title: Transport coefficients in thermal QCD: A probe to the collision integral

abstract:
  We have studied the transport coefficients as a tool to probe the collision
integral appeared in the Boltzmann equation. For this purpose, we have
estimated the transport coefficients (momentum: \{$\eta$,$\zeta$\}, heat:
\{$\kappa$\}, and charge: \{$\sigma_{\rm el}$\}) in the kinetic theory with
Bhatnagar-Gross-Krook (BGK) and the collision integral in RTA and ask whether
we can distinguish between the two collision integrals. For example, $\eta$
gets enhanced while $\zeta$ gets reduced w.r.t. to RTA. As a corollary, we then
investigate the interplay among the aforesaid transport coefficients, {\em
viz.} fluidity and transition point of QCD medium by evaluating the ratios,
$\eta/s$ and $\zeta/s$, respectively, nature of flow (Reynolds number, RI),
sound attenuation (Prandtl number, Pr), and competition between the momentum
and charge diffusion ($\gamma$) etc. as further plausible tools to decipher the
same. With BGK collision integral, the ratios, $\eta/s$ (increase) and
$\zeta/s$ (decrease) shows opposite behavior whereas Pr, RI, $\gamma$ and the
ratio $\zeta/\eta$ get reduced w.r.t RTA. We then examine how a strong $B$
modulate the impact of collision integral, which, in a way, explore the
dimensionality dependence of the momentum transport because the quark dynamics
gets restricted to 1-D only and only the lowest Landau levels are populated. As
a result, $\eta$ ($\zeta$) gets reduced (amplified), which will have
ramifications on the ratios, {\em viz.} $\eta/s$ ($\zeta/s$) becomes smaller
(larger), enhancement of Pr, $\gamma$ and $\zeta/\eta$ etc. In this study, the
thermo-magnetic medium effects have been incorporated by adopting a
quasi-particle model, where the medium generated masses of the partons have
been obtained from the pole of their resummed propagators calculated using
perturbative thermal QCD in strong $B$.


title:
Bayesian Models for Multivariate Difference Boundary Detection in Areal
  Data

abstract:
  Regional aggregates of health outcomes over delineated administrative units
(e.g., states, counties, zip codes), or areal units, are widely used by
epidemiologists to map mortality or incidence rates and capture geographic
variation. To capture health disparities over regions, we seek "difference
boundaries" that separate neighboring regions with significantly different
spatial effects. Matters are more challenging with multiple outcomes over each
unit, where we capture dependence among diseases as well as across the areal
units. Here, we address multivariate difference boundary detection for
correlated diseases. We formulate the problem in terms of Bayesian pairwise
multiple comparisons and seek the posterior probabilities of neighboring
spatial effects being different. To achieve this, we endow the spatial random
effects with a discrete probability law using a class of multivariate
areally-referenced Dirichlet process (MARDP) models that accommodate spatial
and inter-disease dependence. We evaluate our method through simulation studies
and detect difference boundaries for multiple cancers using data from the
Surveillance, Epidemiology, and End Results (SEER) Program of the National
Cancer Institute.


title:
UHI in Fortaleza and trends on screen-level air temperature and
  precipitation

abstract:
  There is a consensus that Urban Heat Island phenomenon - UHI occurs in every
large city. This effect is characterized by higher air temperatures in cities
than in the neighboring countryside at night. However, to date, there has been
no systematic study on the Fortaleza case, the Brazil's 5th largest city. By
the comparison between screen-level air temperature measured by two automatic
weather stations, one located in the city and the other in the neighboring
region, this work shows the occurrence of the UHI in Fortaleza, even during the
rainy season. In an attempt to find some effect of the UHI on precipitation
(and vice-versa), historical series of air temperature and precipitation were
analyzed from 1966 to 2020. Besides the considerable increase in air
temperature over the years, a slight downward trend was observed in UHI and
even more in accumulated precipitation between the hours of 15:00 and 21:00
(local time). However, one believes that these trends may be related to climate
change at large scale rather than an urban scale.


title: Detoxifying Language Models with a Toxic Corpus

abstract:
  Existing studies have investigated the tendency of autoregressive language
models to generate contexts that exhibit undesired biases and toxicity. Various
debiasing approaches have been proposed, which are primarily categorized into
data-based and decoding-based. In our study, we investigate the ensemble of the
two debiasing paradigms, proposing to use toxic corpus as an additional
resource to reduce the toxicity. Our result shows that toxic corpus can indeed
help to reduce the toxicity of the language generation process substantially,
complementing the existing debiasing methods.


title: LHC phenomenology of unusual top partners in composite Higgs models

abstract:
  Composite Higgs models with a fermionic UV completion can contain additional
colored states beside the usual top-partners. We focus here on a model which
contains in addition SU(3) color octet top partners as well as color singlet
ones. The latter can in principle serve as a dark matter candidate. We consider
a particular composite Higgs model which contains SU(3) color octet top
partners besides the usually considered triplet representations. Moreover,
color singlet top partners are present as well which can in principle serve as
dark matter candidates. We investigate the LHC phenomenology of these unusual
top partners. Some of these states could at first glance be confused with
gluinos predicted in supersymmetric models.


title: Artificial Intelligence and Medicine: A literature review

abstract:
  In practically every industry today, artificial intelligence is one of the
most effective ways for machines to assist humans. Since its inception, a large
number of researchers throughout the globe have been pioneering the application
of artificial intelligence in medicine. Although artificial intelligence may
seem to be a 21st-century concept, Alan Turing pioneered the first foundation
concept in the 1940s. Artificial intelligence in medicine has a huge variety of
applications that researchers are continually exploring. The tremendous
increase in computer and human resources has hastened progress in the 21st
century, and it will continue to do so for many years to come. This review of
the literature will highlight the emerging field of artificial intelligence in
medicine and its current level of development.


title:
p5.fab: Direct Control of Digital Fabrication Machines from a Creative
  Coding Environment

abstract:
  Machine settings and tuning are critical for digital fabrication outcomes.
However, exploring these parameters is non-trivial. We seek to enable
exploration of the full design space of digital fabrication. To identify where
we might intervene, we studied how practitioners approach 3D printing. We found
that beyond using CAD/CAM, they create bespoke routines and workflows to
explore interdependent material and machine settings. We seek to provide a
system that supports this workflow development. We identified design goals
around material exploration, fine-tuned control, and iteration. Based on these,
we present p5.fab, a system for controlling digital fabrication machines from
the creative coding environment p5.js. We demonstrate p5.fab with examples of
3D prints that cannot be made with traditional 3D printing software. We
evaluate p5.fab in workshops and find that it encourages novel printing
workflows and artifacts. Finally, we discuss implications for future digital
fabrication systems.


title: Terahertz Spatio-Temporal Deep Learning Computed Tomography

abstract:
  Terahertz computed tomography (THz CT) has drawn significant attention
because of its unique capability to bring multi-dimensional object information
from invisible to visible. However, current physics-model-based THz CT
modalities present low data use efficiency on time-resolved THz signals and low
model fusion extensibility, limiting their application fields' practical use.
In this paper, we propose a supervised THz deep learning computed tomography
(THz DL-CT) framework based on time-domain information. THz DL-CT restores
superior THz tomographic images of 3D objects by extracting features from
spatio-temporal THz signals without any prior material information. Compared
with conventional and machine learning based methods, THz DL-CT delivers at
least 50.2%, and 52.6% superior in root mean square error (RMSE) and structural
similarity index (SSIM), respectively. Additionally, we have experimentally
demonstrated that the pretrained THz DL-CT model can generalize to reconstruct
multi-material systems with no prerequisite information. THz CT through the DL
data fusion approach provides a new pathway for non-invasive functional imaging
in object investigation.


title:
A Comparison of Various Turbulence Models for Analysis of Fluid Microjet
  Injection into the Boundary Layer over a Flat Surface

abstract:
  The present work studied various models for predicting turbulence in the
problem of injecting a fluid microjet into the boundary layer of a turbulent
flow. For this purpose, the one-equation Spalart-Allmaras (SA), two-equation
k-{\epsilon} and k-{\omega}, multi-equation transition k-kL-{\omega},
transition shear stress transport (SST), and Reynolds stress models were used
for solving the steady flow. Moreover, the transition SST, scale-adaptive
simulation (SAS), and detached eddy simulation (DES) models were used for the
transient flow. A comparison of the results indicated that the steady solution
methods performed sufficiently well for this problem. Furthermore, it was found
that the four-equation transition SST model was the most accurate method for
the prediction of turbulence in this problem. This model predicted the velocity
along the x-axis in near- and far-jet locations with about 1% and 5% errors,
respectively. It also outperformed the other methods in predicting Reynolds
stresses, especially at the center (with an about 5% error).


title: Rare transitions in noisy heteroclinic networks

abstract:
  We study small white noise perturbations of planar dynamical systems with
heteroclinic networks in the limit of vanishing noise. We show that the
probabilities of transitions between various cells that the network tessellates
the plane into decay as powers of the noise magnitude. We show that the most
likely scenario for the realization of these rare transition events involves
spending atypically long times in the neighborhoods of certain saddle points of
the network. We describe the hierarchy of time scales and clusters of
accessibility associated with these rare transition events. We discuss
applications of our results to homogenization problems and to the invariant
distribution asymptotics. At the core of our results are local limit theorems
for exit distributions obtained via methods of Malliavin calculus.


title: Physics-guided Terahertz Computational Imaging

abstract:
  Visualizing information inside objects is an ever-lasting need to bridge the
world from physics, chemistry, biology to computation. Among all tomographic
techniques, terahertz (THz) computational imaging has demonstrated its unique
sensing features to digitalize multi-dimensional object information in a
non-destructive, non-ionizing, and non-invasive way. Applying modern signal
processing and physics-guided modalities, THz computational imaging systems are
now launched in various application fields in industrial inspection, security
screening, chemical inspection and non-destructive evaluation. In this article,
we overview recent advances in THz computational imaging modalities in the
aspects of system configuration, wave propagation and interaction models,
physics-guided algorithm for digitalizing interior information of imaged
objects. Several image restoration and reconstruction issues based on
multi-dimensional THz signals are further discussed, which provides a crosslink
between material digitalization, functional property extraction, and
multi-dimensional imager utilization from a signal processing perspective.


