title: Towards Axiomatic Foundations for Conceptual Modeling: An Example

abstract:
  Conceptual modeling is a strongly interdisciplinary field of research.
Although numerous proposals for axiomatic foundations of the main ideas of the
field exist, there is still a lack of understanding main concepts such as
system, process, event, data, and many more. Against the background of the
tremendously gaining importance of digital phenomena, we argue that axiomatic
foundations are needed for our discipline. Besides the general call, we provide
a particular case study using HERAKLIT. This modeling infrastructure
encompasses the architecture, statics, and dynamics of computer-integrated
systems. The case study illustrates how axiomatically well-founded conceptual
models may look like in practice. We argue that axiomatic foundations do not
only have positive effects for theoretical research, but also for empirical
research, because, for instance, assumed axioms can explicitly be tested. It is
now time to spark the discussion on axiomatic foundations of our field.


title: Pseudo-Projective Tensor on Sequential Warped Products

abstract:
  The main objective of this paper is to study pseudo-projective tensor on
sequential warped products and then to obtain necessary and sufficient
conditions for a sequential warped product to be pseudo-projectively flat.
Moreover, we also provide characterization of pseudo-projectively flat
sequential generalized Robertson-Walker and pseudo-projectively flat sequential
standard static spacetimes.S


title:
Overview of Test Coverage Criteria for Test Case Generation from Finite
  State Machines Modelled as Directed Graphs

abstract:
  Test Coverage criteria are an essential concept for test engineers when
generating the test cases from a System Under Test model. They are routinely
used in test case generation for user interfaces, middleware, and back-end
system parts for software, electronics, or Internet of Things (IoT) systems.
Test Coverage criteria define the number of actions or combinations by which a
system is tested, informally determining a potential "strength" of a test set.
As no previous study summarized all commonly used test coverage criteria for
Finite State Machines and comprehensively discussed them regarding their
subsumption, equivalence, or non-comparability, this paper provides this
overview. In this study, 14 most common test coverage criteria and seven of
their synonyms for Finite State Machines defined via a directed graph are
summarized and compared. The results give researchers and industry testing
engineers a helpful overview when setting a software-based or IoT system test
strategy.


title: Spacelike Foliations on Lorentz manifolds

abstract:
  In this work, we study the geometric properties of spacelike foliations by
hypersurfaces on a Lorentz manifold. We investigate conditions for the leaves
being stable, totally geodesic or totally umbilical. We consider that
$\overline{M}^{n+1}$ is equipped with a timelike closed conformal vector field
$\xi$. If the foliation has constant mean curvature, we show that the leaves
are stable. When the leaves are compact spacelike hypersurfaces we show that,
under certain conditions, its are totally umbilic hypersurfaces. In the case of
foliations by complete noncompact hypersurfaces, we using a Maximum Principle
at infinity to conclude that the foliation is totally geodesic.


title: Daily milk yield correction factors: what are they?

abstract:
  Cows are typically milked two or more times on a test day, but not all these
milkings are sampled and weighed. Statistical methods have been proposed to
estimate daily yields in dairy cows, centering on various yield correction
factors in two broad categories. The initial approach estimated a test-day
yield with doubled morning (AM) or evening (PM) yield in the AM-PM milking
plans, assuming equal AM and PM milking intervals. However, AM and PM milking
intervals can vary, and milk secretion rates may be different between day and
night. Additive correction factors (ACF) are evaluated by the average
differences between AM and PM milk yield for various milking interval classes
(MIC). We show that an ACF model is equivalent to a regression model of daily
yield on categorical regressor variables, and a continuous variable for AM or
PM yield with a fixed regression coefficient. Similarly, a linear regression
model can be implemented as an ACF model with the regression coefficient for AM
or PM yield estimated from the data. Multiplicative correction factors (MCF)
are ratio of daily yield to yield from single milkings, but their statistical
interpretations vary. Overall, MCF were more accurate for estimating daily milk
yield than ACF. MCF have biological and statistical challenges. An exponential
regression model was proposed as an alternative model for estimating daily milk
yield, which improved the accuracy in the present study. Characterization of
ACF and MCF showed how ACF and MCF improved the accuracy compared to doubling
AM or PM yield as the daily milk yield. The methods were explicitly described
to estimate daily milk yield in AM and PM milking plans. Still, the principles
are generally applicable to cows milked more than two times a day, and they
apply similarly to the estimation of daily fat and protein yields with some
necessary modifications.


title:
Learning Distributionally Robust Models at Scale via Composite
  Optimization

abstract:
  To train machine learning models that are robust to distribution shifts in
the data, distributionally robust optimization (DRO) has been proven very
effective. However, the existing approaches to learning a distributionally
robust model either require solving complex optimization problems such as
semidefinite programming or a first-order method whose convergence scales
linearly with the number of data samples -- which hinders their scalability to
large datasets. In this paper, we show how different variants of DRO are simply
instances of a finite-sum composite optimization for which we provide scalable
methods. We also provide empirical results that demonstrate the effectiveness
of our proposed algorithm with respect to the prior art in order to learn
robust models from very large datasets.


title: Developing a Successful Bomberman Agent

abstract:
  In this paper, we study AI approaches to successfully play a 2-4 players,
full information, Bomberman variant published on the CodinGame platform. We
compare the behavior of three search algorithms: Monte Carlo Tree Search,
Rolling Horizon Evolution, and Beam Search. We present various enhancements
leading to improve the agents' strength that concern search, opponent
prediction, game state evaluation, and game engine encoding. Our top agent
variant is based on a Beam Search with low-level bit-based state representation
and evaluation function heavy relying on pruning unpromising states based on
simulation-based estimation of survival. It reached the top one position among
the 2,300 AI agents submitted on the CodinGame arena.


title:
An alternative Interpretation of residual feed intake by phenotypic
  recursive relationships in dairy cattle

abstract:
  There has been an increasing interest in residual feed intake (RFI) as a
measure of net feed efficiency in dairy cattle. RFI phenotypes are obtained as
residuals from linear regression encompassing relevant factors (i.e., energy
sinks) to account for body tissue mobilization. However, fitting energy sink
phenotypes as regression variables in standard linear regression was criticized
because phenotypes are subject to measurement errors. Multiple-trait models
have been proposed which derive RFI by follow-up partial regression. By
re-arranging the single-trait linear regression, we showed a causal RFI
interpretation underlying the linear regression for RFI. It postulates
recursive effects in energy allocation from energy sinks on dry matter intake,
but the feedback or simultaneous effects are assumed to be nonexistent. A
Bayesian recursive structural equation model was proposed for directly
predicting RFI and energy sinks and estimating relevant genetic parameters
simultaneously. A simplified Markov chain Monte Carlo algorithm that
implemented the Bayesian recursive model was described. The recursive model is
asymptotically equivalent to one-step linear regression for RFI, yet extends
the analytical capacity to multiple-trait analysis. It is equivalent to
Bayesian-implemented, multiple-trait model reparameterized based on Cholesky
decomposition of phenotypic (co)variance matrix for evaluating RFI, but varied
in assumptions about relationships between energy sinks.


title:
A relation among tangle, 3-tangle, and von Neumann entropy of
  entanglement for three qubits

abstract:
  In this paper, we derive a general formula of the tangle for pure states of
three qubits and present three explicit local unitary (LU) polynomial
invariants. Our result goes beyond the classical work of tangle, 3-tangle and
von Neumann entropy of entanglement (vNee) for Acin et al.' Schmidt
decomposition (ASD) of three qubits by connecting the tangle, 3-tangle, and
vNee for ASD with Acin et al.'s LU invariants. In particular, our result
reveals a general relation among tangle, 3-tangle, and vNee, together with a
relation among their averages. Moreover, we obtain all the states of three
qubits of which tangles, concurrence, 3-tangle and vNee don't vanish and these
states are endurable when one of three qubits is traced out. Via the tangles
and vNee for ASD, we indicate that the three-qubit W state does not retain the
maximal tangle, while its average vNee is maximal only within the W SLOCC
class, and that under ASD the three-qubit GHZ state is the unique state of
which the reduced density operator (RDO) obtained by tracing any two qubits has
the maximal vNee.


title:
STICC: A multivariate spatial clustering method for repeated geographic
  pattern discovery with consideration of spatial contiguity

abstract:
  Spatial clustering has been widely used for spatial data mining and knowledge
discovery. An ideal multivariate spatial clustering should consider both
spatial contiguity and aspatial attributes. Existing spatial clustering
approaches may face challenges for discovering repeated geographic patterns
with spatial contiguity maintained. In this paper, we propose a Spatial
Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both
attributes and spatial relationships of geographic objects for multivariate
spatial clustering. A subregion is created for each geographic object serving
as the basic unit when performing clustering. A Markov random field is then
constructed to characterize the attribute dependencies of subregions. Using a
spatial consistency strategy, nearby objects are encouraged to belong to the
same cluster. To test the performance of the proposed STICC algorithm, we apply
it in two use cases. The comparison results with several baseline methods show
that the STICC outperforms others significantly in terms of adjusted rand index
and macro-F1 score. Join count statistics is also calculated and shows that the
spatial contiguity is well preserved by STICC. Such a spatial clustering method
may benefit various applications in the fields of geography, remote sensing,
transportation, and urban planning, etc.


title: Markov decision processes with Kusuoka-type conditional risk mappings

abstract:
  The Kusuoka representation of proper lower semi-continuous law invariant
coherent risk measures allows one to cast them in terms of average
value-at-risk. Here, we introduce the notion of Kusuoka-type conditional
risk-mappings and use it to define a dynamic risk measure. We use such dynamic
risk measures to study infinite horizon Markov decision processes with random
costs and random actions. Under mild assumptions, we derive a dynamic
programming principle and prove the existence of an optimal policy. We also
derive the Q-learning version of the dynamic programming principle, which is
important for applications. Furthermore, we provide a sufficient condition for
when deterministic actions are optimal.


