Abstract:
  In this work, we investigate how flexible assets within a residential
building influence the long-term impact of operation. We use a measured-peak
grid tariff (MPGT) that puts a cost on the highest single-hour peak import over
the month. We apply a mathematical model of a Home Energy Management System
(HEMS) together with Stochastic Dynamic Programming (SDP), which calculates the
long-term impact of operating as a non-linear expected future cost curve (EFCC)
from the end of the scheduling period to the start. The proposed model is
applied to a case study for a Norwegian building with smart control of a
battery energy storage system (BESS), Electric vehicle (EV) charging and space
heating (SH). Each of the flexible assets are investigated individually with
MPGT and for an energy-based grid tariff. The results showed that EV charging
has the highest peak-power impact in the system, decreasing the total
electricity cost by 14.6% with MPGT when controllable compared to a reference
case with passive charging. It is further shown how the EFCC helps achieve
optimal timing and level of the peak demand, where it co-optimizes both
real-time pricing and the MPGT.


Abstract:
  Ahlgren and Samart relate three cusp forms with complex multiplication to
certain weakly holomorphic modular forms using $p$-adic bounds related to their
Fourier coefficients. In these three examples, their result strengthens a
theorem of Guerzhoy, Kent, and Ono which pairs certain CM forms with weakly
holomorphic modular forms via $p$-adic limits. Ahlgren and Samart use only the
theory of modular forms and Hecke operators, whereas Guerzhoy, Kent, and Ono
use the theory of harmonic Maass forms. Here we extend Ahlgren and Samart's
work to all cases where the cusp form space is one-dimensional and has trivial
Nebentypus. Along the way, we obtain a duality result relating two families of
modular forms that arise naturally in each case.


Abstract:
  This paper targets to explore the inter-subject variations eliminated facial
expression representation in the compressed video domain. Most of the previous
methods process the RGB images of a sequence, while the off-the-shelf and
valuable expression-related muscle movement already embedded in the compression
format. In the up to two orders of magnitude compressed domain, we can
explicitly infer the expression from the residual frames and possible to
extract identity factors from the I frame with a pre-trained face recognition
network. By enforcing the marginal independent of them, the expression feature
is expected to be purer for the expression and be robust to identity shifts. We
do not need the identity label or multiple expression samples from the same
person for identity elimination. Moreover, when the apex frame is annotated in
the dataset, the complementary constraint can be further added to regularize
the feature-level game. In testing, only the compressed residual frames are
required to achieve expression prediction. Our solution can achieve comparable
or better performance than the recent decoded image based methods on the
typical FER benchmarks with about 3$\times$ faster inference with compressed
data.


Abstract:
  This paper presents both a method and a result. The result presents a closed
formula for the sum of the first $m+1,m \ge 0,$ squares of the sequence
$F^{(k)}$ where each member is the sum of the previous $k$ members and with
initial conditions of $k-1$ zeroes followed by a 1. The generalized result
includes the known result of sums of squares of the Fibonacci numbers and a
recent result of Schumaker on sums of squares of Tribonacci numbers. To prove
the identities uniformly for all $k,$ the Algebraic Verification method is
presented which reduces proof of an identity to verification of the equality of
finitely many pairs of finite-degree polynomials, possibly in several
variables. Several other papers proving families of identities are examined,
and it is suggested that the collection of the uniform proof methods used in
these papers could produce a new trend in stating and proving identities.


Abstract:
  Statistical modeling for massive spatial data sets has generated a
substantial literature on scalable spatial processes based upon a likelihood
approximation proposed by Vecchia in 1988. Vecchia's approximation for Gaussian
process models enables fast evaluation of the likelihood by restricting
dependencies at a location to its neighbors. We establish inferential
properties of microergodic spatial covariance parameters within the paradigm of
fixed-domain asymptotics when they are estimated using Vecchia's approximation.
The conditions required to formally establish these properties are explored,
theoretically and empirically, and the effectiveness of Vecchia's approximation
is further corroborated from the standpoint of fixed-domain asymptotics. These
explorations suggest some practical diagnostics for evaluating the quality of
the approximation.


Abstract:
  Fiber-based micro-endoscopes are a critically important tool for
minimally-invasive deep-tissue imaging. However, the state-of-the-art
micro-endoscopes cannot perform three-dimensional imaging through
dynamically-bent fibers without the use of bulky optical elements such as
lenses and scanners at the distal end, increasing the footprint and
tissue-damage. While great efforts have been invested in developing approaches
that avoid distal bulky optical elements, the fundamental barrier of dynamic
optical wavefront-distortions in propagation through flexible fibers, limits
current approaches to nearly-static or non-flexible fibers. Here, we present an
approach that allows holographic 3D bend-insensitive, coherence-gated,
micro-endoscopic imaging, using commercially available multi-core fibers
(MCFs). We achieve this by adding a miniature partially-reflecting mirror to
the distal fiber-tip, allowing us to perform low-coherence full-field
phase-shifting holography. We demonstrate widefield diffraction-limited
reflection imaging of amplitude and phase targets through dynamically bent
fibers at video-rates. Our approach holds great potential for label-free
investigations of dynamic samples.


Abstract:
  Authentication of the quantum sources is a crucial task in building reliable
and efficient protocols for quantum information processing. Steady progress
vis-\`{a}-vis verification of quantum devices in the scenario with fully
characterised measurement devices has been observed in the last years. When it
comes to the scenario with uncharacterised measurements, the so-called
black-box scenario, practical verification methods are still rather scarce.
Development of self-testing methods is an important step forward, but these
results so far have been used for reliable verification only by considering the
asymptotic behavior of large, identically and independently distributed (IID)
samples of a quantum resource. Such strong assumptions deprive the verification
procedure of its truly device-independent character. In this letter, we develop
a systematic approach to device-independent verification of quantum states free
of IID assumptions in the finite copy regime. Remarkably, we show that
device-independent verification can be performed with optimal sample
efficiency. Furthermore, we develop device-independent protocols for quantum
state certification: a protocol in which a fragment of the resource copies is
measured to warrant the rest of the copies to be close to some target state.


Abstract:
  The output of text-to-image synthesis systems should be coherent, clear,
photo-realistic scenes with high semantic fidelity to their conditioned text
descriptions. Our Cross-Modal Contrastive Generative Adversarial Network
(XMC-GAN) addresses this challenge by maximizing the mutual information between
image and text. It does this via multiple contrastive losses which capture
inter-modality and intra-modality correspondences. XMC-GAN uses an attentional
self-modulation generator, which enforces strong text-image correspondence, and
a contrastive discriminator, which acts as a critic as well as a feature
encoder for contrastive learning. The quality of XMC-GAN's output is a major
step up from previous models, as we show on three challenging datasets. On
MS-COCO, not only does XMC-GAN improve state-of-the-art FID from 24.70 to 9.33,
but--more importantly--people prefer XMC-GAN by 77.3 for image quality and 74.1
for image-text alignment, compared to three other recent models. XMC-GAN also
generalizes to the challenging Localized Narratives dataset (which has longer,
more detailed descriptions), improving state-of-the-art FID from 48.70 to
14.12. Lastly, we train and evaluate XMC-GAN on the challenging Open Images
data, establishing a strong benchmark FID score of 26.91.


Abstract:
  Nonlinear responses give rise to various exciting phenomena, which are
forbidden in linear responses. Among them, one of the most fascinating
phenomena is the recently observed giant spontaneous Hall effect in
$\mathrm{Ce_{3}Bi_{4}Pd_{3}}$. This material is a promising candidate for a
Weyl-Kondo semimetal, and this experiment implies that strong correlation
effects can enhance the nonlinear Hall effect. However, most theoretical
studies on nonlinear responses have been limited to free systems, and the
connection between nonlinear responses and strong correlation effects is poorly
understood. Motivated by these experiments and recent theoretical advances to
analyze strong correlation effects on the nonlinear response, we study a
periodic Anderson model describing $\mathrm{Ce_{3}Bi_{4}Pd_{3}}$ using the
dynamical mean-field theory. We calculate the nonlinear longitudinal
conductivity and the nonlinear Hall conductivity using the Kubo formula
extended to the nonlinear response regime and clarify their temperature
dependences. We numerically show that strong correlations can enhance nonlinear
conductivities, and we conclude that the magnitude of the experimentally
observed giant nonlinear Hall effect can be explained by strong correlation
effects.


Abstract:
  We present a large-scale bibliometric analysis of gender differences in
scientific careers, covering all scientific disciplines and a large number of
countries worldwide. We take a longitudinal perspective in which we trace the
publication careers of almost six million male and female researchers in the
period 1996-2018. Our analysis reveals an increasing trend in the percentage of
women starting a career as publishing researcher, from 33% in 2000 to about 40%
in recent years. Looking at cohorts of male and female researchers that started
their publication career in the same year, we find that women seem to be
somewhat less likely to continue their career as publishing researcher than
men, but the difference is small. We also observe that men produce on average
between 15% and 20% more publications than women. Moreover, in biomedical
disciplines, men are about 25% more likely than women to be last author of a
publication, suggesting that men tend to have more senior roles than women.
Compared with cross-sectional studies, our longitudinal analysis has the
advantage of providing a more in-depth understanding of gender imbalances among
authors of scientific publications.


Abstract:
  We report on a large-scale empirical study investigating the relevance of
socio-technical congruence over key basic software quality metrics, namely,
bugs and churn. In particular, we explore whether alignment or misalignment of
social communication structures and technical dependencies in large software
projects influences software quality. To this end, we have defined a
quantitative and operational notion of socio-technical congruence, which we
call socio-technical motif congruence (STMC). STMC is a measure of the degree
to which developers working on the same file or on two related files, need to
communicate. As socio-technical congruence is a complex and multi-faceted
phenomenon, the interpretability of the results is one of our main concerns, so
we have employed a careful mixed-methods statistical analysis. In particular,
we provide analyses with similar techniques as employed by seminal work in the
field to ensure comparability of our results with the existing body of work.
The major result of our study, based on an analysis of 25 large open-source
projects, is that STMC is not related to project quality measures -- software
bugs and churn -- in any temporal scenario. That is, we find no statistical
relationship between the alignment of developer tasks and developer
communications on the one hand, and project outcomes on the other hand. We
conclude that, wherefore congruence does matter as literature shows, then its
measurable effect lies elsewhere.