title: HateCheckHIn: Evaluating Hindi Hate Speech Detection Models

abstract:
  Due to the sheer volume of online hate, the AI and NLP communities have
started building models to detect such hateful content. Recently, multilingual
hate is a major emerging challenge for automated detection where code-mixing or
more than one language have been used for conversation in social media.
Typically, hate speech detection models are evaluated by measuring their
performance on the held-out test data using metrics such as accuracy and
F1-score. While these metrics are useful, it becomes difficult to identify
using them where the model is failing, and how to resolve it. To enable more
targeted diagnostic insights of such multilingual hate speech models, we
introduce a set of functionalities for the purpose of evaluation. We have been
inspired to design this kind of functionalities based on real-world
conversation on social media. Considering Hindi as a base language, we craft
test cases for each functionality. We name our evaluation dataset HateCheckHIn.
To illustrate the utility of these functionalities , we test state-of-the-art
transformer based m-BERT model and the Perspective API.


title:
Foundational Models for Continual Learning: An Empirical Study of Latent
  Replay

abstract:
  Rapid development of large-scale pre-training has resulted in foundation
models that can act as effective feature extractors on a variety of downstream
tasks and domains. Motivated by this, we study the efficacy of pre-trained
vision models as a foundation for downstream continual learning (CL) scenarios.
Our goal is twofold. First, we want to understand the compute-accuracy
trade-off between CL in the raw-data space and in the latent space of
pre-trained encoders. Second, we investigate how the characteristics of the
encoder, the pre-training algorithm and data, as well as of the resulting
latent space affect CL performance. For this, we compare the efficacy of
various pre-trained models in large-scale benchmarking scenarios with a vanilla
replay setting applied in the latent and in the raw-data space. Notably, this
study shows how transfer, forgetting, task similarity and learning are
dependent on the input data characteristics and not necessarily on the CL
algorithms. First, we show that under some circumstances reasonable CL
performance can readily be achieved with a non-parametric classifier at
negligible compute. We then show how models pre-trained on broader data result
in better performance for various replay sizes. We explain this with
representational similarity and transfer properties of these representations.
Finally, we show the effectiveness of self-supervised pre-training for
downstream domains that are out-of-distribution as compared to the pre-training
domain. We point out and validate several research directions that can further
increase the efficacy of latent CL including representation ensembling. The
diverse set of datasets used in this study can serve as a compute-efficient
playground for further CL research. The codebase is available under
https://github.com/oleksost/latent_CL.


title: An evolution model with uncountably many alleles

abstract:
  We study a class of evolution models, where the breeding process involves an
arbitrary exchangeable process, allowing for mutations to appear. The
population size $n$ is fixed, hence after breeding, selection is applied.
Individuals are characterized by their genome, picked inside a set $X$ (which
may be uncountable), and there is a fitness associated to each genome. Being
less fit implies a higher chance of being discarded in the selection process.
The stationary distribution of the process can be described and studied. We are
interested in the asymptotic behavior of this stationary distribution as $n$
goes to infinity. Choosing a parameter $\lambda>0$ to tune the scaling of the
fitness when $n$ grows, we prove limiting theorems both for the case when the
breeding process does not depend on $n$, and for the case when it is given by a
Dirichlet process prior. In both cases, the limit exhibits phase transitions
depending on the parameter $\lambda


title:
Dual-Beam Intelligent Reflecting Surface for Millimeter and THz
  Communications

abstract:
  Intelligent reflecting surface (IRS) is a cost-efficient technique to improve
power efficiency and spectral efficiency. However, IRS-aided multi-antenna
transmission needs to jointly optimize the passive and active beamforming,
imposing a high computational burden and high latency due to its iterative
optimization process. Making use of hybrid analog-digital beamforming in
high-frequency transmission systems, a novel technique, coined dual-beam IRS,
is proposed in this paper. The key idea is to form a pair of beams towards the
IRS and user, respectively. Then, the optimization of passive and active
beamforming can be decoupled, resulting in a simplified system design.
Simulation results corroborate that it achieves a good balance between the
cell-edge and cell-center performance. Compared with the performance bound, the
gap is moderate, but it remarkably outperforms other sub-optimal schemes.


title: Geometry in the large on Hadamard manifolds

abstract:
  In this paper, we prove several Liouville-type theorems on the non-existence
of Killing-Yano tensors, Killing tensors, and harmonic symmetric tensors on
Hadamard manifolds and, in particular, on Riemannian symmetric spaces of
non-compact type. These theorems supplement the well-known vanishing theorems
for the above tensors, obtained using the Bochner technique for compact
Riemannian manifolds. In turn, the proofs of our theorems will use well-known
Liouville-type theorems on the non-existence of subharmonic and harmonic
functions on complete Riemannian manifolds, which we have partially modified
for the case of Hadamard manifolds and, in particular, Riemannian symmetric
spaces of noncompact type.


title: Opportunistic AP Selection in Cell-Free Massive MIMO-OFDM Systems

abstract:
  Exploiting the degree of freedom in the frequency domain and the near-far
effect among different access points (APs), this paper proposes an
opportunistic transmission scheme in cell-free massive MIMO-OFDM systems. The
key idea is to orthogonally assign subcarriers among different users, so that
there is only one user on each subcarrier. Then, a user is only served by its
near APs through opportunistic selection, while the far APs are deactivated to
avoid wasting power over their channels with severe propagation losses.
Moreover, the number of active APs per subcarrier becomes small due to the
opportunistic selection, making the use of downlink pilots and coherent
detection feasible. As corroborated by numerical results, the proposed scheme
can bring a significant performance boost in terms of both power efficiency and
spectral efficiency.


title:
Engineering flexible machine learning systems by traversing functionally
  invariant paths in weight space

abstract:
  Deep neural networks achieve human-like performance on a variety of
perceptual and decision making tasks. However, deep networks perform poorly
when confronted with changing tasks or goals, and broadly fail to match the
flexibility and robustness of human intelligence. Here, we develop a
mathematical and algorithmic framework that enables continual training of deep
neural networks on a broad range of objectives by defining path connected sets
of neural networks that achieve equivalent functional performance on a given
machine learning task while modulating network weights to achieve
high-performance on a secondary objective. We view the weight space of a neural
network as a curved Riemannian manifold and move a neural network along a
functionally invariant path in weight space while searching for networks that
satisfy a secondary objective. We introduce a path-sampling algorithm that
trains networks with millions of weight parameters to learn a series of image
classification tasks without performance loss. The algorithm generalizes to
accommodate a range of secondary objectives including weight-pruning and weight
diversification and exhibits state of the art performance on network
compression and adversarial robustness benchmarks. Broadly, we demonstrate how
the intrinsic geometry of machine learning problems can be harnessed to
construct flexible and robust neural networks.


title:
Evaluating the Impact of Bitcoin on International Asset Allocation using
  Mean-Variance, Conditional Value-at-Risk (CVaR), and Markov Regime Switching
  Approaches

abstract:
  This paper aims to analyze the effect of Bitcoin on portfolio optimization
using mean-variance, conditional value-at-risk (CVaR), and Markov regime
switching approaches. I assessed each approach and developed the next based on
the prior approach's weaknesses until I ended with a high level of confidence
in the final approach. Though the results of mean-variance and CVaR frameworks
indicate that Bitcoin improves the diversification of a well-diversified
international portfolio, they assume that assets' returns are developed
linearly and normally distributed. However, the Bitcoin return does not have
both of these characteristics. Due to this, I developed a Markov regime
switching approach to analyze the effect of Bitcoin on an international
portfolio performance. The results show that there are two regimes based on the
assets' returns: 1- bear state, where returns have low means and high
volatility, 2- bull state, where returns have high means and low volatility.


title:
A nonparametric regression approach to asymptotically optimal estimation
  of normal means

abstract:
  Simultaneous estimation of multiple parameters has received a great deal of
recent interest, with applications in multiple testing, causal inference, and
large-scale data analysis. Most approaches to simultaneous estimation use
empirical Bayes methodology. Here we propose an alternative, completely
frequentist approach based on nonparametric regression. We show that
simultaneous estimation can be viewed as a constrained and penalized
least-squares regression problem, so that empirical risk minimization can be
used to estimate the optimal estimator within a certain class. We show that
under mild conditions, our data-driven decision rules have asymptotically
optimal risk that can match the best known convergence rates for this compound
estimation problem. Our approach provides another perspective to understand
sufficient conditions for asymptotic optimality of simultaneous estimation. Our
proposed estimators demonstrate comparable performance to state-of-the-art
empirical Bayes methods in a variety of simulation settings and our methodology
can be extended to apply to many practically interesting settings.


title: Product decompositions of moment-angle manifolds and $B$-rigidity

abstract:
  A simple polytope $P$ is called $B$-rigid if its combinatorial type is
determined by the cohomology ring of the moment-angle manifold $\mathcal{Z}_P$
over $P$. We show that any tensor product decomposition of this cohomology ring
is geometrically realized by a product decomposition of the moment-angle
manifold up to equivariant diffeomorphism. As an application, we find that
$B$-rigid polytopes are closed under products, generalizing some recent results
in the toric topology literature. Algebraically, our proof establishes that the
Koszul homology of a Gorenstein Stanley-Reisner ring admits a nontrivial tensor
product decomposition if and only if the underlying simplicial complex
decomposes as a join of full subcomplexes.


title: Rotating Hybrid Stars with Color-Flavor-Locked Quark Matter

abstract:
  In the present work we have achieved phase transition from $\beta$ stable
hadronic matter to color-flavor locked (CFL) quark matter with Maxwell
construction. The hybrid equation of state (EoS), obtained for different values
of bag pressure $B$ and gap parameter $\Delta$, have been used to compute the
speed of sound in hybrid star (HS) matter. The structural properties of the HSs
in both static and rotating conditions have been calculated in the light of
various constraints from different astrophysical and empirical perspectives.
The effects of $B$ and $\Delta$ on the EoS and structural properties have been
investigated. At a certain density, shortly after phase transition, the HSs
become unstable. In static conditions, the mass-radius solutions satisfy the
constraints from GW190425, NICER experiment for PSR J0030+0451 and PSR
J0740+6620 and from massive pulsars like PSR J0348+0432 and PSR J0740+6620. In
rapidly rotating conditions at Kepler frequency, the constraints on maximum
mass from the secondary component of GW190814 and that on rotational frequency
from fast pulsars like PSR B1937+21 and PSR J1748-2446ad are all satisfied. In
slowly rotating conditions, the universality relations in terms of normalized
moment of inertia also hold quite well for most of our HS configurations.


