title:
Band Alignment in BP/MoS2 heterostructure: Role of Charge
  Redistribution, Electric Field, Biaxial Strain, and Layer Engineering

abstract:
  The objective of this work is to study the effects of charge redistribution,
applied layer-normal electric fields, applied strain, and layer engineering on
the band alignment of Black Phosphorus (BP)/Molybdenum disulphide (MoS2)
heterostructure through Density Functional Theory (DFT) simulations. Black
phosphorus works as a p-type material with high mobility, mechanical
flexibility, and sensitivity to number of layers. Combining it with the more
electronegative material, MoS2 results in strong carrier confinement and a Type
II heterostructure. Charge redistribution among the layers shifts the band
alignment expected from the Electron Affinity Rule. Applied external fields,
strain and multiple BP layers provide band-alignment tunability within the Type
II range and/or, transition to Type I and Type III heterostructures. The
tunability in BP/MoS2 heterostructure may be useful as tunnel field effect
transistors, rectifier diodes with tunable barrier height, reconfigurable FETs,
and electro-optical modulators. Furthermore, considering heterostructures of
monolayer BP with other monolayer Transitional Metal Dichalcogenides (TMD)
suggests the ability to achieve different band alignment types. In our
simulations, a Type I alignment is found with Tungsten diselenide (WSe2),
Molybdenum diselenide (MoSe2), and Tungsten disulphide (WS2), and a Type III
for Hafnium disulphide (HfS2) and Hafnium diselenide (HfSe2).


title:
Equivalences between different forms of the Kakeya conjecture and
  duality of Hausdorff and packing dimensions for additive complements

abstract:
  The Kakeya conjecture is generally formulated as one the following
statements: every compact/Borel/arbitrary subset of ${\mathbb R}^n$ that
contains a (unit) line segment in every direction has Hausdorff dimension $n$;
or, sometimes, that every closed/Borel/arbitrary subset of ${\mathbb R}^n$ that
contains a full line in every direction has Hausdorff dimension $n$. These
statements are generally expected to be equivalent. Moreover, the condition
that the set contains a line (segment) in every direction is often relaxed by
requiring a line (segment) for a "large" set of directions only, where large
could mean a set of positive $(n-1)$-dimensional Lebesgue measure.
  Here we prove that all the above forms of the Kakeya conjecture are indeed
equivalent. In fact, we prove that there exist $d\le n$ and a compact subset
$C$ of ${\mathbb R}^n$ of Hausdorff dimension $d$ that contains a unit line
segment in every direction (and also a closed set of dimension $d$ that
contains a line in every direction) such that every subset $S$ of ${\mathbb
R}^n$ that contains a line segment in every direction of a set of Hausdorff
dimension $n-1$, must have dimension at least $d$.
  We also obtain results on the duality of Hausdorff and packing dimensions via
additive complements: For any non-empty Borel set $A$ of ${\mathbb R}^n$ we
show that
  (1) the Hausdorff dimension of $A$ can be obtained as $n-p$, where $p$ is the
infimum of the packing dimension of those Borel subsets $B$ of ${\mathbb R}^n$
for which $A+B={\mathbb R}^n$; and
  (2) the packing dimension of $A$ can be obtained as $n-h$, where $h$ is the
infimum of the Hausdorff dimension of those Borel subsets $B$ of ${\mathbb
R}^n$ for which $A+B={\mathbb R}^n$.


title:
Accurate electronic properties and intercalation voltages of
  olivine-type Li-ion cathode materials from extended Hubbard functionals

abstract:
  The design of novel cathode materials for Li-ion batteries requires accurate
first-principles predictions of structural, electronic, and magnetic properties
as well as intercalation voltages in compounds containing transition-metal (TM)
elements. For such systems, density-functional theory (DFT) with standard
(semi-)local exchange-correlation functionals is of no use as it fails
dramatically due to strong self-interaction (delocalization) errors for the
partially filled d shells of the TMs. Here, we perform the first comparative
study of the phospho-olivine cathode materials Li$_x$MnPO$_4$, Li$_x$FePO$_4$,
and mixed-TM Li$_x$Mn$_{1/2}$Fe$_{1/2}$PO$_4$ ($x=0, 1/4, 1/2, 3/4, 1$) using
four electronic structure methods: DFT, DFT+$U$, DFT+$U$+$V$, and HSE06. We
show that DFT+$U$+$V$ outperforms the other three methods, provided that the
on-site $U$ and intersite $V$ Hubbard parameters are determined from
first-principles and self-consistently with respect to the structural
parameters by means of density-functional perturbation theory (linear
response). In particular, we demonstrate that DFT+$U$+$V$ is the only method
that correctly predicts the digital change in oxidation states of the TM ions
in all compounds for the mixed-valence phases occurring at intermediate Li
concentrations, leading to voltages in remarkable agreement with experiments.
We thus show that the inclusion of intersite Hubbard interactions is essential
for the accurate prediction of thermodynamic quantities when electronic
localization occurs while in the presence of inter-atomic orbital
hybridization. At variance with the other methods, DFT+$U$+$V$ alone is capable
to describe such localization-hybridization interplay, and thus opens the door
for the study of more complex cathode materials as well as for a reliable
exploration of the chemical space of compounds for Li-ion batteries.


title: Energy Balancing Algorithm for Wireless Sensor Network

abstract:
  A Wireless Sensor Network (WSN) is made up of a large number of nodes that
are spread randomly or on a regular basis to detect the surrounding environment
and transfer data to a base station (BS) over the Internet to the user. It is
widely used in a variety of civil and military concerns. Because the sensor has
limited battery capacity, energy efficiency is a critical issue with WSNs. As a
result, developing a routing protocol that decreases energy consumption in
sensor nodes to extend the lifetime of the WSN using an intelligence algorithm
has become difficult. LEACH is the first hierarchical routing protocol that
divides the WSN into clusters to reduce energy usage. However, it has reached
its limit in selecting a suitable cluster head and the sensor nodes to be
joined, as well as their quantity. Thus, this research proposes an algorithm
called Wireless Energy Balancing algorithm (WEB) that works on energy balancing
distribution by identifying a suitable cluster head with minimum distance and
high energy. Then it uses the knapsack-problem as a novel algorithm to design
the cluster members. The simulation results demonstrate that the WEB algorithm
outperforms LEACH by 31% in terms of energy conservation and WSN lifetime
extension.


title:
Implicit Likelihood Inference of Reionization Parameters from the 21 cm
  Power Spectrum

abstract:
  The first measurements of the 21 cm brightness temperature power spectrum
from the epoch of reionization will be very likely achieved in the near future
by radio interferometric array experiments such as the Hydrogen Epoch of
Reionization Array (HERA) and the precursors of the Square Kilometre Array
(SKA). Standard MCMC analyses use an explicit likelihood approximation to infer
the reionization and astrophysical parameters from the 21 cm power spectrum. In
this paper, we present a new Bayesian inference of the reionization parameters
where the likelihood is implicitly defined through forward simulations using
density estimation likelihood-free inference (DELFI). Realistic effects
including thermal noise and foreground avoidance are also applied to the mock
observations from the HERA and SKA. We demonstrate that this method recovers
accurate posterior distributions for the reionization parameters, and
outperforms the standard MCMC analysis in terms of the location and size of
credible parameter regions. With the minutes-level processing time once the
network is trained, this technique is a promising approach for the scientific
interpretation of future 21 cm power spectrum observation data. Our code
21cmDELFI-PS is publicly available at this link.


title: Derived equivalences between one-branch extensions of "rectangles"

abstract:
  In this paper we investigate the incidence algebras arising from one-branch
extensions of "rectangles". There are four different ways to form such
extensions, and all four kinds of incidence algebras turn out to be derived
equivalent. We provide realizations for all of them by tilting complexes in a
Nakayama algebra. As an application, we obtain the explicit formulas of the
Coxeter polynomials for a half of Nakayama algebras (i.e., the Nakayama
algebras $N(n,r)$ with $2r\geq n+2$). Meanwhile, an unexpected derived
equivalence between Nakayama algebras $N(2r-1,r)$ and $N(2r-1,r+1)$ has been
found.


title: Exact Community Recovery in Correlated Stochastic Block Models

abstract:
  We consider the problem of learning latent community structure from multiple
correlated networks. We study edge-correlated stochastic block models with two
balanced communities, focusing on the regime where the average degree is
logarithmic in the number of vertices. Our main result derives the precise
information-theoretic threshold for exact community recovery using multiple
correlated graphs. This threshold captures the interplay between the community
recovery and graph matching tasks. In particular, we uncover and characterize a
region of the parameter space where exact community recovery is possible using
multiple correlated graphs, even though (1) this is information-theoretically
impossible using a single graph and (2) exact graph matching is also
information-theoretically impossible. In this regime, we develop a novel
algorithm that carefully synthesizes algorithms from the community recovery and
graph matching literatures.

categories: math.ST cs.IT cs.LG cs.SI math.IT math.PR stat.TH


title:
Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full
  Version

abstract:
  Traffic time series forecasting is challenging due to complex spatio-temporal
dynamics time series from different locations often have distinct patterns; and
for the same time series, patterns may vary across time, where, for example,
there exist certain periods across a day showing stronger temporal
correlations. Although recent forecasting models, in particular deep learning
based models, show promising results, they suffer from being spatio-temporal
agnostic. Such spatio-temporal agnostic models employ a shared parameter space
irrespective of the time series locations and the time periods and they assume
that the temporal patterns are similar across locations and do not evolve
across time, which may not always hold, thus leading to sub-optimal results. In
this work, we propose a framework that aims at turning spatio-temporal agnostic
models to spatio-temporal aware models. To do so, we encode time series from
different locations into stochastic variables, from which we generate
location-specific and time-varying model parameters to better capture the
spatio-temporal dynamics. We show how to integrate the framework with canonical
attentions to enable spatio-temporal aware attentions. Next, to compensate for
the additional overhead introduced by the spatio-temporal aware model parameter
generation process, we propose a novel window attention scheme, which helps
reduce the complexity from quadratic to linear, making spatio-temporal aware
attentions also have competitive efficiency. We show strong empirical evidence
on four traffic time series datasets, where the proposed spatio-temporal aware
attentions outperform state-of-the-art methods in term of accuracy and
efficiency. This is an extended version of "Towards Spatio-Temporal Aware
Traffic Time Series Forecasting", to appear in ICDE 2022 [1], including
additional experimental results.


