title:
Mesoscopic fluctuations in superconductor-topological insulator
  Josephson junctions

abstract:
  We study mesoscopic fluctuations in the supercurrent of a Josephson junction
consisting of a topological insulator microbridge between two conventional
superconductors. In the model, we account for the strong proximity effect when
superconductors induce a gap in the spectrum of surface states as well as a
magnetic field piercing the junction area that causes depairing and gap
filling. The overall magnitude and functional form of the Josephson current
fluctuations are determined analytically, and found to sensitively depend on
the coupling strength to surface states, Thouless energy, and pair-breaking
energy scales in the problem. We also study the density of states that can be
measured by scanning probes. Technically, mesoscopic fluctuations on top of the
mean field description of the proximity effect in the topological region are
described by a field theory approach, the replica nonlinear $\sigma$-model in
the class-D of the extended symmetry classification.


title:
An Adaptive Incremental Gradient Method With Support for Non-Euclidean
  Norms

abstract:
  Stochastic variance reduced methods have shown strong performance in solving
finite-sum problems. However, these methods usually require the users to
manually tune the step-size, which is time-consuming or even infeasible for
some large-scale optimization tasks. To overcome the problem, we propose and
analyze several novel adaptive variants of the popular SAGA algorithm.
Eventually, we design a variant of Barzilai-Borwein step-size which is tailored
for the incremental gradient method to ensure memory efficiency and fast
convergence. We establish its convergence guarantees under general settings
that allow non-Euclidean norms in the definition of smoothness and the
composite objectives, which cover a broad range of applications in machine
learning. We improve the analysis of SAGA to support non-Euclidean norms, which
fills the void of existing work. Numerical experiments on standard datasets
demonstrate a competitive performance of the proposed algorithm compared with
existing variance-reduced methods and their adaptive variants.


title: Reducing Marketplace Interference Bias Via Shadow Prices

abstract:
  Marketplace companies rely heavily on experimentation when making changes to
the design or operation of their platforms. The workhorse of experimentation is
the randomized controlled trial (RCT), or A/B test, in which users are randomly
assigned to treatment or control groups. However, marketplace interference
causes the Stable Unit Treatment Value Assumption (SUTVA) to be violated,
leading to bias in the standard RCT metric. In this work, we propose a
technique for platforms to run standard RCTs and still obtain meaningful
estimates despite the presence of marketplace interference. We specifically
consider a matching setting, in which the platform explicitly matches supply
with demand via a matching algorithm. Our proposed technique is quite simple:
instead of comparing the total value accrued by the treatment and control
groups, we instead compare each group's average shadow price in the matching
linear program. We prove that, in the fluid limit, our proposed technique
corresponds to the correct first-order approximation (in a Taylor series sense)
of the value function of interest. We then use this result to prove that, under
reasonable assumptions, our estimator is less biased than the RCT estimator. At
the heart of our result is the idea that it is relatively easy to model
interference in matching-driven marketplaces since, in such markets, the
platform intermediates the spillover.


title: Poset Ramsey Number $R(P,Q_n)$. II. Antichains

abstract:
  For two posets $(P,\le_P)$ and $(P',\le_{P'})$, we say that $P'$ contains a
copy of $P$ if there exists an injective function $f\colon P'\to P$ such that
for every two $X,Y\in P$, $X\le_P Y$ if and only if $f(X)\le_{P'} f(Y)$. Given
two posets $P$ and $Q$, let the poset Ramsey number $R(P,Q)$ be the smallest
integer $N$ such that any coloring of the elements of an $N$-dimensional
Boolean lattice in blue or red contains either a copy of $P$ where all elements
are blue or a copy of $Q$ where all elements are red. We determine the poset
Ramsey number $R(A_t,Q_n)$ of an antichain versus a Boolean lattice for small
$t$ by showing that $R(A_t,Q_n)=n+3$ for $3\le t\le \log \log n$.


title:
Iterated line graphs with only negative eigenvalues $-2$, their
  complements and energy

abstract:
  The graphs with all equal negative or positive eigenvalues are special kind
in the spectral graph theory. In this article, several iterated line graphs
$\mathcal{L}^k(G)$ with all equal negative eigenvalues $-2$ are characterized
for $k\ge 1$ and their energy consequences are presented. Also, the spectra and
the energy of complement of these graphs are obtained, interestingly they have
exactly two positive eigenvalues with different multiplicities. Moreover, we
characterize a large class of equienergetic graphs which generalize some of the
existing results. There are two different quotient matrices defined for an
equitable partition of $H$-join (generalized composition) of regular graphs to
find the spectrum (partial) of adjacency matrix, Laplacian matrix and signless
Laplacian matrix, it has been proved that these two quotient matrices give the
same respective spectrum of graphs.


title:
Improved error bounds for the distance distribution of Reed-Solomon
  codes

abstract:
  We use the generating function approach to derive simple expressions for the
factorial moments of the distance distribution over Reed-Solomon codes. We
obtain better upper bounds for the error term of a counting formula given by Li
and Wan, which gives nontrivial estimates on the number of polynomials over
finite fields with prescribed leading coefficients and a given number of linear
factors. This improvement leads to new results on the classification of deep
holes of Reed Solomon codes.


title:
A 62-minute orbital period black widow binary in a wide hierarchical
  triple

abstract:
  Over a dozen millisecond pulsars are ablating low-mass companions in close
binary systems. In the original "black widow", the 8-hour orbital period
eclipsing pulsar PSR J1959+2048 (PSR B1957+20), high energy emission
originating from the pulsar is irradiating and may eventually destroy a
low-mass companion. These systems are not only physical laboratories that
reveal the dramatic result of exposing a close companion star to the
relativistic energy output of a pulsar, but are also believed to harbour some
of the most massive neutron stars, allowing for robust tests of the neutron
star equation of state. Here, we report observations of ZTF J1406+1222, a wide
hierarchical triple hosting a 62-minute orbital period black widow candidate
whose optical flux varies by a factor of more than 10. ZTF J1406+1222 pushes
the boundaries of evolutionary models, falling below the 80 minute minimum
orbital period of hydrogen-rich systems. The wide tertiary companion is a rare
low metallicity cool subdwarf star, and the system has a Galactic halo orbit
consistent with passing near the Galactic center, making it a probe of
formation channels, neutron star kick physics, and binary evolution.


title: A scaled-up planetary system around a supernova progenitor

abstract:
  Virtually all known exoplanets reside around stars with $M<2.3~M_\odot$; to
clarify if the dearth of planets around more massive stars is real, we launched
the direct-imaging B-star Exoplanet Abundance STudy (BEAST) survey targeting B
stars ($M>2.4~M_\odot$) in the young (5-20 Myr) Scorpius-Centaurus association
(Sco-Cen). Here we present the case of a massive ($M \sim 9~M_\odot$) BEAST
target, $\mu^2$ Sco. Based on kinematic information, we found that $\mu^2$ Sco
is a member of a small group which we label Eastern Lower Scorpius, refining in
turn the precision on stellar parameters. Around this star we identified a
robustly detected substellar companion ($14.4\pm 0.8 M_J$) at a projected
separation of $290\pm 10$ au, and a probable second object ($18.5\pm 1.5 M_J$)
at $21\pm 1$ au. The planet-to-star mass ratios of these objects are similar to
that of Jupiter to the Sun, and their irradiation is similar to those of
Jupiter and Mercury, respectively. The two companions of $\mu^2$ Sco are
naturally added to the giant planet b Cen b recently discovered by BEAST;
although slightly more massive than the deuterium burning limit, their
properties resemble those of giant planets around less massive stars and they
are better reproduced by a formation under a planet-like, rather than a
star-like scenario. Irrespective of the (needed) confirmation of the inner
companion, $\mu^2$ Sco is the first star that would end its life as a supernova
that hosts such a system. The tentative high frequency of BEAST discoveries
shows that giant planets or small-mass brown dwarfs can form around B stars.
When putting this finding in the context of core accretion and gravitational
instability, we conclude that the current modeling of both mechanisms is not
able to produce this kind of companion. BEAST will pave the way for the first
time to an extension of these models to intermediate and massive stars.
(abridged)


title:
A Rigorous Formalism of Unconventional Symmetry Breaking in Fermi Liquid
  Theory and Its Application to Nematicity in FeSe

abstract:
  Unconventional symmetry breaking due to nonlocal order parameters has
attracted considerable attention in many strongly correlated metals. Famous
examples are the nematic order in Fe-based superconductors and the
star-of-David charge density order in kagome metals. Such exotic symmetry
breaking in metals is a central issue of modern condensed matter physics, while
its theoretical foundation is still unclear in comparison with the
well-established theory of superconductivity. To overcome this difficulty, here
we introduce the "form factor" that generalizes the nonlocal order parameter
into the Luttinger-Ward (LW) Fermi liquid theory. We then construct a rigorous
formalism of the "density-wave equation" that gives the thermodynamically
stable form factor, similarly to the superconducting-gap equation. In addition,
a rigorous expression of the Ginzburg-Landau free-energy for the unconventional
order is presented to calculate various thermodynamic properties. In the next
stage, we apply the derived formalism to a typical Fe-based superconductor
FeSe, by using the one-loop LW function that represents the free-energy gain
due to the interference among paramagnons. The following key experiments are
naturally explained: (i) Lifshitz transition (=disappearance of an
electron-pocket) due to the bond+orbital order below $T_c$. (ii) Curie-Weiss
behavior of the nematic susceptibility at higher T, and the deviation from the
Curie-Weiss behavior at lower T near the nematic quantum-critical-point. (iii)
Scaling relation of the specific heat jump at $T_c$, $\Delta C/T_c \propto
T_c^b$ with $b \sim 3$. (Note that b=0 in the BCS theory.) These results lead
to a conclusion that the nematicity in FeSe is the bond+orbital order due to
the "paramagnon interference mechanism". The present theory paves the way for
solving various unconventional phase transition systems.