title:
Entangling Solid Solutions: Machine Learning of Tensor Networks for
  Materials Property Prediction

abstract:
  Progress in the application of machine learning techniques to the prediction
of solid-state and molecular materials properties has been greatly facilitated
by the development state-of-the-art feature representations and novel deep
learning architectures. A large class of atomic structure representations based
on expansions of smoothed atomic densities have been shown to correspond to
specific choices of basis sets in an abstract many-body Hilbert space.
Concurrently, tensor network structures, conventionally the purview of quantum
many-body physics and quantum information, have been successfully applied in
supervised and unsupervised learning tasks in computer vision and natural
language processing. In this work, we argue that architectures based on tensor
networks are well-suited to machine learning on Hilbert-space representations
of atomic structures. This is demonstrated on supervised learning tasks
involving widely available datasets of density functional theory calculations
of metal and semiconductor alloys. In particular, we show that certain standard
tensor network topologies exhibit strong generalizability even on small
training datasets while being parametrically efficient. We further relate this
generalizability to the presence of complex entanglement in the trained tensor
networks. We also discuss connections to learning with generalized structural
kernels and related strategies for compressing large input feature spaces.


title:
Soliton resolution for the energy-critical nonlinear wave equation in
  the radial case

abstract:
  We consider the focusing energy-critical nonlinear wave equation for radially
symmetric initial data in space dimensions $D \ge 4$. This equation has a
unique (up to sign and scale) nontrivial, finite energy stationary solution
$W$, called the ground state. We prove that every finite energy solution with
bounded energy norm resolves, continuously in time, into a finite superposition
of asymptotically decoupled copies of the ground state and free radiation.


title:
Canvas: Isolated and Adaptive Swapping for Multi-Applications on Remote
  Memory

abstract:
  Remote memory techniques for datacenter applications have recently gained a
great deal of popularity. Existing remote memory techniques focus on the
efficiency of a single application setting only. However, when multiple
applications co-run on a remote-memory system, significant interference could
occur, resulting in unexpected slowdowns even if the same amounts of physical
resources are granted to each application. This slowdown stems from massive
sharing in applications' swap data paths. Canvas is a redesigned swap system
that fully isolates swap paths for remote-memory applications. Canvas allows
each application to possess its dedicated swap partition, swap cache,
prefetcher, and RDMA bandwidth. Swap isolation lays a foundation for adaptive
optimization techniques based on each application's own access patterns and
needs. We develop three such techniques: (1) adaptive swap entry allocation,
(2) semantics-aware prefetching, and (3) two-dimensional RDMA scheduling. A
thorough evaluation with a set of widely-deployed applications demonstrates
that Canvas minimizes performance variation and dramatically reduces
performance degradation.


title:
DeepLSS: breaking parameter degeneracies in large scale structure with
  deep learning analysis of combined probes

abstract:
  In classical cosmological analysis of large scale structure surveys with 2-pt
functions, the parameter measurement precision is limited by several key
degeneracies within the cosmology and astrophysics sectors. For cosmic shear,
clustering amplitude $\sigma_8$ and matter density $\Omega_m$ roughly follow
the $S_8=\sigma_8(\Omega_m/0.3)^{0.5}$ relation. In turn, $S_8$ is highly
correlated with the intrinsic galaxy alignment amplitude $A_{\rm{IA}}$. For
galaxy clustering, the bias $b_g$ is degenerate with both $\sigma_8$ and
$\Omega_m$, as well as the stochasticity $r_g$. Moreover, the redshift
evolution of IA and bias can cause further parameter confusion. A tomographic
2-pt probe combination can partially lift these degeneracies. In this work we
demonstrate that a deep learning analysis of combined probes of weak
gravitational lensing and galaxy clustering, which we call DeepLSS, can
effectively break these degeneracies and yield significantly more precise
constraints on $\sigma_8$, $\Omega_m$, $A_{\rm{IA}}$, $b_g$, $r_g$, and IA
redshift evolution parameter $\eta_{\rm{IA}}$. The most significant gains are
in the IA sector: the precision of $A_{\rm{IA}}$ is increased by approximately
8x and is almost perfectly decorrelated from $S_8$. Galaxy bias $b_g$ is
improved by 1.5x, stochasticity $r_g$ by 3x, and the redshift evolution
$\eta_{\rm{IA}}$ and $\eta_b$ by 1.6x. Breaking these degeneracies leads to a
significant gain in constraining power for $\sigma_8$ and $\Omega_m$, with the
figure of merit improved by 15x. We give an intuitive explanation for the
origin of this information gain using sensitivity maps. These results indicate
that the fully numerical, map-based forward modeling approach to cosmological
inference with machine learning may play an important role in upcoming LSS
surveys. We discuss perspectives and challenges in its practical deployment for
a full survey analysis.


title:
EMPRESS. VIII. A New Determination of Primordial He Abundance with
  Extremely Metal-Poor Galaxies: A Suggestion of the Lepton Asymmetry and
  Implications for the Hubble Tension

abstract:
  The primordial He abundance $Y_P$ is a powerful probe of cosmology.
Currently, $Y_P$ is best determined by observations of metal-poor galaxies,
while there are only a few known local extremely metal-poor ($<0.1 Z_\odot$)
galaxies (EMPGs) having reliable He/H measurements with HeI$\lambda$10830
near-infrared (NIR) emission. Here we present deep Subaru NIR spectroscopy and
He/H determinations for 10 EMPGs, combining the existing optical data and the
Markov chain Monte Carlo algorithm. Adding the existing 3 EMPGs and 51
moderately metal-poor ($0.1-0.4 Z_\odot$) galaxies with reliable He/H
estimates, we obtain $Y_P=0.2379^{+0.0031}_{-0.0030}$ by linear regression in
the (He/H)-(O/H) plane, where our observations increase the number of EMPGs
from 3 to 13 anchoring He/H of the most metal-poor gas in galaxies. Although
our $Y_P$ measurement and previous measurements are consistent, our result is
slightly (~ 1$\sigma$) smaller due to our EMPGs. Including the existing
primordial deuterium $D_P$ constraints, we estimate the effective number of
neutrino species to be $N_{eff}=2.41^{+0.19}_{-0.21}$ showing a > 2 $\sigma$
tension with the Standard Model value ($N_{eff}=3.046$), which may be a hint of
an asymmetry in electron-neutrino $\nu_e$ and anti-electron neutrino
$\bar{\nu}_e$. Allowing the degeneracy parameter of electron-neutrino $\xi_e$
to vary as well as $N_{eff}$ and the baryon-to-photon ratio $\eta$, we obtain
$\xi_e$ = $0.05^{+0.03}_{-0.03}$, $N_{eff}=3.22^{+0.33}_{-0.30}$, and
$\eta\times10^{10}=6.13^{+0.04}_{-0.04}$ from the $Y_P$ and $D_P$ measurements
with a prior of $\eta$ taken from Planck Collaboration et al. (2020). Our
constraints suggest a $\nu_e - \bar{\nu}_e$ asymmetry and allow for a high
value of $N_{eff}$ within the 1$\sigma$ level, which could mitigate the Hubble
tension.


title:
Gravity darkening and tidally perturbed stellar pulsation in the
  misaligned exoplanet system WASP-33

abstract:
  WASP-33 is one of the few $\delta$ Sct stars with a known planetary
companion. By analyzing the stellar oscillations, we search for possible
star-planet interactions in the pattern of the pulsation. We made use of the
Transit and Light Curve Modeller (TLCM) to solve the light curve from the
Transiting Exoplanet Survey Satellite (TESS). We include gravity darkening into
our analysis. The stellar oscillation pattern of WASP-33 clearly shows signs of
several tidally perturbed modes. We find that there are peaks in the frequency
spectrum that are at or near the $3$rd, $12$th and $25$th orbital harmonics
($f_{orb} \sim 0.82$ d$^{-1}$). Also, there is a prominent overabundance of
pulsational frequencies rightwards of the orbital harmonics, being
characteristic of a tidally perturbed stellar pulsation, which is an outcome of
star-planet interactions in the misaligned system. There are peaks in both the
$\delta$ Sct and $\gamma$ Dor ranges of the Fourier spectrum, implying that
WASP-33 is a $\gamma$ Dor -- $\delta$ Sct hybrid pulsator. The transit light
curves are best fitted by a gravity darkened stellar model, and the planet
parameters are consistent with earlier determinations.


title:
The Analysis of Online Event Streams: Predicting the Next Activity for
  Anomaly Detection

abstract:
  Anomaly detection in process mining focuses on identifying anomalous cases or
events in process executions. The resulting diagnostics are used to provide
measures to prevent fraudulent behavior, as well as to derive recommendations
for improving process compliance and security. Most existing techniques focus
on detecting anomalous cases in an offline setting. However, to identify
potential anomalies in a timely manner and take immediate countermeasures, it
is necessary to detect event-level anomalies online, in real-time. In this
paper, we propose to tackle the online event anomaly detection problem using
next-activity prediction methods. More specifically, we investigate the use of
both ML models (such as RF and XGBoost) and deep models (such as LSTM) to
predict the probabilities of next-activities and consider the events predicted
unlikely as anomalies. We compare these predictive anomaly detection methods to
four classical unsupervised anomaly detection approaches (such as Isolation
forest and LOF) in the online setting. Our evaluation shows that the proposed
method using ML models tends to outperform the one using a deep model, while
both methods outperform the classical unsupervised approaches in detecting
anomalous events.


title:
Message recovery attack to NTRU using a lattice independent from the
  public key

abstract:
  In the present paper we introduce a new attack on NTRU-HPS cryptosystem using
lattice theory and Babai's Nearest Plane Algorithm. This attack has many
similarities with the classic CVP attack on NTRU, but in our case we use a
different lattice, instead of the classic lattice which is constructed with the
public key. Finally, the attack is illustrated by many examples.


title: Quantum perturbation theory using Tensor cores and a deep neural network

abstract:
  Time-independent quantum response calculations are performed using Tensor
cores. This is achieved by mapping density matrix perturbation theory onto the
computational structure of a deep neural network. The main computational cost
of each deep layer is dominated by tensor contractions, i.e. dense
matrix-matrix multiplications, in mixed precision arithmetics which achieves
close to peak performance. Quantum response calculations are demonstrated and
analyzed using self-consistent charge density-functional tight-binding theory
as well as coupled-perturbed Hartree-Fock theory. For linear response
calculations, a novel parameter-free convergence criterion is presented that is
well-suited for numerically noisy low precision floating point operations and
we demonstrate a peak performance of almost 200 Tflops using the Tensor cores
of two Nvidia A100 GPUs.