Abstract:
  This paper is devoted to the complete algebraic and geometric classification
of complex $4$-dimensional nilpotent left-symmetric algebras. The corresponding
geometric variety has dimension $15$ and decomposes into $3$ irreducible
components determined by the Zariski closures of two one-parameter families of
algebras and a two-parameter family of algebras (see Theorem B). In particular,
there are no rigid $4$-dimensional complex nilpotent left symmetric algebras.


Abstract:
  We present a novel group collaborative learning framework (GCoNet) capable of
detecting co-salient objects in real time (16ms), by simultaneously mining
consensus representations at group level based on the two necessary criteria:
1) intra-group compactness to better formulate the consistency among co-salient
objects by capturing their inherent shared attributes using our novel group
affinity module; 2) inter-group separability to effectively suppress the
influence of noisy objects on the output by introducing our new group
collaborating module conditioning the inconsistent consensus. To learn a better
embedding space without extra computational overhead, we explicitly employ
auxiliary classification supervision. Extensive experiments on three
challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate that
our simple GCoNet outperforms 10 cutting-edge models and achieves the new
state-of-the-art. We demonstrate this paper's new technical contributions on a
number of important downstream computer vision applications including content
aware co-segmentation, co-localization based automatic thumbnails, etc.


Abstract:
  We consider a loop representation of the $O(n)$ model at the critical point.
When $n=0$ the model represents ensembles of self-avoiding loops (i.e., it
corresponds to SLE with $\kappa=8/3$), and can be described by the logarithmic
conformal field theory (LCFT) with central charge $c=0$. We focus on the
correlation functions in the upper-half plane containing the twist operators in
the bulk, and a pair of the boundary one-leg operators. By using a Coulomb gas
representation for the correlation functions, we obtain explicit results for
probabilities of the SLE${}_{8/3}$ trace to wind in various ways about $N\geq
1$ marked points. When the points collapse pairwise the probabilities reduce to
multi-point Green's functions. We propose an explicit representation for the
Green's functions in terms of the correlation functions of the bulk 1/3-weight
operators, and a pair of the boundary one-leg operators.


Abstract:
  Galaxy groups and poor clusters are more common than rich clusters, and host
the largest fraction of matter content in the Universe. Hence, their studies
are key to understand the gravitational and thermal evolution of the bulk of
the cosmic matter. Moreover, because of their shallower gravitational
potential, galaxy groups are systems where non-gravitational processes (e.g.,
cooling, AGN feedback, star formation) are expected to have a higher impact on
the distribution of baryons, and on the general physical properties, than in
more massive objects, inducing systematic departures from the expected scaling
relations. Despite their paramount importance from the astrophysical and
cosmological point of view, the challenges in their detection have limited the
studies of galaxy groups. Upcoming large surveys will change this picture,
reassigning to galaxy groups their central role in studying the structure
formation and evolution in the Universe, and in measuring the cosmic baryonic
content. Here, we review the recent literature on various scaling relations
between X-ray and optical properties of these systems, focusing on the
observational measurements, and the progress in our understanding of the
deviations from the self-similar expectations on groups' scales. We discuss
some of the sources of these deviations, and how feedback from supernovae
and/or AGNs impacts the general properties and the reconstructed scaling laws.
Finally, we discuss future prospects in the study of galaxy groups.


Abstract:
  The unpolarized and polarized Beam Char\-ge Asymmetries (BCAs) of the
$\vv{e}^{\pm}p \to e^{\pm}p \gamma$ process off unpolarized hydrogen are
discussed. The measurement of BCAs with the CLAS12 spectrometer at the Thomas
Jefferson National Accelerator Facility, using polarized positron and electron
beams at 10.6 GeV is investigated. This experimental configuration allows to
measure azimuthal and $t$-dependences of the unpolarized and polarized BCAs
over a large $(x_B,Q^2)$ phase space, providing a direct access to the real
part of the Compton Form Factor (CFF) ${\mathcal H}$. Additionally, these
measurements confront the Bethe-Heitler dominance hypothesis and eventual
effects beyond leading twist. The impact of potential positron beam data on the
determination of CFFs is also investigated within a local fitting approach of
experimental observables. Positron data are shown to strongly reduce
correlations between CFFs and consequently improve significantly the
determination of $\Re {\rm e} [\mathcal{H}]$.


Abstract:
  Tracking by natural language specification is a new rising research topic
that aims at locating the target object in the video sequence based on its
language description. Compared with traditional bounding box (BBox) based
tracking, this setting guides object tracking with high-level semantic
information, addresses the ambiguity of BBox, and links local and global search
organically together. Those benefits may bring more flexible, robust and
accurate tracking performance in practical scenarios. However, existing natural
language initialized trackers are developed and compared on benchmark datasets
proposed for tracking-by-BBox, which can't reflect the true power of
tracking-by-language. In this work, we propose a new benchmark specifically
dedicated to the tracking-by-language, including a large scale dataset, strong
and diverse baseline methods. Specifically, we collect 2k video sequences
(contains a total of 1,244,340 frames, 663 words) and split 1300/700 for the
train/testing respectively. We densely annotate one sentence in English and
corresponding bounding boxes of the target object for each video. We also
introduce two new challenges into TNL2K for the object tracking task, i.e.,
adversarial samples and modality switch. A strong baseline method based on an
adaptive local-global-search scheme is proposed for future works to compare. We
believe this benchmark will greatly boost related researches on natural
language guided tracking.


Abstract:
  We describe certain Lagrangian fibrations by Prym varieties on holomorphic
symplectic varieties that deform to compactifications of Sp-Hitchin systems.


Abstract:
  We review and extend results on higher-curvature corrections to different
configurations describing a superposition of heterotic strings, KK monopoles,
solitonic 5-branes and momentum waves. Depending on which sources are present,
the low-energy fields describe a black hole, a soliton or a naked singularity.
We show that this property is unaltered when perturbative higher-curvature
corrections are included, provided the sources are fixed. On the other hand,
this character may be changed by appropriate introduction (or removal) of
sources regardless of the presence of curvature corrections, which constitutes
a non-perturbative modification of the departing system. The general system of
multicenter KK monopoles and their 5-brane charge induced by higher-curvature
corrections is discussed in some detail, with special attention paid to the
possibility of merging monopoles. Our results are particularly relevant for
small black holes (Dabholkar-Harvey states, DH), which remain singular after
quadratic curvature corrections are taken into account. When there are four
non-compact dimensions, we notice the existence of a black hole with regular
horizon whose entropy coincides with that of the DH states, but the charges and
supersymmetry preserved by both configurations are different. A similar
construction with five non-compact dimensions is possible, in this case with
the same charges as DH, although it fails to reproduce the DH entropy and
supersymmetry. No such configuration exists if $d>5$, which we interpret as
reflecting the necessity of having a 5-brane wrapping the compact space.


Abstract:
  In this paper, we propose a novel conservative formulation for solving
kinetic equations via neural networks. More precisely, we formulate the
learning problem as a constrained optimization problem with constraints that
represent the physical conservation laws. The constraints are relaxed toward
the residual loss function by the Lagrangian duality. By imposing physical
conservation properties of the solution as constraints of the learning problem,
we demonstrate far more accurate approximations of the solutions in terms of
errors and the conservation laws, for the kinetic Fokker-Planck equation and
the homogeneous Boltzmann equation.


Abstract:
  Astrophysical plasmas are often subject to both rotation and large-scale
background magnetic fields. Individually, each is known to two-dimensionalize
the flow in the perpendicular plane. In realistic flows, both of these effects
are simultaneously present and, importantly, need not be aligned. In this work,
we numerically investigate three-dimensional forced magnetohydrodynamic (MHD)
turbulence subject to the competing effects of global rotation and a
perpendicular background magnetic field. We focus on the case of a strong
background field and find that increasing the rotation rate from zero produces
significant changes in the structure of the turbulent flow. Starting with a
two-dimensional inverse cascade at zero rotation, the flow first transitions to
a forward cascade of kinetic energy, then to a shear-layer dominated regime,
and finally to a second shear-layer regime where the kinetic energy flux is
strongly suppressed and the energy transfer is mediated by the induced magnetic
field. We show that the first two transitions occur at distinct values of the
Rossby number, and the third occurs at a distinct value of the Lehnert number.
More generally, our results demonstrate that, when considering the simultaneous
limits of strong rotation and a strong guide field, the order in which those
limits are taken matters in the misaligned case.


Abstract:
  Software engineering researchers look for software artifacts to study their
characteristics or to evaluate new techniques. In this paper, we introduce
DUETS, a new dataset of software libraries and their clients. This dataset can
be exploited to gain many different insights, such as API usage, usage inputs,
or novel observations about the test suites of clients and libraries. DUETS is
meant to support both static and dynamic analysis. This means that the
libraries and the clients compile correctly, they are executable and their test
suites pass. The dataset is composed of open-source projects that have more
than five stars on GitHub. The final dataset contains 395 libraries and 2,874
clients. Additionally, we provide the raw data that we use to create this
dataset, such as 34,560 pom.xml files or the complete file list from 34,560
projects. This dataset can be used to study how libraries are used by their
clients or as a list of software projects that successfully build. The client's
test suite can be used as an additional verification step for code
transformation techniques that modify the libraries.


Abstract:
  The thermal equilibrium properties of physical systems can be described using
Gibbs states. It is therefore of great interest to know when such states allow
for an easy description. In particular, this is the case if correlations
between distant regions are small. In this work, we consider 1D quantum spin
systems with local, finite-range, translation-invariant interactions at any
temperature. In this setting, we show that Gibbs states satisfy uniform
exponential decay of correlations and, moreover, the mutual information between
two regions decays exponentially with their distance, irrespective of the
temperature. The methods we use are based on the Belavkin-Staszewski relative
entropy and on techniques developed by Araki. Moreover, we find that the Gibbs
states of the systems we consider are superexponentially close to saturating
the data-processing inequality for the Belavkin-Staszewski relative entropy.