title:
Encryption and encoding of facial images into quick response and high
  capacity color 2d code for biometric passport security system

abstract:
  In this thesis, a multimodal biometric, secure encrypted data and encrypted
biometric encoded into the QR code-based biometric-passport authentication
method is proposed for national security applications. Firstly, using the
Extended Profile - Local Binary Patterns (EP-LBP), a Canny edge detector, and
the Scale Invariant Feature Transform (SIFT) algorithm with Image File
Information (IMFINFO) process, the facial mark size recognition is initially
achieved. Secondly, by using the Active Shape Model (ASM) into Active
Appearance Model (AAM) to follow the hand and infusion the hand geometry
characteristics for verification and identification, hand geometry recognition
is achieved. Thirdly, the encrypted biometric passport information that is
publicly accessible is encoded into the QR code and inserted into the
electronic passport to improve protection. Further, Personal information and
biometric data are encrypted by applying the Advanced Encryption Standard (AES)
and the Secure Hash Algorithm (SHA) 256 algorithm. It will enhance the
biometric passport security system.


title:
Accurate Deep Learning-aided Density-free Strategy for Many-Body
  Dispersion-corrected Density Functional Theory

abstract:
  Using a Deep Neuronal Network model (DNN) trained on the large ANI-1 data set
of small organic molecules, we propose a transferable density-free many-body
dispersion model (DNN-MBD). The DNN strategy bypasses the explicit Hirshfeld
partitioning of the Kohn-Sham electron density required by MBD models to obtain
the atom-in-molecules volumes used by the Tkatchenko-Scheffler polarizability
rescaling. The resulting DNN-MBD model is trained with minimal basis iterative
Stockholder atomic volumes and, coupled to Density Functional Theory (DFT),
exhibits comparable (if not greater) accuracy to other approaches based on
different partitioning schemes. Implemented in the Tinker-HP package, the
DNN-MBD model decreases the overall computational cost compared to MBD models
where the explicit density partitioning is performed. Its coupling with the
recently introduced Stochastic formulation of the MBD equations (J. Chem.
Theory. Comput., 2022, 18, 3, 1633-1645) enables large routine
dispersion-corrected DFT calculations at preserved accuracy. Furthermore, the
DNN electron density-free features extend MBD's applicability beyond electronic
structure theory within methodologies such as force fields and neural networks.


title: Exotic Calder\'on-Zygmund operators

abstract:
  We study singular integral operators with kernels that are more singular than
standard Calder\'on-Zygmund kernels, but less singular than bi-parameter
product Calder\'on-Zygmund kernels. These kernels arise as restrictions to two
dimensions of certain three-dimensional kernels adapted to so-called Zygmund
dilations, which is part of our motivation for studying these objects. We make
the case that such kernels can, in many ways, be seen as part of the extended
realm of standard kernels by proving that they satisfy both a T1 theorem and
commutator estimates in a form reminiscent of the corresponding results for
standard Calder\'on-Zygmund kernels. However, we show that one-parameter
weighted estimates, in general, fail.


title: Zeta functions in higher Teichmuller theory

abstract:
  In this note we introduce zeta functions and L-functions for discrete and
faithful representations of surface groups in PSL(d, R), for d >= 3. These are
natural generalizations of the wellknown classical Selberg zeta function and
L-function for Fuchsian groups, corresponding to the case d=2.We show that
these complex functions have meromorphic extensions to the entire complex plane
C.


title: The Hopping Forcing Rule

abstract:
  Zero forcing is a combinatorial game played on graphs that can be used to
model the spread of information with repeated applications of a color change
rule. In general, a zero forcing parameter is the minimum number of initial
blue vertices that are needed to eventually color every vertex blue with a
given color change rule. Furthermore, the throttling number minimizes the sum
of the number of initial blue vertices and the time taken for all vertices to
become blue. In 2013, Barioli et al. added a new rule, called hopping, to
existing color change rules in order to demonstrate that the minor monotone
floor of various zero forcing parameters is itself, a zero forcing parameter.
In this paper, we examine the hopping color change rule independently from the
other classic rules. Specifically, we study the hopping forcing number and the
hopping throttling number. We investigate the ways in which these numbers are
related to various graph theory parameters (such as vertex connectivity and
independence number) as well as other zero forcing parameters.


title: Phototactic cyanobacteria as an active matter system

abstract:
  Flocks of birds, schools of fish, mixtures of motors and cytoskeletal
filaments, swimming bacteria and driven granular media are systems of
interacting motile units that exhibit collective behaviour. These can all be
described as active matter systems, since each individual unit takes energy
from an internal energy depot and transduces it into work performed on the
environment. We review a model for cyanobacterial phototaxis, emphasising the
differences from other models for collective behaviour in active matter
systems. The interactions between individual cells during phototaxis are
dominated by mechanical forces mediated by their physical attachments through
type IV pili (T4P) and through the production of "slime", a complex mixture of
non-diffusible polysaccharides deposited by cells that acts to decrease
friction locally. The slime, in particular, adds a component to the interaction
that is local in space but non-local in time, perhaps most comparable to the
pheromones laid down in ant trails. Our results suggest that the time-delayed
component of the interactions between bacteria qualify their description as a
novel active system, which we refer to as "damp" active matter.


title: The $L_{\infty}$-structure of noncommutative gravity

abstract:
  We summarise recent perspectives on symmetries of noncommutative field
theories based on homotopy algebras. We show how these viewpoints naturally
lead to a new class of noncommutative field theories which possess braided
gauge symmetries, and explain in detail their uses in gravity. We review how
these considerations lead to a new theory of noncommutative gravity in four
dimensions within the Einstein-Cartan-Palatini formalism.


title:
Super-resolving multiple scatterers detection in SAR Tomography assisted
  by correlation information

abstract:
  This paper proposes a method for detecting multiple scatterers (targets) in
the elevation direction for synthetic aperture radar (SAR) tomography. The
proposed method can resolve closely spaced targets through a twostep procedure.
In the first step, coarse detection is performed with a successive cancellation
scheme in which possible locations of targets are marked. Then, in the second
step, by searching in the reduced search space which is finely 10 gridded, the
accurate location of the targets is found. For estimating the actual number of
targets, a model order selection scheme is used in two cases of known and
unknown noise variance. Also, by analytical investigation of the probability of
detection for the proposed method, the effect of the influential parameters on
the detection ability is explicitly demonstrated. Compared to the
super-resolution methods based on compressed sensing (CS), the proposed method
has a lower computational cost and higher estimation accuracy, especially at
low signal-to-noise ratio regime. 15 Simulation results show the superiority of
the proposed method in terms of both 3D scatterer reconstruction and detection
ability


title:
Selectivity Trends and Role of Adsorbate-Adsorbate Interactions in CO
  Hydrogenation on Rhodium Catalysts

abstract:
  Predictive-quality computational modeling of heterogeneously catalyzed
reactions has emerged as an important tool for the analysis and assessment of
activity and activity trends. In contrast, more subtle selectivities and
selectivity trends still pose a significant challenge to prevalent microkinetic
modeling approaches that typically employ a mean-field approximation (MFA).
Here, we focus on CO hydrogenation on Rh catalysts with the possible products
methane, acetaldehyde, ethanol and water. This reaction has already been
subject to a number of experimental and theoretical studies with conflicting
views on the factors controlling activity and selectivity towards the more
valuable higher oxygenates. Using accelerated first-principles kinetic Monte
Carlo (KMC) simulations and explicitly and systematically accounting for
adsorbate-adsorbate interactions through a cluster expansion approach, we model
the reaction on the low-index Rh(111) and stepped Rh(211) surfaces. We find
that the Rh(111) facet is selective towards methane, while the Rh(211) facet
exhibits a similar selectivity towards methane and acetaldehyde. This is
consistent with the experimental selectivity observed for larger, predominantly
(111)-exposing Rh nanoparticles and resolves the discrepancy to earlier
first-principles MFA microkinetic work that found the Rh(111) facet to be
selective towards acetaldehyde. While the latter work tried to approximately
account for lateral interactions through coverage-dependent rate expressions,
our analysis demonstrates that this fails to sufficiently capture concomitant
correlations among the adsorbed reaction intermediates that crucially determine
the overall selectivity.


title: A new approach to the mean-field limit of Vlasov-Fokker-Planck equations

abstract:
  This article introduces a novel approach to the mean-field limit of
stochastic systems of interacting particles, leading to the first ever
derivation of the mean-field limit to the Vlasov-Poisson-Fokker-Planck system
for plasmas in dimension~$2$ together with a partial result in dimension~$3$.
The method is broadly compatible with second order systems that lead to kinetic
equations and it relies on novel estimates on the BBGKY hierarchy. By taking
advantage of the diffusion in velocity, those estimates bound weighted $L^p$
norms of the marginals or observables of the system, uniformly in the number of
particles. This allows to treat very singular interaction kernels between the
particles, including repulsive Poisson interactions.


title: An Adaptive Benchmark for Modeling User Exploration of Large Datasets

abstract:
  Interactive analysis systems provide efficient and accessible means by which
users of varying technical experience can comfortably manipulate and analyze
data using interactive widgets. Widgets are elements of interaction within a
user interface (e.g. scrollbar, button, etc). Interactions with these widgets
produce database queries whose results determine the subsequent changes made to
the current visualization made by the user. In this paper, we present a tool
that extends IDEBench to ingest visualization interfaces and a dataset, and
estimate the expected database load that would be generated by real users. Our
tool analyzes the interactive capabilities of the visualization and creates the
queries that support the various interactions. We began with a proof of concept
implementation of every interaction widget, which led us to define three
distinct sets of query templates that can support all interactions. We then
show that these templates can be layered to imitate various interfaces and
tailored to any dataset. Secondly, we simulate how users would interact with
the proposed interface and report on the strain that such use would place on
the database management system.