title:
Spectral Analysis and Preconditioned Iterative Solvers for Large
  Structured Linear Systems

abstract:
  In this thesis, the numerical solution of three different classes of problems
have been studied. Specifically, new techniques have been proposed and their
theoretical analysis has been performed, accompanied by a wide set of numerical
experiments, for investigating further and comparing the effectiveness and
performance of the presented approach. The first two belong to the research
area of numerical linear algebra and concern the spectral analysis and
preconditioning for Krylov subspace methods of the coefficient matrix of large
structured linear systems. The third concerns a problem from the area of
financial computing namely the pricing of an American put option.


title:
Reliability and limitations of inferring birth radii in the Milky Way
  disk

abstract:
  Recovering the birth radii of observed stars in the Milky Way is one of the
ultimate goals of Galactic Archaeology. One method to infer the birth radius
and the evolution of the ISM metallicity assumes a linear relation between the
ISM metallicity with radius at any given look-back time. Here we test the
reliability of this assumption by using 4 zoom-in cosmological hydrodynamic
simulations from the NIHAO-UHD project. We find that one can infer precise
birth radii only when the stellar disk starts to form, which for our modeled
galaxies happens ~ 10 Gyr ago, in agreement with recent estimates for the Milky
Way. At later times the linear correlation between the ISM metallicity and
radius increases, as stellar motions become more ordered and the azimuthal
variations of the ISM metallicity start to drop. The formation of a central bar
and perturbations from mergers can increase this uncertainty in the inner and
outer disk, respectively.


title: Quantum capacity and codes for the bosonic loss-dephasing channel

abstract:
  Bosonic qubits encoded in continuous-variable systems provide a promising
alternative to two-level qubits for quantum computation and communication. So
far, photon loss has been the dominant source of errors in bosonic qubits, but
the significant reduction of photon loss in recent bosonic qubit experiments
suggests that dephasing errors should also be considered. However, a detailed
understanding of the combined photon loss and dephasing channel is lacking.
Here, we show that, unlike its constituent parts, the combined loss-dephasing
channel is non-degradable, pointing towards a richer structure of this channel.
We provide bounds for the capacity of the loss-dephasing channel and use
numerical optimization to find optimal single-mode codes for a wide range of
error rates.


title: Fast Multivariate Multipoint Evaluation Over All Finite Fields

abstract:
  Multivariate multipoint evaluation is the problem of evaluating a
multivariate polynomial, given as a coefficient vector, simultaneously at
multiple evaluation points. In this work, we show that there exists a
deterministic algorithm for multivariate multipoint evaluation over any finite
field $\mathbb{F}$ that outputs the evaluations of an $m$-variate polynomial of
degree less than $d$ in each variable at $N$ points in time \[
(d^m+N)^{1+o(1)}\cdot\poly(m,d,\log|\mathbb{F}|) \] for all $m\in\N$ and all
sufficiently large $d\in\mathbb{N}$.
  A previous work of Kedlaya and Umans (FOCS 2008, SICOMP 2011) achieved the
same time complexity when the number of variables $m$ is at most $d^{o(1)}$ and
had left the problem of removing this condition as an open problem. A recent
work of Bhargava, Ghosh, Kumar and Mohapatra (STOC 2022) answered this question
when the underlying field is not \emph{too} large and has characteristic less
than $d^{o(1)}$. In this work, we remove this constraint on the number of
variables over all finite fields, thereby answering the question of Kedlaya and
Umans over all finite fields.
  Our algorithm relies on a non-trivial combination of ideas from three
seemingly different previously known algorithms for multivariate multipoint
evaluation, namely the algorithms of Kedlaya and Umans, that of Bj\"orklund,
Kaski and Williams (IPEC 2017, Algorithmica 2019), and that of Bhargava, Ghosh,
Kumar and Mohapatra, together with a result of Bombieri and Vinogradov from
analytic number theory about the distribution of primes in an arithmetic
progression.
  We also present a second algorithm for multivariate multipoint evaluation
that is completely elementary and in particular, avoids the use of the
Bombieri--Vinogradov Theorem. However, it requires a mild assumption that the
field size is bounded by an exponential-tower in $d$ of bounded
\textit{height}.


title: Uncertainty Propagation via Optimal Transport Ambiguity Sets

abstract:
  Uncertainty propagation has established itself as a fundamental area of
research in all fields of science and engineering. Among its central topics
stands the problem of modeling and propagating distributional uncertainty,
i.e., the uncertainty about probability distributions. In this paper, we employ
tools from Optimal Transport to capture distributional uncertainty via Optimal
Transport ambiguity sets, which we show to be very natural and expressive, and
to enjoy powerful topological, geometrical, computational, and statistical
features and guarantees. We show that these ambiguity sets propagate nicely and
intuitively through nonlinear, possibly corrupted by noise, transformations,
and that in many cases the result of the propagation is again an Optimal
Transport ambiguity set. Moreover, whenever this is not the case, we show that
the result of the propagation can be tightly upper bounded by another Optimal
Transport ambiguity set. Importantly, our methodology allows us to capture
exactly how complex systems shape distributional uncertainty. To conclude, we
exemplify our findings in three fundamental applications in forward
propagation, inverse problems, and distributionally robust optimization.


title: Opponent Modeling in Negotiation Dialogues by Related Data Adaptation

abstract:
  Opponent modeling is the task of inferring another party's mental state
within the context of social interactions. In a multi-issue negotiation, it
involves inferring the relative importance that the opponent assigns to each
issue under discussion, which is crucial for finding high-value deals. A
practical model for this task needs to infer these priorities of the opponent
on the fly based on partial dialogues as input, without needing additional
annotations for training. In this work, we propose a ranker for identifying
these priorities from negotiation dialogues. The model takes in a partial
dialogue as input and predicts the priority order of the opponent. We further
devise ways to adapt related data sources for this task to provide more
explicit supervision for incorporating the opponent's preferences and offers,
as a proxy to relying on granular utterance-level annotations. We show the
utility of our proposed approach through extensive experiments based on two
dialogue datasets. We find that the proposed data adaptations lead to strong
performance in zero-shot and few-shot scenarios. Moreover, they allow the model
to perform better than baselines while accessing fewer utterances from the
opponent. We release our code to support future work in this direction.


title: On possible composite structure of scalar fields in expanding universe

abstract:
  Scalar fields in curved backgrounds are assumed to be composite objects. As
an example realizing such a possibility we consider a model of the massless
tensor field $l_{\mu\nu}(x)$ in a 4-dim. background $g_{\mu\nu}(x)$ with
spontaneously broken Weyl and scale symmetries. It is shown that the potential
of $l_{\mu\nu}$, represented by a scalar quartic polynomial, has the degenerate
extremal described by the composite Nambu-Goldstone scalar boson
$\phi(x):=g^{\mu\nu}l_{\mu\nu}$. Removal of the degeneracy shows that $\phi$
acquires a non-zero vev $\langle\phi\rangle_{0}=\mu$ which, together with the
free parameters of the potential, defines the cosmological constant. The latter
is zero for a certain choice of the parameters.


title:
Traffic Management in Smart Cities Using the Weighted Least Squares
  Method

abstract:
  In this paper we demonstrate how one of the most powerful methods in
mathematics and statistics can be used to optimally control and reduce traffics
in smart cities. The main goal of the present work is to use the least squares
method [5] to identify the best location of a portable drone station in a
metropolitan city. By the best location we mean a location that is closest to
traffic congestions and accidents that may occur on a daily basis. It is
generally accepted that drones will play a key role in smart city environments
in the near future. Drones will provide different types of public service, such
as emergency response, disaster relief, and traffic control


title: LayoutBERT: Masked Language Layout Model for Object Insertion

abstract:
  Image compositing is one of the most fundamental steps in creative workflows.
It involves taking objects/parts of several images to create a new image,
called a composite. Currently, this process is done manually by creating
accurate masks of objects to be inserted and carefully blending them with the
target scene or images, usually with the help of tools such as Photoshop or
GIMP. While there have been several works on automatic selection of objects for
creating masks, the problem of object placement within an image with the
correct position, scale, and harmony remains a difficult problem with limited
exploration. Automatic object insertion in images or designs is a difficult
problem as it requires understanding of the scene geometry and the color
harmony between objects. We propose LayoutBERT for the object insertion task.
It uses a novel self-supervised masked language model objective and
bidirectional multi-head self-attention. It outperforms previous layout-based
likelihood models and shows favorable properties in terms of model capacity. We
demonstrate the effectiveness of our approach for object insertion in the image
compositing setting and other settings like documents and design templates. We
further demonstrate the usefulness of the learned representations for
layout-based retrieval tasks. We provide both qualitative and quantitative
evaluations on datasets from diverse domains like COCO, PublayNet, and two new
datasets which we call Image Layouts and Template Layouts. Image Layouts which
consists of 5.8 million images with layout annotations is the largest image
layout dataset to our knowledge. We also share ablation study results on the
effect of dataset size, model size and class sample size for this task.


title:
End-to-End Signal Classification in Signed Cumulative Distribution
  Transform Space

abstract:
  This paper presents a new end-to-end signal classification method using the
signed cumulative distribution transform (SCDT). We adopt a transport-based
generative model to define the classification problem. We then make use of
mathematical properties of the SCDT to render the problem easier in transform
domain, and solve for the class of an unknown sample using a nearest local
subspace (NLS) search algorithm in SCDT domain. Experiments show that the
proposed method provides high accuracy classification results while being data
efficient, robust to out-of-distribution samples, and competitive in terms of
computational complexity with respect to the deep learning end-to-end
classification methods. The implementation of the proposed method in Python
language is integrated as a part of the software package PyTransKit
(https://github.com/rohdelab/PyTransKit).