title:
Modeling of a Quadruped Robot with Spine Joints and Full-Dynamics
  Simulation Environment Construction

abstract:
  This paper presents modeling and simulation of a spined quadruped robot.
Extended literature survey is employed and spine joints researches of the
quadruped robots are classified. Most of the researchers execute simplified
quadruped robot models in their simulations. This survey reveals the need for
the full-body spined quadruped simulation environment. First, the kinematics
and dynamics modeling of the active spined quadruped robot is obtained. Since
quadruped robots are floating-base robots, all derivations are performed with
respect to an inertial frame. The motion equations are acquired by the
Lagrangian approach. The simulation environment is constructed in the
MATLAB/Simulink platform, considering its rich library, powerful solvers, and
suitable and resilient environment in integrating controllers. The computation
speed of the simulation environment is increased by using optimized MATLAB
functions. Precise and accurate contact model is utilized in the simulation
environment. We foreseen that the provided full-dynamics simulation environment
will be helpful for further spine joint studies on the quadruped robot field.


title:
The role of functional thiolated molecules on the enhanced electronic
  transport of interconnected MoS$_2$ nanostructures

abstract:
  Molecular linkers have emerged as an effective strategy to improve electronic
transport properties on solution-processed layered materials via defect
functionalization. However, a detailed discussion on the microscopic mechanisms
behind the beneficial effects of functionalization is still missing. Here, by
first-principles calculations based on density functional theory, we
investigate the effects on the electronic properties of interconnected MoS$_2$
model flakes systems upon functionalization with different thiol molecule
linkers, namely thiophenol, 1,4-benzenedithiol, 1,2-ethanedithiol, and
1,3-propanedithiol. The bonding of benzene- and ethanedithiol bridging adjacent
armchair MoS$_2$ nanoflakes leads to electronic states just above or at the
Fermi level, thus forming a molecular channel for electronic transport between
flakes. In addition, the molecular linker reduces the potential barrier for
thermally activated hopping between neighboring flakes, improving the
conductivity as verified in experiments. The comprehension of such mechanisms
helps in future developments of solution-processed layered materials for use on
2D electronic devices.


title: Explaining Flavour Anomalies with Heavy Scalars

abstract:
  Discrepancies between recent experimental results and their respective
Standard Model predictions, known as flavour anomalies, are reported in
semileptonic charged and neutral-current $B$-decays, the muon magnetic moment
$(g-2)_\mu$, and the extraction of the Cabibbo angle. In this proceedings, we
review two New Physics models that introduce two scalar mediators at the TeV
scale and aim at a combined explanation of the flavour anomalies. The first
model features the leptoquarks $S_1$ and $S_3$ and provides tree level
solutions to both $B$-anomalies and one-loop level solution to the anomalous
$(g-2)_\mu$. The second features the leptoquark $S_1$ and the charged singlet
$\phi^+$. While $S_1$ provides the same solution to the charged-current
$B$-anomaly and $(g-2)_\mu$ as in the first model, $\phi^+$ can accommodate the
Cabibbo-angle anomaly independently and together with $S_1$ can resolve the
neutral-current $B$-anomaly at one-loop.


title: Widom insertion method in simulations with Ewald summation

abstract:
  We discuss the application of Widom insertion method for calculation of the
chemical potential of individual ions in computer simulations with Ewald
summation. Two approaches are considered. In the first approach, an individual
ion is inserted into a periodically replicated overall charge neutral system
representing an electrolyte solution. In the second approach an inserted ion is
also periodically replicated, leading to the violation of the overall charge
neutrality. This requires an introduction of an additional neutralizing
background. We find that the second approach leads to a much better agreement
with the results of grand canonical Monte Carlo simulation for the total
chemical potential of a neutral ionic cluster.


title:
Practices to Improve Teamwork in Software Development During the
  COVID-19 Pandemic: An Ethnographic Study

abstract:
  Context. Due to the COVID-19 pandemic, software professionals had to abruptly
shift to ostensibly temporary home offices, which affected teamwork in several
ways. Goal. This study aims to explore how these professionals coped with
remote work during the pandemic and to identify practices that supported the
team activities. Method. Ethnographic methods, including participant
observation and qualitative data analysis, were used. Results. Three practices
were created by the observed team to improve their engagement: costume meeting,
second-language day, and project happy hour. These practices appear to increase
individual involvement, improve team cohesion, reduce monotony, and create
opportunities for knowledge acquisition. Conclusions. The three observed
practices may help remote software teams cope with adversity. More research is
needed to determine if these practices work in other settings, including
remote-first and hybrid remote / on-site teams, post-pandemic.


title: On the Importance of Data Size in Probing Fine-tuned Models

abstract:
  Several studies have investigated the reasons behind the effectiveness of
fine-tuning, usually through the lens of probing. However, these studies often
neglect the role of the size of the dataset on which the model is fine-tuned.
In this paper, we highlight the importance of this factor and its undeniable
role in probing performance. We show that the extent of encoded linguistic
knowledge depends on the number of fine-tuning samples. The analysis also
reveals that larger training data mainly affects higher layers, and that the
extent of this change is a factor of the number of iterations updating the
model during fine-tuning rather than the diversity of the training samples.
Finally, we show through a set of experiments that fine-tuning data size
affects the recoverability of the changes made to the model's linguistic
knowledge.


title:
Better to be in agreement than in bad company: a critical analysis of
  many kappa-like tests assessing one-million 2x2 contingency tables

abstract:
  We assessed several agreement coefficients applied in 2x2 contingency tables,
which are commonly applied in research due to dicotomization by the conditions
of the subjects (e.g., male or female) or by conveniency of the classification
(e.g., traditional thresholds leading to separations in healthy or diseased,
exposed or non-exposed, etc.). More extreme table configurations (e.g., high
agreement between raters) are also usual, but some of the coefficients have
problems with imbalanced tables. Here, we not only studied some especific
estimators, but also developed a general method to the study for any estimator
candidate to be an agreement measurement. This method was developed in open
source R codes and it is avaliable to the researchers. Here, we tested this
method by verifying the performance of several traditional estimators over all
1,028,789 tables with size ranging from 1 to 68. Cohen's kappa showed
handicapped behavior similar to Pearson's r, Yule's Q, and Yule's Y. Scott's pi
has ambiguity to assess situations of agreement between raters. Shankar and
Bangdiwala's B was mistaken in all situations of neutrality and when there is
greater disagreement between raters. Dice's F1 and McNemar's chi-squared
incompletely assess the information of the contingency table, showing the
poorest performance among all. We concluded that Holley and Guilford's G is the
best agreement estimator, closely followed by Gwet's AC1 and they should be
considered as the first choices for agreement measurement in contingency 2x2
tables. All procedures and data were implemented in R and are available to
download from https://sourceforge.net/projects/tables2x2.


title:
HiStruct+: Improving Extractive Text Summarization with Hierarchical
  Structure Information

abstract:
  Transformer-based language models usually treat texts as linear sequences.
However, most texts also have an inherent hierarchical structure, i.e., parts
of a text can be identified using their position in this hierarchy. In
addition, section titles usually indicate the common topic of their respective
sentences. We propose a novel approach to formulate, extract, encode and inject
hierarchical structure information explicitly into an extractive summarization
model based on a pre-trained, encoder-only Transformer language model
(HiStruct+ model), which improves SOTA ROUGEs for extractive summarization on
PubMed and arXiv substantially. Using various experimental settings on three
datasets (i.e., CNN/DailyMail, PubMed and arXiv), our HiStruct+ model
outperforms a strong baseline collectively, which differs from our model only
in that the hierarchical structure information is not injected. It is also
observed that the more conspicuous hierarchical structure the dataset has, the
larger improvements our method gains. The ablation study demonstrates that the
hierarchical position information is the main contributor to our model's SOTA
performance.


title: Monotonic Differentiable Sorting Networks

abstract:
  Differentiable sorting algorithms allow training with sorting and ranking
supervision, where only the ordering or ranking of samples is known. Various
methods have been proposed to address this challenge, ranging from optimal
transport-based differentiable Sinkhorn sorting algorithms to making classic
sorting networks differentiable. One problem of current differentiable sorting
methods is that they are non-monotonic. To address this issue, we propose a
novel relaxation of conditional swap operations that guarantees monotonicity in
differentiable sorting networks. We introduce a family of sigmoid functions and
prove that they produce differentiable sorting networks that are monotonic.
Monotonicity ensures that the gradients always have the correct sign, which is
an advantage in gradient-based optimization. We demonstrate that monotonic
differentiable sorting networks improve upon previous differentiable sorting
methods.


title:
A Learning Framework for Bandwidth-Efficient Distributed Inference in
  Wireless IoT

abstract:
  In wireless Internet of things (IoT), the sensors usually have limited
bandwidth and power resources. Therefore, in a distributed setup, each sensor
should compress and quantize the sensed observations before transmitting them
to a fusion center (FC) where a global decision is inferred. Most of the
existing compression techniques and entropy quantizers consider only the
reconstruction fidelity as a metric, which means they decouple the compression
from the sensing goal. In this work, we argue that data compression mechanisms
and entropy quantizers should be co-designed with the sensing goal,
specifically for machine-consumed data. To this end, we propose a novel deep
learning-based framework for compressing and quantizing the observations of
correlated sensors. Instead of maximizing the reconstruction fidelity, our
objective is to compress the sensor observations in a way that maximizes the
accuracy of the inferred decision (i.e., sensing goal) at the FC. Unlike prior
work, we do not impose any assumptions about the observations distribution
which emphasizes the wide applicability of our framework. We also propose a
novel loss function that keeps the model focused on learning complementary
features at each sensor. The results show the superior performance of our
framework compared to other benchmark models.


title:
Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for
  Underdocumented Languages

abstract:
  Recent progress in NLP is driven by pretrained models leveraging massive
datasets and has predominantly benefited the world's political and economic
superpowers. Technologically underserved languages are left behind because they
lack such resources. Hundreds of underserved languages, nevertheless, have
available data sources in the form of interlinear glossed text (IGT) from
language documentation efforts. IGT remains underutilized in NLP work, perhaps
because its annotations are only semi-structured and often language-specific.
With this paper, we make the case that IGT data can be leveraged successfully
provided that target language expertise is available. We specifically advocate
for collaboration with documentary linguists. Our paper provides a roadmap for
successful projects utilizing IGT data: (1) It is essential to define which NLP
tasks can be accomplished with the given IGT data and how these will benefit
the speech community. (2) Great care and target language expertise is required
when converting the data into structured formats commonly employed in NLP. (3)
Task-specific and user-specific evaluation can help to ascertain that the tools
which are created benefit the target language speech community. We illustrate
each step through a case study on developing a morphological reinflection
system for the Tsimchianic language Gitksan.