title:
Ergodicity for Three-Dimensional Stochastic Navier-Stokes Equations with
  Markov Switching

abstract:
  Asymptotic behavior of the three-dimensional stochastic Navier-Stokes
equations with Markov switching in additive noises is studied for
incompressible fluid flow in a bounded domain in the three-dimensional space.
To study such a system, we introduce a family of regularized equations and
investigate the asymptotic behavior of the regularized equations first. The
existence an ergodic measure for the regularized system is established via the
Krylov-Bogolyubov method. Then the existence of an stationary measure to the
original system is obtained by extracting a limit from the ergodic measures of
the family of the regularized system.


title: On the Ergodicity of Interacting Particle Systems under Number Rigidity

abstract:
  In this paper, we prove the equivalence among the convergence to the
equilibrium of infinite interacting diffusion processes and two different
concepts stemming from the optimal transport theory and the theory of point
processes. Namely, under the assumption of the rigidity in number of point
processes $\mu$ in the sense of Ghosh-Peres (Duke Math. J. 166 (10) 2017), we
discuss the equivalence of the following three concepts: ${\bf (a)}$ the
ergodicity of interacting diffusion processes with invariant measure $\mu$;
${\bf (b)}$ the finiteness of $L^2$-transportation distance ${\sf
d}_{\boldsymbol\Upsilon}$ between sets of $\mu$-positive measures; ${\bf (c)}$
the tail-triviality of $\mu$. As an application, we obtain the convergence to
the equilibrium of infinite particle diffusions with long-range interaction:
$\mathrm{Sine}_{2}$, $\mathrm{Airy}_{2}$, $\mathrm{Bessel}_{\alpha, 2}$
($\alpha \ge 1$), and $\mathrm{Ginibre}$ point processes, in particular, the
case of Dyson Brownian motion is covered. The key steps of the proofs are
twofold: First, we make full use of results regarding the extended metric
measure geometry on ${\boldsymbol\Upsilon}$ developed in the recent paper
arXiv:2109.03192. Second, we establish the Sobolev-to-Lipschitz property for
Dirichlet forms with respect to ${\sf d}_{{\boldsymbol\Upsilon}}$, which was
conjectured originally by R\"ockner-Schied (J. Func. Anal. 169} (2) 1999).


title:
A Passive Similarity based CNN Filter Pruning for Efficient Acoustic
  Scene Classification

abstract:
  We present a method to develop low-complexity convolutional neural networks
(CNNs) for acoustic scene classification (ASC). The large size and high
computational complexity of typical CNNs is a bottleneck for their deployment
on resource-constrained devices. We propose a passive filter pruning framework,
where a few convolutional filters from the CNNs are eliminated to yield
compressed CNNs. Our hypothesis is that similar filters produce similar
responses and give redundant information allowing such filters to be eliminated
from the network. To identify similar filters, a cosine distance based greedy
algorithm is proposed. A fine-tuning process is then performed to regain much
of the performance lost due to filter elimination. To perform efficient
fine-tuning, we analyze how the performance varies as the number of fine-tuning
training examples changes. An experimental evaluation of the proposed framework
is performed on the publicly available DCASE 2021 Task 1A baseline network
trained for ASC. The proposed method is simple, reduces computations per
inference by 27%, with 25% fewer parameters, with less than 1% drop in
accuracy.


title:
Information Consumption and Boundary Spanning in Decentralized Online
  Social Networks: the case of Mastodon Users

abstract:
  Decentralized Online Social Networks (DOSNs) represent a growing trend in the
social media landscape, as opposed to the well-known centralized peers, which
are often in the spotlight due to privacy concerns and a vision typically
focused on monetization through user relationships. By exploiting open-source
software, DOSNs allow users to create their own servers, or instances, thus
favoring the proliferation of platforms that are independent yet interconnected
with each other in a transparent way. Nonetheless, the resulting cooperation
model, commonly known as the Fediverse, still represents a world to be fully
discovered, since existing studies have mainly focused on a limited number of
structural aspects of interest in DOSNs. In this work, we aim to fill a lack of
study on user relations and roles in DOSNs, by taking two main actions:
understanding the impact of decentralization on how users relate to each other
within their membership instance and/or across different instances, and
unveiling user roles that can explain two interrelated axes of social
behavioral phenomena, namely information consumption and boundary spanning. To
this purpose, we build our analysis on user networks from Mastodon, since it
represents the most widely used DOSN platform. We believe that the findings
drawn from our study on Mastodon users' roles and information flow can pave a
way for further development of fascinating research on DOSNs.


title:
HardVis: Visual Analytics to Handle Instance Hardness Using
  Undersampling and Oversampling Techniques

abstract:
  Despite the tremendous advances in machine learning (ML), training with
imbalanced data still poses challenges in many real-world applications. Among a
series of diverse techniques to solve this problem, sampling algorithms are
regarded as an efficient solution. However, the problem is more fundamental,
with many works emphasizing the importance of instance hardness. This issue
refers to the significance of managing unsafe or potentially noisy instances
that are more likely to be misclassified and serve as the root cause of poor
classification performance. This paper introduces HardVis, a visual analytics
system designed to handle instance hardness mainly in imbalanced classification
scenarios. Our proposed system assists users in visually comparing different
distributions of data types, selecting types of instances based on local
characteristics that will later be affected by the active sampling method, and
validating which suggestions from undersampling or oversampling techniques are
beneficial for the ML model. Additionally, rather than uniformly
undersampling/oversampling a specific class, we allow users to find and sample
easy and difficult to classify training instances from all classes. Users can
explore subsets of data from different perspectives to decide all those
parameters, while HardVis keeps track of their steps and evaluates the model's
predictive performance in a test set separately. The end result is a
well-balanced data set that boosts the predictive power of the ML model. The
efficacy and effectiveness of HardVis are demonstrated with a hypothetical
usage scenario and a use case. Finally, we also look at how useful our system
is based on feedback we received from ML experts.


title: Evaluating Prompts Across Multiple Choice Tasks In a Zero-Shot Setting

abstract:
  Large language models have shown that impressive zero-shot performance can be
achieved through natural language prompts (Radford et al., 2019; Brown et al.,
2020; Sanh et al., 2021). Creating an effective prompt, however, requires
significant trial and error. That \textit{prompts} the question: how do the
qualities of a prompt effects its performance? To this end, we collect and
standardize prompts from a diverse range of tasks for use with tasks they were
not designed for. We then evaluate these prompts across fixed multiple choice
datasets for a quantitative analysis of how certain attributes of a prompt
affect performance. We find that including the choices and using prompts not
used during pre-training provide significant improvements. All experiments and
code can be found https://github.com/gabeorlanski/zero-shot-cross-task.


title:
Demonstration-Bootstrapped Autonomous Practicing via Multi-Task
  Reinforcement Learning

abstract:
  Reinforcement learning systems have the potential to enable continuous
improvement in unstructured environments, leveraging data collected
autonomously. However, in practice these systems require significant amounts of
instrumentation or human intervention to learn in the real world. In this work,
we propose a system for reinforcement learning that leverages multi-task
reinforcement learning bootstrapped with prior data to enable continuous
autonomous practicing, minimizing the number of resets needed while being able
to learn temporally extended behaviors. We show how appropriately provided
prior data can help bootstrap both low-level multi-task policies and strategies
for sequencing these tasks one after another to enable learning with minimal
resets. This mechanism enables our robotic system to practice with minimal
human intervention at training time while being able to solve long horizon
tasks at test time. We show the efficacy of the proposed system on a
challenging kitchen manipulation task both in simulation and in the real world,
demonstrating the ability to practice autonomously in order to solve temporally
extended problems.


title:
Causal de Finetti: On the Identification of Invariant Causal Structure
  in Exchangeable Data

abstract:
  Learning invariant causal structure often relies on conditional independence
testing and assumption of independent and identically distributed data. Recent
work has explored inferring invariant causal structure using data coming from
different environments. These approaches are based on independent causal
mechanism (ICM) principle which postulates that the cause mechanism is
independent of the effect given cause mechanism. Despite its wide application
in machine learning and causal inference, there lacks a statistical
formalization of what independent mechanism means. Here we present Causal de
Finetti which offers a first statistical formalization of ICM principle.


title:
Infrared Optical Spectroscopy of Molten Fluorides: Methods, Electronic
  and Vibrational Data, Structural Interpretation, and Relevance to Radiative
  Heat Transfer

abstract:
  To help address the need for predicting radiative heat transfer (RHT)
behavior of molten salts, we conducted a comprehensive review of methods and
data from infrared optical spectroscopic measurements on molten fluoride salts.
Transmittance, reflectance, and trans-reflectance experimental methods are
discussed, along with the corresponding data reduction methodology and the
limitations of each technique. Optical spectroscopy is a convenient indirect
probe for changes in structural parameters with temperature and composition.
Electronic and vibrational absorption data for transition-metal, lanthanide,
and actinide solutes and vibrational absorption data for alkali and alkaline
earth fluoride solvents are compiled, and the corresponding structural
interpretation is discussed and compared with other experimental and
theoretical work. We find that solvent and solute vibrational absorption can be
significant in the mid-infrared, resulting in near-infrared edges of
significance to RHT. Extrapolation and averaging of existing edge data leads to
estimated gray absorption coefficient values at 700 degrees C of 493 m^(-1) for
FLiBe and 148 m^(-1) for FLiNaK, both within the range of 1 to 6000 m^(-1)
identified to be of engineering relevance for radiative heat transfer analysis.


title: A Sparsity-promoting Dictionary Model for Variational Autoencoders

abstract:
  Structuring the latent space in probabilistic deep generative models, e.g.,
variational autoencoders (VAEs), is important to yield more expressive models
and interpretable representations, and to avoid overfitting. One way to achieve
this objective is to impose a sparsity constraint on the latent variables,
e.g., via a Laplace prior. However, such approaches usually complicate the
training phase, and they sacrifice the reconstruction quality to promote
sparsity. In this paper, we propose a simple yet effective methodology to
structure the latent space via a sparsity-promoting dictionary model, which
assumes that each latent code can be written as a sparse linear combination of
a dictionary's columns. In particular, we leverage a computationally efficient
and tuning-free method, which relies on a zero-mean Gaussian latent prior with
learnable variances. We derive a variational inference scheme to train the
model. Experiments on speech generative modeling demonstrate the advantage of
the proposed approach over competing techniques, since it promotes sparsity
while not deteriorating the output speech quality.