Abstract:
  We discuss $(H_p,L_p)$ and $(H_p,\text{weak}-L_p)$ type inequalities of
weighted maximal operators of $T$ means with respect to the Vilenkin systems
with monotone coefficients, considered in \cite{tut4} and prove that these
results are the best possible in a special sense. As applications, both some
well-known and new results are pointed out.


Abstract:
  Multi criteria decision analysis (MCDA) has been used to provide a holistic
evaluation of the quality of 13 electricity generation technologies in use
today. A group of 19 energy experts cast scores on a scale of 1 to 10 using 12
quality criteria, based around the pillars of sustainability (society,
environment and economy), with the aim of quantifying each criterion for each
technology. The total mean score is employed as a holistic measure of system
quality. The top three technologies to emerge in rank order are nuclear,
combined cycle gas and hydroelectric. The bottom three are solar PV, biomass
and tidal lagoon. All seven new renewable technologies fared badly, perceived
to be expensive, unreliable, and not as environmentally friendly as is often
assumed. We validate our approach by 1) comparing scores for pairs of criteria
where we expect a correlation to exist; 2) comparing our qualitative scores
with quantitative data; and; 3) comparing our qualitative scores with NEEDS
project baseline costs. In many cases, R2>0.8 suggests that the structured
hierarchy of our approach has led to scores that may be used in a
semi-quantitative way. Adopting the results of this survey would lead to a very
different set of energy policy priorities in the OECD and throughout the world.


Abstract:
  Large amounts of threat intelligence information about malware attacks are
available in disparate, typically unstructured, formats. Knowledge graphs can
capture this information and its context using RDF triples represented by
entities and relations. Sparse or inaccurate threat information, however, leads
to challenges such as incomplete or erroneous triples. Generic information
extraction (IE) models used to populate the knowledge graph cannot fully
guarantee domain-specific context. This paper proposes a system to generate a
Malware Knowledge Graph called MalKG, the first open-source automated knowledge
graph for malware threat intelligence. MalKG dataset (MT40K\footnote{ Anonymous
GitHub link: https://github.com/malkg-researcher/MalKG}) contains approximately
40,000 triples generated from 27,354 unique entities and 34 relations. For
ground truth, we manually curate a knowledge graph called MT3K, with 3,027
triples generated from 5,741 unique entities and 22 relations. We demonstrate
the intelligence prediction of MalKG using two use cases. Predicting malware
threat information using the benchmark model achieves 80.4 for the hits@10
metric (predicts the top 10 options for an information class), and 0.75 for the
MRR (mean reciprocal rank). We also propose an automated, contextual framework
for information extraction, both manually and automatically, at the sentence
level from 1,100 malware threat reports and from the common vulnerabilities and
exposures (CVE) database.


Abstract:
  Recent technological advancements have enabled detailed investigation of
associations between the molecular architecture and tumor heterogeneity,
through multi-source integration of radiological imaging and genomic
(radiogenomic) data. In this paper, we integrate and harness radiogenomic data
in patients with lower grade gliomas (LGG), a type of brain cancer, in order to
develop a regression framework called RADIOHEAD (RADIOgenomic analysis
incorporating tumor HEterogeneity in imAging through Densities) to identify
radiogenomic associations. Imaging data is represented through voxel intensity
probability density functions of tumor sub-regions obtained from multimodal
magnetic resonance imaging, and genomic data through molecular signatures in
the form of pathway enrichment scores corresponding to their gene expression
profiles. Employing a Riemannian-geometric framework for principal component
analysis on the set of probability densities functions, we map each probability
density to a vector of principal component scores, which are then included as
predictors in a Bayesian regression model with the pathway enrichment scores as
the response. Variable selection compatible with the grouping structure amongst
the predictors induced through the tumor sub-regions is carried out under a
group spike-and-slab prior. A Bayesian false discovery rate mechanism is then
used to infer significant associations based on the posterior distribution of
the regression coefficients. Our analyses reveal several pathways relevant to
LGG etiology (such as synaptic transmission, nerve impulse and neurotransmitter
pathways), to have significant associations with the corresponding
imaging-based predictors.


Abstract:
  Major theories of military innovation focus on relatively narrow
technological developments, such as nuclear weapons or aircraft carriers.
Arguably the most profound military implications of technological change,
however, come from more fundamental advances arising from general purpose
technologies, such as the steam engine, electricity, and the computer. With few
exceptions, political scientists have not theorized about GPTs. Drawing from
the economics literature on GPTs, we distill several propositions on how and
when GPTs affect military affairs. We call these effects general-purpose
military transformations. In particular, we argue that the impacts of GMTs on
military effectiveness are broad, delayed, and shaped by indirect productivity
spillovers. Additionally, GMTs differentially advantage those militaries that
can draw from a robust industrial base in the GPT. To illustrate the
explanatory value of our theory, we conduct a case study of the military
consequences of electricity, the prototypical GPT. Finally, we apply our
findings to artificial intelligence, which will plausibly cause a profound
general-purpose military transformation.


Abstract:
  Fiber-fed etalons are widely employed in advanced interferometric instruments
such as gravitational-wave detectors, ultrastable lasers and calibration
reference for high-precision spectrographs. We demonstrate that variation in
near-field distribution of the feeding fiber would deteriorate the spectrum
precision of the fiber-fed Fabry-Perot etalon, especially when precision at the
order of 3 * 10-10 or higher is required. The octagonal fiber reinforced with
double scrambler could greatly improve the steadiness and uniformness of the
near-field distribution. When building wavelength calibrators for sub-m s-1
precision radial-velocity instruments, the double scrambler should be
considered meticulously.


Abstract:
  In the search for life in the cosmos, NASA's Transiting Exoplanet Survey
Satellite (TESS) mission has already monitored about 74% of the sky for
transiting extrasolar planets, including potentially habitable worlds. However,
TESS only observed a fraction of the stars long enough to be able to find
planets like Earth. We use the primary mission data - the first two years of
observations - and identify 4,239 stars within 210pc that TESS observed long
enough to see 3 transits of an exoplanet that receives similar irradiation to
Earth: 738 of these stars are located within 30pc. We provide reliable stellar
parameters from the TESS Input Catalog that incorporates Gaia DR2 and also
calculate the transit depth and radial velocity semi-amplitude for an
Earth-analog planet. Of the 4,239 stars in the Revised TESS HZ Catalog, 9 are
known exoplanet hosts - GJ 1061, GJ 1132, GJ 3512, GJ 685, Kepler-42, LHS 1815,
L98-59, RR Cae, TOI 700 - around which TESS could identify additional
Earth-like planetary companions. 37 additional stars host yet unconfirmed TESS
Objects of Interest: three of these orbit in the habitable zone - TOI 203, TOI
715, and TOI 2298. For a subset of 614 of the 4,239 stars, TESS has observed
the star long enough to be able to observe planets throughout the full
temperate, habitable zone out to the equivalent of Mars' orbit. Thus, the
Revised TESS Habitable Zone Catalog provides a tool for observers to prioritize
stars for follow-up observation to discover life in the cosmos. These stars are
the best path towards the discovery of habitable planets using the TESS mission
data.


Abstract:
  Applications of category theory often involve symmetric monoidal categories
(SMCs), in which abstract processes or operations can be composed in series and
parallel. However, in 2020 there remains a dearth of computational tools for
working with SMCs. We present an "unbiased" approach to implementing symmetric
monoidal categories, based on an operad of directed, acyclic wiring diagrams.
Because the interchange law and other laws of a SMC hold identically in a
wiring diagram, no rewrite rules are needed to compare diagrams. We discuss the
mathematics of the operad of wiring diagrams, as well as its implementation in
the software package Catlab.


Abstract:
  We study FO+, a fragment of first-order logic on finite words, where monadic
predicates can only appear positively. We show that there is a FO-definable
language that is monotone in monadic predicates but not definable in FO+. This
provides a simple proof that Lyndon's preservation theorem fails on finite
structures. We additionally show that given a regular language, it is
undecidable whether it is definable in FO+.


Abstract:
  New antibiotics are needed to battle growing antibiotic resistance, but the
development process from hit, to lead, and ultimately to a useful drug, takes
decades. Although progress in molecular property prediction using
machine-learning methods has opened up new pathways for aiding the antibiotics
development process, many existing solutions rely on large datasets and finding
structural similarities to existing antibiotics. Challenges remain in modelling
of unconventional antibiotics classes that are drawing increasing research
attention. In response, we developed an antimicrobial activity prediction model
for conjugated oligoelectrolyte molecules, a new class of antibiotics that
lacks extensive prior structure-activity relationship studies. Our approach
enables us to predict minimum inhibitory concentration for E. coli K12, with 21
molecular descriptors selected by recursive elimination from a set of 5,305
descriptors. This predictive model achieves an R2 of 0.65 with no prior
knowledge of the underlying mechanism. We find the molecular representation
optimum for the domain is the key to good predictions of antimicrobial
activity. In the case of conjugated oligoelectrolytes, a representation
reflecting the 3-dimensional shape of the molecules is most critical. Although
it is demonstrated with a specific example of conjugated oligoelectrolytes, our
proposed approach for creating the predictive model can be readily adapted to
other novel antibiotic candidate domains.


Abstract:
  We demonstrate that 1x1-convolutions in 1D time-channel separable
convolutions may be replaced by constant, sparse random ternary matrices with
weights in $\{-1,0,+1\}$. Such layers do not perform any multiplications and do
not require training. Moreover, the matrices may be generated on the chip
during computation and therefore do not require any memory access. With the
same parameter budget, we can afford deeper and more expressive models,
improving the Pareto frontiers of existing models on several tasks. For command
recognition on Google Speech Commands v1, we improve the state-of-the-art
accuracy from $97.21\%$ to $97.41\%$ at the same network size. Alternatively,
we can lower the cost of existing models. For speech recognition on
Librispeech, we half the number of weights to be trained while only sacrificing
about $1\%$ of the floating-point baseline's word error rate.