title:
A ZTF Look at Optical Variability of Young Stellar Objects in the North
  America and Pelican Nebulae Complex

abstract:
  We present a study of 323 photometrically variable young stellar objects that
are likely members of the North America and Pelican (NAP) nebulae star forming
region. To do so, we utilize over two years of data in the $g$ and $r$
photometric bands from the Zwicky Transient Facility (ZTF). We first
investigate periodic variability, finding 46 objects ($\sim$15\% of the sample)
with significant periods that phase well, and can be attributed to stellar
rotation. We then use the quasi-periodicity (Q) and flux asymmetry (M)
variability metrics to assign morphological classifications to the remaining
aperiodic light curves. Another $\sim$39\% of the variable star sample beyond
the periodic sources are also flux-symmetric, but with a quasi-periodic
(moderate $Q$) or stochastic (high $Q$) nature. Concerning flux-asymmetric
sources, our analysis reveals $\sim$14\% bursters (high negative $M$) and
$\sim$29\% dippers (high positive $M$). We also investigate the relationship
between variability slopes in the $g$ vs $g-r$ color-magnitude diagram, and the
light curve morphological classes. Burster-type objects have shallow slopes,
while dipper-type variables tend to have higher slopes that are consistent with
extinction driven variability. Our work is one of the earliest applications of
the $Q$ and $M$ metrics to ground-based data. We therefore contrast the $Q$
values of high-cadence and high-precision space-based data, for which these
metrics were designed, with $Q$ determinations resulting from degraded
space-based lightcurves that have the cadence and photometric precision
characteristic of ground-based data.


title:
Inventing Relational State and Action Abstractions for Effective and
  Efficient Bilevel Planning

abstract:
  Effective and efficient planning in continuous state and action spaces is
fundamentally hard, even when the transition model is deterministic and known.
One way to alleviate this challenge is to perform bilevel planning with
abstractions, where a high-level search for abstract plans is used to guide
planning in the original transition space. In this paper, we develop a novel
framework for learning state and action abstractions that are explicitly
optimized for both effective (successful) and efficient (fast) bilevel
planning. Given demonstrations of tasks in an environment, our data-efficient
approach learns relational, neuro-symbolic abstractions that generalize over
object identities and numbers. The symbolic components resemble the STRIPS
predicates and operators found in AI planning, and the neural components refine
the abstractions into actions that can be executed in the environment.
Experimentally, we show across four robotic planning environments that our
learned abstractions are able to quickly solve held-out tasks of longer
horizons than were seen in the demonstrations, and can even outperform the
efficiency of abstractions that we manually specified. We also find that as the
planner configuration varies, the learned abstractions adapt accordingly,
indicating that our abstraction learning method is both "task-aware" and
"planner-aware." Code: https://tinyurl.com/predicators-release


title: Localized eigenvectors on metric graphs

abstract:
  Motivated by the recent experimental work on random network fiber lasers
[Gaio,Cipolato], we examine numerically the probability of observing localized
eigenvectors in two arbitrary metric graphs. We find that exact localization is
rare, and that it is necessary to tune the network to observe exactly localized
states. We derive the resonance conditions to obtain localized eigenvectors for
various geometric structures. Using the exact analytical form of the solution
on each edge, we introduce a localization indicator based on the $L_2$ norm.
This indicator estimates the exact number of edges involved in a particular
localized state and is more precise than the standard Inverse Participation
Ratio (IPR).


title:
Design of Compressed Sensing Systems via Density-Evolution Framework for
  Structure Recovery in Graphical Models

abstract:
  It has been shown that the task of learning the structure of Bayesian
networks (BN) from observational data is an NP-Hard problem. Although there
have been attempts made to tackle this problem, these solutions assume direct
access to the observational data which may not be practical in certain
applications. In this paper, we explore the feasibility of recovering the
structure of Gaussian Bayesian Network (GBN) from compressed (low dimensional
and indirect) measurements. We propose a novel density-evolution based
framework for optimizing compressed linear measurement systems that would, by
design, allow for more accurate retrieval of the covariance matrix and thereby
the graph structure. In particular, under the assumption that both the
covariance matrix and the graph are sparse, we show that the structure of GBN
can indeed be recovered from resulting compressed measurements. The numerical
simulations show that our sensing systems outperform the state of the art with
respect to Maximum absolute error (MAE) and have comparable performance with
respect to precision and recall, without any need for ad-hoc parameter tuning.


title: Investigating Compounding Prediction Errors in Learned Dynamics Models

abstract:
  Accurately predicting the consequences of agents' actions is a key
prerequisite for planning in robotic control. Model-based reinforcement
learning (MBRL) is one paradigm which relies on the iterative learning and
prediction of state-action transitions to solve a task. Deep MBRL has become a
popular candidate, using a neural network to learn a dynamics model that
predicts with each pass from high-dimensional states to actions. These
"one-step" predictions are known to become inaccurate over longer horizons of
composed prediction - called the compounding error problem. Given the
prevalence of the compounding error problem in MBRL and related fields of
data-driven control, we set out to understand the properties of and conditions
causing these long-horizon errors. In this paper, we explore the effects of
subcomponents of a control problem on long term prediction error: including
choosing a system, collecting data, and training a model. These detailed
quantitative studies on simulated and real-world data show that the underlying
dynamics of a system are the strongest factor determining the shape and
magnitude of prediction error. Given a clearer understanding of compounding
prediction error, researchers can implement new types of models beyond
"one-step" that are more useful for control.


title: Unified Line and Paragraph Detection by Graph Convolutional Networks

abstract:
  We formulate the task of detecting lines and paragraphs in a document into a
unified two-level clustering problem. Given a set of text detection boxes that
roughly correspond to words, a text line is a cluster of boxes and a paragraph
is a cluster of lines. These clusters form a two-level tree that represents a
major part of the layout of a document. We use a graph convolutional network to
predict the relations between text detection boxes and then build both levels
of clusters from these predictions. Experimentally, we demonstrate that the
unified approach can be highly efficient while still achieving state-of-the-art
quality for detecting paragraphs in public benchmarks and real-world images.


title:
Generating unrepresented proportions of geological facies using
  Generative Adversarial Networks

abstract:
  In this work, we investigate the capacity of Generative Adversarial Networks
(GANs) in interpolating and extrapolating facies proportions in a geological
dataset. The new generated realizations with unrepresented (aka. missing)
proportions are assumed to belong to the same original data distribution.
Specifically, we design a conditional GANs model that can drive the generated
facies toward new proportions not found in the training set. The presented
study includes an investigation of various training settings and model
architectures. In addition, we devised new conditioning routines for an
improved generation of the missing samples. The presented numerical experiments
on images of binary and multiple facies showed good geological consistency as
well as strong correlation with the target conditions.


title:
Echo chambers and information transmission biases in homophilic and
  heterophilic networks

abstract:
  We study how information transmission biases arise by the interplay between
the structural properties of the network and the dynamics of the information in
synthetic scale-free homophilic/heterophilic networks. We provide simple
mathematical tools to quantify these biases. Both Simple and Complex Contagion
models are insufficient to predict significant biases. In contrast, a Hybrid
Contagion model -- in which both Simple and Complex Contagion occur -- gives
rise to three different homophily-dependent biases: emissivity and receptivity
biases,and echo chambers. Simulations in an empirical network with high
homophily confirm the existence of these biases. Our results shed light into
the mechanisms that cause inequalities in the visibility of information
sources, reduced access to information, and lack of communication among
distinct groups.


title:
Rare-earth-free noncollinear metallic ferrimagnets Mn4-xZxN with
  compensation at room temperature

abstract:
  Compensated ferrimagnets, like antiferromagnets, show no net magnetization
but their transport and magneto-optic properties resemble those of
ferromagnets, thereby creating opportunities for applications in high-frequency
spintronics and low-energy loss communications. Here we study the modification
the noncollinear ferrimagnetic spin structure of Mn4N by a variety of metallic
substitutions Z (Z = Cu - Ge and Ag - Sn) to achieve compensation at room
temperature. The noncollinear frustrated 2.35 Bohr magneton moments of Mn on 3c
sites of the (111) kagome planes tilt about 20 degree out-of-plane in Mn4N and
are easily influenced by the substitutions on 1a sites, leading to different
efficiency of compensation in Mn4-xZxN that increases gradually from group 11
(Cu, Ag) to group 14 (Ge, Sn) with increasing number of valance electrons.
Elements from the 5th period are more efficient for compensation than those
from the 4th period due to lattice expansion. The manganese site moments are
determined by Z, orbital hybridization, charge transfer and the tilt angle,
analyzed by constrained density functional theory. The Ga compound with
compensation at room temperature for x = 0.26 is recommended for high-frequency
spintronic applications.


title: Cascade Transformers for End-to-End Person Search

abstract:
  The goal of person search is to localize a target person from a gallery set
of scene images, which is extremely challenging due to large scale variations,
pose/viewpoint changes, and occlusions. In this paper, we propose the Cascade
Occluded Attention Transformer (COAT) for end-to-end person search. Our
three-stage cascade design focuses on detecting people in the first stage,
while later stages simultaneously and progressively refine the representation
for person detection and re-identification. At each stage the occluded
attention transformer applies tighter intersection over union thresholds,
forcing the network to learn coarse-to-fine pose/scale invariant features.
Meanwhile, we calculate each detection's occluded attention to differentiate a
person's tokens from other people or the background. In this way, we simulate
the effect of other objects occluding a person of interest at the token-level.
Through comprehensive experiments, we demonstrate the benefits of our method by
achieving state-of-the-art performance on two benchmark datasets.


title:
A canonical infinitesimally Hilbertian structure on locally Minkowski
  spaces

abstract:
  The aim of this paper is to show the existence of a canonical distance
$\mathsf d'$ defined on a locally Minkowski metric measure space $(\mathsf
X,\mathsf d,\mathfrak m)$ such that:
  i) $\mathsf d'$ is equivalent to $\mathsf d$,
  ii) $(\mathsf X, \mathsf d', \mathfrak m)$ is infinitesimally Hilbertian.
  This new regularity assumption on $(\mathsf X, \mathsf d,\mathfrak m)$
essentially forces the structure to be locally similar to a Minkowski space and
defines a class of metric measure structures which includes all the Finsler
manifolds, and it is actually strictly larger. The required distance $\mathsf
d'$ will be the intrinsic distance $\mathsf d_\mathsf{KS}$ associated to the
so-called Korevaar-Schoen energy, which is proven to be a quadratic form. In
particular, we show that the Cheeger energy associated to the metric measure
space $(\mathsf X, \mathsf d_\mathsf{KS}, \mathfrak m)$ is in fact the
Korevaar-Schoen energy.


