upgrade of the EIC beyond its initial decade of
running. We use the SM effective field theory (SMEFT) framework to parameterize
BSM effects and focus on semi-leptonic four-fermion operators, whereas for our
precision EW study, we determine how well the EIC can measure the weak mixing
angle. New features of our study include the use of an up-to-date detector
design of EIC Comprehensive Chromodynamics Experiment (ECCE) and accurate
running conditions of the EIC, the simultaneous fitting of beam polarization
uncertainties and Wilson coefficients to improve the sensitivity to SMEFT
operators, and the inclusion of the weak mixing angle running in our fit
template. We find that the EIC can probe BSM operators at scales competitive
with and in many cases exceeding LHC Drell-Yan bounds while simultaneously not
suffering from degeneracies between Wilson coefficients.


title: Resolvent Analysis of an Under-expanded Planar Supersonic Impinging Jet

abstract:
  This investigation aims to assess the effect of different types of actuator
forcing on the feedback loop of an under-expanded Mach 1.27 planar impinging
jet using a resolvent framework. To this end, we employ a Large Eddy Simulation
database as a truth model. The time and spanwise-averaged mean flow is taken as
an input to global stability and resolvent analyses with the purpose of
examining both the intrinsic instability and input-output characteristics. The
results show that the inherent instability and primary energy amplification are
attributed to the Kelvin-Helmholtz (K-H) instability. Moreover, the K-H
response modes obtained from the resolvent analysis are in reasonable agreement
with Spectral Proper Orthogonal Decomposition (SPOD) modes from the unsteady
LES data. Insights into noise control are obtained by localizing the actuator
forcing to the nozzle lip and the ground plate by imposing component-wise
forcing to mimic different notional actuators. It is observed that energy
amplification obtained for the localized component-wise forcing is different
from the global resolvent analysis and dependent on the type of actuator. This
provides insights into the type, wavenumber, and frequency of actuators for
active flow control.


title: Large sumsets from small subsets

abstract:
  In this paper we start to investigate a new body of questions in additive
combinatorics. The fundamental Cauchy--Davenport theorem gives a lower bound on
the size of a sumset A+B for subsets of the cyclic group Zp of order p (p
prime), and this is just one example of a large family of results. Our aim in
this paper is to investigate what happens if we restrict the number of elements
of one set that we may use to form the sums. Here is the question we set out to
answer: given two subsets, A and B, does B have a subset C of bounded size such
that A+C is large, perhaps even comparable to the size of A+B? In particular,
can we get close to the lower bound of the Cauchy--Davenport theorem? Our main
results show that, rather surprisingly, in many circumstances it is possible to
obtain not merely an asymptotic version of the usual sumset bound, but even the
exact bound itself.


title: AI-driven Development Is Here: Should You Worry?

abstract:
  AI-Driven Development Environments (AIDEs) Integrate the power of modern AI
into IDEs like Visual Studio Code and JetBrains IntelliJ. By leveraging massive
language models and the plethora of openly available source code, AIDEs promise
to automate many of the obvious, routine tasks in programming. At the same
time, AIDEs come with new challenges to think about, such as bias, legal
compliance, security vulnerabilities, and their impact on learning programming.


title:
Quantum coherence controls the nature of equilibration in coupled
  chaotic systems

abstract:
  A bipartite system whose subsystems are fully quantum chaotic and coupled by
a perturbative interaction with a tunable strength is a paradigmatic model for
investigating how isolated quantum systems relax towards an equilibrium. It is
found that quantum coherence of the initial product states in the uncoupled
eigenbasis can be viewed as a resource for equilibration and approach to
thermalization as manifested by the entanglement. Results are given for four
distinct perturbation strength regimes, the ultra-weak, weak, intermediate, and
strong regimes. For each, three types of initially unentangled states are
considered, coherent random-phase superpositions, random superpositions, and
eigenstate products. A universal time scale is identified involving the
interaction strength parameter. Maximally coherent initial states thermalize
for any perturbation strength in spite of the fact that in the ultra-weak
perturbative regime the underlying eigenstates of the system have a tensor
product structure and are not at all thermal-like; though the time taken to
thermalize tends to infinity as the interaction vanishes. In contrast to the
widespread linear behavior, in this regime the entanglement initially grows
quadratically in time.