title: Three-Body Problem in Modified Dynamics

abstract:
  General properties of the three-body problem in a model of modified dynamics
are investigated. It is shown that the three-body problem in this model shares
some characters with the similar problem in Newtonian dynamics. Moreover, the
planar restricted three-body problem is solved analytically for this type of
extended gravity and it is proved that under certain conditions, which
generally happen at galactic and extragalactic scales, the orbits around
$L_{4}$ and $L_{5}$ Lagrange points are stable. Furthermore, a code is provided
to compare the behavior of orbits in the restricted three-body problem under
Newtonian and modified dynamics. Orbit integrations based on this code show
contrasting orbital behavior under the two dynamics and specially exhibit in a
qualitative way that the rate of ejections is smaller in the modified dynamics
compared to Newtonian gravity. These results could help us to search for
observational signatures of extended gravities.


title: Low-Code Programming Models

abstract:
  Traditionally, computer programming has been the prerogative of professional
developers using textual programming languages such as C, Java, or Python.
Low-code programming promises an alternative: letting citizen developers create
programs using visual abstractions, demonstrations, or natural language. While
low-code programming is currently getting a lot of attention in industry, the
relevant research literature is scattered, and in fact, rarely uses the term
"low-code". This article brings together low-code literature from various
research fields, explaining how techniques work while providing a unified point
of view. Low-code has the potential to empower more people to automate tasks by
creating computer programs, making them more productive and less dependent on
scarce professional software developers.


title: pyRDF2Vec: A Python Implementation and Extension of RDF2Vec

abstract:
  This paper introduces pyRDF2Vec, a Python software package that reimplements
the well-known RDF2Vec algorithm along with several of its extensions. By
making the algorithm available in the most popular data science language, and
by bundling all extensions into a single place, the use of RDF2Vec is
simplified for data scientists. The package is released under a MIT license and
structured in such a way to foster further research into sampling, walking, and
embedding strategies, which are vital components of the RDF2Vec algorithm.
Several optimisations have been implemented in \texttt{pyRDF2Vec} that allow
for more efficient walk extraction than the original algorithm. Furthermore,
best practices in terms of code styling, testing, and documentation were
applied such that the package is future-proof as well as to facilitate external
contributions.


title: Noncommutative analysis of Hermite expansions

abstract:
  This paper is devoted to the study of Hermite operators acting on
noncommutative $L_{p}$-spaces. In the first part, we establish the
noncommutative maximal inequalities for Bochner-Riesz means associated with
Hermite operators and then obtain the corresponding pointwise convergence
theorems. In particular, we develop a noncommutative Stein\textquoteright s
theorem of Bochner-Riesz means for the Hermite operators. The second part of
this paper deals with two multiplier theorems for Hermite operators. Our
analysis on this part is based on a noncommutative analogue of the classical
Littlewood-Paley-Stein theory associated with Hermite expansions.


title: Statistical mechanics of strange metals and black holes

abstract:
  A colloquium style review of the connections between the Sachdev-Ye-Kitaev
model and strange metals without quasiparticles, and between the SYK model and
the quantum properties of black holes.


title: Freezing transitions of Brownian particles in confining potentials

abstract:
  We study the mean first passage time (MFPT) to an absorbing target of a
one-dimensional Brownian particle subject to an external potential $v(x)$ in a
finite domain. We focus on the cases in which the external potential is
confining, of the form $v(x)=k|x-x_0|^n/n$, and where the particle's initial
position coincides with $x_0$. We first consider a particle between an
absorbing target at $x=0$ and a reflective wall at $x=c$. At fixed $x_0$,
target encounter is accelerated by increasing the potential stiffness $k$ from
zero, but only when the size of the domain $c$ is above a critical value. In
this case, the MFPT gets minimized at an optimal stiffness $k_{opt}>0$.
Conversely, if the size of the system is below the critical size, pure
diffusion ($k_{opt}=0$) becomes optimal. Hence, for any value of $n$, the
optimal potential stiffness undergoes a continuous \lq\lq freezing\rq\rq\
transition as the domain size is varied. On the other hand, when the reflective
wall is replaced by a second absorbing target, the freezing transition in
$k_{opt}$ becomes discontinuous. The phase diagram in the $(x_0,n)$-plane then
exhibits three dynamical phases and metastability, with a "triple" point at
$(x_0/c\simeq 0.17185$, $n\simeq 0.39539)$. For harmonic or higher order
potentials $(n\ge 2)$, the MFPT always increases with $k$ at small $k$, for any
$x_0$ or domain size. These results are contrasted with problems of diffusion
under optimal resetting in bounded domains.


title: Twist: Sound Reasoning for Purity and Entanglement in Quantum Programs

abstract:
  Quantum programming languages enable developers to implement algorithms for
quantum computers that promise computational breakthroughs in classically
intractable tasks. Programming quantum computers requires awareness of
entanglement, the phenomenon in which measurement outcomes of qubits are
correlated. Entanglement can determine the correctness of algorithms and
suitability of programming patterns.
  In this work, we formalize purity as a central tool for automating reasoning
about entanglement in quantum programs. A pure expression is one whose
evaluation is unaffected by the measurement outcomes of qubits that it does not
own, implying freedom from entanglement with any other expression in the
computation.
  We present Twist, the first language that features a type system for sound
reasoning about purity. The type system enables the developer to identify pure
expressions using type annotations. Twist also features purity assertion
operators that state the absence of entanglement in the output of quantum
gates. To soundly check these assertions, Twist uses a combination of static
analysis and runtime verification.
  We evaluate Twist's type system and analyses on a benchmark suite of quantum
programs in simulation, demonstrating that Twist can express quantum
algorithms, catch programming errors in them, and support programs that several
languages disallow, while incurring runtime verification overhead of less than
3.5%.


title: Choosing Exogeneity Assumptions in Potential Outcome Models

abstract:
  There are many kinds of exogeneity assumptions. How should researchers choose
among them? When exogeneity is imposed on an unobservable like a potential
outcome, we argue that the form of exogeneity should be chosen based on the
kind of selection on unobservables it allows. Consequently, researchers can
assess the plausibility of any exogeneity assumption by studying the
distributions of treatment given the unobservables that are consistent with
that assumption. We use this approach to study two common exogeneity
assumptions: quantile and mean independence. We show that both assumptions
require a kind of non-monotonic relationship between treatment and the
potential outcomes. We discuss how to assess the plausibility of this kind of
treatment selection. We also show how to define a new and weaker version of
quantile independence that allows for monotonic treatment selection. We then
show the implications of the choice of exogeneity assumption for
identification. We apply these results in an empirical illustration of the
effect of child soldiering on wages.


title: A Dataset for N-ary Relation Extraction of Drug Combinations

abstract:
  Combination therapies have become the standard of care for diseases such as
cancer, tuberculosis, malaria and HIV. However, the combinatorial set of
available multi-drug treatments creates a challenge in identifying effective
combination therapies available in a situation. To assist medical professionals
in identifying beneficial drug-combinations, we construct an expert-annotated
dataset for extracting information about the efficacy of drug combinations from
the scientific literature. Beyond its practical utility, the dataset also
presents a unique NLP challenge, as the first relation extraction dataset
consisting of variable-length relations. Furthermore, the relations in this
dataset predominantly require language understanding beyond the sentence level,
adding to the challenge of this task. We provide a promising baseline model and
identify clear areas for further improvement. We release our dataset, code, and
baseline models publicly to encourage the NLP community to participate in this
task.


title:
Ab initio melting temperatures of bcc and hcp iron under the Earth's
  inner core condition

abstract:
  There has been a long debate on the thermodynamic stability of iron phases
under the Earth's inner core conditions, mainly due to the considerable
uncertainty in determining the melting temperatures of iron phases. In the
present study, we utilized a semi-empirical potential fitted to
high-temperature \textit{ab initio} data to perform a thermodynamic integration
from classical systems described by this potential to \textit{ab initio}
systems. This method provides a smooth path for the thermodynamic integration
and significantly reduces the uncertainty caused by the finite size effect
within 15 K. Our results suggest the hcp phase is the ground state of pure iron
under the inner core conditions, while the free energy difference between the
hcp and bcc phases is tiny, on the order of 10s meV/atom.


title: BrainViewer: interacting with spatial connectome data at the mesoscale

abstract:
  Scientists construct connectomes, comprehensive descriptions of neuronal
connections across a brain, in order to better understand and model brain
function. Interactive visualizations of these pathways would enable exploratory
analysis of such information flows. Current tools can be used to see individual
tracing experiments which are used to build mesoscale connectomes of the mouse
brain, but not the brain network itself. We present a connectivity
visualization program called BrainViewer, which we use with a high-resolution
mouse cortical connectome. This has the ability to display connectomes from
other datasets when they become available and compare spatial connectivity
across multiple brain structures. Our tool, optimized for speed and
portability, presents a GUI visualization in 2-D top view and flatmap
projections, allowing users to select and explore the connections of every
source voxel to everywhere else in the cortex. Anatomists and other
neuroscientists will find BrainViewer useful for building understanding beyond
the known topography of cortical connectivity.


title:
Shifted symplectic structures on derived analytic moduli of $\ell$-adic
  local systems and Galois representations

abstract:
  We develop a characterisation of non-Archimedean derived analytic geometry
based on dg enhancements of dagger algebras. This allows us to formulate
derived analytic moduli functors for many types of pro-\'etale sheaves, and to
construct shifted symplectic structures on them by transgression using
arithmetic duality theorems. In order to handle duality functors involving Tate
twists, we introduce weighted shifted symplectic structures on formal weighted
moduli stacks, with the usual moduli stacks given by taking
$\mathbb{G}_m$-invariants.
  In particular, this establishes the existence of shifted symplectic and
Lagrangian structures on derived moduli stacks of $\ell$-adic constructible
complexes on smooth varieties via Poincar\'e duality, and on derived moduli
stacks of $\ell$-adic Galois representations via Tate and Poitou--Tate duality;
the latter proves a conjecture of Minhyong Kim.