title: Twisted bilayers of thin film magnetic topological insulators

abstract:
  Twisted bilayer graphene (TBG) near "magic angles" has emerged as a rich
platform for strongly correlated states of two-dimensional Dirac semimetals.
Here we show that twisted bilayers of thin-film magnetic topological insulators
(MTI) with large in-plane magnetization can realize flat bands near 2D Dirac
nodes. Using a simple model for thin films of MTIs, we derive a continuum model
for two such MTIs, twisted by a small angle with respect to each other. When
the magnetization is in-plane, we show that interlayer tunneling terms act as
effective $SU(2)$ vector potentials, which are known to lead to flat bands in
TBG. We show that by changing the in-plane magnetization, it is possible to
tune the twisted bilayer MTI band dispersion to quadratic band touching or to
flat bands, similar to the TBG. If realized, this system can be a highly
tunable platform for strongly correlated phases of two-dimensional Dirac
semimetals.


title: Orthogonal Statistical Learning with Self-Concordant Loss

abstract:
  Orthogonal statistical learning and double machine learning have emerged as
general frameworks for two-stage statistical prediction in the presence of a
nuisance component. We establish non-asymptotic bounds on the excess risk of
orthogonal statistical learning methods with a loss function satisfying a
self-concordance property. Our bounds improve upon existing bounds by a
dimension factor while lifting the assumption of strong convexity. We
illustrate the results with examples from multiple treatment effect estimation
and generalized partially linear modeling.


title: Coherent enhancement of optical remission in diffusive media

abstract:
  From the earth's crust to the human brain, remitted waves are used for
sensing and imaging in a diverse range of diffusive media. Separating the
source and detector increases the penetration depth of remitted light, yet
rapidly decreases the signal strength, leading to a poor signal-to-noise ratio.
Here, we experimentally and numerically show that wavefront shaping a laser
beam incident on a diffusive sample enables an order of magnitude remission
enhancement, with a penetration depth of up to 10 transport mean free paths. We
develop a theoretical model which predicts the maximal-remission enhancement.
Our analysis reveals a significant improvement in the sensitivity of remitted
waves, to local changes of absorption deep inside diffusive media. This work
illustrates the potential of coherent wavefront control for non-invasive
diffuse-wave imaging applications, such as diffuse optical tomography and
functional near-infrared spectroscopy.

categories: physics.optics cond-mat.dis-nn physics.app-ph physics.med-ph


title:
Dynamic modelling of near-surface turbulence in large eddy simulation of
  wind farms

abstract:
  In large eddy simulation of atmospheric boundary layer flows over wind farms,
wall-layer models are generally imposed for the surface fluxes without
considering the spatial variability of the surface roughness. In this study, we
consider the near-surface model in conjunction with square of the velocity
gradient tensor to model the adaptive dissipation of turbulence production. The
surface roughness is incorporated through Monin-Obhukhov similarity theory for
the computational cells immediately adjacent to the Earth's surface. The
underlying proposed near-surface model captures the significant amount of
Reynolds stresses in the near-surface and is able to maintain the log-law
profile in wind farms. The present study indicates that the suggested
`near-surface model' is relatively robust in comparison to the classical
`near-wall model'.


title:
Transcription-induced active forces suppress chromatin motion by
  inducing a transient disorder-to-order transition

abstract:
  Recent experiments have shown that the mobility of human interphase
chromosome decreases during transcription, and increases upon inhibiting
transcription, a finding that is counter-intuitive because it is thought that
the active mechanical force ($F$) generated by RNA polymerase II (RNAPII) on
chromatin would render it more open and mobile. We use a polymer model to
investigate how $F$, derived from transcriptional activity, affects the
dynamical properties of chromatin. The movements of the loci in the gene-rich
region are suppressed in an intermediate range of $F$, and are enhanced at
small and large $F$ values. In the intermediate $F$, the bond length between
consecutive loci increases, becoming commensurate with the location of the
minimum in the attractive interaction between the active loci in the chromatin.
This results in a disorder-to-order transition, leading to the decreased
mobility during transcription. Our results suggest that transient ordering of
the loci in the gene-rich region might be a mechanism for nucleating a dynamic
network involving transcription factors, RNAPII, and chromatin.


title: Graph Anisotropic Diffusion

abstract:
  Traditional Graph Neural Networks (GNNs) rely on message passing, which
amounts to permutation-invariant local aggregation of neighbour features. Such
a process is isotropic and there is no notion of `direction' on the graph. We
present a new GNN architecture called Graph Anisotropic Diffusion. Our model
alternates between linear diffusion, for which a closed-form solution is
available, and local anisotropic filters to obtain efficient multi-hop
anisotropic kernels. We test our model on two common molecular property
prediction benchmarks (ZINC and QM9) and show its competitive performance.


title:
Towards Process-Oriented, Modular, and Versatile Question Generation
  that Meets Educational Needs

abstract:
  NLP-powered automatic question generation (QG) techniques carry great
pedagogical potential of saving educators' time and benefiting student
learning. Yet, QG systems have not been widely adopted in classrooms to date.
In this work, we aim to pinpoint key impediments and investigate how to improve
the usability of automatic QG techniques for educational purposes by
understanding how instructors construct questions and identifying touch points
to enhance the underlying NLP models. We perform an in-depth need finding study
with 11 instructors across 7 different universities, and summarize their
thought processes and needs when creating questions. While instructors show
great interests in using NLP systems to support question design, none of them
has used such tools in practice. They resort to multiple sources of
information, ranging from domain knowledge to students' misconceptions, all of
which missing from today's QG systems. We argue that building effective
human-NLP collaborative QG systems that emphasize instructor control and
explainability is imperative for real-world adoption. We call for QG systems to
provide process-oriented support, use modular design, and handle diverse
sources of input.


title:
A Privacy-Aware Distributed Approach for Loosely Coupled Mixed Integer
  Linear Programming Problems

abstract:
  In this paper, we propose two exact distributed algorithms to solve mixed
integer linear programming (MILP) problems with multiple agents where data
privacy is important for the agents. A key challenge is that, because of the
non-convex nature of MILPs, classical distributed and decentralized
optimization approaches cannot be applied directly to find their optimal
solutions. The proposed exact algorithms are based on adding primal cuts and
restricting the Lagrangian relaxation of the original MILP problem. We show
finite convergence of these algorithms for MILPs with only binary and
continuous variables. We test the proposed algorithms on the unit commitment
problem and discuss its pros and cons comparing to the central MILP approach.


title:
A Nanometer-Thick Oxide Semiconductor Transistor with Ultra-High Drain
  Current

abstract:
  High drive current is a critical performance parameter in semiconductor
devices for high-speed, low-power logic applications or high-efficiency,
high-power, high-speed radio frequency (RF) analog applications. In this work,
we demonstrate an In2O3 transistor grown by atomic layer deposition (ALD) at
back-end-of-line (BEOL) compatible temperatures with a record high drain
current exceeding 10 A/mm, the performance of which is 2-3 times better than
all known transistors with semiconductor channels. A record high
transconductance of 4 S/mm is also achieved among all transistors with a planar
structure. It is found that a high carrier density and high electron velocity
both contribute to this remarkably high on-state performance in ALD In2O3
transistors, which is made possible by the high-quality oxide/oxide interface,
the metal-like charge-neutrality-level (CNL) alignment, and the high band
velocities induced by the low density-of-state (DOS). Experimental Hall, I-V
and split C-V measurements at room temperature confirm a high carrier density
up to 6-7*10^13 /cm2 and a high velocity of about 10^7 cm/s. Ultra-thin oxide
semiconductors, with a CNL located deep inside the conduction band, represent a
promising new direction for the search of alternative channel materials for
high-performance semiconductor devices.


title:
Weak solutions to the equations of stationary compressible flows in
  active liquid crystals

abstract:
  The equations of stationary compressible flows of active liquid crystals are
considered in a bounded three-dimensional domain. The system consists of the
stationary Navier-Stokes equations coupled with the equation of Q-tensors and
the equation of the active particles. The existence of weak solutions to the
stationary problem is established through a two-level approximation scheme,
compactness estimates and weak convergence arguments. Novel techniques are
developed to overcome the difficulties due to the lower regularity of
stationary solutions, a Moser-type iteration is used to deal with the strong
coupling of active particles and fluids, and some weighted estimates on the
energy functions are achieved so that the weak solutions can be constructed for
all values of the adiabatic exponent $\gamma>1$.


title:
Adapting and Evaluating Influence-Estimation Methods for
  Gradient-Boosted Decision Trees

abstract:
  Influence estimation analyzes how changes to the training data can lead to
different model predictions; this analysis can help us better understand these
predictions, the models making those predictions, and the data sets they're
trained on. However, most influence-estimation techniques are designed for deep
learning models with continuous parameters. Gradient-boosted decision trees
(GBDTs) are a powerful and widely-used class of models; however, these models
are black boxes with opaque decision-making processes. In the pursuit of better
understanding GBDT predictions and generally improving these models, we adapt
recent and popular influence-estimation methods designed for deep learning
models to GBDTs. Specifically, we adapt representer-point methods and TracIn,
denoting our new methods TREX and BoostIn, respectively; source code is
available at https://github.com/jjbrophy47/tree_influence. We compare these
methods to LeafInfluence and other baselines using 5 different evaluation
measures on 22 real-world data sets with 4 popular GBDT implementations. These
experiments give us a comprehensive overview of how different approaches to
influence estimation work in GBDT models. We find BoostIn is an efficient
influence-estimation method for GBDTs that performs equally well or better than
existing work while being four orders of magnitude faster. Our evaluation also
suggests the gold-standard approach of leave-one-out~(LOO) retraining
consistently identifies the single-most influential training example but
performs poorly at finding the most influential set of training examples for a
given target prediction.


title:
A Gate-All-Around Single-Channel In2O3 Nanoribbon FET with Near 20
  mA/{\mu}m Drain Current

abstract:
  In this work, we demonstrate atomic-layer-deposited (ALD) single-channel
indium oxide (In2O3) gate-all-around (GAA) nanoribbon FETs in a
back-end-of-line (BEOL) compatible process. A maximum on-state current (ION) of
19.3 mA/{\mu}m (near 20 mA/{\mu}m) is achieved in an In2O3 GAA nanoribbon FET
with a channel thickness (TIO) of 3.1 nm, channel length (Lch) of 40 nm,
channel width (Wch) of 30 nm and dielectric HfO2 of 5 nm. The record high drain
current obtained from an In2O3 FET is about one order of magnitude higher than
any conventional single-channel semiconductor FETs. This extraordinary drain
current and its related on-state performance demonstrate ALD In2O3 is a
promising oxide semiconductor channel with great opportunities in BEOL
compatible monolithic 3D integration.