title: Galactic diffuse gamma rays meet the PeV frontier

abstract:
  The Tibet AS$\gamma$ and LHAASO collaborations recently reported the
observation of a $\gamma$-ray diffuse emission with energy up to the PeV from
the Galactic plane. We discuss the relevance of non-uniform cosmic-ray
transport scenarios and the implications of these results for cosmic-ray
physics. We use the {\tt DRAGON} and {\tt HERMES} codes to build
high-resolution maps and spectral distributions of that emission for several
representative models under the condition that they reproduce a wide set of
local cosmic-ray data up to 100 PeV. We show that the energy spectra measured
by Tibet AS$\gamma$, LHAASO, ARGO-YBJ and Fermi-LAT in several regions of
interest in the sky can all be consistently described in terms of the emission
arising by the Galactic cosmic-ray "sea". We also show that all our models are
compatible with IceTop $\gamma$-ray upper limits. Our results favor transport
models characterized by spatial-dependent diffusion although some degeneracy
remains between the choice of the transport scenario and that of the cosmic-ray
spectral shape above 10 TeV. We discuss the role of forthcoming measurements in
resolving that ambiguity.


title:
A New Expression for the Product of Two $\kappa-\mu $ Shadowed Random
  Variables and its Application to Wireless Communication

abstract:
  In this work, the product of two independent and non-identically distributed
(i.n.i.d) $\kappa - \mu $ shadowed random variables is studied. We derive the
series expression for the probability density function (PDF), cumulative
distribution function (CDF), and moment generating function (MGF) of the
product of two (i.n.i.d) $\kappa - \mu $ shadowed random variables. The derived
formulation in this work is quite general as they incorporate most of the
typically used fading channels. As an application example, outage probability
(OP) has been derived for cascaded wireless systems and relay-assisted
communications with a variable gain relay. Extensive Monte-Carlo simulations
have also been carried out.


title:
The maximum domain of attraction of multivariate extreme value
  distributions is small

abstract:
  Consider the set of Borel probability measures on $\mathbf{R}^k$ and endow it
with the topology of weak convergence. We show that the subset of all
probability measures which belong to the domain of attraction of some
multivariate extreme value distributions is dense and of the first Baire
category. In addition, the analogue result holds in the context of free
probability theory.


title: Elastohydrodynamic synchronization of rotating bacterial flagella

abstract:
  To rotate continuously without jamming, the flagellar filaments of bacteria
need to be locked in phase. While several models have been proposed for
eukaryotic flagella, the synchronization of bacterial flagella is less well
understood. Starting from a reduced model of flexible and
hydrodynamically-coupled bacterial flagella, we rigorously coarse-grain the
equations of motion using the method of multiple scales, and hence show that
bacterial flagella generically synchronize to zero phase difference via an
elastohydrodynamic mechanism. Remarkably, the far-field rate of synchronization
is maximized at an intermediate value of elastic compliance, with surprising
implications for bacteria.


title:
Identifying the population of stable ${\nu}_6$ resonant asteroids using
  large databases

abstract:
  Large observational surveys, like those that will be conducted at the Vera C.
Rubin Observatory, are expected to discover up to one million new asteroids in
the first year of operation. This will more than double the database of known
asteroids in a very short time. New methods and techniques will be needed to
handle the large influx of data. Here, we tested some of these new methods by
studying the population of asteroids on stable orbits inside the ${\nu}_6$
secular resonance. This resonance is one of the strongest mechanisms for
destabilizing the orbits of main-belt bodies and producing Near-Earth Asteroids
(NEAs). Yet, stable orbital configurations where the asteroid pericenter is
either aligned or anti-aligned with that of Saturn exist inside the resonance.
The population of stable ${\nu}_6$ resonators, first discovered in the early
2010s, is now the largest population of asteroids in stable orbits inside a
secular resonance.
  Here we obtained the largest sample of asteroids' proper elements for
numbered and multi-opposition objects most likely to be affected by the
resonance. Artificial Neural Networks (ANN) were then used to identify the
images of resonant angles of asteroids in stable orbits, more than doubling
their number. Clustering methods and the use of machine learning algorithms
allowed the identification of the known asteroid families crossed by the
${\nu}_6$ resonance, the Tina, Euphrosyne, and Svea clusters, and of two
entirely new groups: the Tiffanykapler and the 138605 QW177 families.


title: 10 Problems for Partitions of Triangle-free Graphs

abstract:
  We will state 10 problems, and solve some of them, for partitions in
triangle-free graphs related to Erd\H{o}s' Sparse Half Conjecture. Among others
we prove the following variant of it: For every sufficiently large even integer
$n$ the following holds. Every triangle-free graph on $n$ vertices has a
partition $V(G)=A\cup B$ with $|A|=|B|=n/2$ such that $e(G[A])+e(G[B])\leq
n^2/16$. This result is sharp since the complete bipartite graph with class
sizes $3n/4$ and $n/4$ achieves equality, when $n$ is a multiple of 4.
  Additionally, we discuss similar problems for $K_4$-free graphs.


title:
FisherMatch: Semi-Supervised Rotation Regression via Entropy-based
  Filtering

abstract:
  Estimating the 3DoF rotation from a single RGB image is an important yet
challenging problem. Recent works achieve good performance relying on a large
amount of expensive-to-obtain labeled data. To reduce the amount of
supervision, we for the first time propose a general framework, FisherMatch,
for semi-supervised rotation regression, without assuming any domain-specific
knowledge or paired data. Inspired by the popular semi-supervised approach,
FixMatch, we propose to leverage pseudo label filtering to facilitate the
information flow from labeled data to unlabeled data in a teacher-student
mutual learning framework. However, incorporating the pseudo label filtering
mechanism into semi-supervised rotation regression is highly non-trivial,
mainly due to the lack of a reliable confidence measure for rotation
prediction. In this work, we propose to leverage matrix Fisher distribution to
build a probabilistic model of rotation and devise a matrix Fisher-based
regressor for jointly predicting rotation along with its prediction
uncertainty. We then propose to use the entropy of the predicted distribution
as a confidence measure, which enables us to perform pseudo label filtering for
rotation regression. For supervising such distribution-like pseudo labels, we
further investigate the problem of how to enforce loss between two matrix
Fisher distributions. Our extensive experiments show that our method can work
well even under very low labeled data ratios on different benchmarks, achieving
significant and consistent performance improvement over supervised learning and
other semi-supervised learning baselines. Our project page is at
https://yd-yin.github.io/FisherMatch.


title:
From asymptotic freedom to $\theta$ vacua: Qubit embeddings of the O(3)
  nonlinear $\sigma$ model

abstract:
  Conventional lattice formulations of $\theta$ vacua in the $1+1$-dimensional
$\text{O}(3)$ nonlinear sigma model suffer from a sign problem. Here, we
construct the first sign-problem-free regularization for arbitrary $\theta$.
Using efficient lattice Monte Carlo algorithms, we demonstrate how a
Hamiltonian model of spin-$\tfrac12$ degrees of freedom on a 2-dimensional
spatial lattice reproduces both the infrared sector for arbitrary $\theta$, as
well as the ultraviolet physics of asymptotic freedom. Furthermore, as a model
of qubits on a two-dimensional square lattice with only nearest-neighbor
interactions, it is naturally suited for studying the physics of $\theta$ vacua
and asymptotic freedom on near-term quantum devices. Our construction
generalizes to $\theta$ vacua in all $\text{CP}(N-1)$ models, solving a long
standing sign problem.


title:
Polite or Direct? Conversation Design of a Smart Display for Older
  Adults Based on Politeness Theory

abstract:
  Conversational interfaces increasingly rely on human-like dialogue to offer a
natural experience. However, relying on dialogue involving multiple exchanges
for even simple tasks can overburden users, particularly older adults. In this
paper, we explored the use of politeness theory in conversation design to
alleviate this burden and improve user experience. To achieve this goal, we
categorized the voice interaction offered by a smart display application
designed for older adults into seven major speech acts: request, suggest,
instruct, comment, welcome, farewell, and repair. We identified face needs for
each speech act, applied politeness strategies that best address these needs,
and tested the ability of these strategies to shape the perceived politeness of
a voice assistant in an online study ($n=64$). Based on the findings of this
study, we designed direct and polite versions of the system and conducted a
field study ($n=15$) in which participants used each of the versions for five
days at their homes. Based on five factors merged from our qualitative
findings, we identified four distinctive user personas$\unicode{x2013}$socially
oriented follower, socially oriented leader, utility oriented follower, and
utility oriented leader$\unicode{x2013}$that can inform personalized design of
smart displays.


title:
Approximations of Eilenberg-MacLane spaces by smooth projective
  varieties

abstract:
  In this note we construct approximations by smooth projective varieties of
some Eienberg-MacLane spaces in the $A^1$-homotopy category. Using these, we
study the cycle maps from Chow rings to etale cohomology rings.


title: On the level set version of partial uniform ellipticity and applications

abstract:
  We derive level set version of partial uniform ellipticity for symmetric
concave functions. This suggests an effective approach to investigate second
order fully nonlinear equations of elliptic and parabolic type.


title: Target Geometry Estimation Using Deep Neural Networks in Sonar Sensing

abstract:
  Accurate imaging of target shape is a crucial aspect of wideband FM biosonar
in echolocating bats, for which we have developed new algorithms that provide a
solution for the shape of complicated targets in the computational domain. We
use recurrent neural networks and convolutional neural networks to determine
the number of glints (i.e., major reflecting surfaces) making up the target's
structure and the distances between the glints (target shape in sonar). Echoes
are dechirped relative to broadcasts, and the dechirped spectrograms are
scanned in short time segments to find local spectral ripple patterns arising
from different interglint delay separations. By proceeding in successive
time-window slices, we mimic time-frequency neural processing in the bat's
auditory system as a novel means of real-time target discrimination for sonar
sensing in robotics.