title:
Original or Translated? A Causal Analysis of the Impact of
  Translationese on Machine Translation Performance

abstract:
  Human-translated text displays distinct features from naturally written text
in the same language. This phenomena, known as translationese, has been argued
to confound the machine translation (MT) evaluation. Yet, we find that existing
work on translationese neglects some important factors and the conclusions are
mostly correlational but not causal. In this work, we collect CausalMT, a
dataset where the MT training data are also labeled with the human translation
directions. We inspect two critical factors, the train-test direction match
(whether the human translation directions in the training and test sets are
aligned), and data-model direction match (whether the model learns in the same
direction as the human translation direction in the dataset). We show that
these two factors have a large causal effect on the MT performance, in addition
to the test-model direction mismatch highlighted by existing work on the impact
of translationese. In light of our findings, we provide a set of suggestions
for MT training and evaluation. Our code and data are at
https://github.com/EdisonNi-hku/CausalMT


title: Lattices with lots of congruence energy

abstract:
  In 1978, motivated by E. H\"uckel's work in quantum chemistry, I. Gutman
introduced the concept of the energy of a finite simple graph $G$ as the sum of
the absolute values of the eigenvalues of the adjacency matrix of $G$. At the
time of writing, the MathSciNet search for "Title=(graph energy) AND Review
Text=(eigenvalue)" returns 351 publications, most of which going after Gutman's
definition.
  A congruence $\alpha$ of a finite algebra $A$ turns $A$ into a simple graph:
we connect $x\neq y\in A$ by an edge iff $(x,y)\in\alpha$; we let En$(\alpha)$
be the energy of this graph. We introduce the congruence energy CE$(A)$ of $A$
by CE$(A):=\sum\{$En$(\alpha): \alpha\in$ Con$(A)\}$. Let LAT$(n)$ and CDA$(n)$
stand for the class of $n$-element lattices and that of $n$-element congruence
distributive algebras of any type. For a class $\mathcal X$, let CE$(\mathcal
X):= \{$CE$(A): A\in \mathcal X\}$. We prove the following.
  (1) For $\alpha\in A$, En$(\alpha)/2$ is the height of $\alpha$ in the
equivalence lattice of $A$.
  (2) The largest number and the second largest number in CE(LAT($n$)) are
$(n-1)\cdot 2^{n-1}$ and, for $n\geq 4$, $(n-1)\cdot 2^{n-2}+2^{n-3}$; these
numbers are only witnessed by chains and lattices with exactly one two-element
antichain, respectively.
  (3) The largest number in CE(CDA($n$)) is also $(n-1)\cdot 2^{n-1}$, and if
CE$(A)=(n-1)\cdot 2^{n-1}$ for an $A\in$ CDA$(n)$, then Con$(A)$ is a boolean
lattice with size $|$Con$(A)|=2^{n-1}$.


title: On bounded Wang tilings

abstract:
  Wang tiles enable efficient pattern compression while avoiding the
periodicity in tile distribution via programmable matching rules. However, most
research in Wang tilings has considered tiling the infinite plane. Motivated by
emerging applications in materials engineering, we consider the bounded version
of the tiling problem and offer four integer programming formulations to
construct valid or nearly-valid Wang tilings: a decision, maximum-rectangular
tiling, maximum cover, and maximum adjacency constraint satisfaction
formulations. To facilitate a finer control over the resulting tilings, we
extend these programs with tile-based, color-based, packing, and variable-sized
periodic constraints. Furthermore, we introduce an efficient heuristic
algorithm for the maximum-cover variant based on the shortest path search in
directed acyclic graphs and derive simple modifications to provide a $1/2$
approximation guarantee for arbitrary tile sets, and a $2/3$ guarantee for tile
sets with cyclic transducers. Finally, we benchmark the performance of the
integer programming formulations and of the heuristic algorithms showing that
the heuristics provides very competitive outputs in a fraction of time. As a
by-product, we reveal errors in two well-known aperiodic tile sets: the Knuth
tile set contains a tile unusable in two-way infinite tilings, and the Lagae
corner tile set is not aperiodic.


title: Counterexample to a conjecture of Aharoni and Korman

abstract:
  Ron Aharoni and Vladimir Korman conjectured that any hypergraph with only
finite edges has a strongly minimal cover. We present a counterexample.


title: Fredrickson--Andersen model in two dimensions

abstract:
  The present expository article overviews recent mathematical advances on the
Fredrickson--Andersen kinetically constrained spin model in two dimensions. It
was introduced in physics as a toy model for recovering the glassy
phenomenology in supercooled liquids close to the glass transition via dynamic
constraints as opposed to static interactions.

categories: cond-mat.dis-nn cond-mat.stat-mech math-ph math.MP math.PR


title: Zero Day Threat Detection Using Graph and Flow Based Security Telemetry

abstract:
  Zero Day Threats (ZDT) are novel methods used by malicious actors to attack
and exploit information technology (IT) networks or infrastructure. In the past
few years, the number of these threats has been increasing at an alarming rate
and have been costing organizations millions of dollars to remediate. The
increasing expansion of network attack surfaces and the exponentially growing
number of assets on these networks necessitate the need for a robust AI-based
Zero Day Threat detection model that can quickly analyze petabyte-scale data
for potentially malicious and novel activity. In this paper, the authors
introduce a deep learning based approach to Zero Day Threat detection that can
generalize, scale, and effectively identify threats in near real-time. The
methodology utilizes network flow telemetry augmented with asset-level graph
features, which are passed through a dual-autoencoder structure for anomaly and
novelty detection respectively. The models have been trained and tested on four
large scale datasets that are representative of real-world organizational
networks and they produce strong results with high precision and recall values.
The models provide a novel methodology to detect complex threats with low
false-positive rates that allow security operators to avoid alert fatigue while
drastically reducing their mean time to response with near-real-time detection.
Furthermore, the authors also provide a novel, labelled, cyber attack dataset
generated from adversarial activity that can be used for validation or training
of other models. With this paper, the authors' overarching goal is to provide a
novel architecture and training methodology for cyber anomaly detectors that
can generalize to multiple IT networks with minimal to no retraining while
still maintaining strong performance.


title:
Multiple glassy dynamics of a homologous series of triphenylene-based
  columnar liquid crystals -- A study by broadband dielectric spectroscopy and
  advanced calorimetry

abstract:
  Hexakis(n-alkyloxy)triphenylene) (HATn) consisting of an aromatic
triphenylene core and alkyl side chains are model discotic liquid crystal (DLC)
systems forming a columnar mesophase. In the mesophase, the molecules of HATn
self-assemble in columns, which has one-dimensional high charge carrier
mobility along the columns. Here, a homologous series of HATn with different
length of the alkyl chain (n=5,6,8,10,12) is investigated using differential
scanning calorimetry (DSC), broadband dielectric spectroscopy (BDS) and
advanced calorimetric techniques including fast scanning calorimetry (FSC) and
specific heat spectroscopy (SHS). The investigation of the phase behavior was
done utilizing DSC experiments and the influence of the alkyl chain length on
the phase behavior was revealed. By the dielectric investigations probing the
molecular mobility, a $\gamma$-relaxation due to localized fluctuations as well
as two glassy dynamics the $\alpha$ core and $\alpha$ alkyl relaxation were
observed in the temperature range of the plastic crystalline phase. Moreover,
the observed glassy dynamics were further studied employing advanced
calorimetry. All observed relaxation processes are attributed to the possible
specific molecular fluctuations and discussed in detail. From the results a
transition at around n=8 from a rigid constrained (n=5,6) to a softer system
(n=10,12) was revealed with increasing alkyl chain length. A counterbalance of
two competing effects of a polyethylene like behavior of the alkyl chains in
the intercolumnar domains and self-organized confinement is discussed in the
context of a hindered glass transition.

categories: cond-mat.soft cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph physics.chem-ph


title:
P3IV: Probabilistic Procedure Planning from Instructional Videos with
  Weak Supervision

abstract:
  In this paper, we study the problem of procedure planning in instructional
videos. Here, an agent must produce a plausible sequence of actions that can
transform the environment from a given start to a desired goal state. When
learning procedure planning from instructional videos, most recent work
leverages intermediate visual observations as supervision, which requires
expensive annotation efforts to localize precisely all the instructional steps
in training videos. In contrast, we remove the need for expensive temporal
video annotations and propose a weakly supervised approach by learning from
natural language instructions. Our model is based on a transformer equipped
with a memory module, which maps the start and goal observations to a sequence
of plausible actions. Furthermore, we augment our model with a probabilistic
generative module to capture the uncertainty inherent to procedure planning, an
aspect largely overlooked by previous work. We evaluate our model on three
datasets and show our weaklysupervised approach outperforms previous fully
supervised state-of-the-art models on multiple metrics.


title: BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking

abstract:
  Estimating human motion from video is an active research area due to its many
potential applications. Most state-of-the-art methods predict human shape and
posture estimates for individual images and do not leverage the temporal
information available in video. Many "in the wild" sequences of human motion
are captured by a moving camera, which adds the complication of conflated
camera and human motion to the estimation. We therefore present BodySLAM, a
monocular SLAM system that jointly estimates the position, shape, and posture
of human bodies, as well as the camera trajectory. We also introduce a novel
human motion model to constrain sequential body postures and observe the scale
of the scene. Through a series of experiments on video sequences of human
motion captured by a moving monocular camera, we demonstrate that BodySLAM
improves estimates of all human body parameters and camera poses when compared
to estimating these separately.


title:
Machine Learning Operations (MLOps): Overview, Definition, and
  Architecture

abstract:
  The final goal of all industrial machine learning (ML) projects is to develop
ML products and rapidly bring them into production. However, it is highly
challenging to automate and operationalize ML products and thus many ML
endeavors fail to deliver on their expectations. The paradigm of Machine
Learning Operations (MLOps) addresses this issue. MLOps includes several
aspects, such as best practices, sets of concepts, and development culture.
However, MLOps is still a vague term and its consequences for researchers and
professionals are ambiguous. To address this gap, we conduct mixed-method
research, including a literature review, a tool review, and expert interviews.
As a result of these investigations, we provide an aggregated overview of the
necessary principles, components, and roles, as well as the associated
architecture and workflows. Furthermore, we furnish a definition of MLOps and
highlight open challenges in the field. Finally, this work provides guidance
for ML researchers and practitioners who want to automate and operate their ML
products with a designated set of technologies.


title:
Analysing the Robustness of Dual Encoders for Dense Retrieval Against
  Misspellings

abstract:
  Dense retrieval is becoming one of the standard approaches for document and
passage ranking. The dual-encoder architecture is widely adopted for scoring
question-passage pairs due to its efficiency and high performance. Typically,
dense retrieval models are evaluated on clean and curated datasets. However,
when deployed in real-life applications, these models encounter noisy
user-generated text. That said, the performance of state-of-the-art dense
retrievers can substantially deteriorate when exposed to noisy text. In this
work, we study the robustness of dense retrievers against typos in the user
question. We observe a significant drop in the performance of the dual-encoder
model when encountering typos and explore ways to improve its robustness by
combining data augmentation with contrastive learning. Our experiments on two
large-scale passage ranking and open-domain question answering datasets show
that our proposed approach outperforms competing approaches. Additionally, we
perform a thorough analysis on robustness. Finally, we provide insights on how
different typos affect the robustness of embeddings differently and how our
method alleviates the effect of some typos but not of others.


