Abstract:
  We propose a three-dimensional (3D) multimodal medical imaging system that
combines freehand ultrasound and structured light 3D reconstruction in a single
coordinate system without requiring registration. To the best of our knowledge,
these techniques have not been combined before as a multimodal imaging
technique. The system complements the internal 3D information acquired with
ultrasound, with the external surface measured with the structure light
technique. Moreover, the ultrasound probe's optical tracking for pose
estimation was implemented based on a convolutional neural network.
Experimental results show the system's high accuracy and reproducibility, as
well as its potential for preoperative and intraoperative applications. The
experimental multimodal error, or the distance from two surfaces obtained with
different modalities, was 0.12 mm. The code is available as a Github
repository.


Abstract:
  Solving planning and scheduling problems for multiple tasks with highly
coupled state and temporal constraints is notoriously challenging. An appealing
approach to effectively decouple the problem is to judiciously order the events
such that decisions can be made over sequences of tasks. As many problems
encountered in practice are over-constrained, we must instead find relaxed
solutions in which certain requirements are dropped. This motivates a
formulation of optimality with respect to the costs of relaxing constraints and
the problem of finding an optimal ordering under which this relaxing cost is
minimum. In this paper, we present Generalized Conflict-directed Ordering
(GCDO), a branch-and-bound ordering method that generates an optimal total
order of events by leveraging the generalized conflicts of both inconsistency
and suboptimality from sub-solvers for cost estimation and solution space
pruning. Due to its ability to reason over generalized conflicts, GCDO is much
more efficient in finding high-quality total orders than the previous
conflict-directed approach CDITO. We demonstrate this by benchmarking on
temporal network configuration problems, which involves managing networks over
time and makes necessary tradeoffs between network flows against CDITO and
Mixed Integer-Linear Programing (MILP). Our algorithm is able to solve two
orders of magnitude more benchmark problems to optimality and twice the
problems compared to CDITO and MILP within a runtime limit, respectively.


Abstract:
  Cryo-electron microscopy (cryo-EM) has become a major experimental technique
to determine the structures of large protein complexes and molecular
assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been
drastically improved to generate high-resolution three-dimensional (3D) maps
that contain detailed structural information about macromolecules, the
computational methods for using the data to automatically build structure
models are lagging far behind. The traditional cryo-EM model building approach
is template-based homology modeling. Manual de novo modeling is very
time-consuming when no template model is found in the database. In recent
years, de novo cryo-EM modeling using machine learning (ML) and deep learning
(DL) has ranked among the top-performing methods in macromolecular structure
modeling. Deep-learning-based de novo cryo-EM modeling is an important
application of artificial intelligence, with impressive results and great
potential for the next generation of molecular biomedicine. Accordingly, we
systematically review the representative ML/DL-based de novo cryo-EM modeling
methods. And their significances are discussed from both practical and
methodological viewpoints. We also briefly describe the background of cryo-EM
data processing workflow. Overall, this review provides an introductory guide
to modern research on artificial intelligence (AI) for de novo molecular
structure modeling and future directions in this emerging field.


Abstract:
  2020 has been a year marked by the COVID-19 pandemic. This event has caused
disruptions to many aspects of normal life. An important aspect in reducing the
impact of the pandemic is to control its spread. Studies have shown that one
effective method in reducing the transmission of COVID-19 is to wear masks.
Strict mask-wearing policies have been met with not only public sensation but
also practical difficulty. We cannot hope to manually check if everyone on a
street is wearing a mask properly. Existing technology to help automate mask
checking uses deep learning models on real-time surveillance camera footages.
The current dominant method to perform real-time mask detection uses Mask-RCNN
with ResNet as the backbone. While giving good detection results, this method
is computationally intensive and its efficiency in real-time face mask
detection is not ideal. Our research proposes a new approach to mask detection
by replacing Mask-R-CNN with a more efficient model "YOLO" to increase the
processing speed of real-time mask detection and not compromise on accuracy.
Besides, given the small volume as well as extreme imbalance of the mask
detection datasets, we adopt a latest progress made in few-shot visual
classification, simple CNAPs, to improve the classification performance.


Abstract:
  In computed tomography, data consist of measurements of the attenuation of
X-rays passing through an object. The goal is to reconstruct the linear
attenuation coefficient of the object's interior. For each position of the
X-ray source, characterized by its angle with respect to a fixed coordinate
system, one measures a set of data referred to as a view. A common assumption
is that these view angles are known, but in some applications they are known
with imprecision. We propose a framework to solve a Bayesian inverse problem
that jointly estimates the view angles and an image of the object's attenuation
coefficient. We also include a few hyperparameters that characterize the
likelihood and the priors. Our approach is based on a Gibbs sampler where the
associated conditional densities are simulated using different sampling schemes
- hence the term hybrid. In particular, the conditional distribution associated
with the reconstruction is nonlinear in the image pixels, non-Gaussian and
high-dimensional. We approach this distribution by constructing a Laplace
approximation that represents the target conditional locally at each Gibbs
iteration. This enables sampling of the attenuation coefficients in an
efficient manner using iterative reconstruction algorithms. The numerical
results show that our algorithm is able to jointly identify the image and the
view angles, while also providing uncertainty estimates of both. We demonstrate
our method with 2D X-ray computed tomography problems using fan beam
configurations.


Abstract:
  FPGAs are now used in public clouds to accelerate a wide range of
applications, including many that operate on sensitive data such as financial
and medical records. We present ShEF, a trusted execution environment (TEE) for
cloud-based reconfigurable accelerators. ShEF is independent from CPU-based
TEEs and allows secure execution under a threat model where the adversary can
control all software running on the CPU connected to the FPGA, has physical
access to the FPGA, and can compromise the FPGA interface logic of the cloud
provider. ShEF provides a secure boot and remote attestation process that
relies solely on existing FPGA mechanisms for root of trust. It also includes a
Shield component that provides secure access to data while the accelerator is
in use. The Shield is highly customizable and extensible, allowing users to
craft a bespoke security solution that fits their accelerator's memory access
patterns, bandwidth, and security requirements at minimum performance and area
overheads. We describe a prototype implementation of ShEF for existing cloud
FPGAs and measure the performance benefits of customizable security using five
accelerator designs.


Abstract:
  The dual-path RNN (DPRNN) was proposed to more effectively model extremely
long sequences for speech separation in the time domain. By splitting long
sequences to smaller chunks and applying intra-chunk and inter-chunk RNNs, the
DPRNN reached promising performance in speech separation with a limited model
size. In this paper, we combine the DPRNN module with Convolution Recurrent
Network (CRN) and design a model called Dual-Path Convolution Recurrent Network
(DPCRN) for speech enhancement in the time-frequency domain. We replace the
RNNs in the CRN with DPRNN modules, where the intra-chunk RNNs are used to
model the spectrum pattern in a single frame and the inter-chunk RNNs are used
to model the dependence between consecutive frames. With only 0.8M parameters,
the submitted DPCRN model achieves an overall mean opinion score (MOS) of 3.57
in the wide band scenario track of the Interspeech 2021 Deep Noise Suppression
(DNS) challenge. Evaluations on some other test sets also show the efficacy of
our model.


Abstract:
  Quantitative lung measures derived from computed tomography (CT) have been
demonstrated to improve prognostication in coronavirus disease (COVID-19)
patients, but are not part of the clinical routine since required manual
segmentation of lung lesions is prohibitively time-consuming. We propose a new
fully automated deep learning framework for rapid quantification and
differentiation between lung lesions in COVID-19 pneumonia from both contrast
and non-contrast CT images using convolutional Long Short-Term Memory
(ConvLSTM) networks. Utilizing the expert annotations, model training was
performed 5 times with separate hold-out sets using 5-fold cross-validation to
segment ground-glass opacity and high opacity (including consolidation and
pleural effusion). The performance of the method was evaluated on CT data sets
from 197 patients with positive reverse transcription polymerase chain reaction
test result for SARS-CoV-2. Strong agreement between expert manual and
automatic segmentation was obtained for lung lesions with a Dice score
coefficient of 0.876 $\pm$ 0.005; excellent correlations of 0.978 and 0.981 for
ground-glass opacity and high opacity volumes. In the external validation set
of 67 patients, there was dice score coefficient of 0.767 $\pm$ 0.009 as well
as excellent correlations of 0.989 and 0.996 for ground-glass opacity and high
opacity volumes. Computations for a CT scan comprising 120 slices were
performed under 2 seconds on a personal computer equipped with NVIDIA Titan RTX
graphics processing unit. Therefore, our deep learning-based method allows
rapid fully-automated quantitative measurement of pneumonia burden from CT and
may generate results with an accuracy similar to the expert readers.