title: Evaluating Factuality in Text Simplification

abstract:
  Automated simplification models aim to make input texts more readable. Such
methods have the potential to make complex information accessible to a wider
audience, e.g., providing access to recent medical literature which might
otherwise be impenetrable for a lay reader. However, such models risk
introducing errors into automatically simplified texts, for instance by
inserting statements unsupported by the corresponding original text, or by
omitting key information. Providing more readable but inaccurate versions of
texts may in many cases be worse than providing no such access at all. The
problem of factual accuracy (and the lack thereof) has received heightened
attention in the context of summarization models, but the factuality of
automatically simplified texts has not been investigated. We introduce a
taxonomy of errors that we use to analyze both references drawn from standard
simplification datasets and state-of-the-art model outputs. We find that errors
often appear in both that are not captured by existing evaluation metrics,
motivating a need for research into ensuring the factual accuracy of automated
simplification models.


title:
Geometric integration of classical spin dynamics via a mean-field
  Schr\"odinger equation

abstract:
  The Landau-Lifshitz equation describes the time-evolution of magnetic
dipoles, and can be derived by taking the classical limit of a quantum
mechanical spin Hamiltonian. To take this limit, one constrains the many-body
quantum state to a tensor product of coherent states, thereby neglecting
entanglement between sites. Expectation values of the quantum spin operators
produce the usual classical spin dipoles. One may also consider expectation
values of polynomials of the spin operators, leading to quadrupole and
higher-order spin moments, which satisfy a dynamical equation of motion that
generalizes the Landau-Lifshitz dynamics [Zhang and Batista, Phys. Rev. B 104,
104409 (2021)]. Here, we reformulate the dynamics of these $N^2-1$ generalized
spin components as a mean-field Schr\"odinger equation on the $N$-dimensional
coherent state. This viewpoint suggests efficient integration methods that
respect the local symplectic structure of the classical spin dynamics.


title:
Perturbation theory for a non-equilibrium stationary state of a
  one-dimensional stochastic wave equation

abstract:
  We address the problem of constructing a non-equilibrium stationary state for
a one-dimensional stochastic Klein-Gordon wave equation with non-linearity,
using perturbation theory. The linear theory is reviewed, but with the linear
equations of motion including an additional potential term which emerges in the
renormalization of the perturbation expansion for the state corresponding to
the non-linear equations of motion. The potential is the solution to a fixed
point equation. Low order terms in the expansion for the two-point function are
determined.

categories: math-ph cond-mat.stat-mech math.MP physics.data-an


title: Asymptotic lines and parabolic points of plane fields in $\mathbb{R}^3$

abstract:
  In this paper are studied the simplest qualitative properties of asymptotic
lines of a plane field in Euclidean space. These lines are the integral curves
of the null directions of the normal curvature of the plane field, on the
closure of the hyperbolic region, where the Gaussian curvature is negative.
When the plane field is completely integrable, these curves coincides with the
classical asymptotic lines on surfaces.


title:
Improving Frame-Online Neural Speech Enhancement with Overlapped-Frame
  Prediction

abstract:
  Frame-online speech enhancement systems in the short-time Fourier transform
(STFT) domain usually have an algorithmic latency equal to the window size due
to the use of the overlap-add algorithm in the inverse STFT (iSTFT). This
algorithmic latency allows the enhancement models to leverage future contextual
information up to a length equal to the window size. However, current
frame-online systems only partially leverage this future information. To fully
exploit this information, this study proposes an overlapped-frame prediction
technique for deep learning based frame-online speech enhancement, where at
each frame our deep neural network (DNN) predicts the current and several past
frames that are necessary for overlap-add, instead of only predicting the
current frame. In addition, we propose a novel loss function to account for the
scale difference between predicted and oracle target signals. Evaluations
results on a noisy-reverberant speech enhancement task show the effectiveness
of the proposed algorithms.


