Abstract:
  Periodic space crystals are well established and widely used in physical
sciences. Time crystals have been increasingly explored more recently, where
time is disconnected from space. Periodic relativistic spacetime crystals on
the other hand need to account for the mixing of space and time in special
relativity through Lorentz transformation, and have been listed only in
2-dimensions. This work shows that there exists a transformation between the
conventional Minkowski spacetime (MS) and what is referred to here as
renormalized blended spacetime (RBS); they are shown to be equivalent
descriptions of relativistic physics in flat spacetime. There are two elements
to this reformulation of MS, namely, blending and renormalization. One can now
enumerate the RBS point and space groups in various dimensions with their
mapping to the well-known space crystal groups. The RBS point group for flat
isotropic RBS spacetime is identified to be that of cylinders in various
dimensions. An antisymmetry operation is introduced that can swap between
space-like and time-like directions, leading to color spacetime groups. The
formalism reveals RBS symmetries that are not readily apparent in the
conventional MS formulation. Mathematica(R) script is provided for plotting the
MS and RBS geometries discussed in the work.


Abstract:
  Information technology (IT) companies implement multi-dimensional policy
plans that include procedures, sub-plans, and instructions to outline their
business scopes, targets, and communications. This work outlined the IT policy
implementation plan designed by an imaginary company with a random name called
Northcentral Cloud Consulting Firm (NCCF), containing proposed IT policies,
milestones and roadmaps, control framework, stakeholder responsibilities,
knowledge transfer plan, and leadership roles. As NCCF's major customers seek
data-driven solutions in cloud computing, the NCCF IT policy plan provides
various data policies, including security and proper usage of machine learning
services. The plan offers a detailed roadmap of its financial, geographical,
and reputational expansion within three years. The IT policy plan also
compromises an IT risk management, contingency, and emergency communication
plan, mainly for protecting data and business continuity. Stakeholder
responsibilities are incorporated into the IT policy plan, as NCCF considers
any engagement with its customers as a collaborative effort in which both
parties have and share several responsibilities.


Abstract: We generalize the Cauchy-Davenport theorem to locally compact groups.


Abstract:
  Characterization of breast parenchyma on dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI) is a challenging task owing to the complexity of
underlying tissue structures. Current quantitative approaches, including
radiomics and deep learning models, do not explicitly capture the complex and
subtle parenchymal structures, such as fibroglandular tissue. In this paper, we
propose a novel method to direct a neural network's attention to a dedicated
set of voxels surrounding biologically relevant tissue structures. By
extracting multi-dimensional topological structures with high saliency, we
build a topology-derived biomarker, TopoTxR. We demonstrate the efficacy of
TopoTxR in predicting response to neoadjuvant chemotherapy in breast cancer.
Our qualitative and quantitative results suggest differential topological
behavior of breast tissue on treatment-na\"ive imaging, in patients who respond
favorably to therapy versus those who do not.


Abstract:
  Optical implementation of artificial neural networks has been attracting
great attention due to its potential in parallel computation at speed of light.
Although all-optical deep neural networks (AODNNs) with a few neurons have been
experimentally demonstrated with acceptable errors recently, the feasibility of
large scale AODNNs remains unknown because error might accumulate inevitably
with increasing number of neurons and connections. Here, we demonstrate a
scalable AODNN with programmable linear operations and tunable nonlinear
activation functions. We verify its scalability by measuring and analyzing
errors propagating from a single neuron to the entire network. The feasibility
of AODNNs is further confirmed by recognizing handwritten digits and fashions
respectively.


Abstract:
  Rumors are often associated with newly emerging events, thus, an ability to
deal with unseen rumors is crucial for a rumor veracity classification model.
Previous works address this issue by improving the model's generalizability,
with an assumption that the model will stay unchanged even after the new
outbreak of an event. In this work, we propose an alternative solution to
continuously update the model in accordance with the dynamics of rumor domain
creations. The biggest technical challenge associated with this new approach is
the catastrophic forgetting of previous learnings due to new learnings. We
adopt continual learning strategies that control the new learnings to avoid
catastrophic forgetting and propose an additional strategy that can jointly be
used to strengthen the forgetting alleviation.


Abstract:
  The purpose of this project is to evaluate three language models named BERT,
ALBERT, and LongFormer on the Question Answering dataset called DuoRC. The
language model task has two inputs, a question, and a context. The context is a
paragraph or an entire document while the output is the answer based on the
context. The goal is to perform grid search hyperparameter fine-tuning using
DuoRC. Pretrained weights of the models are taken from the Huggingface library.
Different sets of hyperparameters are used to fine-tune the models using two
versions of DuoRC which are the SelfRC and the ParaphraseRC. The results show
that the ALBERT (pretrained using the SQuAD1 dataset) has an F1 score of 76.4
and an accuracy score of 68.52 after fine-tuning on the SelfRC dataset. The
Longformer model (pretrained using the SQuAD and SelfRC datasets) has an F1
score of 52.58 and an accuracy score of 46.60 after fine-tuning on the
ParaphraseRC dataset. The current results outperformed the results from the
previous model by DuoRC.


Abstract:
  It is critical yet challenging for deep learning models to properly
characterize uncertainty that is pervasive in real-world environments. Although
a lot of efforts have been made, such as heteroscedastic neural networks
(HNNs), little work has demonstrated satisfactory practicability due to the
different levels of compromise on learning efficiency, quality of uncertainty
estimates, and predictive performance. Moreover, existing HNNs typically fail
to construct an explicit interaction between the prediction and its associated
uncertainty. This paper aims to remedy these issues by developing SDE-HNN, a
new heteroscedastic neural network equipped with stochastic differential
equations (SDE) to characterize the interaction between the predictive mean and
variance of HNNs for accurate and reliable regression. Theoretically, we show
the existence and uniqueness of the solution to the devised neural SDE.
Moreover, based on the bias-variance trade-off for the optimization in SDE-HNN,
we design an enhanced numerical SDE solver to improve the learning stability.
Finally, to more systematically evaluate the predictive uncertainty, we present
two new diagnostic uncertainty metrics. Experiments on the challenging datasets
show that our method significantly outperforms the state-of-the-art baselines
in terms of both predictive performance and uncertainty quantification,
delivering well-calibrated and sharp prediction intervals.


Abstract:
  We verify the inductive blockwise Alperin weight condition in odd
characteristic $\ell$ for the finite exceptional Chevalley groups $F_4(q)$ for
$q$ not divisible by $\ell$.


Abstract:
  Next-generation plasma-based accelerators can push electron beams to GeV
energies within centimetre distances. The plasma, excited by a driver pulse, is
indeed able to sustain huge electric fields that can efficiently accelerate a
trailing witness bunch, which was experimentally demonstrated on multiple
occasions. Thus, the main focus of the current research is being shifted
towards achieving a high quality of the beam after the plasma acceleration. In
this letter we present beam-driven plasma wakefield acceleration experiment,
where initially preformed high-quality witness beam was accelerated inside the
plasma and characterized. In this experiment the witness beam quality after the
acceleration was maintained on high level, with $0.2\%$ final energy spread and
$3.8~\mu m$ resulting normalized transverse emittance after the acceleration.
In this article, for the first time to our knowledge, the emittance of the PWFA
beam was directly measured.


Abstract:
  Feature importance (FI) estimates are a popular form of explanation, and they
are commonly created and evaluated by computing the change in model confidence
caused by removing certain input features at test time. For example, in the
standard Sufficiency metric, only the top-k most important tokens are kept. In
this paper, we study several under-explored dimensions of FI explanations,
providing conceptual and empirical improvements for this form of explanation.
First, we advance a new argument for why it can be problematic to remove
features from an input when creating or evaluating explanations: the fact that
these counterfactual inputs are out-of-distribution (OOD) to models implies
that the resulting explanations are socially misaligned. The crux of the
problem is that the model prior and random weight initialization influence the
explanations (and explanation metrics) in unintended ways. To resolve this
issue, we propose a simple alteration to the model training process, which
results in more socially aligned explanations and metrics. Second, we compare
among five approaches for removing features from model inputs. We find that
some methods produce more OOD counterfactuals than others, and we make
recommendations for selecting a feature-replacement function. Finally, we
introduce four search-based methods for identifying FI explanations and compare
them to strong baselines, including LIME, Anchors, and Integrated Gradients.
Through experiments with six diverse text classification datasets, we find that
the only method that consistently outperforms random search is a Parallel Local
Search (PLS) that we introduce. Improvements over the second-best method are as
large as 5.4 points for Sufficiency and 17 points for Comprehensiveness. All
supporting code for experiments in this paper is publicly available at
https://github.com/peterbhase/ExplanationSearch.


Abstract:
  The discrepancy between the theoretically calculated and experimentally
measured activation gaps in quantum Hall effect has long been a puzzle. We
revisit this issue in the context of the $\nu=1$ quantum Hall state, while also
incorporating the skyrmion physics. We find that the finite width and the
Landau level mixing (LLM) effects are not sufficient to explain the observed
activation gap. We further show that the presence of charged impurities located
adjacent to the quantum well can cause a significant reduction in the
activation gap, while also causing a suppression of the skyrmion size.


Abstract:
  The class of problems in causal inference which seeks to isolate causal
correlations solely from observational data even without interventions has come
to the forefront of machine learning, neuroscience and social sciences. As new
large scale quantum systems go online, it opens interesting questions of
whether a quantum framework exists on isolating causal correlations without any
interventions on a quantum system. We put forth a theoretical framework for
merging quantum information science and causal inference by exploiting entropic
principles. At the root of our approach is the proposition that the true causal
direction minimizes the entropy of exogenous variables in a non-local hidden
variable theory. The proposed framework uses a quantum causal structural
equation model to build the connection between two fields: entropic causal
inference and the quantum marginal problem. First, inspired by the definition
of geometric quantum discord, we fill the gap between classical and quantum
conditional density matrices to define quantum causal models. Subsequently,
using a greedy approach, we develop a scalable algorithm for quantum entropic
causal inference unifying classical and quantum causality in a principled way.
We apply our proposed algorithm to an experimentally relevant scenario of
identifying the subsystem impacted by noise starting from an entangled state.
This successful inference on a synthetic quantum dataset can have practical
applications in identifying originators of malicious activity on future
multi-node quantum networks as well as quantum error correction. As quantum
datasets and systems grow in complexity, our framework can play a foundational
role in bringing observational causal inference from the classical to the
quantum domain.