Abstract:
  DNA methylation is a well-studied genetic modification that regulates gene
transcription of Eukaryotes. Its alternations have been recognized as a
significant component of cancer development. In this study, we use the DNA
methylation 450k data from The Cancer Genome Atlas to evaluate the efficacy of
DNA methylation data on cancer classification for 30 cancer types. We propose a
new method for gene selection in high dimensional data(over 450 thousand).
Variance filtering is first introduced for dimension reduction and Recursive
feature elimination (RFE) is then used for feature selection. We address the
problem of selecting a small subsets of genes from large number of methylated
sites, and our parsimonious model is demonstrated to be efficient, achieving an
accuracy over 91%, outperforming other studies which use DNA micro-arrays and
RNA-seq Data . The performance of 20 models, which are based on 4 estimators
(Random Forest, Decision Tree, Extra Tree and Support Vector Machine) and 5
classifiers (k-Nearest Neighbours, Support Vector Machine, XGboost, Light GBM
and Multi-Layer Perceptron), is compared and robustness of the RFE algorithm is
examined. Results suggest that the combined model of extra tree plus catboost
classifier offers the best performance in cancer identification, with an
overall validation accuracy of 91% , 92.3%, 93.3% and 93.5% for 20, 30, 40 and
50 features respectively. The biological functions in cancer development of 50
selected genes is also explored through enrichment analysis and the results
show that 12 out of 16 of our top features have already been identified to be
specific with cancer and we also propose some more genes to be tested for
future studies. Therefore, our method may be utilzed as an auxiliary diagnostic
method to determine the actual clinicopathological status of a specific cancer.


Abstract:
  We investigate a one-dimensional quantum emitter chain where transport of
excitations and correlations takes place via nearest neighbor, dipole-dipole
interactions. In the presence of collective radiative emission, we show that a
phase imprinting wavepacket initialization procedure can lead to subradiant
transport and can preserve quantum correlations. In the context of cavity
mediated transport, where emitters are coupled to a common delocalized optical
mode, we analyze the effect of frequency disorder and nonidentical
photon-emitter couplings on excitation transport.


Abstract:
  We associate all small subgraph counting problems with a systematic graph
encoding/representation system which makes a coherent use of graphlet
structures. The system can serve as a unified foundation for studying and
connecting many important graph problems in theory and practice. We describe
topological relations among graphlets (graph elements) in rigorous mathematics
language and from the perspective of graph encoding. We uncover, characterize
and utilize algebraic and numerical relations in graphlet counts/frequencies.
We present a novel algorithm for efficiently counting small subgraphs as a
practical product of our theoretical findings.


Abstract:
  We propose a framework to mine API usage scenarios from Stack Overflow. Each
task consists of a code example, the task description, and the reactions of
developers towards the code example. First, we present an algorithm to
automatically link a code example in a forum post to an API mentioned in the
textual contents of the forum post. Second, we generate a natural language
description of the task by summarizing the discussions around the code example.
Third, we automatically associate developers reactions (i.e., positive and
negative opinions) towards the code example to offer information about code
quality. We evaluate the algorithms using three benchmarks.


Abstract:
  We study the problem of user-scheduling and resource allocation in
distributed multi-user, multiple-input multiple-output (MIMO) networks
implementing user-centric clustering and non-coherent transmission. We
formulate a weighted sum-rate maximization problem which can provide user
proportional fairness. As in this setup, users can be served by many
transmitters, user scheduling is particularly difficult. To solve this issue,
we use block coordinate descent, fractional programming, and compressive
sensing to construct an algorithm that performs user-scheduling and
beamforming. Our results show that the proposed framework provides an 8- to
10-fold gain in the long-term user spectral efficiency compared to benchmark
schemes such as round-robin scheduling. Furthermore, we quantify the
performance loss due to imperfect channel state information and pilot training
overhead using a defined area-based pilot-reuse factor.


Abstract:
  In this paper, we study the graph of homothety classes of stable free
lattices in a two-dimensional representation over a local UFD. This generalizes
a classical result of the case where the base ring is a discrete valuation ring
due to Serre. As applications, we consider the case when the representation
comes from a residually reducible Hida family and we study the control theorem
of Selmer groups. These results enable us to know the precise statement of the
main conjecture in residually reducible case as we will remark in section 4.


Abstract:
  We show that assuming the standard conjectures, for any smooth projective
variety $X$ of dimension $n$ over an algebraically closed field, there is a
constant $C>0$ such that for any positive rational number $r$ and for any
polarized endomorphism $f$ of $X$, we have \[ \| G_r \circ f \| \le C \,
\mathrm{deg}(G_r \circ f), \] where $G_r$ is a correspondence of $X$ so that
for each $0\le i\le 2n$ its pullback action on the $i$-th Weil cohomology group
is the multiplication-by-$r^i$ map. This inequality has been conjectured by the
authors to hold in a more general setting, which - in the special case of
polarized endomorphisms - confirms the validity of the analog of a well known
result by Serre in the K\"ahler setting.


Abstract:
  As we gain access to a greater depth and range of health-related information
about individuals, three questions arise: (1) Can we build better models to
predict individual-level risk of ill health? (2) How much data do we need to
effectively predict ill health? (3) Are new methods required to process the
added complexity that new forms of data bring? The aim of the study is to apply
a machine learning approach to identify the relative contribution of personal,
social, health-related, biomarker and genetic data as predictors of future
health in individuals. Using longitudinal data from 6830 individuals in the UK
from Understanding Society (2010-12 to 2015-17), the study compares the
predictive performance of five types of measures: personal (e.g. age, sex),
social (e.g. occupation, education), health-related (e.g. body weight, grip
strength), biomarker (e.g. cholesterol, hormones) and genetic single nucleotide
polymorphisms (SNPs). The predicted outcome variable was limiting long-term
illness one and five years from baseline. Two machine learning approaches were
used to build predictive models: deep learning via neural networks and XGBoost
(gradient boosting decision trees). Model fit was compared to traditional
logistic regression models. Results found that health-related measures had the
strongest prediction of future health status, with genetic data performing
poorly. Machine learning models only offered marginal improvements in model
accuracy when compared to logistic regression models, but also performed well
on other metrics e.g. neural networks were best on AUC and XGBoost on
precision. The study suggests that increasing complexity of data and methods
does not necessarily translate to improved understanding of the determinants of
health or performance of predictive models of ill health.


Abstract:
  Despite extensive research efforts in the recent years, computational
modeling of argumentation remains one of the most challenging areas of natural
language processing (NLP). This is primarily due to inherent complexity of the
cognitive processes behind human argumentation, which commonly combine and
integrate plethora of different types of knowledge, requiring from
computational models capabilities that are far beyond what is needed for most
other (i.e., simpler) natural language understanding tasks. The existing large
body of work on mining, assessing, generating, and reasoning over arguments
largely acknowledges that much more common sense and world knowledge needs to
be integrated into computational models that would accurately model
argumentation. A systematic overview and organization of the types of knowledge
introduced in existing models of computational argumentation (CA) is, however,
missing and this hinders targeted progress in the field. In this survey paper,
we fill this gap by (1) proposing a pyramid of types of knowledge required in
CA tasks, (2) analysing the state of the art with respect to the reliance and
exploitation of these types of knowledge, for each of the for main research
areas in CA, and (3) outlining and discussing directions for future research
efforts in CA.


Abstract:
  Point clouds, being the simple and compact representation of surface geometry
of 3D objects, have gained increasing popularity with the evolution of deep
learning networks for classification and segmentation tasks. Unlike human,
teaching the machine to analyze the segments of an object is a challenging task
and quite essential in various machine vision applications. In this paper, we
address the problem of segmentation and labelling of the 3D point clouds by
proposing a inception based deep network architecture called PIG-Net, that
effectively characterizes the local and global geometric details of the point
clouds. In PIG-Net, the local features are extracted from the transformed input
points using the proposed inception layers and then aligned by feature
transform. These local features are aggregated using the global average pooling
layer to obtain the global features. Finally, feed the concatenated local and
global features to the convolution layers for segmenting the 3D point clouds.
We perform an exhaustive experimental analysis of the PIG-Net architecture on
two state-of-the-art datasets, namely, ShapeNet [1] and PartNet [2]. We
evaluate the effectiveness of our network by performing ablation study.


Abstract:
  A formula of the $D$-$D$ correlation function is derived. The deuterons are
treated either as elementary particles or as neutron-proton bound states. In
the first case the deuterons are directly emitted from a source and in the
second one the deuteron formation is a final-state process simultaneous with a
generation of $D$-$D$ correlation. The source radius of deuterons formed due to
final-state interactions is bigger by the factor of $\sqrt{2}$ than that of
directly emitted deuterons. To check how sizable is the effect we compute the
$D$-$D$ correlation function taking into the Bose-Einstein statistics of
deuterons, the $s$-wave scattering due to strong interaction and the Coulomb
repulsion. The correlation function is shown to be sensitive to the source
radius for sources which are sufficiently small with RMS radii smaller than 3.5
fm. Otherwise the correlation function is dominated by the Coulomb repulsion
and weakly depends on the source radius. Measurements which can make use of our
finding are discussed.


Abstract:
  Recently developed deep neural networks achieved state-of-the-art results in
the subject of 6D object pose estimation for robot manipulation. However, those
supervised deep learning methods require expensive annotated training data.
Current methods for reducing those costs frequently use synthetic data from
simulations, but rely on expert knowledge and suffer from the "domain gap" when
shifting to the real world. Here, we present a proof of concept for a novel
approach of autonomously generating annotated training data for 6D object pose
estimation. This approach is designed for learning new objects in operational
environments while requiring little interaction and no expertise on the part of
the user. We evaluate our autonomous data generation approach in two grasping
experiments, where we archive a similar grasping success rate as related work
on a non autonomously generated data set.