title: Some remarks on graphs without rainbow triangles

abstract:
  Given graphs $G_1$, $G_2$ and $G_3$ on a common vertex set of size $n$, a
rainbow triangle is a triangle consisting of one edge from each $G_i$. In this
note we provide a counterexample to a conjecture of Frankl on the maximum
product of the sizes of the edge sets of three graphs avoiding a rainbow
triangle. Moreover we propose an alternative conjecture and prove it in the
case when every pair is an edge in at least one of the graphs.


title: Establishing trust in quantum computations

abstract:
  Real-world quantum computers have grown sufficiently complex that they can no
longer be simulated by classical supercomputers, but their computational power
remains limited by errors. These errors corrupt the results of quantum
algorithms, and it is no longer always feasible to use classical simulations to
directly check the correctness of quantum computations. Without practical
methods for quantifying the accuracy with which a quantum algorithm has been
executed, it is difficult to establish trust in the results of a quantum
computation. Here we solve this problem, by introducing a simple and efficient
technique for measuring the fidelity with which an as-built quantum computer
can execute an algorithm. Our technique converts the algorithm's quantum
circuits into a set of closely related circuits whose success rates can be
efficiently measured. It enables measuring the fidelity of quantum algorithm
executions both in the near-term, with algorithms run on hundreds or thousands
of physical qubits, and into the future, with algorithms run on logical qubits
protected by quantum error correction.


title:
Deep Learning-based List Sphere Decoding for Faster-than-Nyquist (FTN)
  Signaling Detection

abstract:
  Faster-than-Nyquist (FTN) signaling is a candidate non-orthonormal
transmission technique to improve the spectral efficiency (SE) of future
communication systems. However, such improvements of the SE are at the cost of
additional computational complexity to remove the intentionally introduced
intersymbol interference. In this paper, we investigate the use of deep
learning (DL) to reduce the detection complexity of FTN signaling. To eliminate
the need of having a noise whitening filter at the receiver, we first present
an equivalent FTN signaling model based on using a set of orthonormal basis
functions and identify its operation region. Second, we propose a DL-based list
sphere decoding (DL-LSD) algorithm that selects and updates the initial radius
of the original LSD to guarantee a pre-defined number $N_{\text{L}}$ of lattice
points inside the hypersphere. This is achieved by training a neural network to
output an approximate initial radius that includes $N_{\text{L}}$ lattice
points. At the testing phase, if the hypersphere has more than $N_{\text{L}}$
lattice points, we keep the $N_{\text{L}}$ closest points to the point
corresponding to the received FTN signal; however, if the hypersphere has less
than $N_{\text{L}}$ points, we increase the approximate initial radius by a
value that depends on the standard deviation of the distribution of the output
radii from the training phase. Then, the approximate value of the
log-likelihood ratio (LLR) is calculated based on the obtained $N_{\text{L}}$
points. Simulation results show that the computational complexity of the
proposed DL-LSD is lower than its counterpart of the original LSD by orders of
magnitude.


title:
TreeStep: Tree Search for Vector Perturbation Precoding under
  per-Antenna Power Constraint

abstract:
  Vector Perturbation Precoding (VPP) can speed up downlink data transmissions
in Large and Massive Multi-User MIMO systems but is known to be NP-hard. While
there are several algorithms in the literature for VPP under total power
constraint, they are not applicable for VPP under per-antenna power constraint.
This paper proposes a novel, parallel tree search algorithm for VPP under
per-antenna power constraint, called \emph{\textbf{TreeStep}}, to find good
quality solutions to the VPP problem with practical computational complexity.
We show that our method can provide huge performance gain over simple linear
precoding like Regularised Zero Forcing. We evaluate TreeStep for several large
MIMO~($16\times16$ and $24\times24$) and massive MIMO~($16\times32$ and
$24\times 48$) and demonstrate that TreeStep outperforms the popular
polynomial-time VPP algorithm, the Fixed Complexity Sphere Encoder, by
achieving the extremely low BER of $10^{-6}$ at a much lower SNR.