title: Operations on spectral partition Lie algebras and TAQ cohomology

abstract:
  We determine all natural operations and their relations on the homotopy
groups of spectral partition Lie algebras, as well as mod $p$ Topological
Andr\'{e}-Quillen cohomology operations at any prime. We construct unary
operations and a shifted restricted Lie algebra structure on the homotopy
groups of spectral partition Lie algebras. Then we prove a composition law for
the unary operations, as well as a compatibility condition between unary
operations and the shifted Lie bracket with restriction. Comparing with
Brantner-Mathew's result on the ranks of the homotopy groups of free spectral
partition Lie algebras, we deduce that these generate all natural operations
and obtain the target category. Since $\mathbb{F}_p$-linear TAQ cohomology
operations coincide with operations on the homotopy groups of spectral
partition Lie algebras, we deduce the structure of $\mathbb{F}_p$-linear TAQ
cohomology operations, thereby clarifying unpublished results by Kriz and
Basterra-Mandell. As a corollary, we determine all natural operations on mod
$p$ $\mathbb{S}$-linear TAQ cohomology, along with relations among the
operations.


title:
Impact of Information Flow Topology on Safety of Tightly-coupled
  Connected and Automated Vehicle Platoons Utilizing Stochastic Control

abstract:
  Cooperative driving, enabled by Vehicle-to-Everything (V2X) communication, is
expected to significantly contribute to the transportation system's safety and
efficiency. Cooperative Adaptive Cruise Control (CACC), a major cooperative
driving application, has been the subject of many studies in recent years. The
primary motivation behind using CACC is to reduce traffic congestion and
improve traffic flow, traffic throughput, and highway capacity. Since the
information flow between cooperative vehicles can significantly affect the
dynamics of a platoon, the design and performance of control components are
tightly dependent on the communication component performance. In addition, the
choice of Information Flow Topology (IFT) can affect certain platoons
properties such as stability and scalability. Although cooperative vehicles
perception can be expanded to multiple predecessors information by using V2X
communication, the communication technologies still suffer from scalability
issues. Therefore, cooperative vehicles are required to predict each other's
behavior to compensate for the effects of non-ideal communication. The notion
of Model-Based Communication (MBC) was proposed to enhance cooperative vehicles
perception under non-ideal communication by introducing a new flexible content
structure for broadcasting joint vehicles dynamic/drivers behavior models. By
utilizing a non-parametric (Bayesian) modeling scheme, i.e., Gaussian Process
Regression (GPR), and the MBC concept, this paper develops a discrete hybrid
stochastic model predictive control approach and examines the impact of
communication losses and different information flow topologies on the
performance and safety of the platoon. The results demonstrate an improvement
in response time and safety using more vehicles information, validating the
potential of cooperation to attenuate disturbances and improve traffic flow and
safety.


title:
Streaming parallel transducer beam search with fast-slow cascaded
  encoders

abstract:
  Streaming ASR with strict latency constraints is required in many speech
recognition applications. In order to achieve the required latency, streaming
ASR models sacrifice accuracy compared to non-streaming ASR models due to lack
of future input context. Previous research has shown that streaming and
non-streaming ASR for RNN Transducers can be unified by cascading causal and
non-causal encoders. This work improves upon this cascaded encoders framework
by leveraging two streaming non-causal encoders with variable input context
sizes that can produce outputs at different audio intervals (e.g. fast and
slow). We propose a novel parallel time-synchronous beam search algorithm for
transducers that decodes from fast-slow encoders, where the slow encoder
corrects the mistakes generated from the fast encoder. The proposed algorithm,
achieves up to 20% WER reduction with a slight increase in token emission
delays on the public Librispeech dataset and in-house datasets. We also explore
techniques to reduce the computation by distributing processing between the
fast and slow encoders. Lastly, we explore sharing the parameters in the fast
encoder to reduce the memory footprint. This enables low latency processing on
edge devices with low computation cost and a low memory footprint.


title:
Weighted Ehrhart series and a type-$\mathsf{B}$ analogue of a formula of
  MacMahon

abstract:
  We present a formula for a generalisation of the Eulerian polynomial, namely
the joint distribution of major index and descent statistic on signed multiset
permutations. It allows a description in terms of the $h^*$-polynomial of a
certain polytope. We associate a family of polytopes to (generalised)
permutations of types $\mathsf{A}$ and $\mathsf{B}$. We use this connection to
study properties of the (generalised) Eulerian numbers, such as palindromicity
and unimodality, by identifying certain properties of the associated polytope.
We also present partial results on generalising the connection between descent
polynomials and polytopes to coloured (multiset) permutations.


title:
Decadal bandwidth phase matching for frequency resolved optical gating
  from the near to the long wavelength infrared

abstract:
  Ultra-fast laser pulses have made possible the study of extreme nonlinear
processes. Many of these experiments however require a full characterization of
the electric field, which typically requires a separate parametric optical
process. As the central wavelength range of new sources continues to increase
so too does the need for nonlinear crystals suited for characterizing these
differing wavelengths. Here we report on the use of polycrystalline Zinc
Selenide as a universal nonlinear crystal in the frequency resolved optical
gating characterization technique from the near to long-wavelength infrared,
capable of phase matching second-harmonic generation and sum-frequency
generation over a decade of bandwidth, as well as ultra-broadband pulses, all
of which being crystal orientation and input polarization independent. With the
majority of ultra-fast laser sources being in this span of wavelengths, this
work demonstrates a greatly simplified approach towards ultra-fast pulse
characterization in the mid to long-wavelength infrared.


title:
Efficiently Evolving Swarm Behaviors Using Grammatical Evolution With
  PPA-style Behavior Trees

abstract:
  Evolving swarm behaviors with artificial agents is computationally expensive
and challenging. Because reward structures are often sparse in swarm problems,
only a few simulations among hundreds evolve successful swarm behaviors.
Additionally, swarm evolutionary algorithms typically rely on ad hoc fitness
structures, and novel fitness functions need to be designed for each swarm
task. This paper evolves swarm behaviors by systematically combining
Postcondition-Precondition-Action (PPA) canonical Behavior Trees (BT) with a
Grammatical Evolution. The PPA structure replaces ad hoc reward structures with
systematic postcondition checks, which allows a common grammar to learn
solutions to different tasks using only environmental cues and BT feedback. The
static performance of learned behaviors is poor because no agent learns all
necessary subtasks, but performance while evolving is excellent because agents
can quickly change behaviors in new contexts. The evolving algorithm succeeded
in 75\% of learning trials for both foraging and nest maintenance tasks, an
eight-fold improvement over prior work.


title: Multiresolution analysis and Zygmund dilations

abstract:
  Zygmund dilations are a group of dilations lying in between the standard
product theory and the one-parameter setting - in $\mathbb{R}^3 = \mathbb{R}
\times \mathbb{R} \times \mathbb{R}$ they are the dilations $(x_1, x_2, x_3)
\mapsto (\delta_1 x_1, \delta_2 x_2, \delta_1 \delta_2 x_3)$. The dyadic
multiresolution analysis and the related dyadic-probabilistic methods have been
very impactful in the modern product singular integral theory. However, the
multiresolution analysis has not been understood in the Zygmund dilation
setting or in other modified product space settings. In this paper we develop
this missing dyadic multiresolution analysis of Zygmund type, and justify its
usefulness by bounding, on weighted spaces, a general class of singular
integrals that are invariant under Zygmund dilations. We provide novel examples
of Zygmund $A_p$ weights and Zygmund kernels showcasing the optimality of our
kernel assumptions for weighted estimates.


title:
Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement
  Learning Method

abstract:
  The growth of videos in our digital age and the users' limited time raise the
demand for processing untrimmed videos to produce shorter versions conveying
the same information. Despite the remarkable progress that summarization
methods have made, most of them can only select a few frames or skims, creating
visual gaps and breaking the video context. This paper presents a novel
weakly-supervised methodology based on a reinforcement learning formulation to
accelerate instructional videos using text. A novel joint reward function
guides our agent to select which frames to remove and reduce the input video to
a target length without creating gaps in the final video. We also propose the
Extended Visually-guided Document Attention Network (VDAN+), which can generate
a highly discriminative embedding space to represent both textual and visual
data. Our experiments show that our method achieves the best performance in
Precision, Recall, and F1 Score against the baselines while effectively
controlling the video's output length. Visit
https://www.verlab.dcc.ufmg.br/semantic-hyperlapse/tpami2022/ for code and
extra results.


title: IIB matrix model, bosonic master field, and emergent spacetime

abstract:
  The IIB matrix model has been suggested as a particular formulation of
nonperturbative superstring theory (M-theory). It has now been realized that an
emerging classical spacetime may reside in its large-$N$ master field. This
bosonic master field can, in principle, give rise to Minkowski and
Robertson-Walker spacetimes. The outstanding task is to solve the bosonic
master-field equation, which is essentially an algebraic equation. In this
article, we present new results for the $(D,\,N)=(10,\,4)$ bosonic master-field
equation of the IIB matrix model, where $D$ is the dimensionality and $N$ the
matrix size. We also give, in a self-contained appendix, explicit results for
critical points of the effective bosonic action. The main physics application
of the (dimensionless) IIB matrix model may be in providing a (conformal) phase
that replaces the Friedmann big bang singularity.


title:
Adaptive Learning with Artificial Barriers Yielding Nash Equilibria in
  General Games

abstract:
  Artificial barriers in Learning Automata (LA) is a powerful and yet
under-explored concept although it was first proposed in the 1980s. Introducing
artificial non-absorbing barriers makes the LA schemes resilient to being
trapped in absorbing barriers, a phenomenon which is often referred to as lock
in probability leading to an exclusive choice of one action after convergence.
Within the field of LA and reinforcement learning in general, there is a
sacristy of theoretical works and applications of schemes with artificial
barriers. In this paper, we devise a LA with artificial barriers for solving a
general form of stochastic bimatrix game. Classical LA systems possess
properties of absorbing barriers and they are a powerful tool in game theory
and were shown to converge to game's of Nash equilibrium under limited
information. However, the stream of works in LA for solving game theoretical
problems can merely solve the case where the Saddle Point of the game exists in
a pure strategy and fail to reach mixed Nash equilibrium when no Saddle Point
exists for a pure strategy. In this paper, by resorting to the powerful concept
of artificial barriers, we suggest a LA that converges to an optimal mixed Nash
equilibrium even though there may be no Saddle Point when a pure strategy is
invoked. Our deployed scheme is of Linear Reward-Inaction ($L_{R-I}$) flavor
which is originally an absorbing LA scheme, however, we render it non-absorbing
by introducing artificial barriers in an elegant and natural manner, in the
sense that that the well-known legacy $L_{R-I}$ scheme can be seen as an
instance of our proposed algorithm for a particular choice of the barrier.
Furthermore, we present an $S$ Learning version of our LA with absorbing
barriers that is able to handle $S$-Learning environment in which the feedback
is continuous and not binary as in the case of the $L_{R-I}$.