Abstract:
  Ablative Rayleigh-Taylor instability growth was investigated to elucidate the
fundamental physics of thermal conduction suppression in a magnetic field.
Experiments found that unstable modulation growth is faster in an external
magnetic field. This result was reproduced by a magnetohydrodynamic simulation
based on a Braginskii model of electron thermal transport. An external magnetic
field reduces the electron thermal conduction across the magnetic field lines
because the Larmor radius of the thermal electrons in the field is much shorter
than the temperature scale length. Thermal conduction suppression leads to
spatially nonuniform pressure and reduced thermal ablative stabilization, which
in turn increases the growth of ablative Rayleigh-Taylor instability.


Abstract:
  X-ray diffraction, thermodynamic measurements, and density-functional
band-structure calculations are used to study the magnetic behavior of
BaCuTe$_{2}$O$_{6}$, a member of the $A$CuTe$_2$O$_6$ structural family that
hosts complex three-dimensional frustrated spin networks with possible
spin-liquid physics. Temperature-dependent magnetic susceptibility and heat
capacity of the Ba compound are well described by the one-dimensional
spin-$\frac12$ Heisenberg chain model reminiscent of the Sr analog
SrCuTe$_2$O$_6$. While the intrachain coupling $J/k_{\rm B}\simeq 37$ K is
reduced compared to 49 K in the Sr compound, the N\'eel temperature increases
from 5.5 K (Sr) to 6.1 K (Ba). Unlike the Sr compound, BaCuTe$_2$O$_6$
undergoes only one magnetic transition as a function of temperature and shows
signatures of weak spin canting. We elucidate the microscopic difference
between the Sr and Ba compounds and suggest that one of the interchain
couplings changes sign as a result of negative pressure caused by the Sr/Ba
substitution. The N\'eel temperature of BaCuTe$_2$O$_6$ is remarkably
insensitive to the magnetic dilution with Zn$^{2+}$ up to the highest reachable
level of about 20%.


Abstract:
  We performed a systematic search for broad-velocity-width molecular features
(BVFs) in the disk part of our Galaxy by using the CO J = 1-0 survey data
obtained with the Nobeyama Radio Observatory 45 m telescope. From this search,
58 BVFs were identified. In comparisons with the infrared and radio continuum
images, 36 BVFs appeared to have both infrared and radio continuum
counterparts, and 15 of them are described as molecular outflows from young
stellar objects in the literature. In addition, 21 BVFs have infrared
counterparts only, and eight of them are described as molecular outflows in the
literature. One BVF (CO 16.134-0.553) does not have any luminous counterpart in
the other wavelengths, which suggests that it may be an analog of high-velocity
compact clouds in the Galactic center.


Abstract:
  The Fermi-Lab Collaboration has announced the results for the measurement of
muon anomalous magnetic moment. Combining with the previous results by the BNL
experiment, we have $4.2 \sigma$ deviation from the Standard Model (SM), which
strongly implies the new physics around 1 TeV. To explain the muon anomalous
magnetic moment naturally, we analyze the corresponding five Feynman diagrams
in the Supersymetric SMs (SSMs), and show that the Electroweak Supersymmetry
(EWSUSY) is definitely needed. We realize the EWSUSY in the Minimal SSM (MSSM)
with Genernalized Mininal Supergravity (GmSUGRA). We find large viable
parameter space, which is consistent with all the current experimental
constraints. In particular, the Lightest Supersymmetric Particle (LSP)
neutralino can be at least as heavy as 550 GeV. Most of the viable parameter
space can be probed at the future HL-LHC, while we do need the future HE-LHC to
probe some viable parameter space. However, it might still be challenge if
R-parity is violated.


Abstract:
  Applying the concept of triadic closure to coauthorship networks means that
scholars are likely to publish a joint paper if they have previously coauthored
with the same people. Prior research has identified moderate to high (20% to
40%) closure rates; suggesting that this mechanism is a reasonable explanation
for tie formation between future coauthors. We show how calculating triadic
closure based on prior operationalizations of closure, namely Newman's measure
for one-mode networks (NCC) and Opsahl's measure for two-mode networks (OCC),
may lead to higher amounts of closure as compared to measuring closure over
time via a metric that we introduce and test in this paper. Based on empirical
experiments using four large-scale, longitudinal datasets, we find a lower
bound of about 1~3% closure rates and an upper bound of about 4~7%. These
results motivate research on new explanatory factors for the formation of
coauthorship links.


Abstract:
  The impact of new and highly precise neutron \b{eta} decay data is reviewed.
We focus on recent results from neutron lifetime, \b{eta} asymmetry, and
electron-neutrino correlation experiments. From these results, weak interaction
parameters are extracted with unprecedented precision, possible also due to
progress in effective field theory and lattice QCD. Limits on New Physics
beyond the Standard Model derived from neutron decay data are sharper than
those derived from high-energy experiments, except for processes involving
right-handed neutrinos.


Abstract:
  In last 50 years, a significant progress was noticed in medicine,
communications and entertainment. Such advanced development of these fields was
directly related to ability of controlling light. Photonics is exactly about
this ability. At the present time, photonics is walking together with a
fundamental physical concept, optical soliton. Optical solitons are
shape-preserving laser beams. They are found potentially useful in data
transmission, which is very significant nowadays. Hence, research in the field
of optical solitons is still a vital issue. When optical soliton is perturbed
in a specific manner, there appear zeros of optical field around the soliton,
which are called optical vortices. In general optical vortices are lines in
space. Hence, we might expect them to become knotted. Knotting optical vortices
around perturbed seems spontaneous and cannot be directly predicted. To explain
this phenomenon, a similar system is constructed based on perturbation theory.
In this system, however, we have a mathematical problem which yet lacks a full
understanding. We address this problem by introducing concepts from three
disciplines: laser physics, knot theory and singular optics. We believe that
understanding the mechanism underlying spontaneous knotting of optical vortices
will be a step forward in other systems too, such as quantum turbulence in
superfluids and formation of optical vortices around other types of optical
solitons.


Abstract:
  We prove that the moduli stack of Bridgeland semistable holomorphic triples
over a curve of $g(C)\geq 1$ with a fixed numerical class and phase is an
algebraic stack of finite type over $\mathbb{C}$ and admits a proper good
moduli space. We prove that this also holds for a class of Bridgeland stability
conditions on the category of holomorphic chains $\mathcal{T}_{C,n}$.
  In the process, we construct an explicit geometric realisation of
$\mathcal{T}_{C,n}$ and prove the open heart property for noetherian hearts in
admissible categories of $D^b(X)$, where $X$ is a smooth projective variety
over $\mathbb{C}$, whose orthogonal complements are geometric triangulated
categories.


Abstract:
  Contextual bandit is a general framework for online learning in sequential
decision-making problems that has found application in a large range of
domains, including recommendation system, online advertising, clinical trials
and many more. A critical aspect of bandit methods is that they require to
observe the contexts -- i.e., individual or group-level data -- and the rewards
in order to solve the sequential problem. The large deployment in industrial
applications has increased interest in methods that preserve the privacy of the
users. In this paper, we introduce a privacy-preserving bandit framework based
on asymmetric encryption. The bandit algorithm only observes encrypted
information (contexts and rewards) and has no ability to decrypt it. Leveraging
homomorphic encryption, we show that despite the complexity of the setting, it
is possible to learn over encrypted data. We introduce an algorithm that
achieves a $\widetilde{O}(d\sqrt{T})$ regret bound in any linear contextual
bandit problem, while keeping data encrypted.


Abstract:
  We present a novel and cost-effective approach of using a second similarity
transformation of the Hamiltonian to include the missing higher-order terms in
the second-order approximate coupled cluster singles and doubles (CC2) model.
The performance of the newly developed ST-CC2 model has been investigated for
the calculation of excitation energies of valence, Rydberg, and charge-transfer
excited states. The method shows significant improvement in the excitation
energies of Rydberg and charge-transfer excited states as compared to the
conventional CC2 method while retaining the good performance of the latter for
the valence excited state. The method retains the charge-transfer separability
of the CT excited states, which is a significant advantage over the traditional
CC2 method. An MBPT2 variant of the new method is also proposed.


Abstract:
  Let $(X,D)$ and $(X',D')$ be two compact Riemann surfaces of genus $g \geq 4$
with the set of marked points $D \subset X$ and $D' \subset X'$. Fix a
parabolic line bundle $L$ with trivial parabolic structure. Let
$\mathcal{N}_{\textnormal{Sp}}(2m,\alpha,L)$ and
$\mathcal{N}'_{\textnormal{Sp}}(2m,\alpha,L)$ be the moduli spaces of stable
symplectic parabolic Higgs bundles over $X$ and $X'$ respectively, with rank
$2m$ and fixed parabolic structure $\alpha$, with the symplectic form taking
values in $L$. We prove that if $\mathcal{N}_{\textnormal{Sp}}(2m,\alpha,L)$ is
isomorphic to $\mathcal{N}'_{\textnormal{Sp}}(2m,\alpha,L)$, then there exist
an isomorphism between $X$ and $X'$ sending $D$ to $D'$.


Abstract:
  We study the Quot scheme $\mathrm{Quot}^l(\mathscr{E})$ of length $l$
coherent sheaf quotients of a locally free sheaf $\mathscr{E}$ on a smooth
projective surface. We conjecture that the singularities of
$\mathrm{Quot}^l(\mathscr{E})$ are rational. For $l=2$ we use the ADHM
description to show that this conjecture holds, and in fact exhibit a modular
resolution of singularities; this leads us to a sheaf-theoretic generalisation
of Severi's double point formula. Given a short exact sequence
$0\rightarrow\mathscr{E}^{'}\rightarrow\mathscr{E}\rightarrow\mathscr{E}^{''}\rightarrow
0$, we show that
$\mathrm{Quot}^l(\mathscr{E}^{''})\subset\mathrm{Quot}^l(\mathscr{E})$ is the
zero scheme of a regular section of the tautological sheaf
$\mathscr{E}^{'\vee[l]}$, which results in a relation between the (ordinary and
virtual) fundamental classes of $\mathrm{Quot}^l(\mathscr{E}^{''})$ and
$\mathrm{Quot}^l(\mathscr{E})$.