title: Evaluation Benchmarks for Spanish Sentence Representations

abstract:
  Due to the success of pre-trained language models, versions of languages
other than English have been released in recent years. This fact implies the
need for resources to evaluate these models. In the case of Spanish, there are
few ways to systematically assess the models' quality. In this paper, we narrow
the gap by building two evaluation benchmarks. Inspired by previous work
(Conneau and Kiela, 2018; Chen et al., 2019), we introduce Spanish SentEval and
Spanish DiscoEval, aiming to assess the capabilities of stand-alone and
discourse-aware sentence representations, respectively. Our benchmarks include
considerable pre-existing and newly constructed datasets that address different
tasks from various domains. In addition, we evaluate and analyze the most
recent pre-trained Spanish language models to exhibit their capabilities and
limitations. As an example, we discover that for the case of discourse
evaluation tasks, mBERT, a language model trained on multiple languages,
usually provides a richer latent representation than models trained only with
documents in Spanish. We hope our contribution will motivate a fairer, more
comparable, and less cumbersome way to evaluate future Spanish language models.


title: Tumor Growth with Nutrients: Regularity and Stability

abstract:
  In this paper we study a tumor growth model with nutrients. The model
presents dynamic patch solutions due to the contact inhibition among the tumor
cells. We show that when the nutrients do not diffuse and the cells do not die,
the tumor density exhibits regularizing dynamics. In particular, we provide
contraction estimates, exponential rate of asymptotic convergence, and boundary
regularity of the tumor patch. These results are in sharp contrast to the
models either with nutrient diffusion or with death rate in tumor cells.


title:
Steepest growth re-examined: repercussions for primordial black hole
  formation

abstract:
  Primordial black holes (PBHs) can be produced by a range of mechanisms in the
early universe. A particular formation channel that connects PBHs with
inflationary phenomenology invokes enhanced primordial curvature perturbations
at small scales. In this paper, we re-examine the impact of the growth of the
primordial power spectrum on PBH formation in terms of its implications for the
PBH mass function. We show that the mass function is relatively insensitive to
the steepness of the growth of the power spectrum and subsequent decay,
depending primarily on the peak amplitude and the presence of any plateaus that
last more than an e-fold. The shape of the power spectrum can of course be
constrained by other tracers, and so understanding the physical limitations on
its shape remains a pertinent question. We elaborate on how rapidly the
background can transition between different values of the parameters of the
Hubble hierarchy, which must ultimately derive from a consistent derivative
expansion for the background inflaton field. We discuss artefacts associated
with matching calculations, and highlight the robustness of the steepest growth
index previously found for single-field inflation with conservatively smoothed
transitions and limits on how much the power spectrum can grow.


title:
VIP-DROP2: A module for characterization of the spread of complex fluids
  droplets under various gravitational accelerations

abstract:
  Axisymmetric droplets provide a demonstration system to study the spread of
complex fluids, which are an essential component of industrial and
technological processes, among which additive manufacturing (AM). Tweaking the
gravitational environment provides access to different regimes, quantified
through the Bond number; in particular, microgravity allows to form large
droplets while remaining in the regime where the surface tension effects and
internal driving stresses are predominant over hydrostatic forces. The
VIP-DROP\textsuperscript{2}} module offers a versatile platform to study a wide
range of complex fluids through the deposition of initially axisymmetric
droplets. It offers the possibility to deposit the droplets on a precursor
layer: a thin, homogeneous layer, which can be made of the same or of a
different fluid. Besides, it allows to deposit simultaneously 4 droplets, while
conducting shadowgraphy on all of them, and observing either the flow field
through particle-image velocimetry (PIV), or the stress distribution within the
droplet in the case of stress birefringent materials. Developed for a drop
tower catapult system, it is designed to withstand a vertical acceleration of
up to 30 times the gravity of Earth in the downwards direction, and can operate
remotely, under microgravity conditions.