title:
Operator inference for non-intrusive model reduction with nonlinear
  manifolds

abstract:
  This paper proposes a novel approach for learning a data-driven quadratic
manifold from high-dimensional data, then employing the quadratic manifold to
derive efficient physics-based reduced-order models. The key ingredient of the
approach is a polynomial mapping between high-dimensional states and a
low-dimensional embedding. This mapping comprises two parts: a representation
in a linear subspace (computed in this work using the proper orthogonal
decomposition) and a quadratic component. The approach can be viewed as a form
of data-driven closure modeling, since the quadratic component introduces
directions into the approximation that lie in the orthogonal complement of the
linear subspace, but without introducing any additional degrees of freedom to
the low-dimensional representation. Combining the quadratic manifold
approximation with the operator inference method for projection-based model
reduction leads to a scalable non-intrusive approach for learning reduced-order
models of dynamical systems. Applying the new approach to transport-dominated
systems of partial differential equations illustrates the gains in efficiency
that can be achieved over approximation in a linear subspace.


title: Order-Disorder Transitions in (Ca$_x$Sr$_{1-x}$)$_3$Rh$_4$Sn$_{13}$

abstract:
  The classification of structural phase transitions as displacive or
order-disorder in character is usually based on spectroscopic data above the
transition. We use single crystal x-ray diffraction to investigate structural
correlations in the quasi-skutterudites, (Ca$_x$Sr$_{1-x}$)$_3$Rh$_4$Sn$_{13}$,
which have a quantum phase transition at $x\sim0.9$. Three-dimensional pair
distribution functions show that the amplitudes of local atomic displacements
are temperature-independent below the transition and persist to well above the
transition, a signature of order-disorder behavior. The implications for the
associated electronic transitions are discussed.


title:
The Dark Matter Tidal Stripping History of the Sagittarius Core with
  N-body simulation

abstract:
  The infall of the Sagittarius (Sgr) Dwarf Spheroidal Galaxy in the Milky Way
is an unique opportunity to understand how the different components of a dwarf
galaxy are tidally removed. In this letter, we reconstruct the Sgr core
morphology and kinematics on the basis of a model that has already successfully
reproduced its stream. The model reproduces most of the observed morphology and
kinematic properties, without specific fine-tuning. We also show that the dark
matter is much more efficiently removed than stars, letting a stellar mass of
$10^{7}M_{\odot}$, and a dark matter mass only twice this value. Dark matter
has been almost entirely removed 2.7 Gyr ago, after few pericenter passages.
The predicted mass distribution for the Sgr core is confirmed by the kinematics
of the three globular clusters that are still attached to the Sgr core. Finally
the model predicts that the Sgr core will be fully disrupted within the next 2
Gyr.


title: BilevelJuMP.jl: Modeling and Solving Bilevel Optimization in Julia

abstract:
  In this paper we present BilevelJuMP, a new Julia package to support bilevel
optimization within the JuMP framework. The package is a Julia library that
enables the user to describe both upper and lower-level optimization problems
using the JuMP algebraic syntax. Due to the generality and flexibility our
library inherits from JuMP's syntax, our package allows users to model bilevel
optimization problems with conic constraints in the lower level and all JuMP
supported constraints in the upper level (Conic, Quadratic, Non-Linear,
Integer, etc.). Moreover, the user-defined problem can be subsequently solved
by various techniques relying on mathematical program with equilibrium
constraints (MPEC) reformulations. Manipulations on the original problem data
are possible due to MathOptInterface.jl's structures and Dualization.jl
features. Hence, the proposed package allows quickly model, deploy, and thereby
experiment bilevel models based on off-the-shelf mixed integer linear
programming and nonlinear solvers.


title: Exceptional points as a route to magnetic nano-oscillators

abstract:
  One of the most fascinating and puzzling aspects of non-Hermitian systems is
their spectral degeneracies, i.e., exceptional points (EPs), at which both
eigenvalues and eigenvectors coalesce to form a defective state space. While
coupled magnetic systems are natural hosts of EPs, the relation between the
linear and nonlinear spin dynamics in the proximity of EPs remains relatively
unexplored. Here we theoretically investigate the spin dynamics of easy-plane
magnetic bilayers in the proximity of exceptional points. We show that the
interplay between the intrinsically dissipative spin dynamics and external
drives can yield a rich dynamical phase diagram. In particular, we find that,
in antiferromagnetically coupled bilayers, a periodic oscillating dynamical
phase emerges in the region enclosed by EPs. Our findings have practical
utility for engineering magnetic nano-oscillator with very large amplitude
oscillations and have the potential to shed light on a plethora of
non-Hermitian systems displaying EPs and nonlinearities.


title:
Towards Robust and Semantically Organised Latent Representations for
  Unsupervised Text Style Transfer

abstract:
  Recent studies show that auto-encoder based approaches successfully perform
language generation, smooth sentence interpolation, and style transfer over
unseen attributes using unlabelled datasets in a zero-shot manner. The latent
space geometry of such models is organised well enough to perform on datasets
where the style is "coarse-grained" i.e. a small fraction of words alone in a
sentence are enough to determine the overall style label. A recent study uses a
discrete token-based perturbation approach to map "similar" sentences
("similar" defined by low Levenshtein distance/ high word overlap) close by in
latent space. This definition of "similarity" does not look into the underlying
nuances of the constituent words while mapping latent space neighbourhoods and
therefore fails to recognise sentences with different style-based semantics
while mapping latent neighbourhoods. We introduce EPAAEs (Embedding Perturbed
Adversarial AutoEncoders) which completes this perturbation model, by adding a
finely adjustable noise component on the continuous embeddings space. We
empirically show that this (a) produces a better organised latent space that
clusters stylistically similar sentences together, (b) performs best on a
diverse set of text style transfer tasks than similar denoising-inspired
baselines, and (c) is capable of fine-grained control of Style Transfer
strength. We also extend the text style transfer tasks to NLI datasets and show
that these more complex definitions of style are learned best by EPAAE. To the
best of our knowledge, extending style transfer to NLI tasks has not been
explored before.


title: Organizing Crime: an Empirical Analysis of the Sicilian Mafia

abstract:
  In this article we study the organizational structure of a large group of
members of the Sicilian Mafia by means of social network analysis and an
econometric analysis of link formation. Our mains results are the following. i)
The Mafia network is a small-world network adjusted by its criminal nature, and
is strongly disassortative. ii) Mafia bosses are not always central in the
network. In particular, consistent with a prediction of Baccara and Bar-Isaac,
we identify a "cell-dominated hierarchy" in the network: a key member is not
central, but is connected to a relative with a central position. iii) The
probability of link formation between two agents is higher if the two agents
belong to the same Mandamento, if they share a high number of similar tasks,
while being a "boss" reduces the probability of link formation between them.
iv) The probability of link formation for an individual agent is higher if he
is in charge of keeping connections outside his Mandamento, of collecting
protection money and or having a directive role, while age has modest role.
These results are interpreted in the light of the efficiency/security trade-off
faced by the Mafia and of its known hierarchical structure.


title:
Efficient single-grid and multi-grid solvers for real-space orbital-free
  density functional theory

abstract:
  To improve the computational efficiencies of the real-space orbital-free
density functional theory, this work develops a new single-grid solver by
directly providing the closed-form solution to the inner iteration and using an
improved bisection method to accelerate the line search process in the outer
iteration, and extended the single-grid solver to a multi-grid solver.
Numerical examples show that the proposed single-grid solver can improve the
computational efficiencies by two orders of magnitude comparing with the
methods in the literature and the multi-grid solver can improve the
computational efficiencies even once for the cases where high-resolution
electron densities are needed.


title:
Asymptotic analysis of diabatic surface hopping algorithm in the
  adiabatic and non-adiabatic limits

abstract:
  Surface hopping algorithms, as an important class of quantum dynamics
simulation algorithms for non-adiabatic dynamics, are typically performed in
the adiabatic representation, which can break down in the presence of
ill-defined adiabatic potential energy surfaces (PESs) and adiabatic coupling
term. Another issue of surface hopping algorithms is the difficulty in
capturing the correct scaling of the transition rate in the Marcus
(weak-coupling/non-adiabatic) regime. Though the first issue can be
circumvented by exploiting the diabatic representation, diabatic surface
hopping algorithms usually lack justification on the theoretical level. We
consider the diabatic surface hopping algorithm proposed in [Fang, Lu.
Multiscale Model. Simul. 16:4, 1603-1622, 2018] and provide the asymptotic
analysis of the transition rate in the Marcus regime that justifies the correct
scaling for the spin-boson model. We propose two conditions that guarantee the
correctness for general potentials. In the opposite (strong-coupling/adiabatic)
regime, we derive the asymptotic behavior of the algorithm that interestingly
matches a type of mean-field description. The techniques used here may shed
light on the analysis for other diabatic-based algorithms.


title:
Maxwell's Conjecture of the Demon creating a Temperature Difference is
  False

abstract:
  We argue that Maxwell's demon is incapable of creating a nonzero temperature
difference. Hence, it does not destroy equilibrium and the second law is never
at risk, contrary to the claim by Maxwell and accepted by many. It is therefore
remarkable that despite this, the demon paradox has been a valuable source of
new ideas. We use two independent arguments, one using classical equilibrium
thermodynamics by extending Brillouin's approach, and the other one using
equilibrium statistical mechanics and the central limit theorem.


title:
Search for CP violation using $\mathrm{t\bar{t}}$ events in the
  lepton+jets channel in pp collisions at $\sqrt{s}$ = 13 TeV

abstract:
  Results are presented on a search for CP violation in the production and
decay of top quark-antiquark pairs in the lepton+jets channel. The search is
based on data from proton-proton collisions at 13 TeV, collected with the CMS
detector, corresponding to an integrated luminosity of 138 fb$^{-1}$. Possible
CP violation effects are evaluated by measuring uncorrected asymmetries in
observables constructed from linearly independent four-momentum vectors of the
final-state particles. The dimensionless chromoelectric dipole moment of the
top quark obtained from the observed asymmetries is measured to be 0.04 $\pm$
0.10 (stat) $\pm$ 0.07 (syst), and the asymmetries exhibit no evidence for
CP-violating effects, consistent with expectations from the standard model.