Abstract:
  We describe the implementation of a three-dimensional Paul ion trap
fabricated from a stack of precision-machined silica glass wafers, which
incorporates a pair of junctions for 2-dimensional ion transport. The trap has
142 dedicated electrodes which can be used to define multiple potential wells
in which strings of ions can be held. By supplying time-varying potentials,
this also allows for transport and re-configuration of ion strings. We describe
the design, simulation, fabrication and packaging of the trap, including
explorations of different parameter regimes and possible optimizations and
design choices. We give results of initial testing of the trap, including
measurements of heating rates and junction transport.


Abstract:
  Laterally large (~3 micrometers), atomically-thin two-dimensional (2D)
Bi2O2CO3 nanosheets (2D bismuth oxycarbonate, 2D bismutite) are fabricated via
sonochemically-assisted template-free synthesis. Key to the synthesis of the
freestanding, laterally large 2D Bi2O2CO3 nanosheets from bulk Bi powder is
choice of suspension medium, controlled reaction temperatures and several hours
processing time. Lateral sizes of 2D Bi2O2CO3 can be controlled between
micrometer-sized nanosheets and tens of nm sized nanoflakes solely based on the
choice of suspension medium. The here introduced 2D Bi2O2CO3 nanosheets/-flakes
are then hybridized by a simple mix-and-match approach with TiO2 nanoparticles
for testing in suspension-type photocatalytic hydrogen production via water
splitting. This introduces the 2D Bi2O2CO3 with TiO2 as a promising
noble-metal-free co-catalyst for photocatalytic hydrogen evolution. Our results
enrich the fabrication toolbox of emerging 2D pnictogen oxycarbonates towards
large 2D nanosheets and demonstrate the promising potential of 2D Bi2O2CO3 as
an advantageous (co-)catalyst for hydrogen evolution in photocatalytic water
splitting.


Abstract:
  A blocking set in a graph $G$ is a subset of vertices that intersects every
maximum independent set of $G$. Let ${\sf mmbs}(G)$ be the size of a maximum
(inclusion-wise) minimal blocking set of $G$. This parameter has recently
played an important role in the kernelization of Vertex Cover parameterized by
the distance to a graph class ${\cal F}$. Indeed, it turns out that the
existence of a polynomial kernel for this problem is closely related to the
property that ${\sf mmbs}({\cal F})=\sup_{G \in {\cal F}}{\sf mmbs}(G)$ is
bounded by a constant, and thus several recent results focused on determining
${\sf mmbs}({\cal F})$ for different classes ${\cal F}$. We consider the
parameterized complexity of computing ${\sf mmbs}$ under various
parameterizations, such as the size of a maximum independent set of the input
graph and the natural parameter. We provide a panorama of the complexity of
computing both ${\sf mmbs}$ and ${\sf mmhs}$, which is the size of a maximum
minimal hitting set of a hypergraph, a closely related parameter. Finally, we
consider the problem of computing ${\sf mmbs}$ parameterized by treewidth,
especially relevant in the context of kernelization. Given the "counting"
nature of ${\sf mmbs}$, it does not seem to be expressible in monadic
second-order logic, hence its tractability does not follow from Courcelle's
theorem. Our main technical contribution is a fixed-parameter tractable
algorithm for this problem.


Abstract:
  In this work we propose a batch Bayesian optimization method for
combinatorial problems on permutations, which is well suited for expensive cost
functions on permutations. We introduce LAW, a new efficient batch acquisition
method based on the determinantal point process, using an acquisition weighted
kernel. Relying on multiple parallel evaluations, LAW accelerates the search
for the optimal permutation. We provide a regret analysis for our method to
gain insight in its theoretical properties. We then apply the framework to
permutation problems, which have so far received little attention in the
Bayesian Optimization literature, despite their practical importance. We call
this method LAW2ORDER. We evaluate the method on several standard combinatorial
problems involving permutations such as quadratic assignment, flowshop
scheduling and the traveling salesman, as well as on a structure learning task.


Abstract:
  We investigate the symplectic geometric and differential geometric aspects of
the moduli space of connections on a compact Riemann surface $X$. Fix a theta
characteristic $K^{1/2}_X$ on $X$; it defines a theta divisor on the moduli
space ${\mathcal M}$ of stable vector bundles on $X$ of rank $r$ degree zero.
Given a vector bundle $E \in {\mathcal M}$ lying outside the theta divisor, we
construct a natural holomorphic connection on $E$ that depends holomorphically
on $E$. Using this holomorphic connection, we construct a canonical holomorphic
isomorphism between the following two: \begin{enumerate} \item the moduli space
$\mathcal C$ of pairs $(E, D)$, where $E\in {\mathcal M}$ and $D$ is a
holomorphic connection on $E$, and
  \item the space ${\rm Conn}(\Theta)$ given by the sheaf of holomorphic
connections on the line bundle on $\mathcal M$ associated to the theta divisor.
\end{enumerate} The above isomorphism between $\mathcal C$ and ${\rm
Conn}(\Theta)$ is symplectic structure preserving, and it moves holomorphically
as $X$ runs over a holomorphic family of Riemann surfaces.


Abstract:
  Despite inextricable ties between race and language, little work has
considered race in NLP research and development. In this work, we survey 79
papers from the ACL anthology that mention race. These papers reveal various
types of race-related bias in all stages of NLP model development, highlighting
the need for proactive consideration of how NLP systems can uphold racial
hierarchies. However, persistent gaps in research on race and NLP remain: race
has been siloed as a niche topic and remains ignored in many NLP tasks; most
work operationalizes race as a fixed single-dimensional variable with a
ground-truth label, which risks reinforcing differences produced by historical
racism; and the voices of historically marginalized people are nearly absent in
NLP literature. By identifying where and how NLP literature has and has not
considered race, especially in comparison to related fields, our work calls for
inclusion and racial justice in NLP research practices.


Abstract:
  The Collatz dynamic is known to generate a complex quiver of sequences over
natural numbers which inflation propensity remains so unpredictable it could be
used to generate reliable proof of work algorithms for the cryptocurrency
industry. Here we establish an ad hoc equivalent of modular arithmetic for
Collatz sequences to automatically demonstrate the convergence of infinite
quivers of numbers, based on five arithmetic rules we prove apply on the entire
Collatz dynamic and which we further simulate to gain insight on their graph
geometry and computational properties. We then formally demonstrate these rules
define an automaton that is playing a Hydra game on the graph of undecided
numbers we also prove is embedded in 24N-7, proving that in ZFC the Collatz
conjecture is true, before giving a promising direction to also prove it in
Peano arithmetic.


Abstract:
  We analyze the problem of active covering, where the learner is given an
unlabeled dataset and can sequentially label query examples. The objective is
to label query all of the positive examples in the fewest number of total label
queries. We show under standard non-parametric assumptions that a classical
support estimator can be repurposed as an offline algorithm attaining an excess
query cost of $\widetilde{\Theta}(n^{D/(D+1)})$ compared to the optimal
learner, where $n$ is the number of datapoints and $D$ is the dimension. We
then provide a simple active learning method that attains an improved excess
query cost of $\widetilde{O}(n^{(D-1)/D})$. Furthermore, the proposed
algorithms only require access to the positive labeled examples, which in
certain settings provides additional computational and privacy benefits.
Finally, we show that the active learning method consistently outperforms
offline methods as well as a variety of baselines on a wide range of benchmark
image-based datasets.


Abstract:
  We consider a network of autonomous agents whose outputs are actions in a
game with coupled constraints. In such network scenarios, agents seeking to
minimize coupled cost functions using distributed information while satisfying
the coupled constraints. Current methods consider the small class of
multi-integrator agents using primal-dual methods. These methods can only
ensure constraint satisfaction in steady-state. In contrast, we propose an
inexact penalty method using a barrier function for nonlinear agents with
equilibrium-independent passive dynamics. We show that these dynamics converge
to an epsilon-GNE while satisfying the constraints for all time, not only in
steady-state. We develop these dynamics in both the full-information and
partial-information settings. In the partial-information setting, dynamic
estimates of the others' actions are used to make decisions and are updated
through local communication. Applications to optical networks and velocity
synchronization of flexible robots are provided.


Abstract:
  Two dynamical systems are topologically equivalent when their phase-portraits
can be morphed into each other by a homeomorphic coordinate transformation on
the state space. The induced equivalence classes capture qualitative properties
such as stability or the oscillatory nature of the state trajectories, for
example. In this paper we develop a method to learn the topological class of an
unknown stable system from a single trajectory of finitely many state
observations. Using a moderate deviations principle for the least squares
estimator of the unknown system matrix $\theta$, we prove that the probability
of misclassification decays exponentially with the number of observations at a
rate that is proportional to the square of the smallest singular value of
$\theta$.


Abstract:
  A recent case study from AWS by Chong et al. proposes an effective
methodology for Bounded Model Checking in industry. In this paper, we report on
a follow up case study that explores the methodology from the perspective of
three research questions: (a) can proof artifacts be used across verification
tools; (b) are there bugs in verified code; and (c) can specifications be
improved. To study these questions, we port the verification tasks for
$\texttt{aws-c-common}$ library to SEAHORN and KLEE. We show the benefits of
using compiler semantics and cross-checking specifications with different
verification techniques, and call for standardizing proof library extensions to
increase specification reuse. The verification tasks discussed are publicly
available online.