title: Deep Reinforcement Agent for Efficient Instant Search

abstract:
  Instant Search is a paradigm where a search system retrieves answers on the
fly while typing. The na\"ive implementation of an Instant Search system would
hit the search back-end for results each time a user types a key, imposing a
very high load on the underlying search system. In this paper, we propose to
address the load issue by identifying tokens that are semantically more salient
towards retrieving relevant documents and utilize this knowledge to trigger an
instant search selectively. We train a reinforcement agent that interacts
directly with the search engine and learns to predict the word's importance.
Our proposed method treats the underlying search system as a black box and is
more universally applicable to a diverse set of architectures. Furthermore, a
novel evaluation framework is presented to study the trade-off between the
number of triggered searches and the system's performance. We utilize the
framework to evaluate and compare the proposed reinforcement method with other
intuitive baselines. Experimental results demonstrate the efficacy of the
proposed method towards achieving a superior trade-off.


title: MatchFormer: Interleaving Attention in Transformers for Feature Matching

abstract:
  Local feature matching is a computationally intensive task at the subpixel
level. While detector-based methods coupled with feature descriptors struggle
in low-texture scenes, CNN-based methods with a sequential extract-to-match
pipeline, fail to make use of the matching capacity of the encoder and tend to
overburden the decoder for matching. In contrast, we propose a novel
hierarchical extract-and-match transformer, termed as MatchFormer. Inside each
stage of the hierarchical encoder, we interleave self-attention for feature
extraction and cross-attention for feature matching, enabling a human-intuitive
extract-and-match scheme. Such a match-aware encoder releases the overloaded
decoder and makes the model highly efficient. Further, combining self- and
cross-attention on multi-scale features in a hierarchical architecture improves
matching robustness, particularly in low-texture indoor scenes or with less
outdoor training data. Thanks to such a strategy, MatchFormer is a multi-win
solution in efficiency, robustness, and precision. Compared to the previous
best method in indoor pose estimation, our lite MatchFormer has only 45%
GFLOPs, yet achieves a +1.3% precision gain and a 41% running speed boost. The
large MatchFormer reaches state-of-the-art on four different benchmarks,
including indoor pose estimation (ScanNet), outdoor pose estimation
(MegaDepth), homography estimation and image matching (HPatch), and visual
localization (InLoc). Code will be made publicly available at
https://github.com/jamycheung/MatchFormer.


title:
On the Jacobi capture origin of binaries with applications to the
  Earth-Moon system and black holes in galactic nuclei

abstract:
  Close encounters between two bodies in a disk often result in a single
orbital deflection. However, within their Jacobi volumes, where the
gravitational forces between the two bodies and the central body become
competitive, temporary captures with multiple close encounters become possible
outcomes: a Jacobi capture. We perform 3-body simulations in order to
characterise the dynamics of Jacobi captures in the plane. We find that the
phase space structure resembles a Cantor set with a fractal dimension of 0.4.
The lifetime distribution decreases exponentially, while the distribution of
the closest separation follows a power law with index 0.5. In our first
application, we consider the Jacobi capture of the Moon. We demonstrate that
both tidal captures and giant impacts are possible outcomes. Their respective
1D cross sections differ within an order of magnitude, evaluated at a
heliocentric distance of 1 AU. The impact speed is well approximated by a
parabolic encounter, while the impact angles follow that of a uniform beam on a
circular target. In our second application, we find that Jacobi captures with
gravitational wave dissipation can result in the formation of binary black
holes in galactic nuclei. The eccentricity distribution is approximately
super-thermal and includes both prograde and retrograde orientations. We
estimate a cosmic rate density of 0.083 < R < 14 Gpc^-3 yr^-1. We conclude that
dissipative Jacobi captures form an efficient channel for binary formation,
which motivates further research into establishing the universality of Jacobi
captures across multiple astrophysical scales.

categories: astro-ph.EP astro-ph.CO astro-ph.HE nlin.CD physics.comp-ph


title: Conditional probability density functional theory

abstract:
  We present conditional probability (CP) density functional theory (DFT) as a
formally exact theory. In essence, CP-DFT determines the ground-state energy of
a system by finding the CP density from a series of independent Kohn-Sham (KS)
DFT calculations. By directly calculating CP densities, we bypass the need for
an approximate XC energy functional. In this work we discuss and derive several
key properties of the CP density and corresponding CP-KS potential.
Illustrative examples are used throughout to help guide the reader through the
various concepts and theory presented. We explore a suitable CP-DFT
approximation and discuss exact conditions, limitations, and results for
selected examples.


title: Generic Lines in Projective Space and the Koszul Property

abstract:
  In this paper, we study the Koszul property of the homogeneous coordinate
ring of a generic collection of lines in $\mathbb{P}^n$ and the homogeneous
coordinate ring of a collection of lines in general linear position in
$\mathbb{P}^n.$ We show that if $\mathcal{M}$ is a collection of $m$ lines in
general linear position in $\mathbb{P}^n$ with $2m \leq n+1$ and $R$ is the
coordinate ring of $\mathcal{M},$ then $R$ is Koszul. Further, if $\mathcal{M}$
is a generic collection of $m$ lines in $\mathbb{P}^n$ and $R$ is the
coordinate ring of $\mathcal{M}$ with $m$ even and $m +1\leq n$ or $m$ is odd
and $m +2\leq n,$ then $R$ is Koszul. Lastly, we show if $\mathcal{M}$ is a
generic collection of $m$ lines such that \[ m >
\frac{1}{72}\left(3(n^2+10n+13)+\sqrt{3(n-1)^3(3n+5)}\right),\] then $R$ is not
Koszul. We give a complete characterization of the Koszul property of the
coordinate ring of a generic collection of lines for $n \leq 6$ or $m \leq 6$.
We also determine the Castelnuovo-Mumford regularity of the coordinate ring for
a generic collection of lines and the projective dimension of the coordinate
ring of collection of lines in general linear position.


title: Open-string integrals with multiple unintegrated punctures at genus one

abstract:
  We study integrals appearing in intermediate steps of one-loop open-string
amplitudes, with multiple unintegrated punctures on the $A$-cycle of a torus.
We construct a vector of such integrals which closes after taking a total
differential with respect to the $N$ unintegrated punctures and the modular
parameter $\tau$. These integrals are found to satisfy the elliptic
Knizhnik-Zamolodchikov-Bernard (KZB) equations, and can be written as a power
series in $\alpha$' -- the string length squared -- in terms of elliptic
multiple polylogarithms (eMPLs). In the $N$-puncture case, the KZB equation
reveals a representation of $B_{1,N}$, the braid group of $N$ strands on a
torus, acting on its solutions. We write the simplest of these braid group
elements -- the braiding one puncture around another -- and obtain generating
functions of analytic continuations of eMPLs. The KZB equations in the
so-called universal case is written in terms of the genus-one Drinfeld-Kohno
algebra $\mathfrak{t}_{1,N} \rtimes \mathfrak{d}$, a graded algebra. Our
construction determines matrix representations of various dimensions for
several generators of this algebra which respect its grading up to commuting
terms.


title: Coherent Backscattering of Entangled Photon Pairs

abstract:
  Correlations between entangled photons are a key ingredient for testing
fundamental aspects of quantum mechanics and an invaluable resource for quantum
technologies. However, scattering from a dynamic medium typically scrambles and
averages out such correlations. Here we show that multiply-scattered entangled
photons reflected from a dynamic complex medium remain partially correlated. We
observe in experiments and in full-wave simulations enhanced correlations,
within an angular range determined by the transport mean free path, which
prevail disorder averaging. Theoretical analysis reveals that this enhancement
arises from the interference between scattering trajectories, in which the
photons leave the sample and are then virtually reinjected back into it. These
paths are the quantum counterpart of the paths that lead to the coherent
backscattering of classical light. This work points to opportunities for
entanglement transport despite dynamic multiple scattering in complex systems.


title: Local-global principles for hermitian spaces over semi-global fields

abstract:
  Let $K$ be a complete discrete valued field with residue field $k$ and $F$
the function field of a curve over $K$. Let $A \in {}_2Br(F)$ be a central
simple algebra with an involution $\sigma$ of any kind and $F_0 =F^{\sigma}$.
Let $h$ be an hermitian space over $(A, \sigma)$ and $G = SU(A, \sigma, h)$ if
$\sigma$ is of first kind and $G = U(A, \sigma, h)$ if $\sigma$ is of second
kind. Suppose that $\text{char}(k) \neq 2$ and ind$(A)\leq 4$. Then we prove
that projective homogeneous spaces under $G$ over $F_0$ satisfy a local-global
principle for rational points with respect to discrete valuations of $F$.


title: Revisiting the Dunn-Belnap logic

abstract:
  In the present work I introduce a semantics based on the cognitive attitudes
of acception and rejection entertained by a given society of agents for logics
inspired on Dunn and Belnap's First Degree Entailment ($\mathbf{E}$). In
contrast to the epistemic situations originally employed by $\mathbf{E}$, the
cognitive attitudes do not coincide with truth-values and they seem more
suitable to logics that intend to consider the informational content of
propositions "said to be true" as well as of propositions "said to be false" as
determinant of the notion of logical validity. After analyzing some logics
associated to the proposed semantics, we introduce the logic $\mathbf{E}^B$,
whose underlying entailment relation -- the $B$-entailment -- is able to
express several kinds of reasoning involving the cognitive attitudes of
acceptance and rejection. A sound and complete sequent calculus for
$\mathbf{E}^B$ is also presented.