title:
Les nombres de Cuesta-Conway comme extension des ordinaux de Cantor: une
  courte introduction aux nombres surr\'eels

abstract:
  On Cuesta-Conway numbers as an extension of Cantor's ordinals: A short
introduction to surreal numbers. The class of Cuesta-Conway numbers, the
surreal numbers, can be defined simply, starting from their normal forms
(families of exponentials indexed by ordinals), as an extension of the reals
and ordinals from which easily follow addition, multiplication, and total order
relation. A construction of this class yields an increasing sequence of
real-closed fields, preliminary, so to say. A complete proof is also given the
well-known result that the class of surreal numbers is a totally ordered,
commutative, real-closed field!


title:
The Distributed Information Bottleneck reveals the explanatory structure
  of complex systems

abstract:
  The fruits of science are relationships made comprehensible, often by way of
approximation. While deep learning is an extremely powerful way to find
relationships in data, its use in science has been hindered by the difficulty
of understanding the learned relationships. The Information Bottleneck (IB) is
an information theoretic framework for understanding a relationship between an
input and an output in terms of a trade-off between the fidelity and complexity
of approximations to the relationship. Here we show that a crucial modification
-- distributing bottlenecks across multiple components of the input -- opens
fundamentally new avenues for interpretable deep learning in science. The
Distributed Information Bottleneck throttles the downstream complexity of
interactions between the components of the input, deconstructing a relationship
into meaningful approximations found through deep learning without requiring
custom-made datasets or neural network architectures. Applied to a complex
system, the approximations illuminate aspects of the system's nature by
restricting -- and monitoring -- the information about different components
incorporated into the approximation. We demonstrate the Distributed IB's
explanatory utility in systems drawn from applied mathematics and condensed
matter physics. In the former, we deconstruct a Boolean circuit into
approximations that isolate the most informative subsets of input components
without requiring exhaustive search. In the latter, we localize information
about future plastic rearrangement in the static structure of a sheared glass,
and find the information to be more or less diffuse depending on the system's
preparation. By way of a principled scheme of approximations, the Distributed
IB brings much-needed interpretability to deep learning and enables
unprecedented analysis of information flow through a system.


title: The Particle in a Box \Warrants an Examination

abstract:
  The particle in a box is a simple model that has a classical Hamiltonian
$H=p^2$ (using $2m=1$), with a limited coordinate space, $-b<q<b$, where
$0<b<\infty$. Using canonical quantization, this example has been fully studied
thanks to its simplicity, and it is a common example for beginners to
understand. Despite its repeated analysis, there is a feature that puts the
past results into question. In addition to pointing out the quantization issue,
the procedures of affine quantization can lead to a proper quantization that
nesaeccsrily points toward more complicated eigenfunctions and eigenvalues,
which deserve to be solved.


title: On Graded Classical S-Primary Submodules

abstract:
  The purpose of this article is to introduce the graded classical S-primary
submodules which are extensions of graded classical primary submodules. We
state that P is a graded classical S-primary submodule of R-module M if there
exists $s\in S$ such that $x,y \in h(R)$ and $m \in h(M)$, if $xym \in P$, then
$sxm \in P$ or $sy^nm \in P$ for some positive integer n. Several properties
and characteristics of graded classical S-primary submodules have been studied.


title:
Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal
  Logic Neural Network

abstract:
  Machine learning-based methods have achieved successful applications in
machinery fault diagnosis. However, the main limitation that exists for these
methods is that they operate as a black box and are generally not
interpretable. This paper proposes a novel neural network structure, called
temporal logic neural network (TLNN), in which the neurons of the network are
logic propositions. More importantly, the network can be described and
interpreted as a weighted signal temporal logic. TLNN not only keeps the nice
properties of traditional neuron networks but also provides a formal
interpretation of itself with formal language. Experiments with real datasets
show the proposed neural network can obtain highly accurate fault diagnosis
results with good computation efficiency. Additionally, the embedded formal
language of the neuron network can provide explanations about the decision
process, thus achieve interpretable fault diagnosis.