Abstract:
  Despite the significant advances in deep learning over the past decade, a
major challenge that limits the wide-spread adoption of deep learning has been
their fragility to adversarial attacks. This sensitivity to making erroneous
predictions in the presence of adversarially perturbed data makes deep neural
networks difficult to adopt for certain real-world, mission-critical
applications. While much of the research focus has revolved around adversarial
example creation and adversarial hardening, the area of performance measures
for assessing adversarial robustness is not well explored. Motivated by this,
this study presents the concept of residual error, a new performance measure
for not only assessing the adversarial robustness of a deep neural network at
the individual sample level, but also can be used to differentiate between
adversarial and non-adversarial examples to facilitate for adversarial example
detection. Furthermore, we introduce a hybrid model for approximating the
residual error in a tractable manner. Experimental results using the case of
image classification demonstrates the effectiveness and efficacy of the
proposed residual error metric for assessing several well-known deep neural
network architectures. These results thus illustrate that the proposed measure
could be a useful tool for not only assessing the robustness of deep neural
networks used in mission-critical scenarios, but also in the design of
adversarially robust models.


Abstract:
  We present QuantumSync, the first quantum algorithm for solving a
synchronization problem in the context of computer vision. In particular, we
focus on permutation synchronization which involves solving a non-convex
optimization problem in discrete variables. We start by formulating
synchronization into a quadratic unconstrained binary optimization problem
(QUBO). While such formulation respects the binary nature of the problem,
ensuring that the result is a set of permutations requires extra care. Hence,
we: (I) show how to insert permutation constraints into a QUBO problem and (ii)
solve the constrained QUBO problem on the current generation of the adiabatic
quantum computers D-Wave. Thanks to the quantum annealing, we guarantee global
optimality with high probability while sampling the energy landscape to yield
confidence estimates. Our proof-of-concepts realization on the adiabatic D-Wave
computer demonstrates that quantum machines offer a promising way to solve the
prevalent yet difficult synchronization problems.


Abstract:
  The present paper gives new elements for the light variations of the EA
variable star AY Peg, on the basis of new times of minimum performed visually
and with ccd by members of GEOS between 1985 and 2018, and the ASAS-SN set of
data available. On one hand, we can establish a new ephemeris with a possible
quadratic term, and on the other hand, the amplitude of the primary minimum
appears much deeper than the one given in GCVS. AY Peg varies between 13.1 and
15.6 magnitude at its primary eclipse.


Abstract:
  Diagnostic classification models (DCMs) offer statistical tools to inspect
the fined-grained attribute of respondents' strengths and weaknesses. However,
the diagnosis accuracy deteriorates when misspecification occurs in the
predefined item-attribute relationship, which is encoded into a Q-matrix. To
prevent such misspecification, methodologists have recently developed several
Bayesian Q-matrix estimation methods for greater estimation flexibility.
However, these methods become infeasible in the case of large-scale assessments
with a large number of attributes and items. In this study, we focused on the
deterministic inputs, noisy ``and'' gate (DINA) model and proposed a new
framework for the Q-matrix estimation to find the Q-matrix with the maximum
marginal likelihood. Based on this framework, we developed a scalable
estimation algorithm for the DINA Q-matrix by constructing an iteration
algorithm that utilizes stochastic optimization and variational inference. The
simulation and empirical studies reveal that the proposed method achieves
high-speed computation, good accuracy, and robustness to potential
misspecifications, such as initial value's choices and hyperparameter settings.
Thus, the proposed method can be a useful tool for estimating a Q-matrix in
large-scale settings.


Abstract:
  Novae are some of the most commonly detected optical transients and have the
potential to provide valuable information about binary evolution. Binary
population synthesis codes have emerged as the most effective tool for
modelling populations of binary systems, but such codes have traditionally
employed greatly simplified nova physics, precluding detailed study. In this
work, we implement a model treating H and He novae as individual events into
the binary population synthesis code \binaryc. This treatment of novae
represents a significant improvement on the `averaging' treatment currently
employed in modern population synthesis codes. We discuss the evolutionary
pathways leading to these phenomena and present nova event rates and
distributions of several important physical parameters. Most novae are produced
on massive white dwarfs, with approximately 70 and 55 per cent of nova events
occurring on O/Ne white dwarfs for H and He novae respectively. Only 15 per
cent of H-nova systems undergo a common-envelope phase, but these systems are
responsible for the majority of H nova events. All He-accreting He-nova systems
are considered post-common-envelope systems, and almost all will merge with
their donor star in a gravitational-wave driven inspiral. We estimate the
current annual rate of novae in M31 (Andromeda) to be approximately $41 \pm 4$
for H novae, underpredicting the current observational estimate of
$65^{+15}_{-16}$, and $0.14\pm0.015$ for He novae. When varying common-envelope
parameters, the H nova rate varies between 20 and 80 events per year.


Abstract:
  Here we introduce a new reconstruction technique for two-dimensional Bragg
Scattering Tomography (BST), based on the Radon transform models of [arXiv
preprint, arXiv:2004.10961 (2020)]. Our method uses a combination of ideas from
multibang control and microlocal analysis to construct an objective function
which can regularize the BST artifacts; specifically the boundary artifacts due
to sharp cutoff in sinogram space (as observed in [arXiv preprint,
arXiv:2007.00208 (2020)]), and artifacts arising from approximations made in
constructing the model used for inversion. We then test our algorithm in a
variety of Monte Carlo (MC) simulated examples of practical interest in airport
baggage screening and threat detection. The data used in our studies is
generated with a novel Monte-Carlo code presented here. The model, which is
available from the authors upon request, captures both the Bragg scatter
effects described by BST as well as beam attenuation and Compton scatter.


Abstract:
  The automated analysis of medical images is currently limited by technical
and biological noise and bias. The same source tissue can be represented by
vastly different images if the image acquisition or processing protocols vary.
For an image analysis pipeline, it is crucial to compensate such biases to
avoid misinterpretations. Here, we evaluate, compare, and improve existing
generative model architectures to overcome domain shifts for immunofluorescence
(IF) and Hematoxylin and Eosin (H&E) stained microscopy images. To determine
the performance of the generative models, the original and transformed images
were segmented or classified by deep neural networks that were trained only on
images of the target bias. In the scope of our analysis, U-Net cycleGANs
trained with an additional identity and an MS-SSIM-based loss and Fixed-Point
GANs trained with an additional structure loss led to the best results for the
IF and H&E stained samples, respectively. Adapting the bias of the samples
significantly improved the pixel-level segmentation for human kidney glomeruli
and podocytes and improved the classification accuracy for human prostate
biopsies by up to 14%.


Abstract:
  We prove a quantitative equidistribution theorem for the eigenfunctions of a
Schr\"odinger operator -\Delta+V on a rectangular torus T for V\in L^2(T). A
key application of our theorem is a quantitative equidistribution theorem for
the eigenfunctions of a Schr\"odinger operator whose potential models
disordered systems with N obstacles. We prove the validity of this
equidistribution theorem in the thermodynamic limit, as N \to\infty, under the
assumption that a weak disorder hypothesis is satisfied.
  In particular, we show that this scale-invariant equidistribution theorem
holds for the eigenfunctions of random displacement models almost surely with
respect to the joint density of the random positions of the potentials. In the
case of a general random Schr\"odinger operator, where disorder may be strong,
we deduce an equidistribution theorem on certain length scales, which
establishes a lower bound for the Anderson localization length as a function of
the energy, coupling parameter, density of scatterers and the L^2 norm of the
potential.


Abstract:
  The heavy fermion superconductor URu$_2$Si$_2$ is a candidate for chiral,
time-reversal symmetry-breaking superconductivity with a nodal gap structure.
Here, we microscopically visualized superconductivity and spatially
inhomogeneous ferromagnetism in URu$_2$Si$_2$. We observed linear-$T$
superfluid density, consistent with d-wave pairing symmetries including chiral
d-wave, but did not observe the spontaneous magnetization expected for chiral
d-wave. Local vortex pinning potentials had either four- or two-fold rotational
symmetries with various orientations at different locations. Taken together,
these data support a nodal gap structure in URu$_2$Si$_2$ and suggest that
chirality either is not present or does not lead to detectable spontaneous
magnetization.


Abstract:
  The realization of practical intelligent reflecting surface (IRS)-assisted
multi-user communication (IRS-MUC) systems critically depends on the proper
beamforming design exploiting accurate channel state information (CSI).
However, channel estimation (CE) in IRS-MUC systems requires a significantly
large training overhead due to the numerous reflection elements involved in
IRS. In this paper, we adopt a deep learning approach to implicitly learn the
historical channel features and directly predict the IRS phase shifts for the
next time slot to maximize the average achievable sum-rate of an IRS-MUC system
taking into account the user mobility. By doing this, only a low-dimension
multiple-input single-output (MISO) CE is needed for transmit beamforming
design, thus significantly reducing the CE overhead. To this end, a
location-aware convolutional long short-term memory network (LA-CLNet) is first
developed to facilitate predictive beamforming at IRS, where the convolutional
and recurrent units are jointly adopted to exploit both the spatial and
temporal features of channels simultaneously. Given the predictive IRS phase
shift beamforming, an instantaneous CSI (ICSI)-aware fully-connected neural
network (IA-FNN) is then proposed to optimize the transmit beamforming matrix
at the access point. Simulation results demonstrate that the sum-rate
performance achieved by the proposed method approaches that of the genie-aided
scheme with the full perfect ICSI.