Abstract:
  We propose a simple and effective strategy for data augmentation for
low-resource machine reading comprehension (MRC). Our approach first pretrains
the answer extraction components of a MRC system on the augmented data that
contains approximate context of the correct answers, before training it on the
exact answer spans. The approximate context helps the QA method components in
narrowing the location of the answers. We demonstrate that our simple strategy
substantially improves both document retrieval and answer extraction
performance by providing larger context of the answers and additional training
data. In particular, our method significantly improves the performance of BERT
based retriever (15.12\%), and answer extractor (4.33\% F1) on TechQA, a
complex, low-resource MRC task. Further, our data augmentation strategy yields
significant improvements of up to 3.9\% exact match (EM) and 2.7\% F1 for
answer extraction on PolicyQA, another practical but moderate sized QA dataset
that also contains long answer spans.


Abstract:
  In this paper, we investigate thick branes generated by a scalar field in
mimetic gravity theory. By introducing two auxiliary super-potentials, we
transform the second-order field equations of the system into a set of
first-order equations. With this first-order formalism, several types of
analytical thick brane solutions are obtained. Then, tensor and scalar
perturbations are analysed. We find that both kinds of perturbations are
stable. The effective potentials for the tensor and scalar perturbations are
dual to each other. The tensor zero mode can be localized on the brane while
the scalar zero mode cannot. Thus, the four-dimensional Newtonian potential can
be recovered on the brane.


Abstract:
  We study the strong ill-posedness (norm inflation with infinite loss of
regularity) for the nonlinear wave equation at every initial data in Wiener
amalgam and Fourier amalgam spaces with negative regularity. In particular
these spaces contain Fourier-Lebesgue, Sobolev and some modulation spaces. The
equations are posed on $\mathbb R^d$ and on torus $\mathbb T^d$ and involve a
smooth power nonlinearity. Our results are sharp with respect to well-posedness
results of B\'enyi and Okoudjou (2009) and Cordero and Nicola (2009) in the
Wiener amalgam and modulation space cases. In particular, we also complement
norm inflation result of Christ, Colliander and Tao (2003) and Forlano and
Okamoto (2020) by establishing infinite loss of regularity in the aforesaid
spaces.


Abstract:
  In this article, we study several probabilistic properties of polynomials
defined over the ring of $p$-adic integers under the Haar measure. First, we
calculate the probability that a monic polynomial is separable, generalizing a
result of Polak. Second, we introduce the notion of two polynomials being
strongly coprime and calculate the probability of two monic polynomials {being}
strongly coprime. Finally, we explain how our method can be used to extrapolate
other probabilistic properties of polynomials over the ring of $p$-adic
integers from polynomials defined over the integers modulo powers of $p$.


Abstract:
  Defects are inevitably present in two-dimensional (2D) materials and usually
govern their various properties. Here a comprehensive density functional
theory-based investigation of 7 kinds of point defects in a recently produced
{\gamma} allotrope of 2D phosphorus carbide ({\gamma}-PC) is conducted. The
defects, such as antisites, single C or P, and double C and P and C and C
vacancies, are found to be stable in {\gamma}-PC, while the Stone-Wales defect
is not presented in {\gamma}-PC due to its transition metal
dichalcogenides-like structure. The formation energies, stability, and surface
density of the considered defect species as well as their influence on the
electronic structure of {\gamma}-PC is systematically identified. The formation
of point defects in {\gamma}-PC is found to be less energetically favourable
then in graphene, phosphorene, and MoS2. Meanwhile, defects can significantly
modulate the electronic structure of {\gamma}-PC by inducing hole/electron
doping. The predicted scanning tunneling microscopy images suggest that most of
the point defects are easy to distinguish from each other and that they can be
easily recognized in experiments.


Abstract:
  The Hosmer-Lemeshow (HL) test is a commonly used global goodness-of-fit (GOF)
test that assesses the quality of the overall fit of a logistic regression
model. In this paper, we give results from simulations showing that the type 1
error rate (and hence power) of the HL test decreases as model complexity
grows, provided that the sample size remains fixed and binary replicates are
present in the data. We demonstrate that the generalized version of the HL test
by Surjanovic et al. (2020) can offer some protection against this power loss.
We conclude with a brief discussion explaining the behaviour of the HL test,
along with some guidance on how to choose between the two tests.


Abstract:
  We propose a platform for braiding Majorana non-Abelian anyons based on a
heterostructure between a $d$-wave high-$T_c$ superconductor and a quantum
spin-Hall insulator. It has been recently shown that such a setup for a quantum
spin-Hall insulator leads to a pair of Majorana zero modes at each corner of
the sample, and thus can be regarded as a higher-order topological
superconductor. We show that upon applying a Zeeman field in the region, these
Majorana modes split in space and can be manipulated for braiding processes by
tuning the field and pairing phase. We show that such a setup can achieve full
braiding, exchanging, and arbitrary phase gates (including the $\pi/8$ magic
gates) of the Majorana zero modes, all of which are robust and protected by
symmetries. As many of the ingredients of our proposed platform have been
realized in recent experiments, our results provide a new route toward
universal topological quantum computation.


Abstract:
  Graph signal processing (GSP) provides a powerful framework for analyzing
signals arising in a variety of domains. In many applications of GSP, multiple
network structures are available, each of which captures different aspects of
the same underlying phenomenon. To integrate these different data sources,
graph alignment techniques attempt to find the best correspondence between
vertices of two graphs. We consider a generalization of this problem, where
there is no natural one-to-one mapping between vertices, but where there is
correspondence between the community structures of each graph. Because we seek
to learn structure at this higher community level, we refer to this problem as
"coarse" graph alignment. To this end, we propose a novel regularized partial
least squares method which both incorporates the observed graph structures and
imposes sparsity in order to reflect the underlying block community structure.
We provide efficient algorithms for our method and demonstrate its
effectiveness in simulations.


Abstract:
  An equivariant minimal surface in $\mathbb{CH}^n$ is a minimal map of the
Poincar\'{e} disc into $\mathbb{CH}^n$ which intertwines two actions of the
fundamental group of a closed surface $\Sigma$: a Fuchsian representation on
the disc and an irreducible action by isometries on $\mathbb{CH}^n$. The moduli
space of these can been studied by relating it to the nilpotent cone in each
moduli space of $PU(n,1)$-Higgs bundles over the conformal surface
corresponding to the map. By providing a necessary condition for points on this
nilpotent cone to be smooth this article shows that away from the points
corresponding to branched minimal immersions or $\pm$-holomorphic immersions
the moduli space is smooth. The argument is easily adapted to show that for
$\mathbb{RH}^n$ the full space of (unbranched) immersions is smooth. For
$\mathbb{CH}^2$ we show the connected components of the moduli space of minimal
immersions are indexed by the Toledo invariant and the Euler number of the
normal bundle of the immersion. This is achieved by studying the limit points
of the $\mathbb{C}^\times$-action on the nilpotent cone. It is shown that the
limit points as $t\to 0$ lead only to branched minimal immersions or
$\pm$-holomorphic immersions. In particular, the Euler number of the normal
bundle can only jump by passing through branched minimal maps.


Abstract:
  Behavior of deep neural networks can be inconsistent between different
versions. Regressions during model update are a common cause of concern that
often over-weigh the benefits in accuracy or efficiency gain. This work focuses
on quantifying, reducing and analyzing regression errors in the NLP model
updates. Using negative flip rate as regression measure, we show that
regression has a prevalent presence across tasks in the GLUE benchmark. We
formulate the regression-free model updates into a constrained optimization
problem, and further reduce it into a relaxed form which can be approximately
optimized through knowledge distillation training method. We empirically
analyze how model ensemble reduces regression. Finally, we conduct CheckList
behavioral testing to understand the distribution of regressions across
linguistic phenomena, and the efficacy of ensemble and distillation methods.


Abstract:
  Within the framework of a Dirac bubble potential model for the C60 fullerene
shell we investigated the angular time delay in slow-electron elastic
scattering by C60 as well as average time delay of electrons in this process.
It is demonstrated how the angular time delay is connected to the
Eisenbud-Wigner-Smith (EWS) time delay. The angular and energy dependences of
these times are investigated. The studies conducted shed light to some extent
on the specific features of these dependencies.


Abstract:
  Koopman mode analysis has provided a framework for analysis of nonlinear
phenomena across a plethora of fields. Its numerical implementation via Dynamic
Mode Decomposition (DMD) has been extensively deployed and improved upon over
the last decade. We address the problems of mean subtraction and DMD mode
selection in the context of finite dimensional Koopman invariant subspaces.
  Preprocessing of data by subtraction of the temporal mean of a time series
has been a point of contention in companion matrix-based DMD. This stems from
the potential of said preprocessing to render DMD equivalent to temporal DFT.
We prove that this equivalence is impossible when the order of the DMD-based
representation of the dynamics exceeds the dimension of the system. Moreover,
this parity of DMD and DFT is mostly indicative of an inadequacy of data, in
the sense that the number of snapshots taken is not enough to represent the
true dynamics of the system.
  We then vindicate the practice of pruning DMD eigenvalues based on the norm
of the respective modes. Once a minimum number of time delays has been taken,
DMD eigenvalues corresponding to DMD modes with low norm are shown to be
spurious, and hence must be discarded. When dealing with mean-subtracted data,
the above criterion for detecting synthetic eigenvalues can be applied after
additional pre-processing. This takes the form of an eigenvalue constraint on
Companion DMD, or yet another time delay.


Abstract:
  Digital image processing techniques have wide applications in different
scientific fields including the medicine. By use of image processing
algorithms, physicians have been more successful in diagnosis of different
diseases and have achieved much better treatment results. In this paper, we
propose an automatic method for segmenting the skin lesions and extracting
features that are associated to them. At this aim, a combination of Speeded-Up
Robust Features (SURF) and Active Contour Model (ACM), is used. In the
suggested method, at first region of skin lesion is segmented from the whole
skin image, and then some features like the mean, variance, RGB and HSV
parameters are extracted from the segmented region. Comparing the segmentation
results, by use of Otsu thresholding, our proposed method, shows the
superiority of our procedure over the Otsu theresholding method. Segmentation
of the skin lesion by the proposed method and Otsu thresholding compared the
results with physician's manual method. The proposed method for skin lesion
segmentation, which is a combination of SURF and ACM, gives the best result.
For empirical evaluation of our method, we have applied it on twenty different
skin lesion images. Obtained results confirm the high performance, speed and
accuracy of our method.


Abstract:
  The XGIS (X and Gamma Imaging Spectrometer) is one of the three instruments
onboard the THESEUS mission (ESA M5, currently in Phase-A). Thanks to its wide
field of view and good imaging capabilities, it will efficiently detect and
localize gamma-ray bursts and other transients in the 2-150 keV sky, and also
provide spectroscopy up to 10 MeV. Its current design has been optimized by
means of scientific simulations based on a Monte Carlo model of the instrument
coupled to a state-of-the-art description of the populations of long and short
GRBs extending to high redshifts. We describe the optimization process that led
to the current design of the XGIS, based on two identical units with partially
overlapping fields of view, and discuss the expected performance of the
instrument.