title:
Combined Learning of Neural Network Weights for Privacy in Collaborative
  Tasks

abstract:
  We introduce CoLN, Combined Learning of Neural network weights, a novel
method to securely combine Machine Learning models over sensitive data with no
sharing of data. With CoLN, local hosts use the same Neural Network
architecture and base parameters to train a model using only locally available
data. Locally trained models are then submitted to a combining agent, which
produces a combined model. The new model's parameters can be sent back to
hosts, and can then be used as initial parameters for a new training iteration.
CoLN is capable of combining several distributed neural networks of the same
kind but is not restricted to any single neural architecture. In this paper we
detail the combination algorithm and present experiments with feed-forward,
convolutional, and recurrent Neural Network architectures, showing that the
CoLN combined model approximates the performance of a hypothetical ideal
centralized model, trained using the combination of the local datasets. CoLN
can contribute to secure collaborative research, as required in the medical
area, where privacy issues preclude data sharing, but where the limitations of
local data demand information derived from larger datasets.


title:
A Simple Duality Proof for Wasserstein Distributionally Robust
  Optimization

abstract:
  We present a short and elementary proof of the duality for Wasserstein
distributionally robust optimization, which holds for any arbitrary Kantorovich
transport distance, any arbitrary measurable loss function, and any arbitrary
nominal probability distribution, as long as certain interchangeability
principle holds.


title: Visual Spatial Reasoning

abstract:
  Spatial relations are fundamental to human cognition and are the most basic
knowledge for us to understand and communicate about our physical surroundings.
In this paper, we ask the critical question: Are current vision-and-language
models (VLMs) able to correctly understand spatial relations? To answer this
question, we propose Visual Spatial Reasoning (VSR), a novel benchmark task
with human labelled dataset for investigating VLMs' capabilities in recognising
65 types of spatial relationships (e.g., under, in front of, facing etc.) in
natural text-image pairs. Specifically, given a caption and an image, the model
needs to perform binary classification and decide if the caption accurately
describes the spatial relationships of two objects presented in the image.
While being seemingly simple and straightforward, the task shows a large gap
between human and model performance (human ceiling on the VSR task is above 95%
and models only achieve around 70%). With fine-grained categorisation and
control on both concepts and relations, our VSR benchmark enables us to perform
interesting probing analysis to pinpoint VLMs' failure cases and the reasons
behind. We observe that VLMs' by-relation performances have little correlation
with the number of training examples and the tested models are in general
incapable of recognising relations that concern orientations of objects. Also,
VLMs have poor zero-shot generalisation toward unseen concepts. The dataset and
code are released at github.com/cambridgeltl/visual-spatial-reasoning.


title:
RADNet: A Deep Neural Network Model for Robust Perception in Moving
  Autonomous Systems

abstract:
  Interactive autonomous applications require robustness of the perception
engine to artifacts in unconstrained videos. In this paper, we examine the
effect of camera motion on the task of action detection. We develop a novel
ranking method to rank videos based on the degree of global camera motion. For
the high ranking camera videos we show that the accuracy of action detection is
decreased. We propose an action detection pipeline that is robust to the camera
motion effect and verify it empirically. Specifically, we do actor feature
alignment across frames and couple global scene features with local
actor-specific features. We do feature alignment using a novel formulation of
the Spatio-temporal Sampling Network (STSN) but with multi-scale offset
prediction and refinement using a pyramid structure. We also propose a novel
input dependent weighted averaging strategy for fusing local and global
features. We show the applicability of our network on our dataset of moving
camera videos with high camera motion (MOVE dataset) with a 4.1% increase in
frame mAP and 17% increase in video mAP.


title: Scattering mechanisms in state-of-the-art GaAs/AlGaAs quantum wells

abstract:
  Motivated by recent breakthrough in molecular beam epitaxy of GaAs/AlGaAs
quantum wells [Y. J. Chung \textit{et al.}, Nature Materials \textbf{20}, 632
(2021)], we examine contributions to mobility and quantum mobility from various
scattering mechanisms and their dependencies on the electron density. We find
that at lower electron densities, $n_e \lesssim 1 \times 10^{11}$ cm$^{-2}$,
both transport and quantum mobility are limited by unintentional background
impurities and follow a power law dependence, $\propto n_e^{\alpha}$, with
$\alpha \approx 0.85$. Our predictions for quantum mobility are in reasonable
agreement with an estimate obtained from the resistivity at filling factor
$\nu= 1/2$ in a sample of Y. J. Chung \textit{et al.} with $n_e = 1 \times
10^{11}$ cm$^{-2}$. Consideration of other scattering mechanisms indicates that
interface roughness (remote donors) is a likely limiting factor of transport
(quantum) mobility at higher electron densities. Future measurements of quantum
mobility should yield information on the distribution of background impurities
in GaAs and AlGaAs.


title:
Fractional Vegetation Cover Estimation using Hough Lines and Linear
  Iterative Clustering

abstract:
  A common requirement of plant breeding programs across the country is
companion planting -- growing different species of plants in close proximity so
they can mutually benefit each other. However, the determination of companion
plants requires meticulous monitoring of plant growth. The technique of ocular
monitoring is often laborious and error prone. The availability of image
processing techniques can be used to address the challenge of plant growth
monitoring and provide robust solutions that assist plant scientists to
identify companion plants. This paper presents a new image processing algorithm
to determine the amount of vegetation cover present in a given area, called
fractional vegetation cover. The proposed technique draws inspiration from the
trusted Daubenmire method for vegetation cover estimation and expands upon it.
Briefly, the idea is to estimate vegetation cover from images containing
multiple rows of plant species growing in close proximity separated by a
multi-segment PVC frame of known size. The proposed algorithm applies a Hough
Transform and Simple Linear Iterative Clustering (SLIC) to estimate the amount
of vegetation cover within each segment of the PVC frame. The analysis when
repeated over images captured at regular intervals of time provides crucial
insights into plant growth. As a means of comparison, the proposed algorithm is
compared with SamplePoint and Canopeo, two trusted applications used for
vegetation cover estimation. The comparison shows a 99% similarity with both
SamplePoint and Canopeo demonstrating the accuracy and feasibility of the
algorithm for fractional vegetation cover estimation.


title:
Determination of quantum yield of NADH and FAD in alcohol-water
  solutions: the analysis of radiative and nonradiative relaxation pathways

abstract:
  Combined studies on fluorescence quantum yield in coenzymes NADH and FAD in
water-methanol, water-ethanol and water-propylene glycol mixtures and on
time-resolved fluorescence of the same molecules under excitation at 450 and
355~nm by means of the TCSPC method have been carried out. The dependence of
quantum yield in NADH on alcohol concentration was found to be similar in
methanol and ethanol and different from that in propylene glycol. The behavior
of quantum yield in FAD was found to be almost independent of the type of
alcohol and exhibited a dramatic 5--6 times increase with alcohol
concentration. A model describing molecular excited state relaxation dynamics
in pico- and nanosecond time domain was developed on the basis on the quantum
mechanical theory and used for analysis of the experimental results. In
particular, the model provided a new insight into the nature of the Decay
Associated Spectra (DAS) phenomenon. The analysis of the role of pico- and
nanosecond quenching in NADH suggested that the picosecond decay in methanol
and ethanol likely does not occur through electron transfer in the stacking
configuration of the nicotinamide and adenine moieties, but through other
mechanisms. However in propylene glycol the picosecond quenching in NADH
depended strongly on propylene glycol concentration and sharp increase of the
measured quantum yield was associated with the contribution of both picosecond
and nanosecond quenching mechanisms. The picosecond quenching to FAD was found
to depend significantly on the any alcohol concentration suggesting that it
gives a profound contribution to the rise of the measured quantum yield with
alcohol concentration. This conclusion supports the established mechanism of
the fluorescence quenching in FAD through electron transfer reaction in the
$\pi$-stacked conformation between isoalloxazine and adenine moieties.


title:
Direct closed-loop identification of continuous-time systems using
  fixed-pole observer model

abstract:
  This paper provides a method for obtaining a continuous-time model of a
target system in a closed-loop environment from input-output data alone. The
proposed method is based on a fixed-pole observer model, which is a reasonable
continuous-time version of the innovation model in discrete-time and allows the
identification of unstable target systems in closed-loop environments with
unknown controllers. The method is within the framework of the stabilized
output error method and shares usability advantages such as robustness to noise
with complex dynamics and applicability to a wide class of models, in addition
to advantages derived from continuous-time models such as tolerance to system
stiffness and easy access to physical knowledge. The effectiveness of the
method is illustrated through numerical examples.


title:
Optimizing an Adaptive Fuzzy Logic Controller of a 3-DOF Helicopter with
  a Modified PSO Algorithm

abstract:
  This paper investigates the controller optimization for a helicopter system
with three degrees of freedom (3-DOF). To control the system, we combined fuzzy
logic with adaptive control theory. The system is extensively nonlinear and
highly sensitive to the controller's parameters, making it a real challenge to
study these parameters' effect on the controller's performance. Using
metaheuristic algorithms for determining these parameters is a promising
solution. This paper proposes using a modified particle swarm optimization
(MPSO) algorithm to optimize the controller. The algorithm shows a high ability
to perform the global search and find a reasonable search space. The algorithm
modifies the search space of each particle based on its fitness function value
and substitutes weak particles for new ones. These modifications have led to
better accuracy and convergence rate. We prove the efficiency of the MPSO
algorithm by comparing it with the standard PSO and six other well-known
metaheuristic algorithms when optimizing the adaptive fuzzy logic controller of
the 3-DOF helicopter. The proposed method's effectiveness is shown through
computer simulations while the system is subject to uncertainties and
disturbance. We demonstrate the method's superiority by comparing the results
when the MPSO and the standard PSO optimize the controller.