title:
Regional Semantic Contrast and Aggregation for Weakly Supervised
  Semantic Segmentation

abstract:
  Learning semantic segmentation from weakly-labeled (e.g., image tags only)
data is challenging since it is hard to infer dense object regions from sparse
semantic tags. Despite being broadly studied, most current efforts directly
learn from limited semantic annotations carried by individual image or image
pairs, and struggle to obtain integral localization maps. Our work alleviates
this from a novel perspective, by exploring rich semantic contexts
synergistically among abundant weakly-labeled training data for network
learning and inference. In particular, we propose regional semantic contrast
and aggregation (RCA) . RCA is equipped with a regional memory bank to store
massive, diverse object patterns appearing in training data, which acts as
strong support for exploration of dataset-level semantic structure.
Particularly, we propose i) semantic contrast to drive network learning by
contrasting massive categorical object regions, leading to a more holistic
object pattern understanding, and ii) semantic aggregation to gather diverse
relational contexts in the memory to enrich semantic representations. In this
manner, RCA earns a strong capability of fine-grained semantic understanding,
and eventually establishes new state-of-the-art results on two popular
benchmarks, i.e., PASCAL VOC 2012 and COCO 2014.


title: Conditional Quantum Plasmonic Sensing

abstract:
  The possibility of using weak optical signals to perform sensing of delicate
samples constitutes one of the main goals of quantum photonic sensing.
Furthermore, the nanoscale confinement of electromagnetic near fields in
photonic platforms through surface plasmon polaritons has motivated the
development of highly sensitive quantum plasmonic sensors. Despite the enormous
potential of plasmonic platforms for sensing, this class of sensors is
ultimately limited by the quantum statistical fluctuations of surface plasmons.
Indeed, the fluctuations of the electromagnetic field severely limit the
performance of quantum plasmonic sensing platforms in which delicate samples
are characterized using weak near-field signals. Furthermore, the inherent
losses associated with plasmonic fields levy additional constraints that
challenge the realization of sensitivities beyond the shot-noise limit. Here,
we introduce a protocol for quantum plasmonic sensing based on the conditional
detection of plasmons. We demonstrate that the conditional detection of
plasmonic fields, via plasmon subtraction, provides a new degree of freedom to
control quantum fluctuations of plasmonic fields. This mechanism enables
improvement of the signal-to-noise ratio of photonic sensors relying on
plasmonic signals that are comparable to their associated field fluctuations.
Consequently, the possibility of using weak plasmonic signals to sense delicate
samples, while preserving the sample properties, has important implications for
molecule sensing, and chemical detection .


title:
Cores in Friend-Oriented Hedonic Games: Verification is Surprisingly
  Harder than Searching

abstract:
  We resolve an open question by Woeginger [Open Problem 13, SOFSEM '13]
regarding the computational complexity of verifying whether a given coalition
structure for friend-oriented preferences is stable. We show that this
verification problem is coNP-complete. It remains so even when the directed
friendship graph underlying the friend-oriented preferences is planar, has
constant vertex-degree (i.e., each player has constant number of friends and is
regarded as a friend by a constant number of other players), and the friendship
preferences are symmetric. On the positive side, we observe that the
verification problem becomes polynomial-time solvable if the friendship graph
is acyclic or has maximum vertex-degree two.


title:
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive
  Sensing

abstract:
  The compressive sensing (CS) scheme exploits much fewer measurements than
suggested by the Nyquist-Shannon sampling theorem to accurately reconstruct
images, which has attracted considerable attention in the computational imaging
community. While classic image CS schemes employed sparsity using analytical
transforms or bases, the learning-based approaches have become increasingly
popular in recent years. Such methods can effectively model the structures of
image patches by optimizing their sparse representations or learning deep
neural networks, while preserving the known or modeled sensing process. Beyond
exploiting local image properties, advanced CS schemes adopt nonlocal image
modeling, by extracting similar or highly correlated patches at different
locations of an image to form a group to process jointly. More recent
learning-based CS schemes apply nonlocal structured sparsity prior using group
sparse representation (GSR) and/or low-rank (LR) modeling, which have
demonstrated promising performance in various computational imaging and image
processing applications. This article reviews some recent works in image CS
tasks with a focus on the advanced GSR and LR based methods. Furthermore, we
present a unified framework for incorporating various GSR and LR models and
discuss the relationship between GSR and LR models. Finally, we discuss the
open problems and future directions in the field.


title:
One- and two-particle properties of the weakly interacting
  two-dimensional Hubbard model in proximity to the van Hove singularity

abstract:
  We study the weak-coupling limit of the $t-t^\prime-U$ Hubbard model on a
two-dimensional square lattice using a direct perturbative approach. Aided by
symbolic computational tools, we compute the longitudinal density-density
correlation functions in the $\chi_{\uparrow \uparrow}$ and $\chi_{\uparrow
\downarrow}$ basis from which we can obtain the dynamical spin and charge
susceptibilities at arbitrary doping and temperature. We find that for non-zero
$t^\prime$, the zero frequency commensurate $\mathbf{q} = (\pi, \pi)$ spin and
charge excitations are each strongest at different densities and we observe a
clear behavioral change that appears tied to the van Hove singularity of the
non-interacting dispersion upon which the perturbative expansion is built. We
find a strongly reduced compressibility in the vicinity of the van Hove
singularity as well as a behavioral change in the double occupancy. For finite
$t^\prime$, the observed van Hove singularity occurs away from half-filling
leading us to conclude that that this reduction in compressibility is distinct
from Mott insulating physics that one expects in the strong-coupling regime. We
compute the full dynamical spin and charge excitations and observe distinct
structure for electron and hole doped scenarios in agreement with experiments
on cuprate materials. Finally, we observe a peculiar splitting in spin and
charge excitations in the vicinity of the van Hove singularity, the origin of
which is traced to a splitting near the bottom of the band.


title:
Lupa: A Framework for Large Scale Analysis of the Programming Language
  Usage

abstract:
  In this paper, we present Lupa - a framework for large-scale analysis of the
programming language usage. Lupa is a command line tool that uses the power of
the IntelliJ Platform under the hood, which gives it access to powerful static
analysis tools used in modern IDEs. The tool supports custom analyzers that
process the rich concrete syntax tree of the code and can calculate its various
features: the presence of entities, their dependencies, definition-usage
chains, etc. Currently, Lupa supports analyzing Python and Kotlin, but can be
extended to other languages supported by IntelliJ-based IDEs. We explain the
internals of the tool, show how it can be extended and customized, and describe
an example analysis that we carried out with its help: analyzing the syntax of
ranges in Kotlin.


title: Low-degree learning and the metric entropy of polynomials

abstract:
  Let $\mathscr{F}_{n,d}$ be the class of all functions $f:\{-1,1\}^n\to[-1,1]$
on the $n$-dimensional discrete hypercube of degree at most $d$. In the first
part of this paper, we prove that any (deterministic or randomized) algorithm
which learns $\mathscr{F}_{n,d}$ with $L_2$-accuracy $\varepsilon$ requires at
least $\Omega((1-\sqrt{\varepsilon})2^d\log n)$ queries for large enough $n$,
thus establishing the sharpness as $n\to\infty$ of a recent upper bound of
Eskenazis and Ivanisvili (2021). To do this, we show that the $L_2$-packing
numbers $\mathsf{M}(\mathscr{F}_{n,d},\|\cdot\|_{L_2},\varepsilon)$ of the
concept class $\mathscr{F}_{n,d}$ satisfy the two-sided estimate
$$c(1-\varepsilon)2^d\log n \leq \log
\mathsf{M}(\mathscr{F}_{n,d},\|\cdot\|_{L_2},\varepsilon) \leq \frac{2^{Cd}\log
n}{\varepsilon^4}$$ for large enough $n$, where $c, C>0$ are universal
constants. In the second part of the paper, we present a logarithmic upper
bound for the randomized query complexity of classes of bounded approximate
polynomials whose Fourier spectra are concentrated on few subsets. As an
application, we prove new estimates for the number of random queries required
to learn approximate juntas of a given degree, functions with rapidly decaying
Fourier tails and constant depth circuits of given size. Finally, we obtain
bounds for the number of queries required to learn the polynomial class
$\mathscr{F}_{n,d}$ without error in the query and random example models.


title:
Simulating the behaviour of travelling wave superconducting parametric
  amplifiers using a commercial circuit simulator

abstract:
  Travelling wave kinetic inductance parametric amplifiers (KI-TWPAs) have been
simulated using Keysight's Advanced Design System (ADS). A lumped element
representation of a superconducting transmission line is developed using
nonlinear inductor elements to represent the current dependency of the
intrinsic kinetic inductance. This provides a fast, simple and easily
modifiable method for analysing the parametric processes that occur in
nonlinear kinetic inductance devices such as parametric oscillators or
amplifiers, while also allowing the accompanying RF network to be optimised.
This methodology is used to model the behaviour of a select number of KI-TWPA
designs operating in the 3-wave mixing (3WM) and 4-wave mixing (4WM) regimes.
S-parameters and dispersion properties are extracted from large signal
S-parameter simulations and the gain curve of each amplifier design is
generated from harmonic balance simulations. Results of these are compared to
the gain expression from the analytic solutions of the coupled mode equations,
while making reference to the gain curves of the equivalent TWPAs from
literature.


title: Meta Reinforcement Learning for Adaptive Control: An Offline Approach

abstract:
  Meta-learning is a branch of machine learning which trains neural network
models to synthesize a wide variety of data in order to rapidly solve new
problems. In process control, many systems have similar and well-understood
dynamics, which suggests it is feasible to create a generalizable controller
through meta-learning. In this work, we formulate a meta reinforcement learning
(meta-RL) control strategy that takes advantage of known, offline information
for training, such as the system gain or time constant, yet efficiently
controls novel systems in a completely model-free fashion. Our meta-RL agent
has a recurrent structure that accumulates "context" for its current dynamics
through a hidden state variable. This end-to-end architecture enables the agent
to automatically adapt to changes in the process dynamics. Moreover, the same
agent can be deployed on systems with previously unseen nonlinearities and
timescales. In tests reported here, the meta-RL agent was trained entirely
offline, yet produced excellent results in novel settings. A key design element
is the ability to leverage model-based information offline during training,
while maintaining a model-free policy structure for interacting with novel
environments. To illustrate the approach, we take the actions proposed by the
meta-RL agent to be changes to gains of a proportional-integral controller,
resulting in a generalized, adaptive, closed-loop tuning strategy.
Meta-learning is a promising approach for constructing sample-efficient
intelligent controllers.