Abstract:
  The motion of our solar system relative to the CMB rest frame leads to subtle
distortions in the observed CMB sky map due to the aberration effect. Usually
the corresponding peculiar velocity is determined from the CMB dipole but
neglecting intrinsic dipole contributions. Here it is investigated whether
certain invariant scalar measures, which are derived from first and second
order covariant derivatives on the sphere, can detect the distortions caused by
the aberration effect at high multipoles. This would in principle allow to
disentangle the Doppler from intrinsic dipole contributions providing an
independent method for the determination of our peculiar velocity. It is found
that the eigenvalues of the Hessian matrix of the temperature field are well
suited for that task.


Abstract:
  The multi-access variant of the coded caching problem in the presence of an
external wiretapper is investigated . A multi-access coded caching scheme with
$K$ users, $K$ caches and $N$ files, where each user has access to $L$
neighbouring caches in a cyclic wrap-around manner, is proposed, which is
secure against the wiretappers. Each transmission in the conventional insecure
scheme will be now encrypted by a random key. The proposed scheme uses a novel
technique for the key placement in the caches. It is also shown that the
proposed secure multi-access coded caching scheme is within a constant
multiplicative factor from the information-theoretic optimal rate for $L\geq
\frac{K}{2}$ and $N\geq 2K$.


Abstract:
  Many studies have revealed that confined water chain flipping is closely
related to the spatial size and even quantum effects of the confinement
environment. Here, we show that these are not the only factors that affect the
flipping process of a confined water chain. First-principles calculations and
analyses confirm that quantum tunnelling effects from the water chain itself,
especially resonant tunnelling, enhance the hydrogen bond rotation process.
Importantly, resonant tunnelling can result in tunnelling rotation of hydrogen
bonds with a probability close to 1 with only 0.597 eV provided energy.
Compared to sequential tunnelling, resonant tunnelling dominants water chain
flipping at temperatures up to 20 K higher. Additionally, the ratio of the
resonant tunnelling probability to the thermal disturbance probability at 200 K
is at least ten times larger than that of sequential tunnelling, which further
illustrates the enhancement of hydrogen bond rotation brought about by resonant
tunnelling.


Abstract:
  We present the results of Atacama Large Millimeter/submillimeter Array (ALMA)
observation in $^{12}$CO(1-0) emission at 0.58 $\times$ 0.52 pc$^2$ resolution
toward the brightest HII region N66 of the Small Magellanic Cloud (SMC). The
$^{12}$CO(1-0) emission toward the north of N66 reveals the clumpy filaments
with multiple velocity components. Our analysis shows that a blueshifted
filament at a velocity range 154.4-158.6 km s$^{-1}$ interacts with a
redshifted filament at a velocity 158.0-161.8 km s$^{-1}$. A third velocity
component in a velocity range 161-165.0 km s$^{-1}$ constitutes hub-filaments.
An intermediate-mass young stellar object (YSO) and a young pre-main sequence
star cluster have hitherto been reported in the intersection of these
filaments. We find a V-shape distribution in the position-velocity diagram at
the intersection of two filaments. This indicates the physical association of
those filaments due to a cloud-cloud collision. We determine the collision
timescale $\sim$ 0.2 Myr using the relative velocity ($\sim$ 5.1 km s$^{-1}$)
and displacement ($\sim$ 1.1 pc) of those interacting filaments. These results
suggest that the event occurred at about 0.2 Myr ago and triggered the star
formation, possibly an intermediate-mass YSO. We report the first observational
evidence for a cloud-cloud collision that triggers star formation in N66N of
the low metallicity $\sim$0.2 Z$_{\odot}$ galaxy, the SMC, with similar
kinematics as in N159W-South and N159E of the Large Magellanic Cloud.


Abstract:
  The polarization dependence of magnon-photon scattering in an optical
microcavity is reported. Due to the short cavity length, the mode-matching
conditions found in previously explored, large path-length whispering gallery
resonators are absent. Nonetheless, for cross-polarized scattering a strong and
broadband suppression of one side-band is observed. This arises due to an
interference between the Faraday and second-order Cotton-Mouton effects. To
fully account for the suppression of the cross-polarized scattering, it is
necessary to consider the squeezing of magnon modes intrinsic to thin-film
geometry. A co-polarized scattering due to Cotton-Mouton effect is also
observed. In addition, the magnon modes involved are identified as
Damon-Eshbach surface modes, whose non-reciprocal propagation could be
exploited in devices applications. This work experimentally demonstrates the
important role of second order Cotton-Mouton effect for optomagnonic devices.


Abstract:
  We perform a systematic study showing the evolution of the multipoles along
with the spectra for a hybrid metal-dielectric nanoantenna, a Si cylinder and
an Ag disk stacked one on top of another, as its dimensions are varied one by
one. We broaden our analysis to demonstrate the "magnetic light" at energies
above 1 eV by varying the height of the Ag on the Si cylinder and below 1 eV by
introducing insulating spacing between them. We also explore the appearance of
the anapole state along with some exceptionally narrow spectral features by
varying the radius of the Ag disk.


Abstract:
  Cryo-EM reconstruction algorithms seek to determine a molecule's 3D density
map from a series of noisy, unlabeled 2D projection images captured with an
electron microscope. Although reconstruction algorithms typically model the 3D
volume as a generic function parameterized as a voxel array or neural network,
the underlying atomic structure of the protein of interest places well-defined
physical constraints on the reconstructed structure. In this work, we exploit
prior information provided by an atomic model to reconstruct distributions of
3D structures from a cryo-EM dataset. We propose Cryofold, a generative model
for a continuous distribution of 3D volumes based on a coarse-grained model of
the protein's atomic structure, with radial basis functions used to model atom
locations and their physics-based constraints. Although the reconstruction
objective is highly non-convex when formulated in terms of atomic coordinates
(similar to the protein folding problem), we show that gradient descent-based
methods can reconstruct a continuous distribution of atomic structures when
initialized from a structure within the underlying distribution. This approach
is a promising direction for integrating biophysical simulation, learned neural
models, and experimental data for 3D protein structure determination.


Abstract:
  In this paper we will describe two new optimisations implemented in
MadGraph5_aMC@NLO, both of which are designed to speed-up the computation of
leading-order processes (for any model). First we implement a new method to
evaluate the squared matrix element, dubbed helicity recycling, which results
in factor of two speed-up. Second, we have modified the multi-channel handling
of the phase-space integrator providing tremendous speed-up for VBF-like
processes (up to thousands times faster).


Abstract:
  The quest for determinism in machine learning has disproportionately focused
on characterizing the impact of noise introduced by algorithmic design choices.
In this work, we address a less well understood and studied question: how does
our choice of tooling introduce randomness to deep neural network training. We
conduct large scale experiments across different types of hardware,
accelerators, state of art networks, and open-source datasets, to characterize
how tooling choices contribute to the level of non-determinism in a system, the
impact of said non-determinism, and the cost of eliminating different sources
of noise.
  Our findings are surprising, and suggest that the impact of non-determinism
in nuanced. While top-line metrics such as top-1 accuracy are not noticeably
impacted, model performance on certain parts of the data distribution is far
more sensitive to the introduction of randomness. Our results suggest that
deterministic tooling is critical for AI safety. However, we also find that the
cost of ensuring determinism varies dramatically between neural network
architectures and hardware types, e.g., with overhead up to $746\%$, $241\%$,
and $196\%$ on a spectrum of widely used GPU accelerator architectures,
relative to non-deterministic training. The source code used in this paper is
available at https://github.com/usyd-fsalab/NeuralNetworkRandomness.


Abstract:
  We report the low-temperature magnetothermal properties and spin dynamics of
a mixed pyrochlore, Dy2GaSbO7 through the coordination of measurements of dc
magnetization, ac susceptibility, and heat capacity, and CF computation. In
Dy2GaSbO7, the spins freeze at temperature Tice = 3.1 K, corresponding to
dynamical spin-freezing into a disordered state in compliance with the spin-ice
rules, but with enhanced zero-point entropy, unlike cannonical dipolar spin-ice
materials, Dy2Ti2O7 and Ho2Ti2O7, due to random disorder and varied chemical
pressure of B-site (GaSb tetrahedra) ions.


Abstract:
  Spin alignments of vector mesons and hyperons in relativistic heavy-ion
collisions have been proposed as signals of the global polarization. The STAR
experiment first observed the $\rm \Lambda$ polarization. Recently, the ALICE
collaboration measured the transverse momentum ($p_T$) and the collision
centrality dependence of $K^*$ and $\phi$ spin alignments in Pb-Pb collisions
at $\rm \sqrt{s_{NN}}$ = 2.76 TeV. A large signal is observed in the low $p_T$
region of mid-central collisions for $K^*$ while the signal is much smaller for
$\phi$, and these have not been understood yet. Since vector mesons have
different lifetimes and their decay products have different scattering cross
sections, they suffer from different hadronic effects. In this paper, we study
the effect of hadronic interactions on the spin alignment of $K^*$, $\phi$ and
$\rho$ mesons in relativistic heavy-ion collisions with a multi-phase transport
model. We find that hadronic scatterings lead to a deviation of the observed
spin alignment matrix element $\rho_{00}$ away from the true value for $\rho$
and $K^*$ mesons (with a bigger effect on $\rho$) while the effect is
negligible for the $\phi$ meson. The effect depends on the kinematic
acceptance: the observed $\rho_{00}$ value is lower than the true value when
the pseudorapidity ($\eta$) coverage is small while there is little effect when
the $\eta$ coverage is big. Our study thus provides valuable information to
understand the vector meson spin alignment signals observed in the experiments.


Abstract:
  We introduce the notion of a rank function on a triangulated category
$\mathcal{C}$ which generalizes the Sylvester rank function in the case when
$\mathcal{C}=\operatorname{Perf}(A)$ is the perfect derived category of a ring
$A$. We show that rank functions are closely related to functors into simple
triangulated categories and classify Verdier quotients into simple triangulated
categories in terms of particular rank functions called localizing. If
$\mathcal{C}=\operatorname{Perf}(A)$ as above, localizing rank functions also
classify finite homological epimorphisms from $A$ into differential graded
skew-fields or, more generally, differential graded Artining rings. To
establish these results, we develop the theory of derived localization of
differential graded algebras at thick subcategories of their perfect derived
categories. This is a far-reaching generalization of Cohn's matrix localization
of rings and has independent interest.