title:
Deep Reinforcement Learning Aided Platoon Control Relying on V2X
  Information

abstract:
  The impact of Vehicle-to-Everything (V2X) communications on platoon control
performance is investigated. Platoon control is essentially a sequential
stochastic decision problem (SSDP), which can be solved by Deep Reinforcement
Learning (DRL) to deal with both the control constraints and uncertainty in the
platoon leading vehicle's behavior. In this context, the value of V2X
communications for DRL-based platoon controllers is studied with an emphasis on
the tradeoff between the gain of including exogenous information in the system
state for reducing uncertainty and the performance erosion due to the
curse-of-dimensionality. Our objective is to find the specific set of
information that should be shared among the vehicles for the construction of
the most appropriate state space. SSDP models are conceived for platoon control
under different information topologies (IFT) by taking into account `just
sufficient' information. Furthermore, theorems are established for comparing
the performance of their optimal policies. In order to determine whether a
piece of information should or should not be transmitted for improving the
DRL-based control policy, we quantify its value by deriving the conditional KL
divergence of the transition models. More meritorious information is given
higher priority in transmission, since including it in the state space has a
higher probability in offsetting the negative effect of having higher state
dimensions. Finally, simulation results are provided to illustrate the
theoretical analysis.


title:
Model Selection for Maternal Hypertensive Disorders with Symmetric
  Hierarchical Dirichlet Processes

abstract:
  Hypertensive disorders of pregnancy occur in about 10% of pregnant women
around the world. Though there is evidence that hypertension impacts maternal
cardiac functions, the relation between hypertension and cardiac dysfunctions
is only partially understood. The study of this relationship can be framed as a
joint inferential problem on multiple populations, each corresponding to a
different hypertensive disorder diagnosis, that combines multivariate
information provided by a collection of cardiac function indexes. A Bayesian
nonparametric approach seems particularly suited for this setup and we
demonstrate it on a dataset consisting of transthoracic echocardiography
results of a cohort of Indian pregnant women. We are able to perform model
selection, provide density estimates of cardiac function indexes and a latent
clustering of patients: these readily interpretable inferential outputs allow
to single out modified cardiac functions in hypertensive patients compared to
healthy subjects and progressively increased alterations with the severity of
the disorder. The analysis is based on a Bayesian nonparametric model that
relies on a novel hierarchical structure, called symmetric hierarchical
Dirichlet process. This is suitably designed so that the mean parameters are
identified and used for model selection across populations, a penalization for
multiplicity is enforced, and the presence of unobserved relevant factors is
investigated through a latent clustering of subjects. Posterior inference
relies on a suitable Markov Chain Monte Carlo algorithm and the model behaviour
is also showcased on simulated data.


title: A Systematic Review on Interactive Virtual Reality Laboratory

abstract:
  Virtual Reality has become a significant element of education throughout the
years. To understand the quality and advantages of these techniques, it is
important to understand how they were developed and evaluated. Since COVID-19,
the education system has drastically changed a lot. It has shifted from being
in a classroom with a whiteboard and projectors to having your own room in
front of your laptop in a virtual meeting. In this respect, virtual reality in
the laboratory or Virtual Laboratory is the main focus of this research, which
is intended to comprehend the work done in quality education from a distance
using VR. As per the findings of the study, adopting virtual reality in
education can help students learn more effectively and also help them increase
perspective, enthusiasm, and knowledge of complex notions by offering them an
interactive experience in which they can engage and learn more effectively.
This highlights the importance of a significant expansion of VR use in
learning, the majority of which employ scientific comparison approaches to
compare students who use VR to those who use the traditional method for
learning.


title: Implementation of an Automated Learning System for Non-experts

abstract:
  Automated machine learning systems for non-experts could be critical for
industries to adopt artificial intelligence to their own applications. This
paper detailed the engineering system implementation of an automated machine
learning system called YMIR, which completely relies on graphical interface to
interact with users. After importing training/validation data into the system,
a user without AI knowledge can label the data, train models, perform data
mining and evaluation by simply clicking buttons. The paper described: 1) Open
implementation of model training and inference through docker containers. 2)
Implementation of task and resource management. 3) Integration of Labeling
software. 4) Implementation of HCI (Human Computer Interaction) with a rebuilt
collaborative development paradigm. We also provide subsequent case study on
training models with the system. We hope this paper can facilitate the
prosperity of our automated machine learning community from industry
application perspective. The code of the system has already been released to
GitHub (https://github.com/industryessentials/ymir).


title: Eliminating black holes singularity in Brane world Cosmology

abstract:
  In recent works, a construction was proposed resulting emergent universes
inside black holes. This result can be obtained from a 4D black hole embedded
in a 5D space time with the fifth dimension compactified on a circle [0, 2] (0
and 2 are identified) such that the two branes are at 0 and 1. In the present
work, we study this setup by deriving particles equations of motion in the new
universes, based on redefining energy and angular momentum. This leads to
disappearance of the singularity in centres of black holes in classical General
Relativity.


title:
Electric-field-coupled oscillators for collective electrochemical
  perception in underwater robotics

abstract:
  This work explores the application of nonlinear oscillators coupled by
electric field in water for collective tasks in underwater robotics. Such
coupled oscillators operate in clear and colloidal (mud, bottom silt) water and
represent a collective electrochemical sensor that is sensitive to global
environmental parameters, geometry of common electric field and spatial
dynamics of autonomous underwater vehicles (AUVs). Implemented in hardware and
software, this approach can be used to create global awareness in the group of
robots, which possess limited sensing and communication capabilities. Using
oscillators from different AUVs enables extending the range limitations related
to electric dipole of a single AUV. Applications of this technique are
demonstrated for detecting the number of AUVs, distances between them,
perception of dielectric objects, synchronization of behavior and
discrimination between 'collective self' and 'collective non-self' through an
'electrical mirror'. These approaches have been implemented in several research
projects with AUVs in fresh and salt water.


title:
Effective and Acceptable Eco-Driving Guidance for Human-Driving
  Vehicles: A Review

abstract:
  Ecodriving guidance includes courses or suggestions for human drivers to
improve driving behaviour, reducing energy use and emissions. This paper
presents a systematic review of existing eco-driving guidance studies and
identifies challenges to tackle in the future. A standard agreement on the
guidance design has not been reached, leading to difficulties in designing and
implementing eco-driving guidance for human drivers. Both static and dynamic
guidance systems have a great variety of guidance results. In addition, the
influencing factors, such as the suggestion content, the displaying methods,
and drivers socio-demographic characteristics, have opposite effects on the
guidance result across studies, while the reason has not been revealed. Drivers
motivation to practice eco behaviour, especially long-term, is overlooked.
Besides, the relationship between users acceptance and system effectiveness is
still unclear. Adaptive driving suggestions based on drivers habits can improve
the effectiveness, while this field is under investigation.


title: COMPASS: Contrastive Multimodal Pretraining for Autonomous Systems

abstract:
  Learning representations that generalize across tasks and domains is
challenging yet necessary for autonomous systems. Although task-driven
approaches are appealing, designing models specific to each application can be
difficult in the face of limited data, especially when dealing with highly
variable multimodal input spaces arising from different tasks in different
environments.We introduce the first general-purpose pretraining pipeline,
COntrastive Multimodal Pretraining for AutonomouS Systems (COMPASS), to
overcome the limitations of task-specific models and existing pretraining
approaches. COMPASS constructs a multimodal graph by considering the essential
information for autonomous systems and the properties of different modalities.
Through this graph, multimodal signals are connected and mapped into two
factorized spatio-temporal latent spaces: a "motion pattern space" and a
"current state space." By learning from multimodal correspondences in each
latent space, COMPASS creates state representations that models necessary
information such as temporal dynamics, geometry, and semantics. We pretrain
COMPASS on a large-scale multimodal simulation dataset TartanAir
\cite{tartanair2020iros} and evaluate it on drone navigation, vehicle racing,
and visual odometry tasks. The experiments indicate that COMPASS can tackle all
three scenarios and can also generalize to unseen environments and real-world
data.


title:
Revisiting Neighborhood-based Link Prediction for Collaborative
  Filtering

abstract:
  Collaborative filtering (CF) is one of the most successful and fundamental
techniques in recommendation systems. In recent years, Graph Neural Network
(GNN)-based CF models, such as NGCF [31], LightGCN [10] and GTN [9] have
achieved tremendous success and significantly advanced the state-of-the-art.
While there is a rich literature of such works using advanced models for
learning user and item representations separately, item recommendation is
essentially a link prediction problem between users and items. Furthermore,
while there have been early works employing link prediction for collaborative
filtering [5, 6], this trend has largely given way to works focused on
aggregating information from user and item nodes, rather than modeling links
directly. In this paper, we propose a new linkage (connectivity) score for
bipartite graphs, generalizing multiple standard link prediction methods. We
combine this new score with an iterative degree update process in the user-item
interaction bipartite graph to exploit local graph structures without any node
modeling. The result is a simple, non-deep learning model with only six
learnable parameters. Despite its simplicity, we demonstrate our approach
significantly outperforms existing state-of-the-art GNN-based CF approaches on
four widely used benchmarks. In particular, on Amazon-Book, we demonstrate an
over 60% improvement for both Recall and NDCG. We hope our work would invite
the community to revisit the link prediction aspect of collaborative filtering,
where significant performance gains could be achieved through aligning link
prediction with item recommendations.


title:
Asteroid Polarimetric-Phase Behavior in the Near-Infrared: S- and
  C-Complex Objects

abstract:
  We present the first results of our survey of asteroid polarization-phase
curves in the near-infrared J and H bands using the WIRC+Pol instrument on the
Palomar 200-inch telescope. We confirm through observations of standard stars
that WIRC+Pol can reach the 0.1% precision needed for asteroid phase curve
characterization, and show that C-complex asteroids could act as an alternate
calibration source as they show less wavelength variation than stellar
polarized standards. Initial polarization-phase curve results for S-complex
asteroids show a shift in behavior as a function of wavelength from visible to
near-infrared bands, extending previously observed trends. Full near-infrared
polarization-phase curve characterization of individual asteroids will provide
a unique constraint on surface composition of these objects by probing the
wavelength dependence of albedo and index of refraction of the surface
material.