title:
Interplay of charge density waves, disorder, and superconductivity in
  2$H$-TaSe$_2$ elucidated by NMR

abstract:
  Single crystals of pristine and 6% Pd-intercalated 2H-TaSe$_2$ have been
studied by means of $^{77}$Se nuclear magnetic resonance (NMR). The temperature
dependence of the $^{77}$Se spectrum, with an unexpected line narrowing upon Pd
intercalation, unravels the presence of correlated local lattice distortions
far above the transition temperature of the charge density wave (CDW) order,
thereby supporting a strong-coupling CDW mechanism in 2H-TaSe$_2$. While, the
Knight shift data suggest that the incommensurate CDW transition involves a
partial Fermi surface gap opening. As for spin dynamics, the $^{77}$Se
spin-lattice relaxation rate $T_1^{-1}$ as a function of temperature shows that
a pseudogap behavior dominates the low-energy spin excitations even within the
CDW phase, and gets stronger along with superconductivity in the Pd-6% sample.
We discuss that CDW fluctuations may be responsible for the pseudogap as well
as superconductivity, although the two phenomena are unlikely to be directly
linked each other.


title:
An Improved Subject-Independent Stress Detection Model Applied to
  Consumer-grade Wearable Devices

abstract:
  Stress is a complex issue with wide-ranging physical and psychological
impacts on human daily performance. Specifically, acute stress detection is
becoming a valuable application in contextual human understanding. Two common
approaches to training a stress detection model are subject-dependent and
subject-independent training methods. Although subject-dependent training
methods have proven to be the most accurate approach to build stress detection
models, subject-independent models are a more practical and cost-efficient
method, as they allow for the deployment of stress level detection and
management systems in consumer-grade wearable devices without requiring
training data for the end-user. To improve the performance of
subject-independent stress detection models, in this paper, we introduce a
stress-related bio-signal processing pipeline with a simple neural network
architecture using statistical features extracted from multimodal contextual
sensing sources including Electrodermal Activity (EDA), Blood Volume Pulse
(BVP), and Skin Temperature (ST) captured from a consumer-grade wearable
device. Using our proposed model architecture, we compare the accuracy between
stress detection models that use measures from each individual signal source,
and one model employing the fusion of multiple sensor sources. Extensive
experiments on the publicly available WESAD dataset demonstrate that our
proposed model outperforms conventional methods as well as providing 1.63%
higher mean accuracy score compared to the state-of-the-art model while
maintaining a low standard deviation. Our experiments also show that combining
features from multiple sources produce more accurate predictions than using
only one sensor source individually.


title:
Emerging Artificial Intelligence Applications in Spatial Transcriptomics
  Analysis

abstract:
  Spatial transcriptomics (ST) has advanced significantly in the last few
years. Such advancement comes with the urgent need for novel computational
methods to handle the unique challenges of ST data analysis. Many artificial
intelligence (AI) methods have been developed to utilize various machine
learning and deep learning techniques for computational ST analysis. This
review provides a comprehensive and up-to-date survey of current AI methods for
ST analysis.


title: A Cost-Efficient Look-Up Table Based Binary Coded Decimal Adder Design

abstract:
  The BCD (Binary Coded Decimal) being the more accurate and human-readable
representation with ease of conversion, is prevailing in the computing and
electronic communication.In this paper, a tree-structured parallel BCD addition
algorithm is proposed with the reduced time complexity. BCD adder is more
effective with a LUT (Look-Up Table)-based design, due to FPGA (Field
Programmable Gate Array) technology's enumerable benefits and applications. A
size-minimal and depth-minimal LUT-based BCD adder circuit construction is the
main contribution of this paper.


title: A new duality on closed convex sets

abstract:
  In this paper we study a subclass of convex sets which are called closed
pseudo-cones and introduce a new duality on this subclass. It turns out that
the new duality characterizes closed pseudo-cones and is essentially the only
possible abstract duality on them. The related classification of endomorphisms
are also established.


title: Choice-Free de Vries Duality

abstract:
  De Vries Duality generalizes Stone's duality between Boolean algebras and
Stone spaces to a duality between de Vries algebras (complete Boolean algebras
equipped with a subordination relation satisfying some axioms) and compact
Hausdorff spaces. This duality allows for an algebraic approach to region-based
theories of space that differs from point-free topology. Building on the recent
choice-free version of Stone duality developed by Bezhanishvili and Holliday,
this paper establishes a choice-free duality between de Vries algebras and a
category of de Vries spaces. We also investigate connections with the Vietoris
functor on the category of compact Hausdorff spaces and with the category of
compact regular frames in point-free topology, and we provide an alternative,
choice-free topological semantics for the Symmetric Strict Implication Calculus
of Bezhanishvili et al.


title: Setigen: Simulating Radio Technosignatures for SETI

abstract:
  The goal of the search for extraterrestrial intelligence (SETI) is the
detection of non-human technosignatures, such as technology-produced emission
in radio observations. While many have speculated about the character of such
technosignatures, radio SETI fundamentally involves searching for signals that
not only have never been detected, but also have a vast range of potential
morphologies. Given that we have not yet detected a radio SETI signal, we must
make assumptions about their form to develop search algorithms. The lack of
positive detections also makes it difficult to test these algorithms' inherent
efficacy. To address these challenges, we present Setigen, a Python-based,
open-source library for heuristic-based signal synthesis and injection for both
spectrograms (dynamic spectra) and raw voltage data. Setigen facilitates the
production of synthetic radio observations, interfaces with standard data
products used extensively by the Breakthrough Listen project (BL), and focuses
on providing a physically-motivated synthesis framework compatible with real
observational data and associated search methods. We discuss the core routines
of Setigen and present existing and future use cases in the development and
evaluation of SETI search algorithms.


title:
Analysing the Performance of Stress Detection Models on Consumer-Grade
  Wearable Devices

abstract:
  Identifying stress levels can provide valuable data for mental health
analytics as well as labels for annotation systems. Although much research has
been conducted into stress detection models using heart rate variability at a
higher cost of data collection, there is a lack of research on the potential of
using low-resolution Electrodermal Activity (EDA) signals from consumer-grade
wearable devices to identify stress patterns. In this paper, we concentrate on
performing statistical analyses on the stress detection capability of two
popular approaches of training stress detection models with stress-related
biometric signals: user-dependent and user-independent models. Our research
manages to show that user-dependent models are statistically more accurate for
stress detection. In terms of effectiveness assessment, the balanced accuracy
(BA) metric is employed to evaluate the capability of distinguishing stress and
non-stress conditions of the models trained on either low-resolution or
high-resolution Electrodermal Activity (EDA) signals. The results from the
experiment show that training the model with (comparatively low-cost)
low-resolution EDA signal does not affect the stress detection accuracy of the
model significantly compared to using a high-resolution EDA signal. Our
research results demonstrate the potential of attaching the user-dependent
stress detection model trained on personal low-resolution EDA signal recorded
to collect data in daily life to provide users with personal stress level
insight and analysis.


title:
Latency Optimization for Blockchain-Empowered Federated Learning in
  Multi-Server Edge Computing

abstract:
  In this paper, we study a new latency optimization problem for
Blockchain-based federated learning (BFL) in multi-server edge computing. In
this system model, distributed mobile devices (MDs) communicate with a set of
edge servers (ESs) to handle both machine learning (ML) model training and
block mining simultaneously. To assist the ML model training for
resource-constrained MDs, we develop an offloading strategy that enables MDs to
transmit their data to one of the associated ESs. We then propose a new
decentralized ML model aggregation solution at the edge layer based on a
consensus mechanism to build a global ML model via peer-to-peer (P2P)-based
Blockchain communications. We then formulate latency-aware BFL as an
optimization aiming to minimize the system latency via joint consideration of
the data offloading decisions, MDs' transmit power, channel bandwidth
allocation for MDs' data offloading, MDs' computational allocation, and hash
power allocation. To address the mixed action space of discrete offloading and
continuous allocation variables, we propose a novel deep reinforcement learning
scheme with a holistic design of a parameterized advantage actor critic (A2C)
algorithm. Additionally, we theoretically characterize the convergence
properties of the proposed BFL system in terms of the aggregation delay,
mini-batch size, and number of P2P communication rounds. Our subsequent
numerical evaluation demonstrates the superior performance of our proposed
scheme over existing approaches in terms of model training efficiency,
convergence rate, and system latency.


title:
New analysis of Mixed finite element methods for incompressible
  Magnetohydrodynamics

abstract:
  The paper focuses on a new error analysis of a class of mixed FEMs for
stationary incompressible magnetohydrodynamics with the standard inf-sup stable
velocity-pressure space pairs to Navier-Stokes equations and the N\'ed\'elec's
edge element for the magnetic field. The methods have been widely used in
various numerical simulations in the last several decades, while the existing
analysis is not optimal due to the strong coupling of system and the pollution
of the lower-order N\'ed\'elec's edge approximation in analysis. In terms of a
newly modified Maxwell projection we establish new and optimal error estimates.
In particular, we prove that the method based on the commonly-used
Taylor-Hood/lowest-order N\'ed\'elec's edge element is efficient and the method
provides the second-order accuracy for numerical velocity. Two numerical
examples for the problem in both convex and nonconvex polygonal domains are
presented. Numerical results confirm our theoretical analysis.


title: Multi-Modal Causal Inference with Deep Structural Equation Models

abstract:
  Accounting for the effects of confounders is one of the central challenges in
causal inference. Unstructured multi-modal data (images, time series, text)
contains valuable information about diverse types of confounders, yet it is
typically left unused by most existing methods. This paper seeks to develop
techniques that leverage this unstructured data within causal inference to
correct for additional confounders that may otherwise not be accounted for. We
formalize this task and we propose algorithms based on deep structural
equations that treat multi-modal unstructured data as proxy variables. We
empirically demonstrate on tasks in genomics and healthcare that unstructured
data can be used to correct for diverse sources of confounding, potentially
enabling the use of large amounts of data that were previously not used in
causal inference.


title:
Hate speech, Censorship, and Freedom of Speech: The Changing Policies of
  Reddit

abstract:
  This paper examines the shift in focus on content policies and user attitudes
on the social media platform Reddit. We do this by focusing on comments from
general Reddit users from five posts made by admins (moderators) on updates to
Reddit Content Policy. All five concern the nature of what kind of content is
allowed to be posted on Reddit, and which measures will be taken against
content that violates these policies. We use topic modeling to probe how the
general discourse for Redditors has changed around limitations on content, and
later, limitations on hate speech, or speech that incites violence against a
particular group. We show that there is a clear shift in both the contents and
the user attitudes that can be linked to contemporary societal upheaval as well
as newly passed laws and regulations, and contribute to the wider discussion on
hate speech moderation.