Abstract:
  Analysis of the fetal heart rate during pregnancy is essential for monitoring
the proper development of the fetus. Current fetal heart monitoring techniques
lack the accuracy in fetal heart rate monitoring and features acquisition,
resulting in diagnostic medical issues. The challenge lies in the extraction of
the fetal ECG from the mother's ECG during pregnancy. This approach has the
advantage of being a reliable and non-invasive technique. For this aim, we
propose in this paper a wavelet/multi-wavelet method allowing to extract
perfectly the feta ECG parameters from the abdominal mother ECG. The method is
essentially due to the exploitation of Clifford wavelets as recent variants in
the field. We prove that these wavelets are more efficient and performing
against classical ones. The experimental results are therefore due to two basic
classes of wavelets and multi-wavelets. A first-class is the classical Haar
Schauder, and a second one is due to Clifford valued wavelets and
multi-wavelets. These results showed that wavelets/multiwavelets are already
good bases for the FECG processing, provided that Clifford ones are the best.


Abstract:
  We consider situations where a user feeds her attributes to a machine
learning method that tries to predict her best option based on a random sample
of other users. The predictor is incentive-compatible if the user has no
incentive to misreport her covariates. Focusing on the popular Lasso estimation
technique, we borrow tools from high-dimensional statistics to characterize
sufficient conditions that ensure that Lasso is incentive compatible in large
samples. We extend our results to the Conservative Lasso estimator and provide
new moment bounds for this generalized weighted version of Lasso. Our results
show that incentive compatibility is achieved if the tuning parameter is kept
above some threshold. We present simulations that illustrate how this can be
done in practice.


Abstract:
  We analyse the emergent cosmological dynamics corresponding to the mean field
hydrodynamics of quantum gravity condensates, in the tensorial group field
theory formalism. We focus in particular on the cosmological effects of
fundamental interactions, and on the contributions from different quantum
geometric modes. The general consequence of such interactions is to produce an
accelerated expansion of the universe, which can happen both at early times,
after the quantum bounce predicted by the model, and at late times. Our main
result is that, while this fails to give a compelling inflationary scenario in
the early universe, it produces naturally a phantom-like dark energy dynamics
at late times, compatible with cosmological observations. By recasting the
emergent cosmological dynamics in terms of an effective equation of state, we
show that it can generically cross the phantom divide, purely out of quantum
gravity effects without the need of any additional phantom matter. Furthermore,
we show that the dynamics avoids any Big Rip singularity, approaching instead a
de Sitter universe asymptotically.


Abstract:
  Power-grid systems constitute one of the most complex man-made spatially
extended structures. These operate with strict operational bounds to ensure
synchrony across the grid. This is particularly relevant for power-grid
frequency, which operates strictly at $50\,$Hz ($60\,$Hz). Nevertheless, small
fluctuations around the mean frequency are present at very short time scales
$<2$ seconds and can exhibit highly complex spatio-temporal behaviour. Here we
apply superstatistical data analysis techniques to measured frequency
fluctuations in the Nordic Grid. We study the increment statistics and extract
the relevant time scales and superstatistical distribution functions from the
data. We show that different synchronous recordings of power-grid frequency
have very distinct stochastic fluctuations with different types of
superstatistics at different spatial locations, and with transitions from one
superstatistics to another when the time lag of the increment statistics is
changed.


Abstract:
  We study the wealth distribution of UK households through a detailed analysis
of data from wealth surveys and rich lists, and propose a non-linear Kesten
process to model the dynamics of household wealth. The main features of our
model are that we focus on wealth growth and disregard exchange, and that the
rate of return on wealth is increasing with wealth. The linear case with
wealth-independent return rate has been well studied, leading to a log-normal
wealth distribution in the long time limit which is essentially independent of
initial conditions. We find through theoretical analysis and simulations that
the non-linearity in our model leads to more realistic power-law tails, and can
explain an apparent two-tailed structure in the empirical wealth distribution
of the UK and other countries. Other realistic features of our model include an
increase in inequality over time, and a stronger dependence on initial
conditions compared to linear models.


Abstract:
  An $(n,r)$-arc in $PG(2,q)$ is a set $B$ of points in $PG(2,q)$ such that
each line in $PG(2,q)$ contains at most $r$ elements of $B$ and such that there
is at least one line containing exactly $r$ elements of $B$. The value
$m_r(2,q)$ denotes the maximal number $n$ of points in the projective geometry
$PG(2,q)$ for which an $(n,r)$-arc exists. By explicitly constructing
$(n,r)$-arcs using prescribed automorphisms and integer linear programming we
obtain some improved lower bounds for $m_r(2,q)$: $m_{10}(2,16)\ge 144$,
$m_3(2,25)\ge 39$, $m_{18}(2,25)\ge 418$, $m_9(2,27)\ge 201$, $m_{14}(2,29)\ge
364$, $m_{25}(2,29)\ge 697$, $m_{25}(2,31)\ge 734$. Furthermore, we show by
systematically excluding possible automorphisms that putative $(44,5)$-arcs,
$(90,9)$-arcs in $PG(2,11)$, and $(39,4)$-arcs in $PG(2,13)$ -- in case of
their existence -- are rigid, i.e. they all would only admit the trivial
automorphism group of order $1$. In addition, putative $(50,5)$-arcs,
$(65,6)$-arcs, $(119,10)$-arcs, $(133,11)$-arcs, and $(146,12)$-arcs in
$PG(2,13)$ would be rigid or would admit a unique automorphism group (up to
conjugation) of order $2$.


Abstract:
  We show Goodwillie's calculus of functors and $n$-geometric $D^{-}$-stacks
share similar features by starting to focus on the convergence of Taylor towers
for homotopy functors and the fact that $\mathbb{R} F(A) \cong \text{holim}
\mathbb{R} F(A_{\leq n})$ for geometric stacks, where $\{ A_{\leq n} \}$
provides a Postnikov tower of some given $A \in s \text{k-Alg}$. From there we
show parallel results, such as similar homotopy fibers of connecting maps in
towers, as well as polynomial approximations, pointwise approximations and
reconstruction theorems for towers.


Abstract:
  The detection of gravity modes is expected to give us unprecedented insights
into the inner dynamics of the Sun. Within this framework, predicting their
amplitudes is essential to guide future observational strategies and seismic
studies. In this work, we predict the amplitude of low-frequency asymptotic
gravity modes generated by penetrative convection at the top of the radiative
zone. The result is found to depend critically on the time evolution of the
plumes inside the generation region. Using a solar model, we compute the GOLF
apparent surface radial velocity of low-degree gravity modes in the frequency
range $10~\mu H_z\le \nu \le 100~\mu H_z$. In case of a Gaussian plume time
evolution, gravity modes turn out to be undetectable because of too small
surface amplitudes. This holds true despite a wide range of values considered
for the parameters of the model. In the other limiting case of an exponential
time evolution, plumes are expected to drive gravity modes in a much more
efficient way because of a much higher temporal coupling between the plumes and
the modes than in the Gaussian case. Using reasonable values for the plume
parameters based on semi-analytical models, the apparent surface velocities in
this case turn out to be one order of magnitude smaller than the 22-years GOLF
detection threshold and than the previous estimates considering turbulent
pressure as the driving mechanism, with a maximum value of $0.05$ cm s${}^{-1}$
for $\ell =1$ and $\nu\approx 100~\mu H_z$. When accounting for uncertainties
on the plume parameters, the apparent surface velocities in the most favorable
plausible case become comparable to those predicted with turbulent pressure,
and the GOLF observation time required for a detection at $ \nu \approx100~\mu
H_z$ and $\ell=1$ is reduced to about 50 yrs.


Abstract:
  We study the time evolution of the chirality imbalance $n_5$ and the chiral
magnetic effect (CME) under the external parallel electromagnetic fields
without assuming the artificial chiral asymmetric source. We adopt the
time-dependent Sauter-type electric and constant magnetic field, and obtain
analytical solutions of the Dirac equation for a massive fermion. We use the
point-split regularization to calculate the vacuum contribution in the gauge
invariant way. As a result, we find that $n_5$ and CME current increase
substantially as the electric field increases, and stay finite after the
electric field is switched off. The chirality imbalance and CME current are
shown to consist of a dominant contribution, which is essentially proportional
to relativistic velocity, and a small oscillating part. We find a simple
analytical relation between $n_5$ and the fermion pair-production rate from the
vacuum. We also discuss dynamical origin of the chirality imbalance in detail.


Abstract:
  Flat bands provide a natural platform for emergent electronic states beyond
Landau paradigm. Among those of particular importance are flat Chern bands,
including bands of higher Chern numbers ($C$$>$$1$). We introduce a new
framework for band flatness through wave functions, and classify the existing
isolated flat bands in a "periodic table" according to tight binding features
and wave function properties. Our flat band categorization encompasses
seemingly different classes of flat bands ranging from atomic insulators to
perfectly flat Chern bands and Landau Levels. The perfectly flat Chern bands
satisfy Berry curvature condition $F_{xy} = \text{Tr} \, \mathcal G_{ij}$ which
on the tight-binding level is fulfilled only for infinite-range models. Most of
the natural Chern bands fall into category of $C=1$; the complexity of creating
higher-$C$ flat bands is beyond the current technology. This is due to the
breakdown of the microscopic stability for higher-$C$ flatness, seen
atomistically e.g. in the increase of the hopping range bound as
$\propto$$\sqrt{C} a$. Within our new formalism, we indicate strategies for
bypassing higher-$C$ constraints and thus dramatically decreasing the
implementation complexity.


Abstract:
  In this paper, we develop a theory on the degenerations of Blaschke products
$\mathcal{B}_d$ to study the boundaries of hyperbolic components. We give a
combinatorial classification of geometrically finite polynomials on the
boundary of the main hyperbolic component $\mathcal{H}_d$ containing $z^d$. We
show the closure $\overline{\mathcal{H}_d}$ is not a topological manifold with
boundary for $d\geq 4$ by constructing self-bumps on its boundary.


Abstract:
  Let $\kappa$ be a positive real number and $m\in\mathbb{N}\cup\{\infty\}$ be
given. Let $p_{\kappa, m}(n)$ denote the number of partitions of $n$ into the
parts from the Piatestki-Shapiro sequence $(\lfloor
\ell^{\kappa}\rfloor)_{\ell\in \mathbb{N}}$ with at most $m$ times (repetition
allowed). In this paper we establish asymptotic formulas of Hardy-Ramanujan
type for $p_{\kappa, m}(n)$, by employing a framework of asymptotics of
partitions established by Roth-Szekeres in 1953, as well as some results on
equidistribution.