Abstract:
  We consider linear network error correction (LNEC) coding when errors may
occur on edges of a communication network of which the topology is known. In
this paper, we first revisit and explore the framework of LNEC coding, and then
unify two well-known LNEC coding approaches. Furthermore, by developing a
graph-theoretic approach to the framework of LNEC coding, we obtain a
significantly enhanced characterization of the error correction capability of
LNEC codes in terms of the minimum distances at the sink nodes. In LNEC coding,
the minimum required field size for the existence of LNEC codes, in particular
LNEC maximum distance separable (MDS) codes which are a type of most important
optimal codes, is an open problem not only of theoretical interest but also of
practical importance, because it is closely related to the implementation of
the coding scheme in terms of computational complexity and storage requirement.
By applying the graph-theoretic approach, we obtain an improved upper bound on
the minimum required field size. The improvement over the existing results is
in general significant. The improved upper bound, which is graph-theoretic,
depends only on the network topology and requirement of the error correction
capability but not on a specific code construction. However, this bound is not
given in an explicit form. We thus develop an efficient algorithm that can
compute the bound in linear time. In developing the upper bound and the
efficient algorithm for computing this bound, various graph-theoretic concepts
are introduced. These concepts appear to be of fundamental interest in graph
theory and they may have further applications in graph theory and beyond.


Abstract:
  In ground-based astronomy, starlight distorted by the atmosphere couples
poorly into single-mode waveguides but a correction by adaptive optics, even if
only partial, can boost coupling into the few-mode regime allowing the use of
photonic lanterns to convert into multiple single-mode beams. Corrected
wavefronts result in focal patterns that couple mostly with the circularly
symmetric waveguide modes. A mode-selective photonic lantern is hence proposed
to convert the multimode light into a subset of the single-mode waveguides of
the standard photonic lantern, thereby reducing the required number of outputs.
We ran simulations to show that only two out of the six waveguides of a 1x6
photonic lantern carry >95% of the coupled light to the outputs at $D/r_0 < 10$
if the wavefront is partially corrected and the photonic lantern is made
mode-selective.


Abstract:
  In Statistics, log-concave density estimation is a central problem within the
field of nonparametric inference under shape constraints. Despite great
progress in recent years on the statistical theory of the canonical estimator,
namely the log-concave maximum likelihood estimator, adoption of this method
has been hampered by the complexities of the non-smooth convex optimization
problem that underpins its computation. We provide enhanced understanding of
the structural properties of this optimization problem, which motivates the
proposal of new algorithms, based on both randomized and Nesterov smoothing,
combined with an appropriate integral discretization of increasing accuracy. We
prove that these methods enjoy, both with high probability and in expectation,
a convergence rate of order $1/T$ up to logarithmic factors on the objective
function scale, where $T$ denotes the number of iterations. The benefits of our
new computational framework are demonstrated on both synthetic and real data,
and our implementation is available in a github repository \texttt{LogConcComp}
(Log-Concave Computation).


Abstract:
  We establish existence, uniqueness as well as quantitative estimates for
solutions to the fractional nonlinear diffusion equation, $\partial_t u
+{\mathcal L}_{s,p} (u)=0$, where ${\mathcal L}_{s,p}=(-\Delta)_p^s$ is the
standard fractional $p$-Laplacian operator. We work in the range of exponents
$0<s<1$ and $1<p<2$, and in some sections $sp<1$. The equation is posed in the
whole space $x\in {\mathbb R}^N$. We first obtain weighted global integral
estimates that allow establishing the existence of solutions for a class of
large data that is proved to be roughly optimal. We study the class of
self-similar solutions of forward type, that we describe in detail when they
exist. We also explain what happens when possible self-similar solutions do not
exist. We establish the dichotomy positivity versus extinction for nonnegative
solutions at any given time. We analyze the conditions for extinction in finite
time.


Abstract:
  An important problem in deep learning is the privacy and security of neural
networks (NNs). Both aspects have long been considered separately. To date, it
is still poorly understood how privacy enhancing training affects the
robustness of NNs. This paper experimentally evaluates the impact of training
with Differential Privacy (DP), a standard method for privacy preservation, on
model vulnerability against a broad range of adversarial attacks. The results
suggest that private models are less robust than their non-private
counterparts, and that adversarial examples transfer better among DP models
than between non-private and private ones. Furthermore, detailed analyses of DP
and non-DP models suggest significant differences between their gradients.
Additionally, this work is the first to observe that an unfavorable choice of
parameters in DP training can lead to gradient masking, and, thereby, results
in a wrong sense of security.


Abstract:
  Biological, physical, medical, and numerical applications involving membrane
problems on different scales are numerous. We propose an extension of the
standard Turing theory to the case of two domains separated by a permeable
membrane. To this aim, we study a reaction-diffusion system with zero-flux
boundary conditions on the external boundary and Kedem-Katchalsky membrane
conditions on the inner membrane. We use the same approach as in the classical
Turing analysis but applied to membrane operators. The introduction of a
diagonalization theory for compact and self-adjoint membrane operators is
needed. Here, Turing instability is proven with the addition of new
constraints, due to the presence of membrane permeability coefficients. We
perform an explicit one-dimensional analysis of the eigenvalue problem,
combined with numerical simulations, to validate the theoretical results.
Finally, we observe the formation of discontinuous patterns in a system which
combines diffusion and dissipative membrane conditions, varying both diffusion
and membrane permeability coefficients. The case of a fast reaction-diffusion
system is also considered.


Abstract:
  We use analytic calculations and time-dependent spherically-symmetric
simulations to study the properties of isothermal galactic winds driven by
cosmic-rays (CRs) streaming at the Alfv\'en velocity. The simulations produce
time-dependent flows permeated by strong shocks; we identify a new linear
instability of sound waves that sources these shocks. The shocks substantially
modify the wind dynamics, invalidating previous steady state models: the CR
pressure $p_c$ has a staircase-like structure with $dp_c/dr \simeq 0$ in most
of the volume, and the time-averaged CR energetics are in many cases better
approximated by $p_c \propto \rho^{1/2}$, rather than the canonical $p_c
\propto \rho^{2/3}$. Accounting for this change in CR energetics, we
analytically derive new expressions for the mass-loss rate, momentum flux, wind
speed, and wind kinetic power in galactic winds driven by CR streaming. We show
that streaming CRs are ineffective at directly driving cold gas out of
galaxies, though CR-driven winds in hotter ISM phases may entrain cool gas. For
the same physical conditions, diffusive CR transport (Paper I) yields mass-loss
rates that are a few-100 times larger than streaming transport, and asymptotic
wind powers that are a factor of $\simeq 4$ larger. We discuss the implications
of our results for galactic wind theory and observations; strong shocks driven
by CR-streaming-induced instabilities produce gas with a wide range of
densities and temperatures, consistent with the multiphase nature of observed
winds. We also quantify the applicability of the isothermal gas approximation
for modeling streaming CRs and highlight the need for calculations with more
realistic thermodynamics.


Abstract:
  The past year has seen numerous publications underlining the importance of a
space mission to the ice giants in the upcoming decade. Proposed mission plans
involve a $\sim$10 year cruise time to the ice giants. This cruise time can be
utilized to search for low-frequency gravitational waves (GWs) by observing the
Doppler shift caused by them in the Earth-spacecraft radio link. We calculate
the sensitivity of prospective ice giant missions to GWs. Then, adopting a
steady-state black hole binary population, we derive a conservative estimate
for the detection rate of extreme mass ratio inspirals (EMRIs), supermassive-
(SMBH) and stellar mass binary black hole (sBBH) mergers. We link the SMBH
population to the fraction of quasars $f_\rm{bin}$ resulting from galaxy
mergers that pair SMBHs to a binary. For a total of ten 40-day observations
during the cruise of a single spacecraft, $\mathcal{O}(f_\rm{bin})\sim0.5$
detections of SMBH mergers are likely, if Allan deviation of Cassini-era noise
is improved by $\sim 10^2$ in the $10^{-5}-10^{-3}$ Hz range. For EMRIs the
number of detections lies between $\mathcal{O}(0.1) - \mathcal{O}(100)$.
Furthermore, ice giant missions combined with the Laser Interferometer Space
Antenna (LISA) would improve the localisation by an order of magnitude compared
to LISA by itself.


Abstract:
  We report the occurrence of a self-emerging frequency chimera state in
spatially extended systems of coupled oscillators, where the coherence and
incoherence are defined with respect to the emergent frequency of the
oscillations. This is generated by the local coupling among nonlinear
oscillators evolving under differing dynamical timescales starting from random
initial conditions. We show how they self-organize to structured patterns with
spatial domains of coherence that are in frequency synchronization, coexisting
with domains that are incoherent in frequencies. Our study has relevance in
understanding such patterns observed in real-world systems like neuronal
systems, power grids, social and ecological networks, where differing dynamical
timescales is natural and realistic amongthe interacting systems.


Abstract:
  We prove that perfect $3$-hash linear codes in $\mathbb{F}_{3}^{n}$ must have
dimension at most $ \left(\frac{1}{4}-\epsilon\right)n$ for some absolute
constant $\epsilon > 0$.