title: mGPT: Few-Shot Learners Go Multilingual

abstract:
  Recent studies report that autoregressive language models can successfully
solve many NLP tasks via zero- and few-shot learning paradigms, which opens up
new possibilities for using the pre-trained language models. This paper
introduces two autoregressive GPT-like models with 1.3 billion and 13 billion
parameters trained on 60 languages from 25 language families using Wikipedia
and Colossal Clean Crawled Corpus. We reproduce the GPT-3 architecture using
GPT-2 sources and the sparse attention mechanism; Deepspeed and Megatron
frameworks allow us to parallelize the training and inference steps
effectively. The resulting models show performance on par with the recently
released XGLM models by Facebook, covering more languages and enhancing NLP
possibilities for low resource languages of CIS countries and Russian small
nations. We detail the motivation for the choices of the architecture design,
thoroughly describe the data preparation pipeline, and train five small
versions of the model to choose the most optimal multilingual tokenization
strategy. We measure the model perplexity in all covered languages and evaluate
it on the wide spectre of multilingual tasks, including classification,
generative, sequence labeling and knowledge probing. The models were evaluated
with the zero-shot and few-shot methods. Furthermore, we compared the
classification tasks with the state-of-the-art multilingual model XGLM. source
code and the mGPT XL model are publicly released.


title:
Time-Variant System Reliability with Infinite Delay Based on Girsanov's
  Transformation

abstract:
  This work addresses the reliability of time-variant system appreciation
models of dynamic systems, where regulatory equations are expressed as an
infinite delay collection of stochastic functional differential equations
(SFDEwID). Reliability estimation forms of series and parallel systems tackled
depending on Monte Carlo simulations based on extends Girsanov's transformation
for infinite delay SFDEs.


title:
High resolution TCSPC imaging of diffuse light with a one-dimensional
  SPAD array scanning system

abstract:
  We report a time-resolved single photon counting (TCSPC) imaging system based
on a line-scanning architecture. The system benefits from the high fill-factor,
active area, and large dimension of an advanced CMOS single photon avalanche
diode (SPAD) array line-sensor. A two-dimensional image is constructed using a
moving mirror to scan the line-sensor field-of-view (FOV) across the target, to
enable the efficient acquisition of a two-dimensional 0.26 Mpixel TCSPC image.
We demonstrate the capabilities of the system for TCSPC imaging and locating
objects obscured in scattering media - specifically to locate a series of
discrete point sources of light along an optical fibre submerged in a highly
scattering solution. We demonstrate that by selectively imaging using early
arriving photons which have undergone less scattering than later arriving
photons, our TCSPC imaging system is able to locate the position of discrete
point sources of light than a non-time-resolved imaging system.


title: Quantum chaos in 2D gravity

abstract:
  We present a quantitative and fully non-perturbative description of the
ergodic phase of quantum chaos in the setting of two-dimensional gravity. To
this end we describe the doubly non-perturbative completion of semiclassical 2D
gravity in terms of its associated universe field theory. The guiding principle
of our analysis is a flavor-matrix theory (fMT) description of the ergodic
phase of holographic gravity, which exhibits $\mathrm{U}(n|n)$ causal symmetry
breaking and restoration. JT gravity and its 2D-gravity cousins alone do not
realize an action principle with causal symmetry, however we demonstrate that
their {\it universe field theory}, the Kodaira-Spencer (KS) theory of gravity,
does. After directly deriving the fMT from brane-antibrane correlators in KS
theory, we show that causal symmetry breaking and restoration can be understood
geometrically in terms of different (topological) D-brane vacua. We interpret
our results in terms of an open-closed string duality between holomorphic
Chern-Simons theory and its closed-string equivalent, the KS theory of gravity.
Emphasis will be put on relating these geometric principles to the
characteristic spectral correlations of the quantum ergodic phase.