Abstract:
  Major depressive disorder (MDD) is a prevalent psychiatric disorder that is
associated with significant healthcare burden worldwide. Phenotyping of MDD can
help early diagnosis and consequently may have significant advantages in
patient management. In prior research MDD phenotypes have been extracted from
structured Electronic Health Records (EHR) or using Electroencephalographic
(EEG) data with traditional machine learning models to predict MDD phenotypes.
However, MDD phenotypic information is also documented in free-text EHR data,
such as clinical notes. While clinical notes may provide more accurate
phenotyping information, natural language processing (NLP) algorithms must be
developed to abstract such information. Recent advancements in NLP resulted in
state-of-the-art neural language models, such as Bidirectional Encoder
Representations for Transformers (BERT) model, which is a transformer-based
model that can be pre-trained from a corpus of unsupervised text data and then
fine-tuned on specific tasks. However, such neural language models have been
underutilized in clinical NLP tasks due to the lack of large training datasets.
In the literature, researchers have utilized the distant supervision paradigm
to train machine learning models on clinical text classification tasks to
mitigate the issue of lacking annotated training data. It is still unknown
whether the paradigm is effective for neural language models. In this paper, we
propose to leverage the neural language models in a distant supervision
paradigm to identify MDD phenotypes from clinical notes. The experimental
results indicate that our proposed approach is effective in identifying MDD
phenotypes and that the Bio- Clinical BERT, a specific BERT model for clinical
data, achieved the best performance in comparison with conventional machine
learning models.


Abstract:
  We say that a group is anti-solvable if all of its composition factors are
non-abelian. We consider a particular family of anti-solvable finite groups
containing the simple alternating groups for $n\neq 6$ and all 26 sporadic
simple groups. We prove that, if $K$ is a perfect field and $X$ is a
homogeneous space of a smooth algebraic $K$-group $G$ with finite geometric
stabilizers lying in this family, then $X$ is dominated by a $G$-torsor. In
particular, if $G=\mathrm{SL}_n$, all such homogeneous spaces have rational
points.


Abstract:
  Several evidences indicate that Lyman Break Galaxies (LBG) in the Epoch of
Reionization (redshift $z>6$) might host massive black holes (MBH). We address
this question by using a merger-tree model combined with tight constraints from
the 7 Ms Chandra survey, and the known high-$z$ super-MBH population. We find
that a typical LBG with $M_{\rm UV}=-22$ residing in a $M_h\approx 10^{12}
M_\odot$ halo at $z=6$ host a MBH with mass $M_\bullet \approx 2\times 10^8
M_\odot$. Depending on the fraction, $f_{\rm seed}$, of early halos planted
with a direct collapse black hole seed ($M_{\rm seed}=10^5 M_\odot$), the model
suggests two possible scenarios: (a) if $f_{\rm seed}=1$, MBH in LBGs mostly
grow by merging, and must accrete at a low ($\lambda_E\simeq 10^{-3}$)
Eddington ratio not to exceed the experimental X-ray luminosity upper bound
$L_X^* = 10^{42.5} {\rm erg\, s}^{-1}$; (b) if $f_{\rm seed}=0.05$ accretion
dominates ($\lambda_E\simeq 0.22$), and MBH emission in LBGs must be heavily
obscured. In both scenarios the UV luminosity function is largely dominated by
stellar emission up to very bright mag, $M_{\rm UV} > -23$, with BH emission
playing a subdominant role. Scenario (a) poses extremely challenging, and
possibly unphysical, requirements on DCBH formation. Scenario (b) entails
testable implications on the physical properties of LBGs involving the FIR
luminosity, emission lines, and presence of outflows.


Abstract:
  Inferring parameters related to the aggregation pattern of parasites and to
their dispersal propensity is important for predicting their ecological
consequences and evolutionary potential. Nonetheless, it is notoriously
difficult to infer these parameters from wildlife parasites given the
difficulty in tracking these organisms. Molecular-based inferences constitute a
promising approach that has yet rarely been applied in the wild.Here, we
combinedseveral population genetic analyses including sibship reconstruction to
documentthe genetic structure, patterns of sibship aggregation and the
dispersal dynamics of a non-native parasite of fish, the freshwater copepod
ectoparasiteTracheliastespolycolpus. We collected parasites according to a
hierarchical sampling design,withthe sampling of all parasites from all host
individualscapturedineight sites spread along an upstream-downstream river
gradient. Individual multilocus genotypes were obtained from 14 microsatellite
markers, and used to assign parasites to full-sib families and to investigate
the genetic structure of T.polycolpus among both hosts and sampling sites. The
distribution of full-sibs obtainedamong the sampling sites was used to estimate
individual dispersal distances within families. Our results showed that T.
polycolpus sibs tend to be aggregated within sites but not withinhost
individuals. We detected important upstream-to-downstream dispersal events of
T.polycolpusbetween sites (modal distance: 25.4 km; 95% CI [22.9, 27.7]),
becoming scarcer as the geographic distance from their family core location
increases. Such a dispersal pattern likely contributes to the strong
isolation-by-distance observed at the river scale. We also detected some
downstream-to-upstream dispersal events (modal distance: 2.6 km; 95% CI
[2.2-23.3]) that likely result from movementsof infected hosts.Within each
site, the dispersal of free-living infective larvae among hosts likely
contributes to increasing genetic diversity on hosts, possibly fostering the
evolutionary potential of T. polycolpus.


Abstract:
  In machine learning, classifiers are typically susceptible to noise in the
training data. In this work, we aim at reducing intra-class noise with the help
of graph filtering to improve the classification performance. Considered graphs
are obtained by connecting samples of the training set that belong to a same
class depending on the similarity of their representation in a latent space. We
show that the proposed graph filtering methodology has the effect of
asymptotically reducing intra-class variance, while maintaining the mean. While
our approach applies to all classification problems in general, it is
particularly useful in few-shot settings, where intra-class noise can have a
huge impact due to the small sample selection. Using standardized benchmarks in
the field of vision, we empirically demonstrate the ability of the proposed
method to slightly improve state-of-the-art results in both cases of few-shot
and standard classification.


Abstract:
  Cluster assignment of large and complex images is a crucial but challenging
task in pattern recognition and computer vision. In this study, we explore the
possibility of employing fuzzy clustering in a deep neural network framework.
Thus, we present a novel evolutionary unsupervised learning representation
model with iterative optimization. It implements the deep adaptive fuzzy
clustering (DAFC) strategy that learns a convolutional neural network
classifier from given only unlabeled data samples. DAFC consists of a deep
feature quality-verifying model and a fuzzy clustering model, where deep
feature representation learning loss function and embedded fuzzy clustering
with the weighted adaptive entropy is implemented. We joint fuzzy clustering to
the deep reconstruction model, in which fuzzy membership is utilized to
represent a clear structure of deep cluster assignments and jointly optimize
for the deep representation learning and clustering. Also, the joint model
evaluates current clustering performance by inspecting whether the re-sampled
data from estimated bottleneck space have consistent clustering properties to
progressively improve the deep clustering model. Comprehensive experiments on a
variety of datasets show that the proposed method obtains a substantially
better performance for both reconstruction and clustering quality when compared
to the other state-of-the-art deep clustering methods, as demonstrated with the
in-depth analysis in the extensive experiments.


Abstract:
  Recent developments in advanced driving assistance systems (ADAS) that rely
on some level of autonomy have led the automobile industry and research
community to investigate the impact they might have on driving performance.
However, most of the research performed so far is based on simulated
environments. In this study, we investigated the behavior of drivers in a
vehicle with automated driving system (ADS) capabilities in a real-life driving
scenario. We analyzed their response to a take over request (TOR) at two
different driving speeds while being engaged in non-driving-related tasks
(NDRT). Results from the performed experiments showed that driver reaction time
to a TOR, gaze behavior and self-reported trust in automation were affected by
the type of NDRT being concurrently performed and driver reaction time and gaze
behavior additionally depended on the driving or vehicle speed at the time of
TOR.


Abstract:
  In this paper, we propose a simple sparse approximate inverse for triangular
matrices (SAIT). Using the Jacobi iteration method, we obtain an expression of
the exact inverse of triangular matrix, which is a finite series. The SAIT is
constructed based on this series. We apply the SAIT matrices to iterative
methods with ILU preconditioners. The two triangular solvers in the ILU
preconditioning procedure are replaced by two matrix-vector multiplications,
which can be fine-grained parallelized. We test this method by solving some
linear systems and eigenvalue problems with preconditioned iterative methods.


Abstract:
  Here, we analyse the behaviour of the higher order standardised moments of
financial time series when we truncate a large data set into smaller and
smaller subsets, referred to below as time windows. We look at the effect of
the economic environment on the behaviour of higher order moments in these time
windows. We observe two different scaling relations of higher order moments
when the data sub sets' length decreases; one for longer time windows and
another for the shorter time windows. These scaling relations drastically
change when the time window encompasses a financial crisis. We also observe a
qualitative change of higher order standardised moments compared to the
gaussian values in response to a shrinking time window. We extend this analysis
to incorporate the effects these scaling relations have upon risk. We decompose
the return series within these time windows and carry out a Value-at-Risk
calculation. In doing so, we observe the manifestation of the scaling relations
through the change in the Value-at-Risk level. Moreover, we model the observed
scaling laws by analysing the hierarchy of rare events on higher order moments.


Abstract:
  A non-monotonic behavior of the velocity gradient of a test particle
revolving around a rapidly rotating black hole in the locally non-rotating
frame of reference is known as the Aschenbach effect. This effect can serve as
a distinguishing signature of rapidly rotating black holes, being potentially
useful for the measurements of the astrophysical black hole spins. This paper
is the generalization of our previous research to the motion of spinning
particles around a rotating black hole with non-zero cosmological constant. We
show that both the particle's spin $s$ and the cosmological constant $\Lambda$
modify the critical value of the black hole spin $a_c$, for which the
Aschenbach effect can be observed; $a_c$ can increase or decrease depending on
the signs of $s$ and $\Lambda$. We also found that the particle's spin $s$ can
mimic the effect of the cosmological constant $\Lambda$ for a given $a_c$,
causing thus a discrepancy in the measurements of $s$, $\Lambda$ and $a_c$ in
the Aschenbach effect.