title:
Gluing theories of contact instantons and of pseudoholomoprhic curves in
  SFT

abstract:
  We develop the gluing theory of contact instantons in the context of open
strings and in the context of closed strings \emph{with vanishing charge}, for
example in the symplectization context. This is one of the key ingredients for
the study of (virtually) smooth moduli space of (bordered) contact instantons
needed for the construction of contact instanton Floer cohomology and more
generally for the construction of Fukaya-type category of Legendrian
submanifolds in contact manifold $(M,\xi)$. As an application, we apply the
gluing theorem to give the construction of (cylindrical) Legendrian contact
instanton homology that enters in our solution to Sandon's question for the
nondegenerate case. We also apply this gluing theory to that of moduli spaces
of holomorphic buildings arising in Symplectic Field Theory (SFT) by
canonically lifting the former to that of the latter.


title:
The Johnson-Lindenstrauss Lemma for Clustering and Subspace
  Approximation: From Coresets to Dimension Reduction

abstract:
  We study the effect of Johnson-Lindenstrauss transforms in various Euclidean
optimization problems. We ask, for a particular problem and an accuracy
parameter $\epsilon \in (0, 1)$, what is the smallest target dimension $t \in
\mathbb{N}$ such that a Johnson-Lindenstrauss transform $\Pi \colon
\mathbb{R}^d \to \mathbb{R}^t$ preserves the cost of the optimal solution up to
a $(1+\epsilon)$-factor.
  $\bullet$ For center-based $(k,z)$-clustering, we show $t = O( (\log k + z
\log(1/\epsilon)) / \epsilon^2)$ suffices, improving on $O((\log k +
z\log(1/\epsilon) + z^2)/\epsilon^2)$ [MMR19].
  $\bullet$ For $(k,z)$-subspace approximation, we show $t = \tilde{O}(zk^2 /
\epsilon^3)$ suffices. The prior best bound, of $O(k/\epsilon^2)$, only applied
to the case $z = 2$ [CEMMP15].
  $\bullet$ For $(k,z)$-flat approximation, we show $t =
\tilde{O}(zk^2/\epsilon^3)$ suffices, improving on a bound of $\tilde{O}(zk^2
\log n/\epsilon^3)$ [KR15].
  $\bullet$ For $(k,z)$-line approximation, we show $t = O((k \log \log n + z +
\log(1/\epsilon)) / \epsilon^3)$ suffices. No prior results were known.
  All the above results follow from one general technique: we use algorithms
for constructing coresets as an analytical tool in randomized dimensionality
reduction.


title: Ensuring Resilience Against Stealthy Attacks on Cyber-Physical Systems

abstract:
  This article provides a tool for analyzing mechanisms that aim to achieve
resilience against stealthy, or undetectable, attacks on cyber-physical systems
(CPSs). We consider attackers who are able to corrupt all of the inputs and
outputs of the system. To counter such attackers, a response scheme must be
implemented that keeps the attacker from corrupting the inputs and outputs of
the system for certain periods of time. To aid in the design of such a response
scheme, our tool provides sufficient lengths for these periods of time in order
to ensure safety with a particular probability. We provide a conservative upper
bound on how long the system can remain under stealthy attack before the safety
constraints are violated. Furthermore, we show how a detector limits the set of
biases an attacker can exert on the system while still remaining stealthy,
aiding a system operator in the design of the detector. Our contributions are
demonstrated with an illustrative example.


title:
Convergence of Ricci-limit spaces under bounded Ricci curvature and
  local covering geometry I

abstract:
  We extend Cheeger-Gromov's and Anderson's convergence theorems to regular
limit spaces of manifolds with bounded Ricci curvature and local covering
geometry, by establishing the $C^{1,\alpha}$-regularities that are the best one
may expect on those Ricci-limit spaces. As an application we prove an optimal
generalization of Fukaya's fibration theorem on collapsed manifolds with
bounded Ricci curvature, which also improves the original version to
$C^{1,\alpha}$ limit spaces.


title:
Observations of Cross Scale Energy Transfer in the Inner Heliosphere by
  Parker Solar Probe

abstract:
  The solar wind, a continuous flow of plasma from the sun, not only shapes the
near Earth space environment but also serves as a natural laboratory to study
plasma turbulence in conditions that are not achievable in the lab. Starting
with the Mariners, for more than five decades, multiple space missions have
enabled in-depth studies of solar wind turbulence. Parker Solar Probe (PSP) was
launched to explore the origins and evolution of the solar wind. With its
state-of-the-art instrumentation and unprecedented close approaches to the sun,
PSP is starting a new era of inner heliospheric exploration. In this review we
discuss observations of turbulent energy flow across scales in the inner
heliosphere as observed by PSP. After providing a quick theoretical overview
and a quick recap of turbulence before PSP, we discuss in detail the
observations of energy at various scales on its journey from the largest scales
to the internal degrees of freedom of the plasma. We conclude with some open
ended questions, many of which we hope that PSP will help answer.


title:
Computational Insights into Electronic Excitations, Spin-Orbit Coupling
  Effects, and Spin Decoherence in Cr(IV)-based Molecular Qubits

abstract:
  The great success of point defects and dopants in semiconductors for quantum
information processing has invigorated a search for molecules with analogous
properties. Flexibility and tunability of desired properties in a large
chemical space have great advantages over solid-state systems. The properties
analogous to point defects were demonstrated in Cr(IV)-based molecular family,
Cr(IV)(aryl)$_4$, where the electronic spin states were optically initialized,
read out, and controlled. Despite this kick-start, there is still a large room
for enhancing properties crucial for molecular qubits. Here we provide
computational insights into key properties of the Cr(IV)-based molecules aimed
at assisting chemical design of efficient molecular qubits. Using the
multireference ab-initio methods, we investigate the electronic states of
Cr(IV)(aryl)$_4$ molecules with slightly different ligands, showing that the
zero-phonon line energies agree with the experiment, and that the excited
spin-triplet and spin-singlet states are highly sensitive to small chemical
perturbations. By adding spin-orbit interaction, we find that the sign of the
uniaxial zero-field splitting (ZFS) parameter is negative for all considered
molecules, and discuss optically-induced spin initialization via non-radiative
intersystem crossing. To understand electron spin decoherence, we quantify
hyperfine coupling to the $^{53}$Cr nuclear spin and to the $^{13}$C and $^1$H
nuclear spins. We show that the splitting or broadening of the electronic spin
sub-levels induced by these hyperfine interactions decreases by an order of
magnitude when the molecules have a substantial transverse ZFS parameter. This
feature is applicable to other integer-spin molecules in order to significantly
reduce spin decoherence.


title:
Traffic Context Aware Data Augmentation for Rare Object Detection in
  Autonomous Driving

abstract:
  Detection of rare objects (e.g., traffic cones, traffic barrels and traffic
warning triangles) is an important perception task to improve the safety of
autonomous driving. Training of such models typically requires a large number
of annotated data which is expensive and time consuming to obtain. To address
the above problem, an emerging approach is to apply data augmentation to
automatically generate cost-free training samples. In this work, we propose a
systematic study on simple Copy-Paste data augmentation for rare object
detection in autonomous driving. Specifically, local adaptive instance-level
image transformation is introduced to generate realistic rare object masks from
source domain to the target domain. Moreover, traffic scene context is utilized
to guide the placement of masks of rare objects. To this end, our data
augmentation generates training data with high quality and realistic
characteristics by leveraging both local and global consistency. In addition,
we build a new dataset named NM10k consisting 10k training images, 4k
validation images and the corresponding labels with a diverse range of
scenarios in autonomous driving. Experiments on NM10k show that our method
achieves promising results on rare object detection. We also present a thorough
study to illustrate the effectiveness of our local-adaptive and global
constraints based Copy-Paste data augmentation for rare object detection. The
data, development kit and more information of NM10k dataset are available
online at: \url{https://nullmax-vision.github.io}.


title: Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF

abstract:
  The sharing of fake news and conspiracy theories on social media has
wide-spread negative effects. By designing and applying different machine
learning models, researchers have made progress in detecting fake news from
text. However, existing research places a heavy emphasis on general,
common-sense fake news, while in reality fake news often involves rapidly
changing topics and domain-specific vocabulary. In this paper, we present our
methods and results for three fake news detection tasks at MediaEval benchmark
2021 that specifically involve COVID-19 related topics. We experiment with a
group of text-based models including Support Vector Machines, Random Forest,
BERT, and RoBERTa. We find that a pre-trained transformer yields the best
validation results, but a randomly initialized transformer with smart design
can also be trained to reach accuracies close to that of the pre-trained
transformer.


title:
The Cost of Lunar Landing Pads with a Trade Study of Construction
  Methods

abstract:
  This study estimates the cost of building lunar landing pads and examines
whether any construction methods are economically superior to others. Some
proposed methods require large amounts of mass transported from the Earth,
others require high energy consumption on the lunar surface, and others have a
long construction time. Each of these factors contributes direct and indirect
costs to lunar activities. The most important economic variables turn out to be
the transportation cost to the lunar surface and the magnitude of the program
delay cost imposed by a construction method. The cost of a landing pad depends
sensitively on the optimization of the mass and speed of the construction
equipment, so a minimum-cost set of equipment exists for each construction
method within a specified economic scenario. Several scenarios have been
analyzed across a range of transportation costs with both high and low program
delay costs. It is found that microwave sintering is currently the most
favorable method to build the inner, high temperature zone of a lunar landing
pad, although other methods are within the range of uncertainty. The most
favorable method to build the outer, low temperature zone of the landing pad is
also sintering when transportation costs are high, but it switches to polymer
infusion when transportation costs drop below about \$110K/kg to the lunar
surface. It is estimated that the Artemis Basecamp could build a landing pad
with a budgeted line-item cost of \$229M assuming that transportation costs
will be reduced modestly from the current rate \$1M/kg to the lunar surface to
\$300K/kg. A landing pad drops to \$130M when the transportation cost drops
further to \$100K/kg, or to \$47M if transportation costs fall below \$10K/kg.
Ultimately, landing pads can be built around the Moon at very low cost, due to
economies of scale.

categories: econ.GN astro-ph.EP astro-ph.IM physics.soc-ph q-fin.EC


title: The Cross-lingual Conversation Summarization Challenge

abstract:
  We propose the shared task of cross-lingual conversation summarization,
\emph{ConvSumX Challenge}, opening new avenues for researchers to investigate
solutions that integrate conversation summarization and machine translation.
This task can be particularly useful due to the emergence of online meetings
and conferences. We construct a new benchmark, covering 2 real-world scenarios
and 3 language directions, including a low-resource language. We hope that
\emph{ConvSumX} can motivate researches to go beyond English and break the
barrier for non-English speakers to benefit from recent advances of
conversation summarization.