title: Generation of polarization entanglement via the quantum Zeno effect

abstract:
  The quantum Zeno effect reveals that the continuous observation of a quantum
system can result in significant alterations to its evolution. Here, we present
a method for establishing polarization entanglement between two initially
unentangled photons in coupled waveguides via the quantum Zeno effect. We
support our analytical investigation with numerical simulations of the
underlying Schrodinger equation describing the system. Further, we extend our
technique to three coupled waveguides in a planar configuration and determine
the parameter regime required to generate three-qubit W-states. Our findings
offer a powerful quantum state engineering approach for photonic quantum
information technologies.


title: Cooperation in costly-access environments

abstract:
  Understanding cooperative behavior in biological and social systems
constitutes a scientific challenge, being the object of intense research over
the past decades. Many mechanisms have been proposed to explain the presence
and persistence of cooperation in those systems, showing that there is no
unique explanation, as different scenarios have different possible driving
forces. In this paper, we propose a model to study situations in which
voluntary participation involves an access cost to the cooperative interaction,
besides the cost associated with cooperation. The proposed Costly-Access
Prisoner's Dilemma, a symmetric donation game with voluntary and costly
participation, breaks the symmetry between abstainers and participants of the
Voluntary Prisoner's Dilemma. A mean-field approach shows that, in well-mixed
populations, the dynamic always leads the system to abstention. However,
depending on the return parameter, numerical simulations in structured
populations display an alternating behavior between mono-strategic,
multi-stable, and coexistence phases. This behavior is fully explained through
a theoretical analysis of the strategic motifs, the transitions being
determined by the change in stability of those motifs.


title:
Expansion formulae for two-center non-integer Slater-type orbital charge
  densities. I. Complete orthonormal sets of exponential-type orbitals with
  non-integer principal quantum numbers

abstract:
  The numerical Gram-Schmidt procedure is performed for the non-integer
Slater-type orbitals. Formulas for the linear combination coefficients used to
transform orthonormal basis functions into the Slater-type orbitals are
derived. A closed form expression for the orthogonalized Slater-type orbitals
is achieved. It is used to generalize complete orthonormal exponential-type
orbitals basis sets obtained by Guseinov in [Int. J. Quant. Chem. \textbf{90},
114 (2002)] to non-integer values of principal quantum numbers.


title:
Language Models in the Loop: Incorporating Prompting into Weak
  Supervision

abstract:
  We propose a new strategy for applying large pre-trained language models to
novel tasks when labeled training data is limited. Rather than apply the model
in a typical zero-shot or few-shot fashion, we treat the model as the basis for
labeling functions in a weak supervision framework. To create a classifier, we
first prompt the model to answer multiple distinct queries about an example and
define how the possible responses should be mapped to votes for labels and
abstentions. We then denoise these noisy label sources using the Snorkel system
and train an end classifier with the resulting training data. Our experimental
evaluation shows that prompting large language models within a weak supervision
framework can provide significant gains in accuracy. On the WRENCH weak
supervision benchmark, this approach can significantly improve over zero-shot
performance, an average 19.5% reduction in errors. We also find that this
approach produces classifiers with comparable or superior accuracy to those
trained from hand-engineered rules.


title: Fluctuations of The Symmetric Perceptron

abstract:
  We study the fluctuations in the storage capacity of the symmetric binary
perceptron, or equivalently, the fluctuations in the combinatorial discrepancy
of a Gaussian matrix. Perkins and Xu, and Abbe, Li, and Sly recently
established a sharp threshold: for some explicit constant $K_c$, the
discrepancy of a Gaussian matrix is $K_c + o(1)$ with probability tending to
one, but without a quantitative rate. We sharpen these results significantly.
We show the fluctuations around $K_c$ of the discrepancy are at most of order
$\log(n)/n$, and provide exponential tail bounds. Up to a logarithmic factor,
this yields a tight characterization for the fluctuations of the symmetric
perceptron and combinatorial discrepancy.


title:
Drift diffusion equations with fractional diffusion on compact Lie
  groups

abstract:
  In this work we investigate the well-posedness for difussion equations
associated to subelliptic pseudo-differential operators on compact Lie groups.
The diffusion by strongly elliptic operators is considered as a special case
and in particular the fractional diffusion with respect to the Laplacian. The
general case is studied within the H\"ormander classes associated to a
sub-Riemannian structure on the group (encoded by a H\"ormander system of
vector fields). Applications to diffusion equations for fractional
sub-Laplacians, fractional powers of more general subelliptic operators, and
the corresponding quasi-geostrophic model with drift $D$ are investigated.
Examples on SU(2) for diffusion problems with fractional diffusion are
analysed.


title: Most Activation Functions Can Win the Lottery Without Excessive Depth

abstract:
  The strong lottery ticket hypothesis has highlighted the potential for
training deep neural networks by pruning, which has inspired interesting
practical and theoretical insights into how neural networks can represent
functions. For networks with ReLU activation functions, it has been proven that
a target network with depth $L$ can be approximated by the subnetwork of a
randomly initialized neural network that has double the target's depth $2L$ and
is wider by a logarithmic factor. We show that a depth $L+1$ network is
sufficient. This result indicates that we can expect to find lottery tickets at
realistic, commonly used depths while only requiring logarithmic
overparametrization. Our novel construction approach applies to a large class
of activation functions and is not limited to ReLUs.


title:
Existence of positive solutions of a Hammerstein integral equation using
  the layered compression-expansion fixed point theorem

abstract:
  In this paper, we show the existence of a positive solution of a Hammerstein
integral equation under certain conditions on the kernel. We apply the recent
Layered Compression-Expansion Fixed Point Theorem. Finally, we provide
corollaries to help in application of the main results and present an example.


title:
Fill and dump measurement of the neutron lifetime using an asymmetric
  magneto-gravitational trap

abstract:
  The past two decades have yielded several new measurements and reanalyses of
older measurements of the neutron lifetime. These have led to a 4.4 standard
deviation discrepancy between the most precise measurements of the neutron
decay rate producing protons in cold neutron beams and the lifetime measured in
neutron storage experiments. Measurements using different techniques are
important for investigating whether there are unidentified systematic effects
in any of the measurements. In this paper we report a new measurement using the
Los Alamos asymmetric magneto-gravitational trap where the surviving neutrons
are counted external to the trap using the fill and dump method. The new
measurement gives a free neutron lifetime of . Although this measurement is not
as precise, it is in statistical agreement with previous results using in situ
counting in the same apparatus.


title: Towards the speed limit of high fidelity 2-qubit gates

abstract:
  Most implementations of quantum gate operations rely on external control
fields to drive the evolution of the quantum system. Generating these control
fields requires significant efforts to design the suitable control
Hamiltonians. Furthermore, any error in the control fields reduces the fidelity
of the implemented control operation with respect to the ideal target
operation. Achieving sufficiently fast gate operations at low error rates
remains therefore a huge challenge. In this work, we present a novel approach
to overcome this challenge by eliminating, for specific gate operations, the
time-dependent control fields entirely. This approach appears useful for
maximising the speed of the gate operation while simultaneously eliminating
relevant sources of errors. We present an experimental demonstration of the
concept in a single nitrogen-vacancy (NV) center in diamond at room
temperature.


title:
Lyapunov-Type Inequalities for a Fractional Boundary Value Problem with
  a Fractional Boundary Condition

abstract:
  In this paper, we consider a linear fractional differential equation with
fractional boundary conditions. First, by obtaining Green's function, we derive
the Lyapunov-type inequalities for such boundary value problems. Furthermore,
we use the contraction mapping theorem to study the existence of a unique
solution to a nonlinear problem.


title:
Edge states, transport and topological properties of heterostructures in
  the SSH model

abstract:
  In this paper, we discuss the topological and transport properties of binary
heterostructures of different topological materials. Using the
Su-Schrieffer-Heeger model and the method of Green's functions, we calculate
and characterize the edge states of these heterostructures. We study the
topological phase through an invariant calculated from the Zak phase in order
to build phase diagrams. We obtained an analytical result that allows us to
find the topological phase diagram for a one-dimensional general chain and for
heterostructures from the cross band condition. We also calculate the
differential conductance with the non-equilibrium Green function technique
showing the tunneling of the edge states and discussing its possible design and
experimental application.


title:
Safe Bayesian Optimization using Interior-Point Methods -- Applied to
  Personalized Insulin Dose Guidance

abstract:
  This paper considers the problem of Bayesian optimization for systems with
safety-critical constraints, where both the objective function and the
constraints are unknown, but can be observed by querying the system. In
safety-critical applications, querying the system at an infeasible point can
have catastrophic consequences. Such systems require a safe learning framework,
such that the performance objective can be optimized while satisfying the
safety-critical constraints with high probability. In this paper we propose a
safe Bayesian optimization framework that ensures that the points queried are
always in the interior of the partially revealed safe region, thereby
guaranteeing constraint satisfaction with high probability. The proposed
interior-point Bayesian optimization framework can be used with any acquisition
function, making it broadly applicable. The performance of the proposed method
is demonstrated using a personalized insulin dosing application for patients
with type 1 diabetes.


title: Exploring the Benefits of Teams in Multiagent Learning

abstract:
  For problems requiring cooperation, many multiagent systems implement
solutions among either individual agents or across an entire population towards
a common goal. Multiagent teams are primarily studied when in conflict;
however, organizational psychology (OP) highlights the benefits of teams among
human populations for learning how to coordinate and cooperate. In this paper,
we propose a new model of multiagent teams for reinforcement learning (RL)
agents inspired by OP and early work on teams in artificial intelligence. We
validate our model using complex social dilemmas that are popular in recent
multiagent RL and find that agents divided into teams develop cooperative
pro-social policies despite incentives to not cooperate. Furthermore, agents
are better able to coordinate and learn emergent roles within their teams and
achieve higher rewards compared to when the interests of all agents are
aligned.