title:
A workflow for segmenting soil and plant X-ray CT images with deep
  learning in Googles Colaboratory

abstract:
  X-ray micro-computed tomography (X-ray microCT) has enabled the
characterization of the properties and processes that take place in plants and
soils at the micron scale. Despite the widespread use of this advanced
technique, major limitations in both hardware and software limit the speed and
accuracy of image processing and data analysis. Recent advances in machine
learning, specifically the application of convolutional neural networks to
image analysis, have enabled rapid and accurate segmentation of image data.
Yet, challenges remain in applying convolutional neural networks to the
analysis of environmentally and agriculturally relevant images. Specifically,
there is a disconnect between the computer scientists and engineers, who build
these AI/ML tools, and the potential end users in agricultural research, who
may be unsure of how to apply these tools in their work. Additionally, the
computing resources required for training and applying deep learning models are
unique, more common to computer gaming systems or graphics design work, than to
traditional computational systems. To navigate these challenges, we developed a
modular workflow for applying convolutional neural networks to X-ray microCT
images, using low-cost resources in Googles Colaboratory web application. Here
we present the results of the workflow, illustrating how parameters can be
optimized to achieve best results using example scans from walnut leaves,
almond flower buds, and a soil aggregate. We expect that this framework will
accelerate the adoption and use of emerging deep learning techniques within the
plant and soil sciences.


title: Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement

abstract:
  Bayesian coresets approximate a posterior distribution by building a small
weighted subset of the data points. Any inference procedure that is too
computationally expensive to be run on the full posterior can instead be run
inexpensively on the coreset, with results that approximate those on the full
data. However, current approaches are limited by either a significant run-time
or the need for the user to specify a low-cost approximation to the full
posterior. We propose a Bayesian coreset construction algorithm that first
selects a uniformly random subset of data, and then optimizes the weights using
a novel quasi-Newton method. Our algorithm is simple to implement, does not
require the user to specify a low-cost posterior approximation, and is the
first to come with a general high-probability bound on the KL divergence of the
output coreset posterior. Experiments demonstrate that the method provides
orders of magnitude improvement in construction time against the
state-of-the-art black-box method. Moreover, it provides significant
improvements in coreset quality against alternatives with comparable
construction times, with far less storage cost and user input required.


title:
In-situ probing and stabilizing the power ratio of
  electro-optic-modulated laser pairs based on VIPA etalon for quantum sensing

abstract:
  Monitoring and stabilizing the power ratio of laser pairs is significant to
high-precision atom interferometers, especially as the compact electro-optic
modulated all-fiber laser system prevails. In this Letter, we demonstrate a
novel method to in-situ probe the relative power of laser pairs and to
stabilize the power ratio of two Raman lasers using a high-dispersion virtually
imaged phased array (VIPA) etalon. Sub-microsecond resolution on probing laser
power transformation during atom interferometer sequence is achieved and the
power ratio of two Raman lasers (PRTR) is tightly locked with high bandwidth
despite of environmental disturbances, showing an Allan deviation of
$4.39\times 10^{-5}$ at 1000 s averaging time. This method provides a novel way
to stabilize the PRTR and diagnose the multi-frequency laser systems for atom
interferometers and could find potential application in broad quantum sensing
scenarios.


title:
Geodesics and dynamical information projections on the manifold of
  H\"older equilibrium probabilities

abstract:
  We consider here the discrete time dynamics described by a transformation
$T:M \to M$, where $T$ is either the action of shift $T=\sigma$ on the symbolic
space $M=\{1,2,...,d\}^\mathbb{N}$, or, $T$ describes the action of a $d$ to
$1$ expanding transformation $T:S^1 \to S^1$ of class $C^{1+\alpha}$ (\,for
example $x \to T(x) =d\, x $ (mod $1) $\,), where $M=S^1$ is the unit circle.
It is known that the infinite-dimensional manifold $\mathcal{N}$ of equilibrium
probabilities for H\"older potentials $A:M \to \mathbb{R}$ is an analytical
manifold and carries a natural Riemannian metric associated with the asymptotic
variance. We show here that under the assumption of the existence of a
Fourier-like Hilbert basis for the kernel of the Ruelle operator there exists
geodesics paths. When $T=\sigma$ and $M=\{0,1\}^\mathbb{N}$ such basis exists.
  In a different direction, we also consider the KL-divergence
$D_{KL}(\mu_1,\mu_2)$ for a pair of equilibrium probabilities. If
$D_{KL}(\mu_1,\mu_2)=0$, then $\mu_1=\mu_2$. Although $D_{KL}$ is not a metric
in $\mathcal{N}$, it describes the proximity between $\mu_1$ and $\mu_2$. A
natural problem is: for a fixed probability $\mu_1\in \mathcal{N}$ consider the
probability $\mu_2$ in a convex set of probabilities in $\mathcal{N}$ which
minimizes $D_{KL}(\mu_1,\mu_2)$. This minimization problem is a dynamical
version of the main issues considered in information projections. We consider
this problem in $\mathcal{N}$, a case where all probabilities are dynamically
invariant, getting explicit equations for the solution sought. Triangle and
Pythagorean inequalities will be investigated.

categories: math.DS cs.IT math-ph math.DG math.IT math.MP math.PR


title: Self-Ensemble Adversarial Training for Improved Robustness

abstract:
  Due to numerous breakthroughs in real-world applications brought by machine
intelligence, deep neural networks (DNNs) are widely employed in critical
applications. However, predictions of DNNs are easily manipulated with
imperceptible adversarial perturbations, which impedes the further deployment
of DNNs and may result in profound security and privacy implications. By
incorporating adversarial samples into the training data pool, adversarial
training is the strongest principled strategy against various adversarial
attacks among all sorts of defense methods. Recent works mainly focus on
developing new loss functions or regularizers, attempting to find the unique
optimal point in the weight space. But none of them taps the potentials of
classifiers obtained from standard adversarial training, especially states on
the searching trajectory of training. In this work, we are dedicated to the
weight states of models through the training process and devise a simple but
powerful \emph{Self-Ensemble Adversarial Training} (SEAT) method for yielding a
robust classifier by averaging weights of history models. This considerably
improves the robustness of the target model against several well known
adversarial attacks, even merely utilizing the naive cross-entropy loss to
supervise. We also discuss the relationship between the ensemble of predictions
from different adversarially trained models and the prediction of
weight-ensembled models, as well as provide theoretical and empirical evidence
that the proposed self-ensemble method provides a smoother loss landscape and
better robustness than both individual models and the ensemble of predictions
from different classifiers. We further analyze a subtle but fatal issue in the
general settings for the self-ensemble model, which causes the deterioration of
the weight-ensembled method in the late phases.


title:
Modeling Intensification for Sign Language Generation: A Computational
  Approach

abstract:
  End-to-end sign language generation models do not accurately represent the
prosody in sign language. A lack of temporal and spatial variations leads to
poor-quality generated presentations that confuse human interpreters. In this
paper, we aim to improve the prosody in generated sign languages by modeling
intensification in a data-driven manner. We present different strategies
grounded in linguistics of sign language that inform how intensity modifiers
can be represented in gloss annotations. To employ our strategies, we first
annotate a subset of the benchmark PHOENIX-14T, a German Sign Language dataset,
with different levels of intensification. We then use a supervised intensity
tagger to extend the annotated dataset and obtain labels for the remaining
portion of it. This enhanced dataset is then used to train state-of-the-art
transformer models for sign language generation. We find that our efforts in
intensification modeling yield better results when evaluated with automatic
metrics. Human evaluation also indicates a higher preference of the videos
generated using our model.


title: LeHDC: Learning-Based Hyperdimensional Computing Classifier

abstract:
  Thanks to the tiny storage and efficient execution, hyperdimensional
Computing (HDC) is emerging as a lightweight learning framework on
resource-constrained hardware. Nonetheless, the existing HDC training relies on
various heuristic methods, significantly limiting their inference accuracy. In
this paper, we propose a new HDC framework, called LeHDC, which leverages a
principled learning approach to improve the model accuracy. Concretely, LeHDC
maps the existing HDC framework into an equivalent Binary Neural Network
architecture, and employs a corresponding training strategy to minimize the
training loss. Experimental validation shows that LeHDC outperforms previous
HDC training strategies and can improve on average the inference accuracy over
15% compared to the baseline HDC.


title:
HDLock: Exploiting Privileged Encoding to Protect Hyperdimensional
  Computing Models against IP Stealing

abstract:
  Hyperdimensional Computing (HDC) is facing infringement issues due to
straightforward computations. This work, for the first time, raises a critical
vulnerability of HDC, an attacker can reverse engineer the entire model, only
requiring the unindexed hypervector memory. To mitigate this attack, we propose
a defense strategy, namely HDLock, which significantly increases the reasoning
cost of encoding. Specifically, HDLock adds extra feature hypervector
combination and permutation in the encoding module. Compared to the standard
HDC model, a two-layer-key HDLock can increase the adversarial reasoning
complexity by 10 order of magnitudes without inference accuracy loss, with only
21% latency overhead.


title:
Optimizing Randomized and Deterministic Saturation Designs under
  Interference

abstract:
  Randomized saturation designs are a family of designs which assign a possibly
different treatment proportion to each cluster of a population at random. As a
result, they generalize the well-known (stratified) completely randomized
designs and the cluster-based randomized designs, which are included as special
cases. We show that, under the stable unit treatment value assumption, either
the cluster-based or the stratified completely randomized design are in fact
optimal for the bias and variance of the difference-in-means estimator among
randomized saturation designs. However, this is no longer the case when
interference is present. We provide the closed form of the bias and variance of
the difference-in-means estimator under a linear model of interference and
investigate the optimization of each of these objectives. In addition to the
randomized saturation designs, we propose a deterministic saturation design,
where the treatment proportion for clusters are fixed, rather than randomized,
in order to further improve the estimator under correct model specification.
Through simulations, we illustrate the merits of optimizing randomized
saturation designs to the graph and potential outcome structure, as well as
showcasing the additional improvements yielded by well-chosen deterministic
saturation designs.