title:
Geometric Graph Representation with Learnable Graph Structure and
  Adaptive AU Constraint for Micro-Expression Recognition

abstract:
  Micro-expression recognition (MER) is valuable because the involuntary nature
of micro-expressions (MEs) can reveal genuine emotions. Most works recognize
MEs by taking RGB videos or images as input. In fact, the activated facial
regions in ME images are very small and the subtle motion can be easily
submerged in the unrelated information. Facial landmarks are a low-dimensional
and compact modality, which leads to much lower computational cost and can
potentially concentrate more on ME-related features. However, the
discriminability of landmarks for MER is not clear. Thus, this paper explores
the contribution of facial landmarks and constructs a new framework to
efficiently recognize MEs with sole facial landmark information. Specially, we
design a separate structure module to separately aggregate the spatial and
temporal information in the geometric movement graph based on facial landmarks,
and a Geometric Two-Stream Graph Network is constructed to aggregate the
low-order geometric information and high-order semantic information of facial
landmarks. Furthermore, two core components are proposed to enhance features.
Specifically, a semantic adjacency matrix can automatically model the
relationship between nodes even long-distance nodes in a self-learning fashion;
and an Adaptive Action Unit loss is introduced to guide the learning process
such that the learned features are forced to have a synchronized pattern with
facial action units. Notably, this work tackles MER only utilizing geometric
features, processed based on a graph model, which provides a new idea with much
higher efficiency to promote MER. The experimental results demonstrate that the
proposed method can achieve competitive or even superior performance with a
significantly reduced computational cost, and facial landmarks can
significantly contribute to MER and are worth further study for efficient ME
analysis.


title: Chemical Strain Engineering of MAPbI3 Perovskite Films

abstract:
  This study introduces a new chemical method for controlling the strain in
methylammonium lead iodide (MAPbI3) perovskite crystals by varying the ratio of
Pb(Ac)2 and PbCl2 in the precursor solution. To observe the effect on crystal
strain, a combination of piezoresponse force microscopy (PFM) and X-ray
diffraction (XRD) is used. The PFM images show an increase in the average size
of ferroelastic twin domains upon increasing the PbCl2 content, indicating an
increase in crystal strain. The XRD spectra support this observation with
strong crystal twinning features that appear in the spectra. This behaviour is
caused by a strain gradient during the crystallization due to different
evaporation rates of methylammonium acetate and methylammonium chloride as
revealed by time-of-flight secondary ion mass spectroscopy (ToF-SIMS) and
grazing incidince x-ray diffraction (GIXRD) measurements. Additional
time-resolved photoluminescence (TRPL) show an increased carrier lifetime in
the MAPbI3 films prepared with higher PbCl2 content, suggesting a decreased
trap density in films with larger twin domain structures. The results
demonstrate the potential of chemical strain engineering as an easy method for
controlling strain-related effects in lead halide perovskites.


title: Liouville type Theorems for the stationary compressible 3D MHD equations

abstract:
  In this paper, we investigate the three dimensional stationary compressible
Navier-Stokes equations, and obtain Liouville type theorems if a smooth
solution $(\rho, \mathbf{u})$ satisfies some suitable conditions. In
particular, our results improve and generalize the corresponding result.


title: Regulating stochastic clocks

abstract:
  Stochastic clocks represent a class of time change methods for incorporating
trading activity into continuous-time financial models, with the ability to
deal with typical asymmetrical and tail risks in financial returns. In this
paper we propose a significant improvement of stochastic clocks for the same
objective but without decreasing the number of trades or changing the trading
intensity. Our methodology targets any L\'{e}vy subordinator, or more generally
any process of nonnegative independent increments, and is based on various
choices of regulating kernels motivated from repeated averaging. By way of a
hyperparameter linked to the degree of regulation, arbitrarily large skewness
and excess kurtosis of returns can be easily achieved. Generic-time Laplace
transforms, characterizing triplets, and cumulants of the regulated clocks and
subsequent mixed models are analyzed, serving purposes ranging from statistical
estimation and option price calibration to simulation techniques. Under
specified jump-diffusion processes and tempered stable processes, a robust
moment-based estimation procedure with profile likelihood is employed for
statistical estimation and a comprehensive empirical study involving S\&P500
and Bitcoin daily returns is conducted to demonstrate a series of desirable
effects of the proposed methods.


title:
Blockchain Applicability for the Internet of Things: Performance and
  Scalability Challenges and Solutions

abstract:
  Blockchain has recently been able to draw wider attention throughout the
research community. Since its emergence, the world has seen the mind-blowing
expansion of this new technology, which was initially developed as a pawn of
digital currency more than a decade back. A self-administering ledger that
ensures extensive data immutability over the peer-to-peer network has made it
attractive for cybersecurity applications such as a sensor-enabled system
called the Internet of things (IoT). Brand new challenges and questions now
demand solutions as huge IoT devices are now online in a distributed fashion to
ease our everyday lives. After being motivated by those challenges, the work
here has figured out the issues and perspectives an IoT infrastructure can
suffer because of the wrong choice of blockchain technology. Though it may look
like a typical review, however, unlike that, this paper targets sorting out the
specific security challenges of the blockchain-IoT eco-system through critical
findings and applicable use-cases. Therefore, the contribution includes
directing Blockchain architects, designers, and researchers in the broad domain
to select the unblemished combinations of Blockchain-powered IoT applications.
In addition, the paper promises to bring a deep insight into the
state-of-the-art Blockchain platforms, namely Ethereum, Hyperledger, and IOTA,
to exhibit the respective challenges, constraints, and prospects in terms of
performance and scalability.


title:
Adaptive auxiliary reduced model reanalysis-based efficient topology
  optimization method

abstract:
  Efficient topology optimization based on the adaptive auxiliary reduced model
reanalysis (AARMR) is proposed to improve computational efficiency and scale.
In this method, a projection auxiliary reduced model (PARM) is integrated into
the combined approximation reduced model (CARM) to reduce the dimension of the
model in different aspects. First, the CARM restricts the solution space to
avoid large matrix factorization. Second, the PARM is proposed to construct the
CARM dynamically to save computational cost. Furthermore, the multi-grid
conjugate gradient method is suggested to update PARM adaptively. Finally,
several classic numerical examples are tested to show that the proposed method
not only significantly improves computational efficiency, but also can solve
large-scale problems that are difficult to solve by direct solvers due to the
memory limitations.


title: Internal sums for synthetic fibered $(\infty,1)$-categories

abstract:
  We give structural results about bifibrations of (internal)
$(\infty,1)$-categories with internal sums. This includes a higher version of
Moens' Theorem, characterizing cartesian bifibrations with extensive aka stable
and disjoint internal sums over lex bases as Artin gluings of lex functors.
  We also treat a generalized version of Moens' Theorem due to Streicher which
does not require the Beck--Chevalley condition.
  Furthermore, we show that also in this setting the Moens fibrations can be
characterized via a condition due to Zawadowski.
  Our account overall follows Streicher's presentation of fibered category
theory \`{a} la B\'{e}nabou, generalizing the results to the internal,
higher-categorical case, formulated in a synthetic setting.
  Namely, we work inside simplicial homotopy type theory, which has been
introduced by Riehl and Shulman as a logical system to reason about internal
$(\infty,1)$-categories, interpreted as Rezk objects in any given
Grothendieck--Rezk--Lurie $(\infty,1)$-topos.


title:
Crude Oil-related Events Extraction and Processing: A Transfer Learning
  Approach

abstract:
  One of the challenges in event extraction via traditional supervised learning
paradigm is the need for a sizeable annotated dataset to achieve satisfactory
model performance. It is even more challenging when it comes to event
extraction in the finance and economics domain, a domain with considerably
fewer resources. This paper presents a complete framework for extracting and
processing crude oil-related events found in CrudeOilNews corpus, addressing
the issue of annotation scarcity and class imbalance by leveraging on the
effectiveness of transfer learning. Apart from event extraction, we place
special emphasis on event properties (Polarity, Modality, and Intensity)
classification to determine the factual certainty of each event. We build
baseline models first by supervised learning and then exploit Transfer Learning
methods to boost event extraction model performance despite the limited amount
of annotated data and severe class imbalance. This is done via methods within
the transfer learning framework such as Domain Adaptive Pre-training,
Multi-task Learning and Sequential Transfer Learning. Based on experiment
results, we are able to improve all event extraction sub-task models both in F1
and MCC1-score as compared to baseline models trained via the standard
supervised learning. Accurate and holistic event extraction from crude oil news
is very useful for downstream tasks such as understanding event chains and
learning event-event relations, which can be used for other downstream tasks
such as commodity price prediction, summarisation, etc. to support a wide range
of business decision making.


title:
Abnormal-aware Multi-person Evaluation System with Improved Fuzzy
  Weighting

abstract:
  There exists a phenomenon that subjectivity highly lies in the daily
evaluation process. Our research primarily concentrates on a multi-person
evaluation system with anomaly detection to minimize the possible inaccuracy
that subjective assessment brings. We choose the two-stage screening method,
which consists of rough screening and score-weighted Kendall-$\tau$ Distance to
winnow out abnormal data, coupled with hypothesis testing to narrow global
discrepancy. Then we use Fuzzy Synthetic Evaluation Method(FSE) to determine
the significance of scores given by reviewers as well as their reliability,
culminating in a more impartial weight for each reviewer in the final
conclusion. The results demonstrate a clear and comprehensive ranking instead
of unilateral scores, and we get to have an efficiency in filtering out
abnormal data as well as a reasonably objective weight determination mechanism.
We can sense that through our study, people will have a chance of modifying a
multi-person evaluation system to attain both equity and a relatively superior
competitive atmosphere.