Abstract:
  Meta-learning is a branch of machine learning which aims to quickly adapt
models, such as neural networks, to perform new tasks by learning an underlying
structure across related tasks. In essence, models are being trained to learn
new tasks effectively rather than master a single task. Meta-learning is
appealing for process control applications because the perturbations to a
process required to train an AI controller can be costly and unsafe.
Additionally, the dynamics and control objectives are similar across many
different processes, so it is feasible to create a generalizable controller
through meta-learning capable of quickly adapting to different systems. In this
work, we construct a deep reinforcement learning (DRL) based controller and
meta-train the controller using a latent context variable through a separate
embedding neural network. We test our meta-algorithm on its ability to adapt to
new process dynamics as well as different control objectives on the same
process. In both cases, our meta-learning algorithm adapts very quickly to new
tasks, outperforming a regular DRL controller trained from scratch.
Meta-learning appears to be a promising approach for constructing more
intelligent and sample-efficient controllers.


Abstract:
  Electricity production currently generates approximately 25% of greenhouse
gas emissions in the USA. Thus, increasing the amount of renewable energy is a
key step to carbon neutrality. However, integrating a large amount of
fluctuating renewable generation is a significant challenge for power grid
operating and planning. Grid reliability, i.e., an ability to meet operational
constraints under power fluctuations, is probably the most important of them.
In this paper, we propose computationally efficient and accurate methods to
estimate the probability of failure, i.e. reliability constraints violation,
under a known distribution of renewable energy generation. To this end, we
investigate an importance sampling approach, a flexible extension of
Monte-Carlo methods, which adaptively changes the sampling distribution to
generate more samples near the reliability boundary. The approach allows to
estimate failure probability in real-time based only on a few dozens of random
samples, compared to thousands required by the plain Monte-Carlo. Our study
focuses on high voltage direct current power transmission grids with linear
reliability constraints on power injections and line currents. We propose a
novel theoretically justified physics-informed adaptive importance sampling
algorithm and compare its performance to state-of-the-art methods on multiple
IEEE power grid test cases.


Abstract:
  Expectation-maximization (EM) is a popular and well-established method for
image reconstruction in positron emission tomography (PET) but it often suffers
from slow convergence. Ordered subset EM (OSEM) is an effective reconstruction
algorithm that provides significant acceleration during initial iterations, but
it has been observed to enter a limit cycle. In this work, we investigate two
classes of algorithms for accelerating OSEM based on variance reduction for
penalised PET reconstructions. The first is a stochastic variance reduced EM
algorithm, termed as SVREM, an extension of the classical EM to the stochastic
context, by combining classical OSEM with insights from variance reduction
techniques for gradient descent. The second views OSEM as a preconditioned
stochastic gradient ascent, and applies variance reduction techniques, i.e.,
SAGA and SVRG, to estimate the update direction. We present several numerical
experiments to illustrate the efficiency and accuracy of the approaches. The
numerical results show that these approaches significantly outperform existing
OSEM type methods for penalised PET reconstructions, and hold great potential.


Abstract:
  Topological phenomena are commonly studied in phases of matter which are
separated from a trivial phase by an unavoidable quantum phase transition. This
can be overly restrictive, leaving out scenarios of practical relevance --
similar to the distinction between liquid water and vapor. Indeed, we show that
topological phenomena can be stable over a large part of parameter space even
when the bulk is strictly speaking in a trivial phase of matter. In particular,
we focus on symmetry-protected topological phases which can be trivialized by
extending the symmetry group. The topological Haldane phase in spin chains
serves as a paradigmatic example where the $SO(3)$ symmetry is extended to
$SU(2)$ by tuning away from the Mott limit. Although the Haldane phase is then
adiabatically connected to a product state, we show that characteristic
phenomena -- edge modes, entanglement degeneracies and bulk phase transitions
-- remain parametrically stable. This stability is due to a separation of
energy scales, characterized by quantized invariants which are well-defined
when a subgroup of the symmetry only acts on high-energy degrees of freedom.
The low-energy symmetry group is a quotient group whose emergent anomalies
stabilize edge modes and unnecessary criticality, which can occur in any
dimension.


Abstract:
  A Thickened Flame (TF) modeling approach is combined with a Large Eddy
Simulation (LES) methodology to model premixed combustion and the accuracy of
these model predictions is evaluated by comparing with the piloted premixed
stoichiometric methane-air flame data of Chen et al. [Combust. Flame 107 (1996)
233-244] at a Reynolds number Re = 24,000. In the TF model, the flame front is
artificially thickened to resolve it on the computational LES grid and the
reaction rates are specified using reduced chemistry. The response of the
thickened flame to turbulence is taken care of by incorporating an efficiency
function in the governing equations. The efficiency function depends on the
characteristics of the local turbulence and on the characteristics of the
premixed flame such as laminar flame speed and thickness. Three variants of the
TF model are examined: the original Thickened Flame model, the Power-law flame
wrinkling model, and the dynamically modified TF model. Reasonable agreement is
found when comparing predictions with the experimental data and with
computations reported using a probability distribution function (PDF) modeling
approach. The results of the TF model are in better agreement with data when
compared with the predictions of the G-equation approach


Abstract:
  Surrender poses one of the major risks to life insurance and a sound modeling
of its true probability has direct implication on the risk capital demanded by
the Solvency II directive. We add to the existing literature by performing
extensive experiments that present highly practical results for various
modeling approaches, including XGBoost, random forest, GLM and neural networks.
Further, we detect shortcomings of prevalent model assessments, which are in
essence based on a confusion matrix. Our results indicate that accurate label
predictions and a sound modeling of the true probability can be opposing
objectives. We illustrate this with the example of resampling. While resampling
is capable of improving label prediction in rare event settings, such as
surrender, and thus is commonly applied, we show theoretically and numerically
that models trained on resampled data predict significantly biased event
probabilities. Following a probabilistic perspective on surrender, we further
propose time-dependent confidence bands on predicted mean surrender rates as a
complementary assessment and demonstrate its benefit. This evaluation takes a
very practical, going concern perspective, which respects that the composition
of a portfolio, as well as the nature of underlying risk drivers might change
over time.


Abstract:
  We address a challenging problem of identifying main sources of hate speech
on Twitter. On one hand, we carefully annotate a large set of tweets for hate
speech, and deploy advanced deep learning to produce high quality hate speech
classification models. On the other hand, we create retweet networks, detect
communities and monitor their evolution through time. This combined approach is
applied to three years of Slovenian Twitter data. We report a number of
interesting results. Hate speech is dominated by offensive tweets, related to
political and ideological issues. The share of unacceptable tweets is
moderately increasing with time, from the initial 20% to 30% by the end of
2020. Unacceptable tweets are retweeted significantly more often than
acceptable tweets. About 60% of unacceptable tweets are produced by a single
right-wing community of only moderate size. Institutional Twitter accounts and
media accounts post significantly less unacceptable tweets than individual
accounts. However, the main sources of unacceptable tweets are anonymous
accounts, and accounts that were suspended or closed during the last three
years.


Abstract:
  Successful quantitative investment usually relies on precise predictions of
the future movement of the stock price. Recently, machine learning based
solutions have shown their capacity to give more accurate stock prediction and
become indispensable components in modern quantitative investment systems.
However, the i.i.d. assumption behind existing methods is inconsistent with the
existence of diverse trading patterns in the stock market, which inevitably
limits their ability to achieve better stock prediction performance. In this
paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to
empower existing stock prediction models with the ability to model multiple
stock trading patterns. Essentially, TRA is a lightweight module that consists
of a set of independent predictors for learning multiple patterns as well as a
router to dispatch samples to different predictors. Nevertheless, the lack of
explicit pattern identifiers makes it quite challenging to train an effective
TRA-based model. To tackle this challenge, we further design a learning
algorithm based on Optimal Transport (OT) to obtain the optimal sample to
predictor assignment and effectively optimize the router with such assignment
through an auxiliary loss term. Experiments on the real-world stock ranking
task show that compared to the state-of-the-art baselines, e.g., Attention LSTM
and Transformer, the proposed method can improve information coefficient (IC)
from 0.053 to 0.059 and 0.051 to 0.056 respectively. Our dataset and code used
in this work are publicly available:
https://github.com/microsoft/qlib/tree/main/examples/benchmarks/TRA.


Abstract:
  We continue studying $6D, {\cal N}=(1,1)$ supersymmetric Yang-Mills (SYM)
theory in the ${\cal N}=(1,0)$ harmonic superspace formulation. Using the
superfield background field method we explore the two-loop divergencies of the
effective action in the gauge multiplet sector. It is explicitly demonstrated
that among four two-loop background-field dependent supergraphs contributing to
the effective action, only one diverges off shell. It is also shown that the
divergences are proportional to the superfield classical equations of motion
and hence vanish on shell. Besides, we have analyzed a possible structure of
the two-loop divergences on general gauge and hypermultiplet background.


Abstract:
  This article reviews a class of adaptive group testing procedures that
operate under a probabilistic model assumption as follows. Consider a set of
$N$ items, where item $i$ has the probability $p$ ($p_i$ in the generalized
group testing) to be defective, and the probability $1-p$ to be non-defective
independent from the other items. A group test applied to any subset of size
$n$ is a binary test with two possible outcomes, positive or negative. The
outcome is negative if all $n$ items are non-defective, whereas the outcome is
positive if at least one item among the $n$ items is defective. The goal is
complete identification of all $N$ items with the minimum expected number of
tests.