title: Second-Order Sensitivity Analysis for Bilevel Optimization

abstract:
  In this work we derive a second-order approach to bilevel optimization, a
type of mathematical programming in which the solution to a parameterized
optimization problem (the "lower" problem) is itself to be optimized (in the
"upper" problem) as a function of the parameters. Many existing approaches to
bilevel optimization employ first-order sensitivity analysis, based on the
implicit function theorem (IFT), for the lower problem to derive a gradient of
the lower problem solution with respect to its parameters; this IFT gradient is
then used in a first-order optimization method for the upper problem. This
paper extends this sensitivity analysis to provide second-order derivative
information of the lower problem (which we call the IFT Hessian), enabling the
usage of faster-converging second-order optimization methods at the upper
level. Our analysis shows that (i) much of the computation already used to
produce the IFT gradient can be reused for the IFT Hessian, (ii) errors bounds
derived for the IFT gradient readily apply to the IFT Hessian, (iii) computing
IFT Hessians can significantly reduce overall computation by extracting more
information from each lower level solve. We corroborate our findings and
demonstrate the broad range of applications of our method by applying it to
problem instances of least squares hyperparameter auto-tuning, multi-class SVM
auto-tuning, and inverse optimal control.


title: Reinforcement Learning Algorithm for Mixed Mean Field Control Games

abstract:
  We present a new combined Mean Field Control Game (MFCG) problem which can be
interpreted as a competitive game between collaborating groups and its solution
as a Nash equilibrium between the groups. Within each group the players
coordinate their strategies. An example of such a situation is a modification
of the classical trader's problem. Groups of traders maximize their wealth.
They are faced with transaction cost for their own trades and a cost for their
own terminal position. In addition they face a cost for the average holding
within their group. The asset price is impacted by the trades of all agents. We
propose a reinforcement learning algorithm to approximate the solution of such
mixed Mean Field Control Game problems. We test the algorithm on benchmark
linear-quadratic specifications for which we have analytic solutions.


title:
Galactic Kinematics and Observed Flare Rates of a Volume-Complete Sample
  of Mid-to-Late M-dwarfs: Constraints on the History of the Stellar Radiation
  Environment of Planets Orbiting Low-mass Stars

abstract:
  We present a study of the relationship between galactic kinematics, flare
rates, chromospheric magnetic activity, and rotation periods for a
volume-complete, nearly all-sky sample of 219 single stars within 15 parsecs
and with masses between 0.1$-$0.3 M$\odot$ observed during the primary mission
of TESS. We find that all targeted stars are consistent with a common value of
$\alpha$=1.984 $\pm$ 0.019 for the exponent of the flare frequency
distribution. From multi-epoch high-resolution spectroscopy, we determine the
stellar radial velocity which, when combined with Gaia astrometry, permits us
to determine the galactic UVW space motions. We find that 64% of our stars are
members of the thin disk in the Galaxy, 5% belong to the thick disk, and for
the remaining 31%, we cannot confidently assign membership to either component.
If we assume that star formation has been constant in the thin disk for the
past 8 Gyr, then based on the fraction that we observe to be active, we
estimate the average age at which these stars transition from the saturated to
the unsaturated flaring regime to be 2.4 $\pm$ 0.3 Gyr. This is consistent with
the ages that we assign from galactic kinematics: We find that stars with Prot
$<$ 10 days (which have yet to spin down) have a mean age of 2.0 $\pm$ 1.2 Gyr,
whereas stars with 10 $<$ Prot $\leq$ 90 days have a mean age of 5.6 $\pm$ 2.7
Gyr, and stars with Prot $>$ 90 days have a mean age of 12.9 $\pm$ 3.5 Gyr.
When we divide our sample by mass, we find that the average age of stars with
Prot $<$ 10 days increases from 0.6 $\pm$ 0.3 Gyr (0.2 - 0.3 M$\odot$) to 2.3
$\pm$ 1.3 Gyr (0.1--0.2 M$\odot$).


title:
Learning Individual Interactions from Population Dynamics with
  Discrete-Event Simulation Model

abstract:
  The abundance of data affords researchers to pursue more powerful
computational tools to learn the dynamics of complex system, such as neural
networks, engineered systems and social networks. Traditional machine learning
approaches capture complex system dynamics either with dynamic Bayesian
networks and state space models, which is hard to scale because it is
non-trivial to prescribe the dynamics with a sparse graph or a system of
differential equations; or a deep neural networks, where the distributed
representation of the learned dynamics is hard to interpret. In this paper, we
will explore the possibility of learning a discrete-event simulation
representation of complex system dynamics assuming multivariate normal
distribution of the state variables, based on the observation that many complex
system dynamics can be decomposed into a sequence of local interactions, which
individually change the system state only minimally but in sequence generate
complex and diverse dynamics. Our results show that the algorithm can
data-efficiently capture complex network dynamics in several fields with
meaningful events.


title: Equity and Fairness of Bayesian Knowledge Tracing

abstract:
  We consider the equity and fairness of curricula derived from Knowledge
Tracing models. We begin by defining a unifying notion of an equitable tutoring
system as a system that achieves maximum possible knowledge in minimal time for
each student interacting with it. Realizing perfect equity requires tutoring
systems that can provide individualized curricula per student. In particular,
we investigate the design of equitable tutoring systems that derive their
curricula from Knowledge Tracing models. We first show that many existing
models, including classical Bayesian Knowledge Tracing (BKT) and Deep Knowledge
Tracing (DKT), and their derived curricula can fall short of achieving
equitable tutoring. To overcome this issue, we then propose a novel model,
Bayesian-Bayesian Knowledge Tracing (BBKT), that naturally enables online
individualization and, thereby, more equitable tutoring. We demonstrate that
curricula derived from our model are more effective and equitable than those
derived from classical BKT models. Furthermore, we highlight that improving
models with a focus on the fairness of next-step predictions might be
insufficient to develop equitable tutoring systems.


title:
On Internal and External Alignment of Dust Grains in Protostellar
  Environments

abstract:
  We study the physical processes inducing the alignment of the grain axis of
maximum inertia moment with the angular momentum (${\bf J}$, i.e., internal
alignment) and of ${\bf J}$ with the magnetic field (i.e., external alignment)
of very large grains (VLGs, of radius $a>10\mu$m) using the grain alignment
framework based on radiative torques (RATs) and mechanical torques (METs). We
derive analytical formulae for critical sizes of grain alignment, assuming that
grains are aligned at both low$-J$ and high$-J$ attractors by RATs (METs). For
protostellar cores, we find that super-Barnett relaxation can induce efficient
internal alignment for VLGs with large iron inclusions aligned at high$-J$
attractors by RATs (METs). In contrast, inelastic relaxation can be efficient
for VLGs made of any composition. For external alignment, we find that VLGs
with iron inclusions aligned at high$-J$ attractors can have magnetic alignment
by RATs ($B-$RAT) or METs ($B-$ MET), enabling dust polarization as a reliable
tracer of magnetic fields in such dense regions. Still, grains at low$-J$
attractors or grains without iron inclusions have alignment along the radiation
direction ($k-$RAT) or gas flow ($v-$MET). For protostellar disks, we find that
super-Barnett relaxation can be efficient for grains with large iron inclusions
in the outer disk thanks to spinup by METs, but inelastic relaxation is
inefficient. VLGs aligned at low-J attractors can have $k-$RAT ($v-$MET)
alignment, but grains aligned at high$-J$ attractors have likely $B-$RAT
($B-$MET) alignment. Grain alignment by METs appears to be more important than
RATs in protostellar disks.


title:
Acceleration Strategies for MR-STAT: Achieving High-Resolution
  Reconstructions on a Desktop PC within 3 minutes

abstract:
  MR-STAT is an emerging quantitative magnetic resonance imaging technique
which aims at obtaining multi-parametric tissue parameter maps from single
short scans. It describes the relationship between the spatial-domain tissue
parameters and the time-domain measured signal by using a comprehensive,
volumetric forward model. The MR-STAT reconstruction solves a large-scale
nonlinear problem, thus is very computationally challenging. In previous work,
MR-STAT reconstruction using Cartesian readout data was accelerated by
approximating the Hessian matrix with sparse, banded blocks, and can be done on
high performance CPU clusters with tens of minutes. In the current work, we
propose an accelerated Cartesian MR-STAT algorithm incorporating two different
strategies: firstly, a neural network is trained as a fast surrogate to learn
the magnetization signal not only in the full time-domain but also in the
compressed lowrank domain; secondly, based on the surrogate model, the
Cartesian MR-STAT problem is re-formulated and split into smaller sub-problems
by the alternating direction method of multipliers. The proposed method
substantially reduces the computational requirements for runtime and memory.
Simulated and in-vivo balanced MR-STAT experiments show similar reconstruction
results using the proposed algorithm compared to the previous sparse Hessian
method, and the reconstruction times are at least 40 times shorter.
Incorporating sensitivity encoding and regularization terms is straightforward,
and allows for better image quality with a negligible increase in
reconstruction time. The proposed algorithm could reconstruct both balanced and
gradient-spoiled in-vivo data within 3 minutes on a desktop PC, and could
thereby facilitate the translation of MR-STAT in clinical settings.


title:
On the instabilities of intrinsic thermoacoustic modes in a
  thermoacoustic waveguide with anechoic terminations

abstract:
  A recent study [H. Hao and F. Semperlotti, Phys. Rev. B 104, 104303 (2021)]
investigated the dynamic behavior of an infinite one-dimensional (1D)
thermoacoustic waveguide (TAWG) and illustrated its ability to sustain
nonreciprocal and near zero-index sound propagation behavior; these properties
can be very beneficial in the design of acoustic devices, including acoustic
diodes, amplifiers, and cloaks. Nevertheless, it is critical to realize that
when this concept is implemented in a finite-length waveguide, dynamic
instabilities may occur and either drastically reduce or completely hinder the
ability of the TAWG to control and manipulate sound. In this work, we uncover
and investigate the occurrence of either evanescent or intrinsic thermoacoustic
(ITA) modes in a 1D TAWG with anechoic terminations. The stability analysis
clearly distinguishes these two types of evanescent modes and highlights their
different origin rooted in either acoustic or thermoviscous effects. Numerical
results reveal that ITA modes in anechoic-terminated TAWG are strictly
connected to the acoustic-driven evanescent modes, and evolve towards unstable
modes as the TA coupling strength is increased. This study may have important
implications for the practical design of novel acoustic manipulating devices
enabled by TA coupling elements. The conclusions drawn in this study may also
shed lights on the effective suppression of instabilities in TAWGs.