title:
Scaling of Berry-curvature monopole dominated large linear positive
  magnetoresistance

abstract:
  The emergent topological quantum materials increasingly exhibit exotic
electronic transport behaviors. The linear positive magnetoresistance (LPMR) in
topological materials is a widely observed phenomenon, which is promising for
potential applications on topological spintronics. However, its mechanism
remains ambiguous yet and the effect is thus uncontrollable. Here, we report a
quantitative scaling model that correlates the LPMR with the Berry curvature
near the Fermi surface, based on a ferromagnetic Weyl semimetal CoS2 that bears
the largest LPMR of over 500% at 2 Kelvin and 9 Tesla, among known magnetic
topological semimetals. Theoretical calculations reveal that masses of Weyl
nodes exist near the Fermi level, which serve as Berry-curvature monopoles and
low-effective-mass carriers in this system. We have proposed a relation
\[\text{MR}=\frac{e}{\hbar }\mathbf{B}\cdot \mathbf{\Omega }\], with B being
the applied magnetic field and {\Omega} the average value of the Berry
curvature near the Fermi surface, and provided experimental evidence of Berry
curvature induced LPMR for the first time. A clear picture of the linearly
slowing down of carriers, i.e., the LPMR effect, has been demonstrated under
the cooperation of the k-space Berry curvature and real-space magnetic field.
This intrinsic mechanism promotes the common understanding and functional
designing of the large Berry-curvature MR in topological Dirac/Weyl systems for
magnetic sensing or information storage.


title:
A Conceptual Trust Management Framework under Uncertainty for Smart
  Vehicular Networks

abstract:
  Trust is a fundamental concept in large-scale distributed systems like the
Internet of Things (IoT). Trust helps to resolve choices into a decision.
However, the trust calculation depends on the amount of uncertainty present in
data sources. Trust in an IoT network is proportional to the amount of
uncertainty generated by such sources as hardware malfunctions, network
stability, adversarial issues, and the nature of data exchanged between the
entities. The relationship between trust and uncertainty warrants approaches
designed to maximize the former quality whilst minimizing the latter.
Unfortunately, there is no consensus on an approach to ensure the
trustworthiness of IoT networks, in particular, addressing the uncertainty
issues in a fine-grained way. This paper aims to explore a generalized
framework designed to manage trust in IoT networks of varying scales. In the
proposed framework, several sources of uncertainty are expressed as quantities,
trust ratings are calculated for individual entities in an IoT network, and a
network model capable of effectively distributing workloads to trustworthy
nodes is proposed. We consider a practical use case of smart vehicular
networks. By realizing this paper, a standardized approach to building
trustworthy IoT networks can be established, which can further guide subsequent
works in the field of trust management under uncertainty.


title:
Polarization distribution in the image of a synchrotron emitting ring
  around regular black holes

abstract:
  We have studied the polarized image of a synchrotron emitting ring around
regular Hayward black hole and Bardeen black hole, respectively. Each of them
carries a magnetic field itself in terms of gravity coupled to a nonlinear
electrodynamics. Our results show that the main features of the polarization
images of emitting rings are similar in these two regular black hole
spacetimes. With the increase of magnetic charge parameter, the polarization
intensity and electric vector position angle in the image plane increase in
both Hayward black hole and Bardeen black hole spacetimes. We also investigate
the differences of polarization intensity and electric vector position angle
between in the regular black hole spacetime and in the Schwarzschild case,
which indicates that the differences in the Hayward black hole spacetime are
smaller than those in the Bardeen black hole. We also find the effects of the
magnetic charge parameter on the Strokes $Q-U$ loops slightly larger in Bardeen
black hole spacetime. These information stored in the polarization image around
Hayward and Bardeen black holes could help us to understand regular black holes
and the gravity coupled a nonlinear electrodynamics.


title: Properties of accretion disc models in the background with quadrupoles

abstract:
  We consider a static and axially symmetric metric containing two quadrupole
parameters. In the present contribution, we study the quadrupole moments
constraints on the properties of the relativistic accretion disc models, also
explore the relation of oscillatory frequencies of charged particles to the
frequencies of the twin high-frequency quasi-periodic oscillations observed in
some microquasars. We also compare the results with Schwarzschild and Kerr
metrics.


title:
Lifetime-based Method for Quantum Simulation on a New Sunway
  Supercomputer

abstract:
  Faster classical simulation becomes essential for the validation of quantum
computer, and tensor network contraction is a widely-applied simulation
approach. Due to the memory limitation, slicing is adopted to help cutting down
the memory size by reducing the tensor dimension, which also leads to
additional computation overhead. This paper proposes novel lifetime-based
methods to reduce the slicing overhead and improve the computing efficiency,
including: interpretation for slicing overhead, an in place slicing strategy to
find the smallest slicing set, a corresponding iterative method, and an
adaptive path refiner customized for Sunway architecture. Experiments show that
our in place slicing strategy reduces the slicing overhead to less than 1.2 and
obtains 100-200 times speedups over related efforts. The resulting simulation
time is reduced from 304s (2021 Gordon Bell Prize) to 149.2s on Sycamore RQC,
with a sustainable mixed-precision performance of 416.5 Pflops using over 41M
cores to simulate 1M correlated samples.


title: Neural Network Optimal Feedback Control with Guaranteed Local Stability

abstract:
  Recent research shows that deep learning can be an effective tool for
designing optimal feedback controllers for high-dimensional nonlinear dynamic
systems. But the behavior of these neural network (NN) controllers is still not
well understood. In particular, some NNs with high test accuracy can fail to
even locally stabilize the dynamic system. To address this challenge we propose
several novel NN architectures, which we show guarantee local stability while
retaining the semi-global approximation capacity to learn the optimal feedback
policy. The proposed architectures are compared against standard NN feedback
controllers through numerical simulations of two high-dimensional nonlinear
optimal control problems (OCPs): stabilization of an unstable Burgers-type
partial differential equation (PDE), and altitude and course tracking for a six
degree-of-freedom (6DoF) unmanned aerial vehicle (UAV). The simulations
demonstrate that standard NNs can fail to stabilize the dynamics even when
trained well, while the proposed architectures are always at least locally
stable. Moreover, the proposed controllers are found to be near-optimal in
testing.


title: ELQA: A Corpus of Questions and Answers about the English Language

abstract:
  We introduce a community-sourced dataset for English Language Question
Answering (ELQA), which consists of more than 180k questions and answers on
numerous topics about English language such as grammar, meaning, fluency, and
etymology. The ELQA corpus will enable new NLP applications for language
learners. We introduce three tasks based on the ELQA corpus: 1) answer quality
classification, 2) semantic search for finding similar questions, and 3) answer
generation. We present baselines for each task along with analysis, showing the
strengths and weaknesses of current transformer-based models. The ELQA corpus
and scripts are publicly available for future studies.


title: Elliptic differential inclusions on non-compact Riemannian manifolds

abstract:
  We investigate a large class of elliptic differential inclusions on
non-compact complete Riemannian manifolds which involves the Laplace-Beltrami
operator and a Hardy-type singular term. Depending on the behavior of the
nonlinear term and on the curvature of the Riemannian manifold, we guarantee
non-existence and existence/multiplicity of solutions for the studied
differential inclusion. The proofs are based on nonsmooth variational analysis
as well as isometric actions and fine eigenvalue properties on Riemannian
manifolds. The results are also new in the smooth setting.


title:
Connectivity keeping caterpillars and spiders in bipartite graphs with
  connectivity at most three

abstract:
  A conjecture of Luo, Tian and Wu (2022) says that for every positive integer
$k$ and every finite tree $T$ with bipartition $X$ and $Y$ (denote $t =
\max\{|X|,|Y |\})$, every $k$-connected bipartite graph $G$ with $\delta(G)
\geq k + t$ contains a subtree $T' \cong T$ such that $\kappa(G-V (T')) \geq
k$. In this paper, we confirm this conjecture for caterpillars when $k=3$ and
spiders when $k\leq 3$.


title:
Upper bounds on the size of nodal sets for Gevrey and quasianalytic
  Riemannian manifolds

abstract:
  We find new polynomial upper bounds for the size of nodal sets of
eigenfunctions when the Riemannian manifold has a Gevrey or quasianalytic
regularity.


title:
Learning user-defined sub-goals using memory editing in reinforcement
  learning

abstract:
  The aim of reinforcement learning (RL) is to allow the agent to achieve the
final goal. Most RL studies have focused on improving the efficiency of
learning to achieve the final goal faster. However, the RL model is very
difficult to modify an intermediate route in the process of reaching the final
goal. That is, the agent cannot be under control to achieve other sub-goals in
the existing studies. If the agent can go through the sub-goals on the way to
the destination, the RL can be applied and studied in various fields. In this
study, I propose a methodology to achieve the user-defined sub-goals as well as
the final goal using memory editing. The memory editing is performed to
generate various sub-goals and give an additional reward to the agent. In
addition, the sub-goals are separately learned from the final goal. I set two
simple environments and various scenarios in the test environments. As a
result, the agent almost successfully passed the sub-goals as well as the final
goal under control. Moreover, the agent was able to be induced to visit the
novel state indirectly in the environments. I expect that this methodology can
be used in the fields that need to control the agent in a variety of scenarios.


title:
Convex Combination Consistency between Neighbors for Weakly-supervised
  Action Localization

abstract:
  In weakly-supervised temporal action localization (WS-TAL), the methods
commonly follow the "localization by classification" procedure, which uses the
snippet predictions to form video class scores and then optimizes a video
classification loss. In this procedure, the snippet predictions (or snippet
attention weights) are used to separate foreground and background. However, the
snippet predictions are usually inaccurate due to absence of frame-wise labels,
and then the overall performance is hindered. In this paper, we propose a novel
C$^3$BN to achieve robust snippet predictions. C$^3$BN includes two key designs
by exploring the inherent characteristics of video data. First, because of the
natural continuity of adjacent snippets, we propose a micro data augmentation
strategy to increase the diversity of snippets with convex combination of
adjacent snippets. Second, we propose a macro-micro consistency regularization
strategy to force the model to be invariant (or equivariant) to the
transformations of snippets with respect to video semantics, snippet
predictions and snippet features. Experimental results demonstrate the
effectiveness of our proposed method on top of baselines for the WS-TAL tasks
with video-level and point-level supervision.


title:
A strong maximum principle for minimizers of the one-phase Bernoulli
  problem

abstract:
  We prove a strong maximum principle for minimizers of the one-phase
Alt-Caffarelli functional. We use this to construct a Hardt-Simon-type
foliation associated to any 1-homogenous global minimizer.


title: The theorems on freedom for free products with a relations

abstract:
  In this paper we prove the theorem on freedom for free products with a single
relation (analogous with the well-known result of Magnus) and a generalized
Freiheitssatz for free products (analogous with the well-known result of
Romanovski)