title: Duality, Criticality, Anomaly, and Topology in Quantum Spin-1 Chains

abstract:
  In quantum spin-1 chains, there is a nonlocal unitary transformation, known
as the Kennedy-Tasaki transformation $U_{\text{KT}}$, that defines a duality
between the Haldane phase and the $\mathbb{Z}_2 \times \mathbb{Z}_2$
symmetry-breaking phase. In this paper, we find that $U_{\text{KT}}$ also
defines a duality between a topological Ising critical phase and a trivial
Ising critical phase, which provides a "hidden symmetry breaking"
interpretation for the topological criticality. Moreover, since the duality
relates different phases of matter, we argue that a model with self-duality
(i.e., invariant under $U_{\text{KT}}$) is natural to be at a critical or
multicritical point. We study concrete examples to demonstrate this argument.
In particular, when $H$ is the Hamiltonian of the spin-1 antiferromagnetic
Heisenberg chain, we prove that the self-dual model $H + U_{\text{KT}} H
U_{\text{KT}}$ is exactly equivalent to a gapless spin-$1/2$ XY chain, which
also implies an emergent quantum anomaly. On the other hand, we show that the
topological and trivial Ising criticalities that are dual to each other meet at
a multicritical point which is indeed self-dual. Our discussions can be
generalized to other symmetries beyond the spin-1 chains.

categories: cond-mat.str-el cond-mat.stat-mech hep-th quant-ph


title: Target and Task specific Source-Free Domain Adaptive Image Segmentation

abstract:
  Solving the domain shift problem during inference is essential in medical
imaging as most deep-learning based solutions suffer from it. In practice,
domain shifts are tackled by performing Unsupervised Domain Adaptation (UDA),
where a model is adapted to an unlabeled target domain by leveraging the
labelled source domain. In medical scenarios, the data comes with huge privacy
concerns making it difficult to apply standard UDA techniques. Hence, a closer
clinical setting is Source-Free UDA (SFUDA), where we have access to source
trained model but not the source data during adaptation. Methods trying to
solve SFUDA typically address the domain shift using pseudo-label based
self-training techniques. However, due to domain shift, these pseudo-labels are
usually of high entropy and denoising them still does not make them perfect
labels to supervise the model. Therefore, adapting the source model with noisy
pseudo labels reduces its segmentation capability while addressing the domain
shift. To this end, we propose a two-stage approach for source-free domain
adaptive image segmentation: 1) Target-specific adaptation followed by 2)
Task-specific adaptation. In the first stage, we focus on generating
target-specific pseudo labels while suppressing high entropy regions by
proposing an Ensemble Entropy Minimization loss. We also introduce a selective
voting strategy to enhance pseudo-label generation. In the second stage, we
focus on adapting the network for task-specific representation by using a
teacher-student self-training approach based on augmentation-guided
consistency. We evaluate our proposed method on both 2D fundus datasets and 3D
MRI volumes across 7 different domain shifts where we achieve better
performance than recent UDA and SF-UDA methods for medical image segmentation.
Code is available at https://github.com/Vibashan/tt-sfuda.


title:
Instance Relation Graph Guided Source-Free Domain Adaptive Object
  Detection

abstract:
  Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the
issue of domain shift. Specifically, UDA methods try to align the source and
target representations to improve the generalization on the target domain.
Further, UDA methods work under the assumption that the source data is
accessible during the adaptation process. However, in real-world scenarios, the
labelled source data is often restricted due to privacy regulations, data
transmission constraints, or proprietary data concerns. The Source-Free Domain
Adaptation (SFDA) setting aims to alleviate these concerns by adapting a
source-trained model for the target domain without requiring access to the
source data. In this paper, we explore the SFDA setting for the task of
adaptive object detection. To this end, we propose a novel training strategy
for adapting a source-trained object detector to the target domain without
source data. More precisely, we design a novel contrastive loss to enhance the
target representations by exploiting the objects relations for a given target
domain input. These object instance relations are modelled using an Instance
Relation Graph (IRG) network, which are then used to guide the contrastive
representation learning. In addition, we utilize a student-teacher based
knowledge distillation strategy to avoid overfitting to the noisy pseudo-labels
generated by the source-trained model. Extensive experiments on multiple object
detection benchmark datasets show that the proposed approach is able to
efficiently adapt source-trained object detectors to the target domain,
outperforming previous state-of-the-art domain adaptive detection methods. Code
is available at https://github.com/Vibashan/irg-sfda.


title: CHEX: CHannel EXploration for CNN Model Compression

abstract:
  Channel pruning has been broadly recognized as an effective technique to
reduce the computation and memory cost of deep convolutional neural networks.
However, conventional pruning methods have limitations in that: they are
restricted to pruning process only, and they require a fully pre-trained large
model. Such limitations may lead to sub-optimal model quality as well as
excessive memory and training cost. In this paper, we propose a novel Channel
Exploration methodology, dubbed as CHEX, to rectify these problems. As opposed
to pruning-only strategy, we propose to repeatedly prune and regrow the
channels throughout the training process, which reduces the risk of pruning
important channels prematurely. More exactly: From intra-layer's aspect, we
tackle the channel pruning problem via a well known column subset selection
(CSS) formulation. From inter-layer's aspect, our regrowing stages open a path
for dynamically re-allocating the number of channels across all the layers
under a global channel sparsity constraint. In addition, all the exploration
process is done in a single training from scratch without the need of a
pre-trained large model. Experimental results demonstrate that CHEX can
effectively reduce the FLOPs of diverse CNN architectures on a variety of
computer vision tasks, including image classification, object detection,
instance segmentation, and 3D vision. For example, our compressed ResNet-50
model on ImageNet dataset achieves 76% top1 accuracy with only 25% FLOPs of the
original ResNet-50 model, outperforming previous state-of-the-art channel
pruning methods. The checkpoints and code are available at here .


title:
Not Another School Resource Map: Meeting Underserved Families'
  Information Needs Requires Trusting Relationships and Personalized Care

abstract:
  Public school districts across the United States have implemented school
choice systems that have the potential to improve underserved students' access
to educational opportunities. However, research has shown that learning about
and applying for schools can be extremely time-consuming and expensive, making
it difficult for these systems to create more equitable access to resources in
practice. A common factor surfaced in prior work is unequal access to
information about the schools and enrollment process. In response, governments
and non-profits have invested in providing more information about schools to
parents, for instance, through detailed online dashboards. However, we know
little about what information is actually useful for historically marginalized
and underserved families. We conducted interviews with 10 low-income families
and families of color to learn about the challenges they faced navigating an
online school choice and enrollment system. We complement this data with four
interviews with people who have supported families through the enrollment
process in a wide range of roles, from school principal to non-profit staff
("parent advocates"). Our findings highlight the value of personalized support
and trusting relationships to delivering relevant and helpful information. We
contrast this against online information resources and dashboards, which tend
to be impersonal, target a broad audience, and make strong assumptions about
what parents should look for in a school without sensitivity to families'
varying circumstances. We advocate for an assets-based design approach to
information support in public school enrollment, which would ask how we can
support the local, one-on-one support that community members already provide.


title:
Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech
  Recognition

abstract:
  An unsupervised text-to-speech synthesis (TTS) system learns to generate the
speech waveform corresponding to any written sentence in a language by
observing: 1) a collection of untranscribed speech waveforms in that language;
2) a collection of texts written in that language without access to any
transcribed speech. Developing such a system can significantly improve the
availability of speech technology to languages without a large amount of
parallel speech and text data. This paper proposes an unsupervised TTS system
by leveraging recent advances in unsupervised automatic speech recognition
(ASR). Our unsupervised system can achieve comparable performance to the
supervised system in seven languages with about 10-20 hours of speech each. A
careful study on the effect of text units and vocoders has also been conducted
to better understand what factors may affect unsupervised TTS performance. The
samples generated by our models can be found at
https://cactuswiththoughts.github.io/UnsupTTS-Demo.


title:
Convergence and Complexity of Stochastic Subgradient Methods with
  Dependent Data for Nonconvex Optimization

abstract:
  We show that under a general dependent data sampling scheme, the classical
stochastic projected and proximal subgradient methods for weakly convex
functions have worst-case rate of convergence $\tilde{O}(n^{-1/4})$ and
complexity $\tilde{O}(\varepsilon^{-4})$ for achieving an $\varepsilon$-near
stationary point in terms of the norm of the gradient of Moreau envelope. While
classical convergence guarantee requires i.i.d. data sampling from the target
distribution, we only require a mild mixing condition of the conditional
distribution, which holds for a wide class of Markov chain sampling algorithms.
This improves the existing complexity for the specific case of constrained
smooth nonconvex optimization with dependent data from
$\tilde{O}(\varepsilon^{-8})$ to $\tilde{O}(\varepsilon^{-4})$ with a
significantly simpler analysis. We illustrate the generality of our approach by
deriving convergence results with dependent data for adaptive stochastic
subgradient algorithm AdaGrad and stochastic subgradient algorithm with heavy
ball momentum. As an application, we obtain first online nonnegative matrix
factorization algorithms for dependent data based on stochastic projected
gradient methods with adaptive step sizes with optimal rate of convergence
guarantee.


title:
DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance
  Fields for Articulated Avatars

abstract:
  Acquisition and creation of digital human avatars is an important problem
with applications to virtual telepresence, gaming, and human modeling. Most
contemporary approaches for avatar generation can be viewed either as 3D-based
methods, which use multi-view data to learn a 3D representation with appearance
(such as a mesh, implicit surface, or volume), or 2D-based methods which learn
photo-realistic renderings of avatars but lack accurate 3D representations. In
this work, we present, DRaCoN, a framework for learning full-body volumetric
avatars which exploits the advantages of both the 2D and 3D neural rendering
techniques. It consists of a Differentiable Rasterization module, DiffRas, that
synthesizes a low-resolution version of the target image along with additional
latent features guided by a parametric body model. The output of DiffRas is
then used as conditioning to our conditional neural 3D representation module
(c-NeRF) which generates the final high-res image along with body geometry
using volumetric rendering. While DiffRas helps in obtaining photo-realistic
image quality, c-NeRF, which employs signed distance fields (SDF) for 3D
representations, helps to obtain fine 3D geometric details. Experiments on the
challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms
state-of-the-art methods both in terms of error metrics and visual quality.