title:
General spherical harmonic bra-ket overlap integrals of trigonometric
  functions

abstract:
  Closed formulas in terms of double sums of Clebsch-Gordan coefficients are
computed for the evaluation of bra-ket spherical harmonic overlap integrals of
a wide class of trigonometric functions. These analytical expressions can find
useful application in problems involving non-separable wave equations, e.g.
general-relativistic perturbation theory, electromagnetism, quantum theory,
etc, wherein the overlap integrals arise from the coupling among different
angular modes.

categories: math-ph gr-qc hep-th math.MP physics.atom-ph quant-ph


title:
Mixing the solar wind proton and electron scales. Theory and 2D-PIC
  simulations of firehose instability

abstract:
  Firehose-like instabilities (FIs) are cited in multiple astrophysical
applications. Of particular interest are the kinetic manifestations in
weakly-collisional or even collisionless plasmas, where these instabilities are
expected to contribute to the evolution of macroscopic parameters. Relatively
recent studies have initiated a realistic description of FIs, as induced by the
interplay of both species, electrons and protons, dominant in the solar wind
plasma. This work complements the current knowledge with new insights from
linear theory and the first disclosures from 2D PIC simulations, identifying
the fastest growing modes near the instability thresholds and their long-run
consequences on the anisotropic distributions. Thus, unlike previous setups,
these conditions are favorable to those aperiodic branches that propagate
obliquely to the uniform magnetic field, with (maximum) growth rates higher
than periodic, quasi-parallel modes. Theoretical predictions are, in general,
confirmed by the simulations. The aperiodic electron FI (a-EFI) remains
unaffected by the proton anisotropy, and saturates rapidly at low-level
fluctuations. Regarding the firehose instability at proton scales, we see a
stronger competition between the periodic and aperiodic branches. For the
parameters chosen in our analysis, the a-PFI is excited before than the p-PFI,
with the latter reaching a significantly higher fluctuation power. However,
both branches are significantly enhanced by the presence of anisotropic
electrons. The interplay between EFIs and PFIs also produces a more pronounced
proton isotropization.


title:
The First Exit Time Statistics and the Entropic Forces in Single File
  Diffusion

abstract:
  Single file systems are simplified models to study effectively
one-dimensional physical systems. Here we compute analytically the complete
first exit time statistics for an ideal overdamped single file with absorbing
boundary conditions. Then we use these results to study the speed-accuracy
trade-off characterizing this observable in terms of the means square
displacement and the entropic repulsive forces of the system.


title:
Knowledge Distillation of Russian Language Models with Reduction of
  Vocabulary

abstract:
  Today, transformer language models serve as a core component for majority of
natural language processing tasks. Industrial application of such models
requires minimization of computation time and memory footprint. Knowledge
distillation is one of approaches to address this goal. Existing methods in
this field are mainly focused on reducing the number of layers or dimension of
embeddings/hidden representations. Alternative option is to reduce the number
of tokens in vocabulary and therefore the embeddings matrix of the student
model. The main problem with vocabulary minimization is mismatch between input
sequences and output class distributions of a teacher and a student models. As
a result, it is impossible to directly apply KL-based knowledge distillation.
We propose two simple yet effective alignment techniques to make knowledge
distillation to the students with reduced vocabulary. Evaluation of distilled
models on a number of common benchmarks for Russian such as Russian SuperGLUE,
SberQuAD, RuSentiment, ParaPhaser, Collection-3 demonstrated that our
techniques allow to achieve compression from $17\times$ to $49\times$, while
maintaining quality of $1.7\times$ compressed student with the full-sized
vocabulary, but reduced number of Transformer layers only. We make our code and
distilled models available.


title:
Soft Syndrome Decoding of Quantum LDPC Codes for Joint Correction of
  Data and Syndrome Errors

abstract:
  Quantum errors are primarily detected and corrected using the measurement of
syndrome information which itself is an unreliable step in practical error
correction implementations. Typically, such faulty or noisy syndrome
measurements are modeled as a binary measurement outcome flipped with some
probability. However, the measured syndrome is in fact a discretized value of
the continuous voltage or current values obtained in the physical
implementation of the syndrome extraction. In this paper, we use this "soft" or
analog information without the conventional discretization step to benefit the
iterative decoders for decoding quantum low-density parity-check (QLDPC) codes.
Syndrome-based iterative belief propagation decoders are modified to utilize
the syndrome-soft information to successfully correct both data and syndrome
errors simultaneously, without repeated measurements. We demonstrate the
advantages of extracting the soft information from the syndrome in our improved
decoders, not only in terms of comparison of thresholds and logical error rates
for quasi-cyclic lifted-product QLDPC code families, but also for faster
convergence of iterative decoders. In particular, the new BP decoder with noisy
syndrome performs as good as the standard BP decoder under ideal syndrome.


title:
Monotonicity versions of Epstein's Concavity Theorem and related
  inequalities

abstract:
  Many trace inequalities can be expressed either as concavity/convexity
theorems or as monotonicity theorems. A classic example is the joint convexity
of the quantum relative entropy which is equivalent to the Data Processing
Inequality. The latter says that quantum operations can never increase the
relative entropy. The monotonicity versions often have many advantages, and
often have direct physical application, as in the example just mentioned.
Moreover, the monotonicity results are often valid for a larger class of maps
than, say, quantum operations (which are completely positive). In this paper we
prove several new monotonicity results, the first of which is a monotonicity
theorem that has as a simple corollary a celebrated concavity theorem of
Epstein. Our starting points are the monotonicity versions of the Lieb
Concavity and the Lieb Convexity Theorems. We also give two new proofs of these
in their general forms using interpolation. We then prove our new monotonicity
theorems by several duality arguments.


title: Convolutional and Residual Networks Provably Contain Lottery Tickets

abstract:
  The Lottery Ticket Hypothesis continues to have a profound practical impact
on the quest for small scale deep neural networks that solve modern deep
learning tasks at competitive performance. These lottery tickets are identified
by pruning large randomly initialized neural networks with architectures that
are as diverse as their applications. Yet, theoretical insights that attest
their existence have been mostly focused on deep fully-connected feed forward
networks with ReLU activation functions. We prove that also modern
architectures consisting of convolutional and residual layers that can be
equipped with almost arbitrary activation functions can contain lottery tickets
with high probability.


title: Remote Sensing of Coronal Forces During a Solar Prominence Eruption

abstract:
  We present a new methodology -- the Keplerian Optical Dynamics Analysis
(KODA) -- for analyzing the dynamics of dense, cool material in the solar
corona. The technique involves adaptive spatiotemporal tracking of propagating
intensity gradients and their characterization in terms of time-evolving
Keplerian areas swept out by the position vectors of moving plasma blobs.
Whereas gravity induces purely ballistic motions consistent with Kepler's
second law, non-central forces such as the Lorentz force introduce non-zero
torques resulting in more complex motions. KODA algorithms enable direct
evaluation of the line-of-sight component of the net torque density from the
image-plane projection of the areal acceleration. The method is applied to the
prominence eruption of 2011 June 7, observed by the Solar Dynamics
Observatory's Atmospheric Imaging Assembly. Results obtained include
quantitative estimates of the magnetic forces, field intensities, and blob
masses and energies across a vast region impacted by the post-reconnection
redistribution of the prominence material. The magnetic pressure and energy are
strongly dominant during the early, rising phase of the eruption, while the
dynamic pressure and kinetic energy become significant contributors during the
subsequent falling phases. Measured intensive properties of the prominence
blobs are consistent with those of typical active-region prominences; measured
extensive properties are compared with those of the whole pre-eruption
prominence and the post-eruption coronal mass ejection of 2011 June 7, all
derived by other investigators and techniques. We argue that KODA provides
valuable information on characteristics of erupting prominences that are not
readily available via alternative means, thereby shedding new light on their
environment and evolution.


title: Sketching Approximability of (Weak) Monarchy Predicates

abstract:
  We analyze the sketching approximability of constraint satisfaction problems
on Boolean domains, where the constraints are balanced linear threshold
functions applied to literals. In~particular, we explore the approximability of
monarchy-like functions where the value of the function is determined by a
weighted combination of the vote of the first variable (the president) and the
sum of the votes of all remaining variables. The pure version of this function
is when the president can only be overruled by when all remaining variables
agree. For every $k \geq 5$, we show that CSPs where the underlying predicate
is a pure monarchy function on $k$ variables have no non-trivial sketching
approximation algorithm in $o(\sqrt{n})$ space. We also show infinitely many
weaker monarchy functions for which CSPs using such constraints are
non-trivially approximable by $O(\log(n))$ space sketching algorithms.
Moreover, we give the first example of sketching approximable asymmetric
Boolean CSPs. Our results work within the framework of Chou, Golovnev, Sudan,
and Velusamy (FOCS 2021) that characterizes the sketching approximability of
all CSPs. Their framework can be applied naturally to get a computer-aided
analysis of the approximability of any specific constraint satisfaction
problem. The novelty of our work is in using their work to get an analysis that
applies to infinitely many problems simultaneously.


title:
Radial Trapping of Thermal Rossby Waves within the Convection Zones of
  Low-Mass Stars

abstract:
  We explore how thermal Rossby waves propagate within the gravitationally
stratified atmosphere of a low-mass star with an outer convective envelope.
Under the conditions of slow, rotationally constrained dynamics, we derive a
local dispersion relation for atmospheric waves in a fully compressible
stratified fluid. This dispersion relation describes the zonal and radial
propagation of acoustic waves and gravito-inertial waves. Thermal Rossby waves
are just one class of prograde-propagating gravito-inertial wave that manifests
when the buoyancy frequency is small compared to the rotation rate of the star.
From this dispersion relation, we identify the radii at which waves naturally
reflect and demonstrate how thermal Rossby waves can be trapped radially in a
waveguide that permits free propagation in the longitudinal direction. We
explore this trapping further by presenting analytic solutions for thermal
Rossby waves within an isentropically stratified atmosphere that models a zone
of efficient convective heat transport. We find that within such an atmosphere,
waves of short zonal wavelength have a wave cavity that is radially thin and
confined within the outer reaches of the convection zone near the star's
equator. The same behavior is evinced by the thermal Rossby waves that appear
at convective onset in numerical simulations of convection within rotating
spheres. Finally, we suggest that stable thermal Rossby waves could exist in
the lower portion of the Sun's convection zone, despite that region's unstable
stratification. For long wavelengths, the Sun's rotation rate is sufficiently
rapid to stabilize convective motions and the resulting overstable convective
modes are identical to thermal Rossby waves.