Abstract:
  Liquid-liquid phase separation (LLPS) is important to control a wide range of
reactions from gene expression to protein degradation in a cell-sized space. To
bring a better understanding of the compatibility of such phase-separated
structures with protein synthesis, we study emergent LLPS in a cell-free
transcription-translation (TXTL) reaction. When the TXTL reaction composed of
many proteins is concentrated, the uniformly mixed state becomes unstable, and
membrane-less phases form spontaneously. This LLPS droplet formation is induced
when the TXTL reaction is enclosed in water-in-oil emulsion droplets, in which
water evaporates from the surface. As the emulsion droplets shrink, smaller
LLPS droplets appear inside the emulsion droplets and coalesce into large
phase-separated domains that partition the localization of synthesized reporter
proteins. The presence of PEG in the TXTL reaction is important not only for
versatile cell-free protein synthesis but also for the formation of two large
domains capable of protein partitioning. Our results may shed light on the
dynamic interplay of LLPS formation and cell-free protein synthesis toward the
construction of synthetic organelles.


Abstract:
  Mobile and embedded platforms are increasingly required to efficiently
execute computationally demanding DNNs across heterogeneous processing
elements. At runtime, the available hardware resources to DNNs can vary
considerably due to other concurrently running applications. The performance
requirements of the applications could also change under different scenarios.
To achieve the desired performance, dynamic DNNs have been proposed in which
the number of channels/layers can be scaled in real time to meet different
requirements under varying resource constraints. However, the training process
of such dynamic DNNs can be costly, since platform-aware models of different
deployment scenarios must be retrained to become dynamic. This paper proposes
Dynamic-OFA, a novel dynamic DNN approach for state-of-the-art platform-aware
NAS models (i.e. Once-for-all network (OFA)). Dynamic-OFA pre-samples a family
of sub-networks from a static OFA backbone model, and contains a runtime
manager to choose different sub-networks under different runtime environments.
As such, Dynamic-OFA does not need the traditional dynamic DNN training
pipeline. Compared to the state-of-the-art, our experimental results using
ImageNet on a Jetson Xavier NX show that the approach is up to 3.5x (CPU), 2.4x
(GPU) faster for similar ImageNet Top-1 accuracy, or 3.8% (CPU), 5.1% (GPU)
higher accuracy at similar latency.


Abstract:
  NP (search) problems allow easy correctness tests for solutions. Climbing
algorithms allow also easy assessment of how close to yielding the correct
answer is the configuration at any stage of their run. This offers a great
flexibility, as how sensible is any deviation from the standard procedures can
be instantly assessed.
  An example is the Dual Matrix Algorithm (DMA) for linear programming,
variations of which were considered by A.Y. Levin in 1965 and by Yamnitsky and
myself in 1982. It has little sensitivity to numerical errors and to the number
of inequalities. It offers substantial flexibility and, thus, potential for
further developments.


Abstract:
  In high-energy leptonic collisions, such as at a multi-TeV muon collider, the
collinear splittings of the electroweak (EW) gauge bosons and leptons are the
dominant phenomena, and the scattering processes should thus be formulated in
terms of the EW parton distribution functions (EW PDFs). We complete this
formalism in the Standard Model to include the QCD sector and evaluate the
quark and gluon PDFs inside a lepton at the double-log accuracy. The splittings
of the photon and subsequently the quarks and gluons control the quark/gluon
PDFs below the EW scale. The massive gauge bosons lead to substantial
contributions at high scales. The jet production cross section can reach the
order of a few nb (50 pb) in $e^+e^-$ ($\mu^+\mu^-$) collisions, at the TeV
c.m. energies with a moderate acceptance cut, that governs the overall event
shape up to about $p_T^j \sim 60$ GeV.


Abstract:
  This article investigates the energy efficiency issue in non-orthogonal
multiple access (NOMA)-enhanced Internet-of-Things (IoT) networks, where a
mobile unmanned aerial vehicle (UAV) is exploited as a flying base station to
collect data from ground devices via the NOMA protocol. With the aim of
maximizing network energy efficiency, we formulate a joint problem of UAV
deployment, device scheduling and resource allocation. First, we formulate the
joint device scheduling and spectrum allocation problem as a three-sided
matching problem, and propose a novel low-complexity near-optimal algorithm. We
also introduce the novel concept of `exploration' into the matching game for
further performance improvement. By algorithm analysis, we prove the
convergence and stability of the final matching state. Second, in an effort to
allocate proper transmit power to IoT devices, we adopt the Dinkelbach's
algorithm to obtain the optimal power allocation solution. Furthermore, we
provide a simple but effective approach based on disk covering problem to
determine the optimal number and locations of UAV's stop points to ensure that
all IoT devices can be fully covered by the UAV via line-of-sight (LoS) links
for the sake of better channel condition. Numerical results unveil that: i) the
proposed joint UAV deployment, device scheduling and resource allocation scheme
achieves much higher EE compared to predefined stationary UAV deployment case
and fixed power allocation scheme, with acceptable complexity; and ii) the
UAV-aided IoT networks with NOMA greatly outperforms the OMA case in terms of
number of accessed devices.


Abstract:
  Deformable convolution networks (DCNs) proposed to address the image
recognition with geometric or photometric variations typically involve
deformable convolution that convolves on arbitrary locations of input features.
The locations change with different inputs and induce considerable dynamic and
irregular memory accesses which cannot be handled by classic neural network
accelerators (NNAs). Moreover, bilinear interpolation (BLI) operation that is
required to obtain deformed features in DCNs also cannot be deployed on
existing NNAs directly. Although a general purposed processor (GPP) seated
along with classic NNAs can process the deformable convolution, the processing
on GPP can be extremely slow due to the lack of parallel computing capability.
  To address the problem, we develop a DCN accelerator on existing NNAs to
support both the standard convolution and deformable convolution. Specifically,
for the dynamic and irregular accesses in DCNs, we have both the input and
output features divided into tiles and build a tile dependency table (TDT) to
track the irregular tile dependency at runtime. With the TDT, we further
develop an on-chip tile scheduler to handle the dynamic and irregular accesses
efficiently. In addition, we propose a novel mapping strategy to enable
parallel BLI processing on NNAs and apply layer fusion techniques for more
energy-efficient DCN processing. According to our experiments, the proposed
accelerator achieves orders of magnitude higher performance and energy
efficiency compared to the typical computing architectures including ARM,
ARM+TPU, and GPU with 6.6\% chip area penalty to a classic NNA.


Abstract:
  Cloud providers offer end-users various pricing schemes to allow them to
tailor VMs to their needs, e.g., a pay-as-you-go billing scheme, called
\textit{on-demand}, and a discounted contract scheme, called \textit{reserved
instances}. This paper presents a cloud broker which offers users both the
flexibility of on-demand instances and some level of discounts found in
reserved instances. The broker employs a buy-low-and-sell-high strategy that
places user requests into a resource pool of pre-purchased discounted cloud
resources. By analysing user request time-series data, the broker takes a
risk-oriented approach to dynamically adjust the resource pool.
  This approach does not require a training process which is useful at
processing the large data stream. The broker is evaluated with high-frequency
real cloud datasets from Alibaba. The results show that the overall profit of
the broker is close to the theoretical optimal scenario where user requests can
be perfectly predicted.


Abstract:
  Thermal states of light are widely used in quantum optics for various quantum
phenomena testing. Particularly, they can be utilized for characterization of
photon creation and photon annihilation operations. During the last decade the
problem of photon subtraction from multimode quantum states become of much
significance. Therefore, in this work we present a technique for statistical
parameter estimation of multimode multiphoton subtracted thermal states of
light, which can be used for multimode photon annihilation test.


Abstract:
  The recent experimental data of anomalous magnetic moments strongly indicate
the existence of new physics beyond the standard model. An energetic $\mu^+$
beam is a potential option to the expected neutrino factories, the future muon
colliders and the $\mu$SR(the spin rotation, resonance and relaxation)
technology. It is proposed a prompt acceleration scheme of the $\mu^+$ beam in
a donut wakefield driven by a shaped Laguerre-Gaussian (LG) laser pulse. The
forward part of the donut wakefield can accelerate and also focus positive
particle beams effectively. The LG laser is shaped by a near-critical-density
plasma. The shaped LG laser has the shorter rise time and can enlarge the
acceleration field. The acceleration field driven by a shaped LG laser pulse is
six times higher than that driven by a normal LG laser pulse. The simulation
results show that the $ \mu^+$ bunch can be accelerated from $200\mathrm{MeV}$
to 2GeV and the transversal size of the $\mu^+$ bunch is also focused from
initial $\omega_0=5\mu m$ to $\omega=1\mu m$ within several picoseconds.


Abstract:
  HO Puppis (HO Pup) was considered as a Be-star candidate based on its
gamma-Cassiopeiae-type light curve, but lacked spectroscopic confirmation.
Using distance measured from Gaia Data Release 2 and the
spectral-energy-distribution (SED) fit on broadband photometry, the Be-star
nature of HO Pup is ruled out. Furthermore, based on the 28,700 photometric
data points collected from various time-domain surveys and dedicated
intensive-monitoring observations, the light curves of HO Pup closely resemble
IW And-type stars (as pointed out in Kimura et al. 2020a), exhibiting
characteristics such as quasi-standstill phase, brightening, and dips. The
light curve of HO Pup displays various variability timescales, including
brightening cycles ranging from 23 to 61 days, variations with periods between
3.9 days and 50 minutes during the quasi-standstill phase, and a semi-regular
~14-day period for the dip events. We have also collected time-series spectra
(with various spectral resolutions), in which Balmer emission lines and other
expected spectral lines for an IW And-type star were detected (even though some
of these lines were also expected to be present for Be stars). We detect Bowen
fluorescence near the brightening phase, and that can be used to discriminate
between IW And-type stars and Be stars. Finally, despite only observing for
four nights, the polarization variation was detected, indicating that HO Pup
has significant intrinsic polarization.