Abstract:
  The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a
serious threat to public health and the economy. Rapid and accurate diagnosis
of COVID-19 is crucial to prevent the further spread of the disease and reduce
its mortality. Chest Computed tomography (CT) is an effective tool for the
early diagnosis of lung diseases including pneumonia. However, detecting
COVID-19 from CT is demanding and prone to human errors as some early-stage
patients may have negative findings on images. Recently, many deep learning
methods have achieved impressive performance in this regard. Despite their
effectiveness, most of these methods underestimate the rich spatial information
preserved in the 3D structure or suffer from the propagation of errors. To
address this problem, we propose a Dual-Attention Residual Network (DARNet) to
automatically identify COVID-19 from other common pneumonia (CP) and healthy
people using 3D chest CT images. Specifically, we design a dual-attention
module consisting of channel-wise attention and depth-wise attention
mechanisms. The former is utilized to enhance channel independence, while the
latter is developed to recalibrate the depth-level features. Then, we integrate
them in a unified manner to extract and refine the features at different levels
to further improve the diagnostic performance. We evaluate DARNet on a large
public CT dataset and obtain superior performance. Besides, the ablation study
and visualization analysis prove the effectiveness and interpretability of the
proposed method.


Abstract:
  The electrocardiogram (ECG) records electrical signals in a non-invasive way
to observe the condition of the heart, typically looking at the heart from 12
different directions. Several types of the cardiac disease are diagnosed by
using 12-lead ECGs Recently, various wearable devices have enabled immediate
access to the ECG without the use of wieldy equipment. However, they only
provide ECGs with a couple of leads. This results in an inaccurate diagnosis of
cardiac disease due to lacking of required leads. We propose a deep generative
model for ECG synthesis from two asynchronous leads to ten leads. It first
represents a heart condition referring to two leads, and then generates ten
leads based on the represented heart condition. Both the rhythm and amplitude
of leads generated resemble those of the original ones, while the technique
removes noise and the baseline wander appearing in the original leads. As a
data augmentation method, our model improves the classification performance of
models compared with models using ECGs with only one or two leads.


Abstract:
  Recently directed acyclic graph (DAG) structure learning is formulated as a
constrained continuous optimization problem with continuous acyclicity
constraints and was solved iteratively through subproblem optimization. To
further improve efficiency, we propose a novel learning framework to model and
learn the weighted adjacency matrices in the DAG space directly. Specifically,
we first show that the set of weighted adjacency matrices of DAGs are
equivalent to the set of weighted gradients of graph potential functions, and
one may perform structure learning by searching in this equivalent set of DAGs.
To instantiate this idea, we propose a new algorithm, DAG-NoCurl, which solves
the optimization problem efficiently with a two-step procedure: 1) first we
find an initial cyclic solution to the optimization problem, and 2) then we
employ the Hodge decomposition of graphs and learn an acyclic graph by
projecting the cyclic graph to the gradient of a potential function.
Experimental studies on benchmark datasets demonstrate that our method provides
comparable accuracy but better efficiency than baseline DAG structure learning
methods on both linear and generalized structural equation models, often by
more than one order of magnitude.


Abstract:
  Modeling media memorability has been a consistent challenge in the field of
machine learning. The Predicting Media Memorability task in MediaEval2020 is
the latest benchmark among similar challenges addressing this topic. Building
upon techniques developed in previous iterations of the challenge, we developed
ensemble methods with the use of extracted video, image, text, and audio
features. Critically, in this work we introduce and demonstrate the efficacy
and high generalizability of extracted audio embeddings as a feature for the
task of predicting media memorability.


Abstract:
  We investigated low-temperature dynamics in a charge density wave (CDW)
conductor NbSe3, a widely studied representative of a class of systems of
driven periodic media with quenched disorder and relevant to a wider group of
systems exhibiting collective transport behaviors. To date, theoretical efforts
have not converged to produce a consistent description of the rich dynamics
observed in these systems, especially in the low temperature regime. We
developed modern sample preparation techniques and used frequency- and
time-domain transport measurements below the second characteristic Peierls CDW
transition to investigate the regime of temporally-ordered collective creep in
NbSe3 samples in the low temperature regime between 15 K and 32 K. By measuring
the frequency of coherent oscillations between two characteristic threshold
fields, ET and ET*, we show that in nine high-quality samples, pure, Ta-, or
Ti-doped, the current-field relation for the collective transport in this
regime closely follows a modified Anderson-Kim form across five orders of
magnitude with thermally- and field-activated behavior above ET for a range of
temperatures. This study, combined with our transport relaxation measurements,
provides relevant length, energy, and time scales that set the dynamics in this
regime and reveals that the collective dynamics, governed by large length and
energy scales, must be reconciled with microscopic local dynamics, with
barriers at orders of magnitude smaller scales. The interplay between the
collective and local mechanisms set the dynamics that is responsible for
extremely slow (creep-like) collective, yet temporally-ordered behavior.
Combined with the existing work, our results paint a consistent picture of a
transport phase diagram for CDWs, and density-wave systems in general, and
provide essential ingredients for a much-needed correct theoretical description
of these systems.


Abstract:
  Both Stefan-Boltzmann law and the Casimir effect, in a universe described by
the FRW metric with zero curvature, are calculated. These effects are described
by Thermo Field Dynamics (TFD). The gravitational energy-momentum tensor is
defined in the context of Teleparallel Equivalent to General Relativity (TEGR).
Each of the two effects gives a consistent prediction with what is observed on
a cosmological scale. One of the effect establishes a minimum range for the
deceleration parameter. While another leads to the conclusion that a possible
cosmological constant has a very small order of magnitude.


Abstract:
  This paper discusses a detailed design approach to determine the optimal
input impedance (Rin) and bandwidth (BW) for current preamplifiers in Front End
Electronics (FEE) of high-accuracy time measurement systems used in particle
detection. Our study shows the effect of the input impedance Rin including the
parasitic interconnection inductances of bonding wires between the detector and
the FEE. We explain also the development of a new mathematical model for the
estimation of the timing jitter of the current preamplifier in the case of
using a low capacitor detector (Cd) as diamond. Different simulations based on
developed MATLAB Simulink behavioral models are done in addition to an electric
simulation of a transimpedance amplifier (TIA) designed in a 130 nm 1P8M CMOS
technology which demonstrates the accuracy of the design approach and the
timing jitter estimation model.


Abstract:
  Behavioral software models play a key role in many software engineering
tasks; unfortunately, these models either are not available during software
development or, if available, quickly become outdated as implementations
evolve. Model inference techniques have been proposed as a viable solution to
extract finite state models from execution logs. However, existing techniques
do not scale well when processing very large logs that can be commonly found in
practice.
  In this paper, we address the scalability problem of inferring the model of a
component-based system from large system logs, without requiring any extra
information. Our model inference technique, called PRINS, follows a divide and
conquer approach. The idea is to first infer a model of each system component
from the corresponding logs; then, the individual component models are merged
together taking into account the flow of events across components, as reflected
in the logs. We evaluated PRINS in terms of scalability and accuracy, using
nine datasets composed of logs extracted from publicly available benchmarks and
a personal computer running desktop business applications. The results show
that PRINS can process large logs much faster than a publicly available and
well-known state-of-the-art tool, without significantly compromising the
accuracy of inferred models.


Abstract:
  Accurate stellar ages are essential for our understanding of the star
formation history of the Milky Way, Galactic chemical evolution, and to
constrain exoplanet formation models. Gyrochronology, an empirical relationship
between stellar rotation and age, appears to be a reliable age indicator for
main sequence stars over the mass range 0.6 to 1.0 $M_\odot$. Under the
assumption that wide binaries contain two coeval stars, we selected pairs from
the $Kepler$, $K2$, and $TESS$ surveys in order to evaluate three
Gyrochronology models (Barnes 2007; Angus et al. 2015; Mamajek & Hillenbrand
2008). In this first paper in a planned series, we describe a Monte Carlo
approach to assess the precision and accuracy of age derived from these models.
We use this technique to demonstrate that current gyrochronology models achieve
$\sim$25 % precision in ages when $\sigma_P$/P = 0.1 and $\sigma_{B-V}$ = 0.02.
We also outline ways to improve this to $\sim$10 %.


Abstract:
  A novel medium manganese steel named Novalloy with composition
Fe-8.3Mn-3.8Al-1.8Si-0.5C-0.06V-0.05Sn was developed and thermomechanically
processed through hot rolling and intercritical annealing. The steel possessed
a yield strength of 1 GPa, tensile strength of 1.13 GPa and ductility of 41\%.
In order to study the effect of cold rolling after intercritical annealing on
subsequent tensile properties, the steel was further cold rolled up to 20\%
reduction. After cold rolling, it was observed that the strain hardening rate
increased continuously with increasing cold rolling reduction but without a
significant drop in ductility during subsequent tensile tests. The
microstructural evolution with cold rolling reduction was analysed to
understand the mechanisms behind this enhanced TRIP effect. It was found that
cold rolling activated additional twinning systems which provided a large
number of potent nucleation sites for strain induced martensite to form during
subsequent tensile tests.


Abstract:
  To the best of our knowledge, the application of deep learning in the field
of quantitative risk management is still a relatively recent phenomenon. In
this article, we utilize techniques inspired by reinforcement learning in order
to optimize the operation plans of underground natural gas storage facilities.
We provide a theoretical framework and assess the performance of the proposed
method numerically in comparison to a state-of-the-art least-squares
Monte-Carlo approach. Due to the inherent intricacy originating from the
high-dimensional forward market as well as the numerous constraints and
frictions, the optimization exercise can hardly be tackled by means of
traditional techniques.


Abstract:
  We study theoretically spin pumping in bilayers consisting of superconductors
and antiferromagnetic insulators. We consider both compensated and
uncompensated interfaces and include both the regular scattering channel and
the Umklapp scattering channel. We find that at temperatures close to the
critical temperatures and precession frequencies much lower than the gap, the
spin-current is enhanced in superconductors as compared to normal metals.
Otherwise, the spin-current is suppressed. The relevant precession frequencies
where the spin-current in SC/AFI is enhanced compared to NM/AFI is much lower
than the typical resonance frequencies of antiferromagnets, which makes the
detection of this effect experimentally challenging. A possible solution lies
in the shifting of the resonance frequency by a static magnetic field.