title:
Rooted America: Immobility and Segregation of the Inter-county Migration
  Networks

abstract:
  Despite the popular narrative that the United States is a "land of mobility,"
its internal migration rates have declined for decades, and reached a
historical low. Economic and related factors were able to account for a portion
of this trend, but the bulk has remained unexplained. Here, we propose a
systemic, relational model of internal migration in the U.S., combining
demographic, economic, political, and geographical factors with endogenous
social mechanisms, with the objective of identifying factors limiting migration
rates. We implement this model using valued temporal exponential-family random
graph models, allowing us to calibrate it to the (valued) network of
intercounty U.S. migration flows during the 2011-2015 period. Our analysis
reveals a pattern of segmented immobility, where fewer people migrate between
counties with dissimilar political contexts, levels of urbanization, and racial
compositions. Probing our model using "knockout" experiments suggests that one
would have observed approximately 3 million (17%) more intercounty migrants
over the study period were the segmented immobility mechanisms inoperative.
This analysis suggests that internal migration in the current era is driven not
only by demographic and economic factors, but also cultural and political ones.
It also reveals social and political cleavages that underlie geographical
immobility in America.


title: Secure Decentralized Online Gaming with Lending Functionalities

abstract:
  We present a decentralized online gaming platform implemented as a
Decentralized Application (DApp) on the Ethereum blockchain. The gaming
platform enables secure gaming, where the account balances and the stakes of
the players are secured by a smart contract. Moreover, the fair enforcement of
the game rules and the deposit of the winnings of the players and the gaming
platform into their accounts are guaranteed by the smart contract. The gaming
platform proposes lending functionalities that allow players to securely borrow
tokens from the gaming platform in order to participate in the games.


title: Nonlinear Capillary Waves on the Boundary of a Fluid Disk

abstract:
  This is a first draft of a publication on nonlinear traveling waves on the
boundary of a droplet. In the present work we numerically compute exact
solutions of an integro-differential equation for traveling waves that exist on
the boundary of a $2$D blob of an ideal fluid subject to surface tension. We
find that the solutions with many lobes resemble the Crapper waves on the
surface of flat water, whereas the solutions with few lobes tend to become
elongated as they become more nonlinear. We conjecture that waves with few
lobes can become arbitrarily long.


title:
A Fully Adaptive Strategy for Hamiltonian Cycles in the Semi-Random
  Graph Process

abstract:
  The semi-random graph process is a single player game in which the player is
initially presented an empty graph on $n$ vertices. In each round, a vertex $u$
is presented to the player independently and uniformly at random. The player
then adaptively selects a vertex $v$, and adds the edge $uv$ to the graph. For
a fixed monotone graph property, the objective of the player is to force the
graph to satisfy this property with high probability in as few rounds as
possible.
  We focus on the problem of constructing a Hamiltonian cycle in as few rounds
as possible. In particular, we present an adaptive strategy for the player
which achieves it in $\alpha n$ rounds, where $\alpha < 2.01678$ is derived
from the solution to some system of differential equations. We also show that
the player cannot achieve the desired property in less than $\beta n$ rounds,
where $\beta > 1.26575$. These results improve the previously best known bounds
and, as a result, the gap between the upper and lower bounds is decreased from
1.39162 to 0.75102.


title: On Preemptive Scheduling of Unrelated Machines Using Linear Programming

abstract:
  We consider a basic problem of preemptive scheduling of $n$
non-simultaneously released jobs on a group of $m$ unrelated parallel machines
so as to minimize maximum job completion time, the makespan. In the scheduling
literature, the problem is commonly considered to be solvable in polynomial
time by linear programming (LP) techniques proposed in Lawler and Labetoulle
\cite{ll78}. The authors in \cite{ll78} give a LP formulation of the version
with simultaneously released jobs and show how an optimal solution to this LP
can be used to construct an optimal schedule to the latter problem. As the
current study shows, for non-simultaneously released jobs, unlikely, there
exist a linear program such that a schedule with the minimum makespan can be
constructed based on an optimal LP solution. We also prove that, in case no
splitting of the same job on a machine is allowed (i.e., job part assigned to a
machine is to be processed without an interruption on that machine), the
problem is NP-hard. As a side result, we obtain that, whenever job splitting is
not allowed, given an optimal LP solution, it is NP-hard to find an optimal
schedule with the minimum makespan that agrees with that LP solution. We also
extend the schedule construction procedure based on an optimal LP solution from
Lawler and Labetoulle \cite{ll78} for non-simultaneously released jobs. The
extended procedure, among all feasible schedules that agree with any feasible
(not necessarily optimal) LP solution, generates one with the minimum makespan.
Such procedure is helpful, in particular, because, as we show, there may exist
no optimal schedule that agrees with an optimal LP solution if jobs are
non-simultaneously released.


title:
Field emission: calculations supporting a new methodology of comparing
  theory with experiment

abstract:
  This paper presents a new methodology for making comparisons between the
theory of field electron emission (FE) and experiment, and is intended as a
"demonstration of concept". This methodology is based on the value of the
exponent kappa that describes the power to which voltage is raised in the
pre-exponential of a mathematical equation that describes (for an
electronically ideal FE system) the dependence of measured emission current on
measured voltage. The aim is to use experimental exponent-values kappa^expt in
an attempt to decide between two alternative FE theories, for both of which
allowable (but different) ranges of kappa have been established. At present,
there is limited information on what contribution to the "total theoretical
kappa" is made by the voltage-dependence of the notional emission area: this
paper report simulations intended to add to our knowledge about this, for four
common assumed shapes of an emitter apex. The methodology is then applied to
the choice between 1928/29 Fowler-Nordheim (FN) FE theory and 1956 Murphy-Good
(MG) FE theory (which is a situation where it is theoretically certain that
1956 MG FE theory is "better physics" than 1928/29 FN FE theory). Like all
previous attempts to reach the known correct theoretical conclusion by
experimentally based argument, the outcome of the new methodology tends to
favour MG FE theory, but is formally indecisive at this stage of discussion.
There seems an urgent need for better methods of measuring kappa^expt and of
establishing reliable experimental error limits.


title: Pushouts of Dwyer maps are $(\infty,1)$-categorical

abstract:
  The inclusion of 1-categories into $(\infty,1)$-categories fails to preserve
colimits in general, and pushouts in particular. In this note, we observe that
if one functor in a span of categories belongs to a certain
previously-identified class of functors, then the 1-categorical pushout is
preserved under this inclusion. Dwyer maps, a kind of neighborhood deformation
retract of categories, were used by Thomason in the construction of his model
structure on 1-categories. Thomason previously observed that the nerves of such
pushouts have the correct weak homotopy type. We refine this result and show
that the weak homotopical equivalence is a weak categorical equivalence.


title:
Variance of the k-fold divisor function in arithmetic progressions for
  individual modulus

abstract:
  In this paper, we confirm a smoothed version of a recent conjecture on the
variance of the k-fold divisor function in arithmetic progressions to
individual composite moduli, in a restricted range. In contrast to a previous
result of Rodgers and Soundararajan, we do not require averaging over the
moduli. Our proof adapts a technique of S. Lester who treated in the same range
the variance of the k-fold divisor function in the short intervals setting, and
is based on a smoothed Voronoi summation formula but twisted by multiplicative
characters. The use of Dirichlet characters allows us to extend to a wider
range from previous result of Kowalski and Ricotta who used additive
characters. Smoothing also permits us to treat all k unconditionally. This
result is closely related to moments of Dirichlet L-functions.


title:
Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt
  Tuning

abstract:
  Pre-trained language models have contributed significantly to relation
extraction by demonstrating remarkable few-shot learning abilities. However,
prompt tuning methods for relation extraction may still fail to generalize to
those rare or hard patterns. Note that the previous parametric learning
paradigm can be viewed as memorization regarding training data as a book and
inference as the close-book test. Those long-tailed or hard patterns can hardly
be memorized in parameters given few-shot instances. To this end, we regard RE
as an open-book examination and propose a new semiparametric paradigm of
retrieval-enhanced prompt tuning for relation extraction. We construct an
open-book datastore for retrieval regarding prompt-based instance
representations and corresponding relation labels as memorized key-value pairs.
During inference, the model can infer relations by linearly interpolating the
base output of PLM with the non-parametric nearest neighbor distribution over
the datastore. In this way, our model not only infers relation through
knowledge stored in the weights during training but also assists
decision-making by unwinding and querying examples in the open-book datastore.
Extensive experiments on benchmark datasets show that our method can achieve
state-of-the-art in both standard supervised and few-shot settings. Code are
available in https://github.com/zjunlp/PromptKG/tree/main/research/RetrievalRE.


title:
Mass loss of massive helium star supernova progenitors shortly before
  explosion constrained by supernova radio properties

abstract:
  Mass loss of massive helium stars is not well understood even though it plays
an essential role in determining their remnant neutron-star or black-hole
masses as well as ejecta mass of Type Ibc supernovae. Radio emission from Type
Ibc supernovae is strongly affected by circumstellar matter properties formed
by mass loss of their massive helium star progenitors. In this study, we
estimate the rise time and peak luminosity distributions of Type Ibc supernovae
in radio based on a few massive helium star mass-loss prescriptions and compare
them with the observed distribution to constrain the uncertain massive helium
star mass-loss rates. We find that massive helium stars in the luminosity range
expected for ordinary Type Ibc supernova progenitors (4.6 ~< log L/Lsun ~< 5.2)
should generally have large mass-loss rates (> ~ 1e-6 Msun/yr) in order to
account for the observed rise time and peak luminosity distribution. Therefore,
mass-loss prescriptions that predict significantly low mass-loss rates for
helium stars in this luminosity range is inconsistent with the supernova radio
observations. It is also possible that massive helium stars shortly before
their explosion generally undergo mass-loss enhancement in a different way from
the standard radiation-driven wind mechanism.