title: StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis

abstract:
  Although progress has been made for text-to-image synthesis, previous methods
fall short of generalizing to unseen or underrepresented attribute compositions
in the input text. Lacking compositionality could have severe implications for
robustness and fairness, e.g., inability to synthesize the face images of
underrepresented demographic groups. In this paper, we introduce a new
framework, StyleT2I, to improve the compositionality of text-to-image
synthesis. Specifically, we propose a CLIP-guided Contrastive Loss to better
distinguish different compositions among different sentences. To further
improve the compositionality, we design a novel Semantic Matching Loss and a
Spatial Constraint to identify attributes' latent directions for intended
spatial region manipulations, leading to better disentangled latent
representations of attributes. Based on the identified latent directions of
attributes, we propose Compositional Attribute Adjustment to adjust the latent
code, resulting in better compositionality of image synthesis. In addition, we
leverage the $\ell_2$-norm regularization of identified latent directions (norm
penalty) to strike a nice balance between image-text alignment and image
fidelity. In the experiments, we devise a new dataset split and an evaluation
metric to evaluate the compositionality of text-to-image synthesis models. The
results show that StyleT2I outperforms previous approaches in terms of the
consistency between the input text and synthesized images and achieves higher
fidelity.


title:
Engineering ideal helical topological networks in stanene via Zn
  decoration

abstract:
  The xene family of topological insulators plays a key role in many proposals
for topological electronic, spintronic, and valleytronic devices. These
proposals rely on applying local perturbations, including electric fields and
proximity magnetism, to induce topological phase transitions in xenes. However,
these techniques lack control over the geometry of interfaces between
topological regions, a critical aspect of engineering topological devices. We
propose adatom decoration as a method for engineering atomically precise
topological edge modes in xenes. Our first-principles calculations show that
decorating stanene with Zn adatoms exclusively on one of two sublattices
induces a topological phase transition from the quantum spin Hall (QSH) to
quantum valley Hall (QVH) phase and confirm the existence of spin-valley
polarized edge modes propagating at QSH/QVH interfaces. We conclude by
discussing technological applications of these edge modes that are enabled by
the atomic precision afforded by recent advances in adatom manipulation
technology.


title: Current-enabled optical conductivity of superconductors

abstract:
  In most superconductors, optical excitations require impurity scattering or
the presence of multiple bands. This is because in clean single-band
superconductors, the combination of particle-hole and inversion symmetries
prevents momentum-conserving transitions. In this work we show how the flow of
supercurrent can lead to new contributions to optical conductivity. As
supercurrent breaks inversion symmetry, transitions across the superconducting
gap become allowed even in clean superconductors and dominate over
impurity-induced contributions for energies comparable to the gap width. The
response is dependent on the nature of the underlying normal state as well as
on the type of superconducting order. Through use of the screening supercurrent
with controllable magnitude and direction, that arises from an external
magnetic field, this enables a detailed investigation of the superconducting
state and possible gap symmetry determination in unconventional superconductors
for which other techniques have not been practicable.


title: Shift-Robust Node Classification via Graph Adversarial Clustering

abstract:
  Graph Neural Networks (GNNs) are de facto node classification models in graph
structured data. However, during testing-time, these algorithms assume no data
shift, i.e., $\Pr_\text{train}(X,Y) = \Pr_\text{test}(X,Y)$. Domain adaption
methods can be adopted for data shift, yet most of them are designed to only
encourage similar feature distribution between source and target data.
Conditional shift on classes can still affect such adaption. Fortunately, graph
yields graph homophily across different data distributions. In response, we
propose Shift-Robust Node Classification (SRNC) to address these limitations.
We introduce an unsupervised cluster GNN on target graph to group the similar
nodes by graph homophily. An adversarial loss with label information on source
graph is used upon clustering objective. Then a shift-robust classifier is
optimized on training graph and adversarial samples on target graph, which are
generated by cluster GNN. We conduct experiments on both open-set shift and
representation-shift, which demonstrates the superior accuracy of SRNC on
generalizing to test graph with data shift. SRNC is consistently better than
previous SoTA domain adaption algorithm on graph that progressively use model
predictions on target graph for training.


title:
Dose Finding Studies for Therapies with Late-Onset Safety and Efficacy
  Outcomes

abstract:
  In Phase I/II dose-finding trials, the objective is to find the Optimal
Biological Dose (OBD), a dose that is both safe and efficacious that maximises
some optimality criterion based on safety and efficacy. This is further
complicated when the investigated treatment consists of multiple cycles of
therapy, and both toxicity and efficacy outcomes may occur at any point
throughout the follow up of multiple cycles. In this work we present the Joint
TITE-CRM, a model-based design for late onset toxicities and efficacies based
on the well-known TITE-CRM. It is found to be superior to both currently
available alternative designs that account for late onset bivariate outcomes; a
model-assisted method and a bivariate survival design, as well as being both
intuitive and computationally feasible.


title:
Improving The Diagnosis of Thyroid Cancer by Machine Learning and
  Clinical Data

abstract:
  Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid
gland. Much effort has been invested in improving its diagnosis, and
thyroidectomy remains the primary treatment method. A successful operation
without unnecessary side injuries relies on an accurate preoperative diagnosis.
Current human assessment of thyroid nodule malignancy is prone to errors and
may not guarantee an accurate preoperative diagnosis. This study proposed a
machine framework to predict thyroid nodule malignancy based on a novel
clinical dataset we collected. The 10-fold cross-validation, bootstrap
analysis, and permutation predictor importance were applied to estimate and
interpret the model performance under uncertainty. The comparison between model
prediction and expert assessment shows the advantage of our framework over
human judgment in predicting thyroid nodule malignancy. Our method is accurate,
interpretable, and thus useable as additional evidence in the preoperative
diagnosis for thyroid cancer.


title:
A Metaheuristic Algorithm for Large Maximum Weight Independent Set
  Problems

abstract:
  Motivated by a real-world vehicle routing application, we consider the
maximum-weight independent set problem: Given a node-weighted graph, find a set
of independent (mutually nonadjacent) nodes whose node-weight sum is maximum.
Some of the graphs airsing in this application are large, having hundreds of
thousands of nodes and hundreds of millions of edges. To solve instances of
this size, we develop a new local search algorithm, which is a metaheuristic in
the greedy randomized adaptive search (GRASP) framework. This algorithm, which
we call METAMIS, uses a wider range of simple local search operations than
previously described in the literature. We introduce data structures that make
these operations efficient. A new variant of path-relinking is introduced to
escape local optima and so is a new alternating augmenting-path local search
move that improves algorithm performance. We compare an implementation of our
algorithm with a state-of-the-art openly available code on public benchmark
sets, including some large instances with hundreds of millions of vertices. Our
algorithm is, in general, competitive and outperforms this openly available
code on large vehicle routing instances. We hope that our results will lead to
even better MWIS algorithms.


title:
Bayesian Photonic Accelerators for Energy Efficient and Noise Robust
  Neural Processing

abstract:
  Artificial neural networks are efficient computing platforms inspired by the
brain. Such platforms can tackle a vast area of real-life tasks ranging from
image processing to language translation. Silicon photonic integrated chips
(PICs), by employing coherent interactions in Mach-Zehnder interferometers, are
promising accelerators offering record low power consumption and ultra-fast
matrix multiplication. Such photonic accelerators, however, suffer from phase
uncertainty due to fabrication errors and crosstalk effects that inhibit the
development of high-density implementations. In this work, we present a
Bayesian learning framework for such photonic accelerators. In addition to the
conventional log-likelihood optimization path, two novel training schemes are
derived, namely a regularized version and a fully Bayesian learning scheme.
They are applied on a photonic neural network with 512 phase shifters targeting
the MNIST dataset. The new schemes, when combined with a pre-characterization
stage that provides the passive offsets, are able to dramatically decrease the
operational power of the PIC beyond 70%, with just a slight loss in
classification accuracy. The full Bayesian scheme, apart from this energy
reduction, returns information with respect to the sensitivity of the phase
shifters. This information is used to de-activate 31% of the phase actuators
and, thus, significantly simplify the driving system.


title:
High Speed Photonic Neuromorphic Computing Using Recurrent Optical
  Spectrum Slicing Neural Networks

abstract:
  Neuromorphic Computing implemented in photonic hardware is one of the most
promising routes towards achieving machine learning processing at the
picosecond scale, with minimum power consumption. In this work, we present a
new concept for realizing photonic recurrent neural networks and reservoir
computing architectures with the use of recurrent optical spectrum slicing.
This is accomplished through simple optical filters placed in an loop, where
each filter processes a specific spectral slice of the incoming optical signal.
The synaptic weights in our scheme are equivalent to filters central
frequencies and bandwidths. This new method for implementing recurrent neural
processing in the photonic domain, which we call Recurrent Optical Spectrum
Slicing Neural Networks, is numerically evaluated on a demanding,
industry-relevant task such as high baud rate optical signal equalization 100
Gbaud, exhibiting ground-breaking performance. The performance enhancement
surpasses state-of-the-art digital processing techniques by doubling the reach
while minimizing complexity and power consumption by a factor of 10 compared to
state-of-the-art solutions. In this respect, ROSS-NNs can pave the way for the
implementation of ultra-efficient photonic hardware accelerators tailored for
processing high-bandwidth optical signals in optical communication and
high-speed imaging applications


title: On gravitational preheating

abstract:
  We consider dark matter production during the inflaton oscillation epoch. It
is conceivable that renormalizable interactions between dark matter and
inflaton may be negligible. In this case, the leading role is played by higher
dimensional operators generated by gravity and thus suppressed by the Planck
scale. We focus on dim-6 operators and study the corresponding particle
production in perturbative and non-perturbative regimes. We find that the dark
matter production rate is dominated by non-derivative operators involving
higher powers of the inflaton field. Even if they appear with small Wilson
coefficients, such operators can readily account for the correct dark matter
abundance.