Abstract:
  A corner is a set of three points in $\mathbf{Z}^2$ of the form $(x, y), (x +
d, y), (x, y + d)$ with $d \neq 0$. We show that for infinitely many $N$ there
is a set $A \subset [N]^2$ of size $2^{-(c + o(1)) \sqrt{\log_2 N}} N^2$ not
containing any corner, where $c = 2 \sqrt{2 \log_2 \frac{4}{3}} \approx
1.822\dots$.


Abstract:
  There has recently been growing interest in utilizing multimodal sensors to
achieve robust lane line segmentation. In this paper, we introduce a novel
multimodal fusion architecture from an information theory perspective, and
demonstrate its practical utility using Light Detection and Ranging (LiDAR)
camera fusion networks. In particular, we develop, for the first time, a
multimodal fusion network as a joint coding model, where each single node,
layer, and pipeline is represented as a channel. The forward propagation is
thus equal to the information transmission in the channels. Then, we can
qualitatively and quantitatively analyze the effect of different fusion
approaches. We argue the optimal fusion architecture is related to the
essential capacity and its allocation based on the source and channel. To test
this multimodal fusion hypothesis, we progressively determine a series of
multimodal models based on the proposed fusion methods and evaluate them on the
KITTI and the A2D2 datasets. Our optimal fusion network achieves 85%+ lane line
accuracy and 98.7%+ overall. The performance gap among the models will inform
continuing future research into development of optimal fusion algorithms for
the deep multimodal learning community.


Abstract:
  In this paper, we study the Hausdorff dimension of the generalized intrinsic
level set with respect to the given ergodic meausre in a class of non-uniformly
hyperbolic interval maps with finitely many branches.


Abstract:
  The generic Hidden Local Symmetry (HLS) model has given rise to the BHLS$_2$
variant which introduces symmetry breaking in the vector meson sector; the
central mechanism is a modification of the covariant derivative at the root of
the HLS approach. However, the description of the Belle $\tau$ dipion spectra
is not satisfactory whereas the dealing with its annihilation sector ($e^+ e^-
\rightarrow \pi^+ \pi^-/\pi^+ \pi^-\pi^0/ \pi^0 \gamma/\eta \gamma/K^+ K^-/K_L
K_S$) is optimum. This issue is solved by an additional breaking term allowing
to include the $[\pi^0,\eta,\eta^\prime]$ system. This extension of the usual
't~Hooft determinant term only affects the BHLS$_2$ kinetic energy term. One
obtains a fair account for the $\tau$ dipion spectra as well as for the
annihilation channels.
  The Belle dipion spectrum provides evidence for a violation of CVC in the
$\tau$ lepton decay; this evidence is strengthened by imposing the conditions
$<0|J_\mu^q |[q^\prime \overline{q^\prime}](p)>=ip_\mu f_q \delta_{q q^\prime
},
  \{ [q \overline{q}], q=u,d,s\}$ on BHLS$_2$ axial current matrix elements.
BHLS$_2$ is found to recover the usual (completed) formulae for the
[$\pi^0,~\eta,~\eta^\prime$] mixing parameters and the global fits return
mixing parameter values in agreement with expectations and better
uncertainties. Updating the muon HVP, one argues that the strong tension
between the KLOE and BaBar pion form factors imposes to provide two solutions :
$a_\mu^{HVP-LO}({\rm KLOE})=687.31 \pm 2.93$ and $a_\mu^{HVP-LO}({\rm
BaBar})=692.36 \pm 2.95$, in units of $10^{-10}$, rather than some combination
of these. Taking into account common systematics, their differences with the
experimental BNL-FNAL average value exhibit significances $> 5.4\sigma$ (KLOE)
and $> 4.1\sigma$ (BaBar) with fit probabilities favoring the former.


Abstract:
  Thermoelectric technology is an alternate way to efficiently utilize the
energy by converting waste heat into electricity. Thermoelectric requires
material with low thermal conductivity to improves its thermoelectric
performance. In this work, by solving Boltzmann transport equation based on
first principles calculations, we report an ultra-low room temperature thermal
conductivity of 2.08 Wm$^{-1}$K$^{-1}$ and 2.9 Wm$^{-1}$K$^{-1}$ along c-axis
and a-axis respectively for pure MgPb$_2$Te. To explain this ultra-low thermal
conductivity, we analyzed the elastic constants, phonon group velocity,
phonon-phonon scattering and contribution from transverse acoustic,
longitudinal acoustic and optical phonon branches. We also report the thermal
conductivity of MgPb$_2$Te nanostructures. At 50 nm, the room temperature
thermal conductivity of MgPb$_2$Te is 0.957 Wm$^{-1}$K$^{-1}$ and 1.459
Wm$^{-1}$K$^{-1}$ along c-axis and a-axis respectively. Ultra-low thermal
conductivity unraveled in this work shows MgPb$_2$Te would a promising material
for thermoelectric applications.


Abstract:
  Deep reinforcement learning models are vulnerable to adversarial attacks that
can decrease a victim's cumulative expected reward by manipulating the victim's
observations. Despite the efficiency of previous optimization-based methods for
generating adversarial noise in supervised learning, such methods might not be
able to achieve the lowest cumulative reward since they do not explore the
environmental dynamics in general. In this paper, we provide a framework to
better understand the existing methods by reformulating the problem of
adversarial attacks on reinforcement learning in the function space. Our
reformulation generates an optimal adversary in the function space of the
targeted attacks, repelling them via a generic two-stage framework. In the
first stage, we train a deceptive policy by hacking the environment, and
discover a set of trajectories routing to the lowest reward or the worst-case
performance. Next, the adversary misleads the victim to imitate the deceptive
policy by perturbing the observations. Compared to existing approaches, we
theoretically show that our adversary is stronger under an appropriate noise
level. Extensive experiments demonstrate our method's superiority in terms of
efficiency and effectiveness, achieving the state-of-the-art performance in
both Atari and MuJoCo environments.


Abstract:
  This paper presents algorithms for computing the length of a sum of squares
and a Pythagoras element in a global field $K$ of characteristic different from
$2$. In the first part of the paper, we present algorithms for computing the
length in a non-dyadic and dyadic (if $K$ is a number field) completion of $K$.
These two algorithms serve as subsidiary steps for computing lengths in global
fields. In the second part of the paper we present a procedure for constructing
an element whose length equals the Pythagoras number of a global field, termed
a Pythagoras element.


Abstract:
  Vehicle re-identification (Re-ID) distinguishes between the same vehicle and
other vehicles in images. It is challenging due to significant intra-instance
differences between identical vehicles from different views and subtle
inter-instance differences of similar vehicles. Researchers have tried to
address this problem by extracting features robust to variations of viewpoints
and environments. More recently, they tried to improve performance by using
additional metadata such as key points, orientation, and temporal information.
Although these attempts have been relatively successful, they all require
expensive annotations. Therefore, this paper proposes a novel deep neural
network called a multi-attention-based soft partition (MUSP) network to solve
this problem. This network does not use metadata and only uses multiple soft
attentions to identify a specific vehicle area. This function was performed by
metadata in previous studies. Experiments verified that MUSP achieved
state-of-the-art (SOTA) performance for the VehicleID dataset without any
additional annotations and was comparable to VeRi-776 and VERI-Wild.


Abstract:
  Developing artificial intelligence based assistive systems to aid Persons
with Dementia (PwD) requires large amounts of training data. However, data
collection poses ethical, legal, economic, and logistic issues. Synthetic data
generation tools, in this regard, provide a potential solution. However, we
believe that already available such tools do not adequately reflect cognitive
deficiencies in behavior simulation. To counter these issues we propose a
simulation model (SimDem ) that primarily focuses on cognitive impairments
suffered by PwD and can be easily configured and adapted by the users to model
and evaluate assistive solutions.


Abstract:
  Networks found in the real-world are numerous and varied. A common type of
network is the heterogeneous network, where the nodes (and edges) can be of
different types. Accordingly, there have been efforts at learning
representations of these heterogeneous networks in low-dimensional space.
However, most of the existing heterogeneous network embedding methods suffer
from the following two drawbacks: (1) The target space is usually Euclidean.
Conversely, many recent works have shown that complex networks may have
hyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually
rely on meta-paths, which require domain-specific prior knowledge for meta-path
selection. Additionally, different down-streaming tasks on the same network
might require different meta-paths in order to generate task-specific
embeddings. In this paper, we propose a novel self-guided random walk method
that does not require meta-path for embedding heterogeneous networks into
hyperbolic space. We conduct thorough experiments for the tasks of network
reconstruction and link prediction on two public datasets, showing that our
model outperforms a variety of well-known baselines across all tasks.


Abstract:
  In this paper, we develop a general framework to design differentially
private expectation-maximization (EM) algorithms in high-dimensional latent
variable models, based on the noisy iterative hard-thresholding. We derive the
statistical guarantees of the proposed framework and apply it to three specific
models: Gaussian mixture, mixture of regression, and regression with missing
covariates. In each model, we establish the near-optimal rate of convergence
with differential privacy constraints, and show the proposed algorithm is
minimax rate optimal up to logarithm factors. The technical tools developed for
the high-dimensional setting are then extended to the classic low-dimensional
latent variable models, and we propose a near rate-optimal EM algorithm with
differential privacy guarantees in this setting. Simulation studies and real
data analysis are conducted to support our results.


Abstract:
  In this paper, we consider and study a cell-free massive MIMO (CF-mMIMO)
system aided with reconfigurable intelligent surfaces (RISs), where a large
number of access points (APs) cooperate to serve a smaller number of users with
the help of RIS technology. We consider imperfect channel state information
(CSI), where each AP uses the local channel estimates obtained from the uplink
pilots and applies conjugate beamforming for downlink data transmission.
Additionally, we consider random beamforming at the RIS during both training
and data transmission phases. This allows us to eliminate the need of
estimating each RIS assisted link, which has been proven to be a challenging
task in literature. We then derive a closed-form expression for the achievable
rate and use it to evaluate the system's performance supported with numerical
results. We show that the RIS provided array gain improves the system's
coverage, and provides nearly a 2-fold increase in the minimum rate and a
1.5-fold increase in the per-user throughput. We also use the results to
provide preliminary insights on the number of RISs that need to be used to
replace an AP, while achieving similar performance as a typical CF-mMIMO system
with dense AP deployment.