Abstract:
  The recently discovered so-called $s+is$ superconducting state is a
non-topological and non-chiral state in the modern classification scheme. It
does not have topological surface states, and no spontaneous surface currents
are expected in this class of materials. We present a microscopic study that
demonstrates that $s+is$ superconductors can have spontaneous boundary currents
and spontaneous magnetic fields of a different kind. These arise at
lower-dimensional boundaries: corners of two-dimensional samples and corners
and edges of three-dimensional samples.


Abstract:
  Oilfield production optimization is challenging due to subsurface model
complexity and associated non-linearity, large number of control parameters,
large number of production scenarios, and subsurface uncertainties.
Optimization involves time-consuming reservoir simulation studies to compare
different production scenarios and settings. This paper presents efficacy of
two hybrid evolutionary optimization approaches for well control optimization
of a waterflooding operation, and demonstrates their application using Olympus
benchmark. A simpler, weighted sum of cumulative fluid (WCF) is used as
objective function first, which is then replaced by net present value (NPV) of
discounted cash-flow for comparison. Two popular evolutionary optimization
algorithms, genetic algorithm (GA) and particle swarm optimization (PSO), are
first used in standalone mode to solve well control optimization problem. Next,
both GA and PSO methods are used with another popular optimization algorithm,
covariance matrix adaptation-evolution strategy (CMA-ES), in hybrid mode.
Hybrid optimization run is made by transferring the resulting population from
one algorithm to the next as its starting population for further improvement.
Approximately four thousand simulation runs are needed for standalone GA and
PSO methods to converge, while six thousand runs are needed in case of two
hybrid optimization modes (GA-CMA-ES and PSO-CMA-ES). To reduce turn-around
time, commercial cloud computing is used and simulation workload is distributed
using parallel programming. GA and PSO algorithms have a good balance between
exploratory and exploitative properties, thus are able identify regions of
interest. CMA-ES algorithm is able to further refine the solution using its
excellent exploitative properties. Thus, GA or PSO with CMA-ES in hybrid mode
yields better optimization result as compared to standalone GA or PSO
algorithms.


Abstract:
  We study the reachability problem for continuous one-counter automata, COCA
for short. In such automata, transitions are guarded by upper and lower bound
tests against the counter value. Additionally, the counter updates associated
with taking transitions can be (non-deterministically) scaled down by a nonzero
factor between zero and one. Our three main results are as follows: (1) We
prove that the reachability problem for COCA with global upper and lower bound
tests is in NC2; (2) that, in general, the problem is decidable in polynomial
time; and (3) that it is decidable in the polynomial hierarchy for COCA with
parametric counter updates and bound tests.


Abstract:
  Ten protostellar outflows in the Orion molecular clouds were mapped in the
$^{12}$CO/$^{13}$CO ${J=6\rightarrow5}$ and $^{12}$CO ${J=7\rightarrow6}$
lines. The maps of these mid-$J$ CO lines have an angular resolution of about
10$''$ and a typical field size of about 100$''$. Physical parameters of the
molecular outflows were derived, including mass transfer rates, kinetic
luminosities, and outflow forces. The outflow sample was expanded by
re-analyzing archival data of nearby low-luminosity protostars, to cover a wide
range of bolometric luminosities. Outflow parameters derived from other
transitions of CO were compared. The mid-$J$ ($J_{\rm up} \approx 6$) and
low-$J$ ($J_{\rm up} \leq 3$) CO line wings trace essentially the same outflow
component. By contrast, the high-$J$ (up to $J_{\rm up} \approx 50$)
line-emission luminosity of CO shows little correlation with the kinetic
luminosity from the ${J=6\rightarrow5}$ line, which suggests that they trace
distinct components. The low/mid-$J$ CO line wings trace long-term outflow
behaviors while the high-$J$ CO lines are sensitive to short-term activities.
The correlations between the outflow parameters and protostellar properties are
presented, which shows that the strengths of molecular outflows increase with
bolometric luminosity and envelope mass.


Abstract:
  We continue our study of effective field theory via homotopy transfer of
$L_\infty$-algebras, and apply it to tree-level non-Wilsonian effective actions
of the kind discussed by Sen in which the modes integrated out are comparable
in mass to the modes that are kept. We focus on the construction of effective
actions for string states at fixed levels and in particular on the construction
of weakly constrained double field theory. With these examples in mind, we
discuss closed string theory on toroidal backgrounds and resolve some subtle
issues involving vertex operators, including the proper form of cocycle factors
and of the reflector state. This resolves outstanding issues concerning the
construction of covariant closed string field theory on toroidal backgrounds.
The weakly constrained double field theory is formally obtained from closed
string field theory on a toroidal background by integrating out all but the
doubly massless' states and homotopy transfer then gives a prescription for
determining the theory's vertices and symmetries. We also discuss consistent
truncation in the context of homotopy transfer.


Abstract:
  Large pre-trained language models have been shown to encode large amounts of
world and commonsense knowledge in their parameters, leading to substantial
interest in methods for extracting that knowledge. In past work, knowledge was
extracted by taking manually-authored queries and gathering paraphrases for
them using a separate pipeline. In this work, we propose a method for
automatically rewriting queries into "BERTese", a paraphrase query that is
directly optimized towards better knowledge extraction. To encourage meaningful
rewrites, we add auxiliary loss functions that encourage the query to
correspond to actual language tokens. We empirically show our approach
outperforms competing baselines, obviating the need for complex pipelines.
Moreover, BERTese provides some insight into the type of language that helps
language models perform knowledge extraction.


Abstract:
  Earth's climate can be understood as a dynamical system that changes due to
external forcing and internal couplings. Essential climate variables, such as
surface air temperature, describe this dynamics. Our current interglacial, the
Holocene (11,700 years ago to today), has been characterized by great stability
of global mean temperature prior to anthropogenic warming. However, the
mechanisms and spatiotemporal patterns of fluctuations around this mean, called
temperature variability, are poorly understood despite their socio-economic
relevance for climate change mitigation and adaptation. Here, we examine
discrepancies between temperature variability from model simulations and
paleoclimate reconstructions by clarifying the stability of local and global
surface air temperature on the timescale of years to centuries. To this end, we
contrast power spectral densities (PSD) and their power-law scaling using
simulated and observation-based temperature series of the last six thousand
years. We further introduce the spectral gain to disentangle the
externally-forced and internally-generated variability as a function of
timescale. It is based on our estimate of the joint PSD of radiative forcing,
which exhibits a scale break around the period of ten years. We find that local
temperature series from paleoclimate reconstructions tend to show unstable
behavior on periods of 10 to 200 years, while simulated temperatures almost
exclusively show stable behavior. Conversely, the PSD and spectral gain of
global mean temperature are consistent across data sets. Our results point to
the limitation of climate models to fully represent local temperature
statistics over decades to centuries. By highlighting the key characteristics
of temperature variability, we pave a way to better constrain possible changes
in temperature variability with global warming and assess future climate risks.


Abstract:
  Maximally recoverable codes are a class of codes which recover from all
potentially recoverable erasure patterns given the locality constraints of the
code. In earlier works, these codes have been studied in the context of codes
with locality. The notion of locality has been extended to hierarchical
locality, which allows for locality to gradually increase in levels with the
increase in the number of erasures. We consider the locality constraints
imposed by codes with two-level hierarchical locality and define maximally
recoverable codes with data-local and local hierarchical locality. We derive
certain properties related to their punctured codes and minimum distance. We
give a procedure to construct hierarchical data-local MRCs from hierarchical
local MRCs. We provide a construction of hierarchical local MRCs for all
parameters. We also give constructions of MRC with hierarchical locality for
some parameters, whose field size is smaller than that of known constructions
for general parameters. We also derive a field size lower bound on MRC with
hierarchical locality.


Abstract:
  Motivated by the work of T.E. Govindan in [5,8,9], this paper is concerned
with a more general semilinear stochastic evolution equation. The difference
between the equations considered in this paper and the previous one is that it
makes some changes to the nonlinear function in random integral, which also
depends on the probability distribution of stochastic process at that time.
First, this paper considers the existence and uniqueness of mild solutions for
such equations. Furthermore, Trotter-Kato approximation system is introduced
for the mild solutions, and the weak convergence of induced probability
measures and zeroth-order approximations are obtained. Then we consider the
classical limit theorem about the parameter dependence of this kind of
equations. Finally, an example of stochastic partial differential equation is
given to illustrate our results.


Abstract:
  3D printing using robots has garnered significant interest in manufacturing
and construction in recent years. A robot's versatility paired with the design
freedom of 3D printing offers promising opportunities for how parts and
structures are built in the future. However, 3D printed objects are still
limited in size and location due to a lack of vertical mobility of ground
robots. These limitations severely restrict the potential of the 3D printing
process. To overcome these limitations, we develop a hexacopter testbed that
can print via fused deposition modeling during flight. We discuss the design of
this testbed and develop a simple control strategy for initial print tests. By
successfully performing these initial print tests, we demonstrate the
feasibility of this approach and lay the groundwork for printing 3D parts and
structures with drones.


Abstract:
  The gapless modes on the edge of four-dimensional (4D) quantum Hall droplets
are known to be anisotropic: they only propagate in one direction, foliating
the 3D boundary into independent 1D conduction channels. This foliation is
extremely sensitive to the confining potential and generically yields chaotic
flows. Here we study the quantum correlations and entanglement of such edge
modes in 4D droplets confined by harmonic traps, whose boundary is a squashed
three-sphere. Commensurable trapping frequencies lead to periodic trajectories
of electronic guiding centers; the corresponding edge modes propagate
independently along $S^1$ fibers, forming a bundle of 1D conformal field
theories over a 2D base space. By contrast, incommensurable frequencies produce
quasi-periodic, ergodic trajectories, each of which covers its invariant torus
densely; the corresponding correlation function of edge modes has fractal
features. This wealth of behaviors highlights the sharp differences between 4D
Hall droplets and their 2D peers; it also exhibits the dependence of 4D edge
modes on the choice of trap, suggesting the existence of observable
bifurcations due to droplet deformations.