Abstract:
  We refine McMullen's construction of sequences of knots in $S^3$ obeying the
Chebotarev law in two ways. One is to extend his theorem for generalized
pseudo-Anosov flows, which may admit a finite number of 1-pronged singular
orbits. The other is to invoke the notion of rational Fried surgeries to
produce many pseudo-Anosov flows. These methods especially prove that modular
knots around the missing trefoil in $S^3$ obey the Chebotarev law, yielding an
equidistribution theorem for the Rademacher symbol.


Abstract:
  For machine agents to successfully interact with humans in real-world
settings, they will need to develop an understanding of human mental life.
Intuitive psychology, the ability to reason about hidden mental variables that
drive observable actions, comes naturally to people: even pre-verbal infants
can tell agents from objects, expecting agents to act efficiently to achieve
goals given constraints. Despite recent interest in machine agents that reason
about other agents, it is not clear if such agents learn or hold the core
psychology principles that drive human reasoning. Inspired by cognitive
development studies on intuitive psychology, we present a benchmark consisting
of a large dataset of procedurally generated 3D animations, AGENT (Action,
Goal, Efficiency, coNstraint, uTility), structured around four scenarios (goal
preferences, action efficiency, unobserved constraints, and cost-reward
trade-offs) that probe key concepts of core intuitive psychology. We validate
AGENT with human-ratings, propose an evaluation protocol emphasizing
generalization, and compare two strong baselines built on Bayesian inverse
planning and a Theory of Mind neural network. Our results suggest that to pass
the designed tests of core intuitive psychology at human levels, a model must
acquire or have built-in representations of how agents plan, combining utility
computations and core knowledge of objects and physics.


Abstract:
  Many online platforms are providing valuable real-time contents (e.g.,
traffic) by continuously acquiring the status of different Points of Interest
(PoIs). In status acquisition, it is challenging to determine how frequently a
PoI should upload its status to a platform, since they are self-interested with
private and possibly time-varying preferences. This paper considers a general
multi-period status acquisition system, aiming to maximize the aggregate social
welfare and ensure the platform freshness. The freshness is measured by a
metric termed age of information. For this goal, we devise a long-term
decomposition (LtD) mechanism to resolve the time-varying information
asymmetry. The key idea is to construct a virtual social welfare that only
depends on the current private information, and then decompose the per-period
operation into multiple distributed bidding problems for the PoIs and
platforms. The LtD mechanism enables the platforms to achieve a tunable
trade-off between payoff maximization and freshness conditions. Moreover, the
LtD mechanism retains the same social performance compared to the benchmark
with symmetric information and asymptotically ensures the platform freshness
conditions. Numerical results based on real-world data show that when the
platforms pay more attention to payoff maximization, each PoI still obtains a
non-negative payoff in the long-term.


Abstract:
  In Polymer Quantum Mechanics, a quantization scheme that naturally emerges
from Loop Quantum Gravity, position and momentum operators cannot be both
well-defined on the Hilbert space ( H_Poly ). It is henceforth deemed
impossible to define standard creation and annihilation operators. In this
letter we show that a q-oscillator structure, and hence q-deformed
creation/annihilation operators, can be naturally defined on H_Poly , which is
then mapped into the sum of many copies of the q-oscillator Hilbert space. This
shows that the q-calculus is a natural calculus for Polymer Quantum Mechanics.
Moreover, we show that the inequivalence of different superselected sectors of
H Poly is of topological nature.


Abstract:
  We present aperiodic sets of prototiles whose shapes are based on the
well-known Penrose rhomb tiling. Some decorated prototiles lead to an exact
Penrose rhomb tiling without any matching rules. We also give an approximate
solution to an aperiodic monotile that tessellates the plane (including five
types of gaps) only in a nonperiodic way.


Abstract:
  Few-shot learning has been proposed and rapidly emerging as a viable means
for completing various tasks. Many few-shot models have been widely used for
relation learning tasks. However, each of these models has a shortage of
capturing a certain aspect of semantic features, for example, CNN on long-range
dependencies part, Transformer on local features. It is difficult for a single
model to adapt to various relation learning, which results in the high variance
problem. Ensemble strategy could be competitive on improving the accuracy of
few-shot relation extraction and mitigating high variance risks. This paper
explores an ensemble approach to reduce the variance and introduces fine-tuning
and feature attention strategies to calibrate relation-level features. Results
on several few-shot relation learning tasks show that our model significantly
outperforms the previous state-of-the-art models.


Abstract:
  The purpose of this article is to investigate the structure of singular
measures from a microlocal perspective. We introduce a notion of
$L^1$-regularity wave front set for scalar and vector distributions. Our main
result is a proper microlocal characterisation of the support of the singular
part of tempered Radon measures and of their polar functions at these points.
We deduce a sharp $L^1$ elliptic regularity result which appears to be new even
for scalar measures and which enlightens the interest of the techniques from
geometric measure theory to the study of harmonic analysis questions. We also
deduce several consequences including extensions of the results in [10] giving
constraints on the polar function at singular points for measures constrained
by a PDE. Finally, we also illustrate the interest of this microlocal approach
with a result of propagation of singularities for constrained measures.


Abstract:
  We present a flexible, open-source Python package for the accurate simulation
of the $z$-propagation dynamics of ultrashort optical pulses in nonlinear
waveguides, especially valid for few-cycle pulses and their interaction. The
simulation approach is based on unidirectional propagation equations for the
analytic signal. The provided software allows to account for dispersion,
attenuation, four-wave mixing processes including, e.g., third-harmonic
generation, and features various models for the Raman response. The propagation
equations are solved on a periodic temporal domain. For $z$-propagation, a
selection of pseudospectral methods is available. Propagation scenarios for a
custom propagation constant and initial field pulses can either be specified in
terms of a HDF5 based input file format or by direct implementation using a
python script. We demonstrate the functionality for a test-case for which an
exact solution is available, by reproducing exemplary results documented in the
scientific literature, and a complex propagation scenario involving multiple
pulses. The py-fmas code, its reference manual, an extended user guide, and
further usage examples are available online at
https://github.com/omelchert/py-fmas.


Abstract:
  We show that the centroid molecular dynamics (CMD) method provides a
realistic way to calculate the thermal diffusivity $a=\lambda/\rho c_{\rm V}$
of a quantum mechanical liquid such as para-hydrogen. Once $a$ has been
calculated, the thermal conductivity can be obtained from $\lambda=\rho c_{\rm
V}a$, where $\rho$ is the density of the liquid and $c_{\rm V}$ is the
constant-volume heat capacity. The use of this formula requires an accurate
quantum mechanical heat capacity $c_{\rm V}$, which can be obtained from a path
integral molecular dynamics simulation. The thermal diffusivity can be
calculated either from the decay of the equilibrium density fluctuations in the
liquid or by using the Green-Kubo relation to calculate the CMD approximation
to $\lambda$ and then dividing this by the corresponding approximation to $\rho
c_{\rm V}$. We show that both approaches give the same results for liquid
para-hydrogen and that these results are in good agreement with experimental
measurements of the thermal conductivity over a wide temperature range. In
particular, they correctly predict a decrease in the thermal conductivity at
low temperatures -- an effect that stems from the decrease in the quantum
mechanical heat capacity and has eluded previous para-hydrogen simulations. We
also show that the method gives equally good agreement with experimental
measurements for the thermal conductivity of normal liquid helium.


Abstract:
  In modern enterprises, Business Processes (BPs) are realized over a mix of
workflows, IT systems, Web services and direct collaborations of people.
Accordingly, process data (i.e., BP execution data such as logs containing
events, interaction messages and other process artifacts) is scattered across
several systems and data sources, and increasingly show all typical properties
of the Big Data. Understanding the execution of process data is challenging as
key business insights remain hidden in the interactions among process entities:
most objects are interconnected, forming complex, heterogeneous but often
semi-structured networks. In the context of business processes, we consider the
Big Data problem as a massive number of interconnected data islands from
personal, shared and business data. We present a framework to model process
data as graphs, i.e., Process Graph, and present abstractions to summarize the
process graph and to discover concept hierarchies for entities based on both
data objects and their interactions in process graphs. We present a language,
namely BP-SPARQL, for the explorative querying and understanding of process
graphs from various user perspectives. We have implemented a scalable
architecture for querying, exploration and analysis of process graphs. We
report on experiments performed on both synthetic and real-world datasets that
show the viability and efficiency of the approach.


Abstract:
  Time-equispaced inertial measurements are practically used as inputs for
motion determination. Polynomial interpolation is a common technique of
recovering the gyroscope signal but is subject to a fundamentally numerical
stability problem due to the Runge effect on equispaced samples. This paper
reviews the theoretical results of Runge phenomenon in related areas and
proposes a straightforward borrowing-and-cutting (BAC) strategy to depress it.
It employs the neighboring samples for higher-order polynomial interpolation
but only uses the middle polynomial segment in the actual time interval. The
BAC strategy has been incorporated into attitude computation by functional
iteration, leading to accuracy benefit of several orders of magnitude under the
classical coning motion. It would potentially bring significant benefits to the
inertial navigation computation under sustained dynamic motions.


Abstract:
  The past decade has witnessed the tremendous successes of machine learning
techniques in the supervised learning paradigm, where there is a clear
demarcation between training and testing. In the supervised learning paradigm,
learning is inherently passive, seeking to distill human-provided supervision
in large-scale datasets into high capacity models. Following these successes,
machine learning researchers have looked beyond this paradigm and became
interested in tasks that are more dynamic. To them, robotics serve as an
excellent test-bed, for the challenges of robotics break many of the
assumptions that made supervised learning successful. Out of the many different
areas within robotics, robotic manipulation has become a favorite area for
researchers to demonstrate new algorithms because of the vast numbers of
possible applications and its highly dynamical and complex nature. This
document surveys recent robotics conferences and identifies the major trends
with which machine learning techniques have been applied to the challenges of
robotic manipulation.