Abstract:
  The category of complete differential graded Lie algebras provides nice
algebraic models for the rational homotopy types of non-simply connected
spaces. In particular, there is a realization functor, $\langle -\rangle$, of
any complete differential graded Lie algebra as a simplicial set. In a previous
article, we considered the particular case of a complete graded Lie algebra,
$L_{0}$, concentrated in degree 0 and proved that $\langle L_{0}\rangle$ is
isomorphic to the usual bar construction on the Malcev group associated to
$L_{0}$.
  Here we consider the case of a complete differential graded Lie algebra,
$L=L_{0}\oplus L_{1}$, concentrated in degrees 0 and 1. We establish that this
category is equivalent to explicit subcategories of crossed modules and Lie
algebra crossed modules, extending the equivalence between pronilpotent Lie
algebras and Malcev groups. In particular, there is a crossed module
$\mathcal{C}(L)$ associated to $L$. We prove that $\mathcal{C}(L)$ is
isomorphic to the Whitehead crossed module associated to the simplicial pair
$(\langle L\rangle, \langle L_{0}\rangle)$. Our main result is the
identification of $\langle L\rangle$ with the classifying space of
$\mathcal{C}(L)$.


Abstract:
  A systematics of grain boundary (GB) segregation transitions and critical
phenomena has been derived to expand the classical GB segregation theory. This
study uncovers when GB layering vs. prewetting transitions should occur and how
they are related to one another. Moreover, a novel descriptor, normalized
segregation strength, is introduced. It can represent several factors that
control GB segregation, including strain and bond energies for both general and
small-angle GBs, as well as misorientation for small-angle GBs, which had to be
treated separately in prior models. In a strong segregation system with a large
normalized segregation strength, first-order layering transitions occur at low
temperatures and become continuous above GB roughing temperatures. With
reducing normalized segregation strength, the layering transitions gradually
merge and finally lump into prewetting transitions without quantized layer
numbers, akin to Cahn's critical-point wetting model. Furthermore, GB
complexion diagrams with universal characters are constructed as the GB
counterpart to the classical exemplar of Pelton-Thompson regular-solution
binary bulk phase diagrams.


Abstract:
  We consider distributed convex-concave saddle point problems over arbitrary
connected undirected networks and propose a decentralized distributed algorithm
for their solution. The local functions distributed across the nodes are
assumed to have global and local groups of variables. For the proposed
algorithm we prove non-asymptotic convergence rate estimates with explicit
dependence on the network characteristics. To supplement the convergence rate
analysis, we propose lower bounds for strongly-convex-strongly-concave and
convex-concave saddle-point problems over arbitrary connected undirected
networks. We illustrate the considered problem setting by a particular
application to distributed calculation of non-regularized Wasserstein
barycenters.


Abstract:
  Graph signal processing is a ubiquitous task in many applications such as
sensor, social, transportation and brain networks, point cloud processing, and
graph neural networks. Often, graph signals are corrupted in the sensing
process, thus requiring restoration. In this paper, we propose two graph signal
restoration methods based on deep algorithm unrolling (DAU). First, we present
a graph signal denoiser by unrolling iterations of the alternating direction
method of multiplier (ADMM). We then suggest a general restoration method for
linear degradation by unrolling iterations of Plug-and-Play ADMM (PnP-ADMM). In
the second approach, the unrolled ADMM-based denoiser is incorporated as a
submodule, leading to a nested DAU structure. The parameters in the proposed
denoising/restoration methods are trainable in an end-to-end manner. Our
approach is interpretable and keeps the number of parameters small since we
only tune graph-independent regularization parameters. We overcome two main
challenges in existing graph signal restoration methods: 1) limited performance
of convex optimization algorithms due to fixed parameters which are often
determined manually. 2) large number of parameters of graph neural networks
that result in difficulty of training. Several experiments for graph signal
denoising and interpolation are performed on synthetic and real-world data. The
proposed methods show performance improvements over several existing techniques
in terms of root mean squared error in both tasks.


Abstract:
  Recent works show that group equivariance as an inductive bias improves
neural network performance for both classification and generation. However,
designing group-equivariant neural networks is challenging when the group of
interest is large and is unknown. Moreover, inducing equivariance can
significantly reduce the number of independent parameters in a network with
fixed feature size, affecting its overall performance. We address these
problems by proving a new group-theoretic result in the context of equivariant
neural networks that shows that a network is equivariant to a large group if
and only if it is equivariant to smaller groups from which it is constructed.
Using this result, we design a novel fast group equivariant construction
algorithm, and a deep Q-learning-based search algorithm in a reduced search
space, yielding what we call autoequivariant networks (AENs). AENs find the
right balance between equivariance and network size when tested on new
benchmark datasets, G-MNIST and G-Fashion-MNIST, obtained via group
transformations on MNIST and Fashion-MNIST respectively that we release.
Extending these results to group convolutional neural networks, where we
optimize between equivariances, augmentations, and network sizes, we find group
equivariance to be the most dominating factor in all high-performing GCNNs on
several datasets like CIFAR10, SVHN, RotMNIST, ASL, EMNIST, and KMNIST.


Abstract:
  Transverse momentum $p_T$ spectra of final state particles produced in high
energy heavy-ion collision can be divided into two distinct regions based on
the difference in the underlying particle production process. We have provided
a unified formalism to explain both low- and high-$p_T$ regime of spectra in a
consistent manner. The $p_T$ spectra of final state particles produced at RHIC
and LHC energies have been analysed using unified formalism to test its
applicability at different energies, and a good agreement with the data is
obtained across all energies. Further, the prospect of extracting the elliptic
flow coefficient directly from the transverse momentum spectra is explored.


Abstract:
  One of the main drawbacks of the powder metallurgy route for High-Entropy
Alloys (HEAs) is the unavailability of fully pre-alloyed powders in the market.
Using commodity powders (commercial Ni, Fe and Co base fully pre-alloyed
powders, fully available in large quantities and at competitive prices) to
produce HEAs presents a completely new and competitive scenario for obtaining
viable alloys for high-performance applications.


Abstract:
  In this paper, we present our heuristic solutions to the problems of finding
the maximum and minimum area polygons with a given set of vertices. Our
solutions are based mostly on two simple algorithmic paradigms: greedy method
and local search. The greedy heuristic starts with a simple polygon and adds
vertices one by one, according to a weight function. A crucial ingredient to
obtain good solutions is the choice of an appropriate weight function that
avoids long edges. The local search part consists of moving consecutive
vertices to another location in the polygonal chain. We also discuss the
different implementation techniques that are necessary to reduce the running
time.


Abstract:
  Continual learning tackles the setting of learning different tasks
sequentially. Despite the lots of previous solutions, most of them still suffer
significant forgetting or expensive memory cost. In this work, targeted at
these problems, we first study the continual learning process through the lens
of information theory and observe that forgetting of a model stems from the
loss of \emph{information gain} on its parameters from the previous tasks when
learning a new task. From this viewpoint, we then propose a novel continual
learning approach called Bit-Level Information Preserving (BLIP) that preserves
the information gain on model parameters through updating the parameters at the
bit level, which can be conveniently implemented with parameter quantization.
More specifically, BLIP first trains a neural network with weight quantization
on the new incoming task and then estimates information gain on each parameter
provided by the task data to determine the bits to be frozen to prevent
forgetting. We conduct extensive experiments ranging from classification tasks
to reinforcement learning tasks, and the results show that our method produces
better or on par results comparing to previous state-of-the-arts. Indeed, BLIP
achieves close to zero forgetting while only requiring constant memory
overheads throughout continual learning.


Abstract:
  Increasing concerns and regulations about data privacy and sparsity
necessitate the study of privacy-preserving, decentralized learning methods for
natural language processing (NLP) tasks. Federated learning (FL) provides
promising approaches for a large number of clients (e.g., personal devices or
organizations) to collaboratively learn a shared global model to benefit all
clients while allowing users to keep their data locally. Despite interest in
studying FL methods for NLP tasks, a systematic comparison and analysis is
lacking in the literature. Herein, we present the FedNLP, a benchmarking
framework for evaluating federated learning methods on four different task
formulations: text classification, sequence tagging, question answering, and
seq2seq. We propose a universal interface between Transformer-based language
models (e.g., BERT, BART) and FL methods (e.g., FedAvg, FedOPT, etc.) under
various non-IID partitioning strategies. Our extensive experiments with FedNLP
provide empirical comparisons between FL methods and helps us better understand
the inherent challenges of this direction. The comprehensive analysis points to
intriguing and exciting future research aimed at developing FL methods for NLP
tasks.


Abstract:
  The monitoring of rotating machinery is an essential task in today's
production processes. Currently, several machine learning and deep
learning-based modules have achieved excellent results in fault detection and
diagnosis. Nevertheless, to further increase user adoption and diffusion of
such technologies, users and human experts must be provided with explanations
and insights by the modules. Another issue is related, in most cases, with the
unavailability of labeled historical data that makes the use of supervised
models unfeasible. Therefore, a new approach for fault detection and diagnosis
in rotating machinery is here proposed. The methodology consists of three
parts: feature extraction, fault detection and fault diagnosis. In the first
part, the vibration features in the time and frequency domains are extracted.
Secondly, in the fault detection, the presence of fault is verified in an
unsupervised manner based on anomaly detection algorithms. The modularity of
the methodology allows different algorithms to be implemented. Finally, in
fault diagnosis, Shapley Additive Explanations (SHAP), a technique to interpret
black-box models, is used. Through the feature importance ranking obtained by
the model explainability, the fault diagnosis is performed. Two tools for
diagnosis are proposed, namely: unsupervised classification and root cause
analysis. The effectiveness of the proposed approach is shown on three datasets
containing different mechanical faults in rotating machinery. The study also
presents a comparison between models used in machine learning explainability:
SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local-
DIFFI). Lastly, an analysis of several state-of-art anomaly detection
algorithms in rotating machinery is included.


Abstract:
  In the setting of regularly varying time series, a cluster of exceedances is
a short period for which the supremum norm exceeds a high threshold. We propose
to study a generalization of this notion considering short periods, or blocks,
with lp--norm above a high threshold. We derive large deviation principles of
blocks and apply these results to improve cluster inference. We focus on blocks
estimators and show they are consistent when we use large empirical quantiles
from the lp --norm of blocks as threshold levels. We derive an adaptive
threshold selection method for cluster inference in lp. Our approach focuses on
the case p < $\infty$ rather than the classical one for p = $\infty$ where the
bias is more difficult to control.


Abstract:
  In this letter, we show that the wino-Higgsino dark matter (DM) is detectable
in near future DM direct detection experiments for almost all consistent
parameter space in the spontaneously broken supergravity (SUGRA) if the muon
g-2 anomaly is explained by the wino-Higgsino loop diagrams. We also point out
that the present and future LHC experiments can exclude or confirm this SUGRA
explanation of the observed muon g-2 anomaly.