Abstract:
  Background and purpose: Accelerator-Based Boron Neutron Capture Therapy is a
radiotherapy based on compact accelerator neutron sources requiring an
epithermal neutron field for tumour irradiations. Neutrons of 10 keV are
considered as the maximum optimised energy to treat deep-seated tumours. We
investigated, by means of Monte Carlo simulations, the epithermal range from 10
eV to 10 keV in order to optimise the maximum epithermal neutron energy as a
function of the tumour depth.
  Methods: A Snyder head phantom was simulated and mono-energetic neutrons with
4 different incident energies were used: 10 eV, 100 eV, 1 keV and 10 keV.
$^{10}$B capture rates and absorbed dose composition on every tissue were
calculated to describe and compare the effects of lowering the maximum
epithermal energy. The Therapeutic Gain (TG) was estimated considering the
whole brain volume.
  Results: For tumours seated at 4 cm depth, 10 eV, 100 eV and 1 keV neutrons
provided respectively 54 $\%$, 36 $\%$ and 18 $\%$ increase on the TG compared
to 10 keV neutrons. Neutrons with energies between 10 eV and 1 keV provided
higher TG than 10 keV neutrons for tumours seated up to 6.4 cm depth inside the
head. The size of the tumour does not change these results.
  Conclusions: Using lower epithermal energy neutrons for AB-BNCT tumour
irradiation could improve treatment efficacy, delivering more therapeutic dose
while reducing the dose in healthy tissues. This could lead to new Beam Shape
Assembly designs in order to optimise the BNCT irradiation.


Abstract:
  Recently proposed consistency-based Semi-Supervised Learning (SSL) methods
such as the $\Pi$-model, temporal ensembling, the mean teacher, or the virtual
adversarial training, have advanced the state of the art in several SSL tasks.
These methods can typically reach performances that are comparable to their
fully supervised counterparts while using only a fraction of labelled examples.
Despite these methodological advances, the understanding of these methods is
still relatively limited. In this text, we analyse (variations of) the
$\Pi$-model in settings where analytically tractable results can be obtained.
We establish links with Manifold Tangent Classifiers and demonstrate that the
quality of the perturbations is key to obtaining reasonable SSL performances.
Importantly, we propose a simple extension of the Hidden Manifold Model that
naturally incorporates data-augmentation schemes and offers a framework for
understanding and experimenting with SSL methods.


Abstract:
  In multi-center clinical trials, due to various reasons, the individual-level
data are strictly restricted to be assessed publicly. Instead, the summarized
information is widely available from published results. With the advance of
computational technology, it has become very common in data analyses to run on
hundreds or thousands of machines simultaneous, with the data distributed
across those machines and no longer available in a single central location. How
to effectively assemble the summarized clinical data information or information
from each machine in parallel computation has become a challenging task for
statisticians and computer scientists. In this paper, we selectively review
some recently-developed statistical methods, including communication efficient
distributed statistical inference, and renewal estimation and incremental
inference, which can be regarded as the latest development of calibration
information methods in the era of big data. Even though those methods were
developed in different fields and in different statistical frameworks, in
principle, they are asymptotically equivalent to those well known methods
developed in meta analysis. Almost no or little information is lost compared
with the case when full data are available. As a general tool to integrate
information, we also review the generalized method of moments and estimating
equations approach by using empirical likelihood method.


Abstract:
  This study considers modern surgical navigation systems based on augmented
reality technologies. Augmented reality glasses are used to construct holograms
of the patient's organs from MRI and CT data, subsequently transmitted to the
glasses. This, in addition to seeing the actual patient, the surgeon gains
visualization inside the patient's body (bones, soft tissues, blood vessels,
etc.). The solutions developed at Peter the Great St. Petersburg Polytechnic
University allow reducing the invasiveness of the procedure and preserving
healthy tissues. This also improves the navigation process, making it easier to
estimate the location and size of the tumor to be removed. We describe the
application of developed systems to different types of surgical operations
(removal of a malignant brain tumor, removal of a cyst of the cervical spine).
We consider the specifics of novel navigation systems designed for anesthesia,
for endoscopic operations. Furthermore, we discuss the construction of novel
visualization systems for ultrasound machines. Our findings indicate that the
technologies proposed show potential for telemedicine.


Abstract:
  A growing branch of computer vision is object detection. Object detection is
used in many applications such as industrial process, medical imaging analysis,
and autonomous vehicles. The ability to detect objects in videos is crucial.
Object detection systems are trained on large image datasets. For applications
such as autonomous vehicles, it is crucial that the object detection system can
identify objects through multiple frames in video. There are many problems with
applying these systems to video. Shadows or changes in brightness that can
cause the system to incorrectly identify objects frame to frame and cause an
unintended system response. There are many neural networks that have been used
for object detection and if there was a way of connecting objects between
frames then these problems could be eliminated. For these neural networks to
get better at identifying objects in video, they need to be re-trained. A
dataset must be created with images that represent consecutive video frames and
have matching ground-truth layers. A method is proposed that can generate these
datasets. The ground-truth layer contains only moving objects. To generate this
layer, FlowNet2-Pytorch was used to create the flow mask using the novel
Magnitude Method. As well, a segmentation mask will be generated using networks
such as Mask R-CNN or Refinenet. These segmentation masks will contain all
objects detected in a frame. By comparing this segmentation mask to the flow
mask ground-truth layer, a loss function is generated. This loss function can
be used to train a neural network to be better at making consistent predictions
on video. The system was tested on multiple video samples and a loss was
generated for each frame, proving the Magnitude Method's ability to be used to
train object detection neural networks in future work.


Abstract:
  Machine Learning (ML) is widely used to automatically extract meaningful
information from Electronic Health Records (EHR) to support operational,
clinical, and financial decision-making. However, ML models require a large
number of annotated examples to provide satisfactory results, which is not
possible in most healthcare scenarios due to the high cost of clinician-labeled
data. Active Learning (AL) is a process of selecting the most informative
instances to be labeled by an expert to further train a supervised algorithm.
We demonstrate the effectiveness of AL in multi-label text classification in
the clinical domain. In this context, we apply a set of well-known AL methods
to help automatically assign ICD-9 codes on the MIMIC-III dataset. Our results
show that the selection of informative instances provides satisfactory
classification with a significantly reduced training set (8.3\% of the total
instances). We conclude that AL methods can significantly reduce the manual
annotation cost while preserving model performance.


Abstract:
  The present work concerns the transferability of coarse-grained (CG) modeling
in reproducing the dynamic properties of the reference atomistic systems across
a range of parameters. In particular, we focus on implicit-solvent CG modeling
of polymer solutions. The CG model is based on the generalized Langevin
equation, where the memory kernel plays the critical role in determining the
dynamics in all time scales. Thus, we propose methods for transfer learning of
memory kernels. The key ingredient of our methods is Gaussian process
regression. By integration with the model order reduction via proper orthogonal
decomposition and the active learning technique, the transfer learning can be
practically efficient and requires minimum training data. Through two example
polymer solution systems, we demonstrate the accuracy and efficiency of the
proposed transfer learning methods in the construction of transferable memory
kernels. The transferability allows for out-of-sample predictions, even in the
extrapolated domain of parameters. Built on the transferable memory kernels,
the CG models can reproduce the dynamic properties of polymers in all time
scales at different thermodynamic conditions (such as temperature and solvent
viscosity) and for different systems with varying concentrations and lengths of
polymers.


Abstract:
  We propose a quantization-based numerical scheme for a family of decoupled
FBSDEs. We simplify the scheme for the control in Pag\`es and Sagna (2018) so
that our approach is fully based on recursive marginal quantization and does
not involve any Monte Carlo simulation for the computation of conditional
expectations. We analyse in detail the numerical error of our scheme and we
show through some examples the performance of the whole procedure, which proves
to be very effective in view of financial applications.


Abstract:
  This article is dedicated to the asymptotic geometry of wreath products $F\wr
H := \left( \bigoplus_H F \right) \rtimes H$ where $F$ is a finite group and
$H$ a one-ended finitely presented group. Our main result is a complete
classification of these groups up to quasi-isometry. More precisely, given two
finite groups $F_1,F_2$ and two finitely presented one-ended groups $H_1,H_2$,
we show that $F_1 \wr H_1$ and $F_2 \wr H_2$ are quasi-isometric if and only if
either (i) $H_1,H_2$ are non-amenable quasi-isometric groups and $|F_1|,|F_2|$
have the same prime divisors, or (ii) $H_1,H_2$ are amenable, $|F_1|=k^{n_1}$
and $|F_2|=k^{n_2}$ for some $k,n_1,n_2 \geq 1$, and there exists a
quasi-$(n_2/n_1)$-to-one quasi-isometry $H_1 \to H_2$. The article also
contains algebraic information on groups quasi-isometric to such wreath
products. This can be seen as far reaching extension of a celebrated work of
Eskin-Fisher-Whyte who treated the case of $H=\mathbb{Z}$. Our approach is
however fundamentally different, as it crucially exploits the assumption that
$H$ is one-ended. Our central tool is a new geometric interpretation of
lamplighter groups involving natural families of quasi-median spaces.


Abstract:
  We consider distributed online learning for joint regret with communication
constraints. In this setting, there are multiple agents that are connected in a
graph. Each round, an adversary first activates one of the agents to issue a
prediction and provides a corresponding gradient, and then the agents are
allowed to send a $b$-bit message to their neighbors in the graph. All agents
cooperate to control the joint regret, which is the sum of the losses of the
activated agents minus the losses evaluated at the best fixed common comparator
parameters $u$. We observe that it is suboptimal for agents to wait for
gradients that take too long to arrive. Instead, the graph should be
partitioned into local clusters that communicate among themselves. Our main
result is a new method that can adapt to the optimal graph partition for the
adversarial activations and gradients, where the graph partition is selected
from a set of candidate partitions. A crucial building block along the way is a
new algorithm for online convex optimization with delayed gradient information
that is comparator-adaptive, meaning that its joint regret scales with the norm
of the comparator $||u||$. We further provide near-optimal gradient compression
schemes depending on the ratio of $b$ and the dimension times the diameter of
the graph.


Abstract:
  In the last decades the rapid development of technologies and methodologies
in the field of digitization and 3D modelling has led to an increasing
proliferation of 3D technologies in the Cultural Heritage domain. Despite the
great potential of 3D digital heritage, the "special effects" of 3D may often
overwhelm its importance in research. Projects and consortia of scholars have
tried to put order in the different fields of application of these
technologies, providing guidelines and proposing workflows. The use of computer
graphics as an effective methodology for CH research and communication
highlighted the need of transparent provenance data to properly document
digital assets and understand the degree of scientific quality and reliability
of their outcomes. The building and release of provenance knowledge, consisting
in the complete formal documentation of each phase of the process, is therefore
of fundamental importance to ensure its repeatability and to guarantee the
integration and interoperability of the generated metadata on the Semantic Web.
This paper proposes a methodology for documenting the planning and creation of
3D models used in archaeology and Cultural Heritage, by means of an application
profile based on the CIDOC CRM ecosystem and other international standards.


Abstract:
  The eROSITA Final Equatorial-Depth Survey has been carried out during the PV
phase of the SRG/eROSITA telescope and completed in November 2019. This survey
is designed to provide the first eROSITA-selected sample of galaxy clusters and
groups and to test the predictions for the all-sky survey in the context of
cosmological studies with clusters. In the 140 deg$^2$ area covered by eFEDS,
542 candidate clusters and groups are detected as extended X-ray sources, down
to a flux of $\sim10^{-14} $erg/s/cm$^2$ in the soft band (0.5-2 keV) within
1'. In this work, we provide the catalog of candidate galaxy clusters and
groups in eFEDS. We perform imaging and spectral analysis on the eFEDS clusters
with eROSITA X-ray data, and study the properties of the sample. The clusters
are distributed in the redshift range [0.01, 1.3], with the median redshift at
0.35. We obtain the ICM temperature measurement with $>2\sigma$ c.l. for
$\sim$1/5 (102/542) of the sample. The average temperature of these clusters is
$\sim$2 keV. Radial profiles of flux, luminosity, electron density, and gas
mass are measured from the precise modeling of the imaging data. The selection
function, the purity and completeness of the catalog are examined and discussed
in detail. The contamination fraction is $\sim1/5$ in this sample, dominated by
misidentified point sources. The X-ray Luminosity Function of the clusters
agrees well with the results obtained from other recent X-ray surveys. We also
find 19 supercluster candidates in eFEDS, most of which are located at
redshifts between 0.1 and 0.5. The eFEDS cluster and group catalog provides a
benchmark proof-of-concept for the eROSITA All-Sky Survey extended source
detection and characterization. We confirm the excellent performance of eROSITA
for cluster science and expect no significant deviations from our pre-launch
expectations for the final All-Sky Survey.


Abstract:
  The Strictly Correlated Electrons (SCE) limit of the Levy-Lieb functional in
Density Functional Theory (DFT) gives rise to a symmetric multi-marginal
optimal transport problem with Coulomb cost, where the number of marginal laws
is equal to the number of electrons in the system, which can be very large in
relevant applications. In this work, we design a numerical method, built upon
constrained overdamped Langevin processes to solve Moment Constrained Optimal
Transport (MCOT) relaxations (introduced in A. Alfonsi, R. Coyaud, V. Ehrlacher
and D. Lombardi, Math. Comp. 90, 2021, 689--737) of symmetric multi-marginal
optimal transport problems with Coulomb cost. Some minimizers of such
relaxations can be written as discrete measures charging a low number of points
belonging to a space whose dimension, in the symmetrical case, scales linearly
with the number of marginal laws. We leverage the sparsity of those minimizers
in the design of the numerical method and prove that any local minimizer to the
resulting problem is actually a \emph{global} one. We illustrate the
performance of the proposed method by numerical examples which solves MCOT
relaxations of 3D systems with up to 100 electrons.


Abstract:
  In this paper we present our submission for the EACL 2021 SRW; a methodology
that aims at bridging the gap between high and low-resource languages in the
context of Open Information Extraction, showcasing it on the Greek language.
The goals of this paper are twofold: First, we build Neural Machine Translation
(NMT) models for English-to-Greek and Greek-to-English based on the Transformer
architecture. Second, we leverage these NMT models to produce English
translations of Greek text as input for our NLP pipeline, to which we apply a
series of pre-processing and triple extraction tasks. Finally, we
back-translate the extracted triples to Greek. We conduct an evaluation of both
our NMT and OIE methods on benchmark datasets and demonstrate that our approach
outperforms the current state-of-the-art for the Greek natural language.


Abstract:
  We study the temporal evolution of the circuit complexity for a subsystem in
harmonic lattices after a global quantum quench of the mass parameter, choosing
the initial reduced density matrix as the reference state. Upper and lower
bounds are derived for the temporal evolution of the complexity for the entire
system. The subsystem complexity is evaluated by employing the Fisher
information geometry for the covariance matrices. We discuss numerical results
for the temporal evolutions of the subsystem complexity for a block of
consecutive sites in harmonic chains with either periodic or Dirichlet boundary
conditions, comparing them with the temporal evolutions of the entanglement
entropy. For infinite harmonic chains, the asymptotic value of the subsystem
complexity is studied through the generalised Gibbs ensemble.


Abstract:
  Let $(W_1(s), W_2(t)), s,t\ge 0$ be a bivariate Brownian motion with standard
Brownian motion marginals and constant correlation $\rho \in (-1,1).$ Parisian
ruin is defined as a classical ruin that happens over an extended period of
time, the so-called time-in-red. We derive exact asymptotics for the
non-simultaneous Parisian ruin of the company conditioned on the event of
non-simultaneous ruin happening. We are interested in finding asymptotics of
such problem as $u \to \infty$ and with the length of time-in-red being of
order $\frac{1}{u^2},$ where $u$ represents initial capital for the companies.
Approximation of this problem is of interest for the analysis of Parisian ruin
probability in bivariate Brownian risk model, which is a standard way of
defining prolonged ruin models in the financial markets.


Abstract:
  We classify the GL(2,R)-invariant subvarieties M in strata of Abelian
differentials for which any two M-parallel cylinders have homologous core
curves. This answers a question of Mirzakhani and Wright. As a corollary we
show that outside of an explicit list of exceptions, if M is a
GL(2,R)-invariant subvariety, then the Kontsevich-Zorich cocycle has nonzero
Lyapunov exponents in the symplectic orthogonal of the projection of the
tangent bundle of M to absolute cohomology.


Abstract:
  We present an easily defined countable family of permutations of the natural
numbers for which explicit rearrangements (i.e., the sums induced by the
permutations) can be computed. The digamma function proves to be the key tool
for the computations found here for the alternating harmonic series. The
permutations $\phi$ under consideration are simple in a sense: they are
involutions ($\phi\circ\phi$ is the identity function). We show that the
countable set of rearrangements obtained from the simple involutions considered
below are dense in the reals.


Abstract:
  Recently, pre-trained language models (LMs) have achieved strong performance
when fine-tuned on difficult benchmarks like SuperGLUE. However, performance
can suffer when there are very few labeled examples available for fine-tuning.
Pattern Exploiting Training (PET) is a recent approach that leverages patterns
for few-shot learning. However, PET uses task-specific unlabeled data. In this
paper, we focus on few-shot learning without any unlabeled data and introduce
ADAPET, which modifies PET's objective to provide denser supervision during
fine-tuning. As a result, ADAPET outperforms PET on SuperGLUE without any
task-specific unlabeled data. Our code can be found at
https://github.com/rrmenon10/ADAPET.


Abstract:
  Deep generative models, since their inception, have become increasingly more
capable of generating novel and perceptually realistic signals (e.g., images
and sound waves). With the emergence of deep models for graph structured data,
natural interests seek extensions of these generative models for graphs.
Successful extensions were seen recently in the case of learning from a
collection of graphs (e.g., protein data banks), but the learning from a single
graph has been largely under explored. The latter case, however, is important
in practice. For example, graphs in financial and healthcare systems contain so
much confidential information that their public accessibility is nearly
impossible, but open science in these fields can only advance when similar data
are available for benchmarking.
  In this work, we propose an approach to generating a doppelganger graph that
resembles a given one in many graph properties but nonetheless can hardly be
used to reverse engineer the original one, in the sense of a near zero edge
overlap. The approach is an orchestration of graph representation learning,
generative adversarial networks, and graph realization algorithms. Through
comparison with several graph generative models (either parameterized by neural
networks or not), we demonstrate that our result barely reproduces the given
graph but closely matches its properties. We further show that downstream
tasks, such as node classification, on the generated graphs reach similar
performance to the use of the original ones.


Abstract:
  Extensive work has argued in favour of paying crowd workers a wage that is at
least equivalent to the U.S. federal minimum wage. Meanwhile, research on
collecting high quality annotations suggests using a qualification that
requires workers to have previously completed a certain number of tasks. If
most requesters who pay fairly require workers to have completed a large number
of tasks already then workers need to complete a substantial amount of poorly
paid work before they can earn a fair wage. Through analysis of worker
discussions and guidance for researchers, we estimate that workers spend
approximately 2.25 months of full time effort on poorly paid tasks in order to
get the qualifications needed for better paid tasks. We discuss alternatives to
this qualification and conduct a study of the correlation between
qualifications and work quality on two NLP tasks. We find that it is possible
to reduce the burden on workers while still collecting high quality data.


Abstract:
  In a previous paper (P. D. Mannheim, Phys. Rev. D 102, 123535 (2020)) we
studied cosmological perturbation theory in the cosmology associated with the
fourth-order derivative conformal gravity theory, and provided an exact
solution to the theory in the recombination era. In this paper we present an
exact solution that holds all the way from recombination until the current era.


Abstract:
  There is still a long way to go before artificial mini robots are really used
for search and rescue missions in disaster-hit areas due to hindrance in power
consumption, computation load of the locomotion, and obstacle-avoidance system.
Insect-computer hybrid system, which is the fusion of living insect platform
and microcontroller, emerges as an alternative solution. This study
demonstrates the first-ever insect-computer hybrid system conceived for search
and rescue missions, which is capable of autonomous navigation and human
presence detection in an unstructured environment. Customized navigation
control algorithm utilizing the insect's intrinsic navigation capability
achieved exploration and negotiation of complex terrains. On-board
high-accuracy human presence detection using infrared camera was achieved with
a custom machine learning model. Low power consumption suggests system
suitability for hour-long operations and its potential for realization in
real-life missions.