Abstract:
  We study an effective one-dimensional quantum model that includes friction
and spin-orbit coupling (SOC), and show that the model exhibits spin
polarization when both terms are finite. Most important, strong spin
polarization can be observed even for moderate SOC, provided that friction is
strong. Our findings might help to explain the pronounced effect of chirality
on spin distribution and transport in chiral molecules. In particular, our
model implies static magnetic properties of a chiral molecule, which lead to
Shiba-like states when a molecule is placed on a superconductor, in accordance
with recent experimental data.


Abstract:
  A recent paper by Mucino, Okon and Sudarsky attempts an assessment of the
Relational Interpretation of quantum mechanics. The paper presupposes
assumptions that are precisely those questioned in the Relational
Interpretation, thus undermining the value of the assessment.


Abstract:
  Any large-scale spiking neuromorphic system striving for complexity at the
level of the human brain and beyond will need to be co-optimized for
communication and computation. Such reasoning leads to the proposal for
optoelectronic neuromorphic platforms that leverage the complementary
properties of optics and electronics. Starting from the conjecture that future
large-scale neuromorphic systems will utilize integrated photonics and fiber
optics for communication in conjunction with analog electronics for
computation, we consider two possible paths towards achieving this vision. The
first is a semiconductor platform based on analog CMOS circuits and
waveguide-integrated photodiodes. The second is a superconducting approach that
utilizes Josephson junctions and waveguide-integrated superconducting
single-photon detectors. We discuss available devices, assess scaling
potential, and provide a list of key metrics and demonstrations for each
platform. Both platforms hold potential, but their development will diverge in
important respects. Semiconductor systems benefit from a robust fabrication
ecosystem and can build on extensive progress made in purely electronic
neuromorphic computing but will require III-V light source integration with
electronics at an unprecedented scale, further advances in ultra-low
capacitance photodiodes, and success from emerging memory technologies.
Superconducting systems place near theoretically minimum burdens on light
sources (a tremendous boon to one of the most speculative aspects of either
platform) and provide new opportunities for integrated, high-endurance synaptic
memory. However, superconducting optoelectronic systems will also contend with
interfacing low-voltage electronic circuits to semiconductor light sources, the
serial biasing of superconducting devices on an unprecedented scale, a less
mature fabrication ecosystem, and cryogenic infrastructure.


Abstract:
  Motivation: Manual curation of genome-scale reconstructions is laborious, yet
existing automated curation tools typically do not take species-specific
experimental data and manually refined genome annotations into account.
Results: We developed DEMETER, a COBRA Toolbox extension that enables the
efficient simultaneous refinement of thousands of draft genome-scale
reconstructions while ensuring adherence to the quality standards in the field,
agreement with available experimental data, and refinement of pathways based on
manually refined genome annotations. Availability: DEMETER and tutorials are
available at https://github.com/opencobra/cobratoolbox.


Abstract:
  This work proposes to learn fair low-rank tensor decompositions by
regularizing the Canonical Polyadic Decomposition factorization with the kernel
Hilbert-Schmidt independence criterion (KHSIC). It is shown, theoretically and
empirically, that a small KHSIC between a latent factor and the sensitive
features guarantees approximate statistical parity. The proposed algorithm
surpasses the state-of-the-art algorithm, FATR (Zhu et al., 2018), in
controlling the trade-off between fairness and residual fit on synthetic and
real data sets.


Abstract:
  Chinese character recognition has attracted much research interest due to its
wide applications. Although it has been studied for many years, some issues in
this field have not been completely resolved yet, e.g. the zero-shot problem.
Previous character-based and radical-based methods have not fundamentally
addressed the zero-shot problem since some characters or radicals in test sets
may not appear in training sets under a data-hungry condition. Inspired by the
fact that humans can generalize to know how to write characters unseen before
if they have learned stroke orders of some characters, we propose a
stroke-based method by decomposing each character into a sequence of strokes,
which are the most basic units of Chinese characters. However, we observe that
there is a one-to-many relationship between stroke sequences and Chinese
characters. To tackle this challenge, we employ a matching-based strategy to
transform the predicted stroke sequence to a specific character. We evaluate
the proposed method on handwritten characters, printed artistic characters, and
scene characters. The experimental results validate that the proposed method
outperforms existing methods on both character zero-shot and radical zero-shot
tasks. Moreover, the proposed method can be easily generalized to other
languages whose characters can be decomposed into strokes.


Abstract:
  Tracking objects of interest in a video is one of the most popular and widely
applicable problems in computer vision. However, with the years, a Cambrian
explosion of use cases and benchmarks has fragmented the problem in a multitude
of different experimental setups. As a consequence, the literature has
fragmented too, and now novel approaches proposed by the community are usually
specialised to fit only one specific setup. To understand to what extent this
specialisation is necessary, in this work we present UniTrack, a solution to
address five different tasks within the same framework. UniTrack consists of a
single and task-agnostic appearance model, which can be learned in a supervised
or self-supervised fashion, and multiple ``heads'' that address individual
tasks and do not require training. We show how most tracking tasks can be
solved within this framework, and that the same appearance model can be
successfully used to obtain results that are competitive against specialised
methods for most of the tasks considered. The framework also allows us to
analyse appearance models obtained with the most recent self-supervised
methods, thus extending their evaluation and comparison to a larger variety of
important problems.


Abstract:
  We consider the motion of a gyroscope on a closed timelike curve (CTC). A
gyroscope is identified with a unit-length spacelike vector - a spin-vector -
orthogonal to the tangent to the CTC, and satisfying the equations of
Fermi-Walker transport along the curve. We investigate the consequences of the
periodicity of the coefficients of the transport equations, which arise from
the periodicty of the CTC, which is assumed to be piecewise $C^2$. We show that
every CTC with period $T$ admits at least one $T-$periodic spin-vector.
Further, either every other spin-vector is $T-$periodic, or no others are. It
follows that gyroscopes carried by CTCs are either all $T-$periodic, or are
generically not $T-$periodic. We consider examples of spacetimes admitting
CTCs, and address the question of whether $T-$periodicity of gyroscopic motion
occurs generically or only on a negligible set for these CTCs. We discuss these
results from the perspective of principles of consistency in spacetimes
admitting CTCs.


Abstract:
  In this paper I formulate Minimal Requirements for Candidate Predictions in
quantum field theories, inspired by viewing the standard model as an effective
field theory. I then survey standard effective field theory regularization
procedures, to see if the vacuum expectation value of energy density
($\langle\rho\rangle$) is a quantity that meets these requirements. The verdict
is negative, leading to the conclusion that $\langle\rho\rangle$ is not a
physically significant quantity in the standard model. Rigorous extensions of
flat space quantum field theory eliminate $\langle\rho\rangle$ from their
conceptual framework, indicating that it lacks physical significance in the
framework of quantum field theory more broadly. This result has consequences
for problems in cosmology and quantum gravity, as it suggests that the correct
solution to the cosmological constant problem involves a revision of the vacuum
concept within quantum field theory.


Abstract:
  In this work, we obtain the Schr\"odinger equation solutions for the Varshni
potential using the Nikiforov-Uvarov method. The energy eigenvalues are
obtained in non-relativistic regime. The corresponding eigenfunction is
obtained in terms of Laguerre polynomials. We applied the present results to
calculate heavy-meson masses of charmonium and bottomonium .The mass spectra
for charmonium and bottomonium multiplets have predicted numerically. The
results are in good agreement with experimental data and the work of other
researchers.


Abstract:
  We determine explicitly and discuss in detail the effects of the joint
presence of a longitudinal and a transversal (random) magnetic field on the
phases of the Random Energy Model (REM) and its hierarchical generalization,
the GREM. Our results extent known results both in the classical case of
vanishing transversal field and in the quantum case for vanishing longitudinal
field. Following Derrida and Gardner, we argue that the longitudinal field has
to be implemented hierarchically also in the Quantum GREM. We show that this
ensures the shrinking of the spin glass phase in the presence of the magnetic
fields as also expected for the Quantum Sherrington-Kirkpatrick model.


Abstract:
  Starting from a discrete $C^*$-dynamical system $(\mathfrak{A}, \theta,
\omega_o)$, we define and study most of the main ergodic properties of the
crossed product $C^*$-dynamical system $(\mathfrak{A}\rtimes_\alpha\mathbb{Z},
\Phi_{\theta, u},\om_o\circ E)$,
$E:\mathfrak{A}\rtimes_\alpha\mathbb{Z}\rightarrow\ga$ being the canonical
conditional expectation of $\mathfrak{A}\rtimes_\alpha\mathbb{Z}$ onto
$\mathfrak{A}$, provided $\a\in\aut(\ga)$ commute with the $*$-automorphism
$\th$ up tu a unitary $u\in\ga$. Here, $\Phi_{\theta,
u}\in\aut(\mathfrak{A}\rtimes_\alpha\mathbb{Z})$ can be considered as the fully
noncommutative generalisation of the celebrated skew-product defined by H.
Anzai for the product of two tori in the classical case.


Abstract:
  Extended scalar sectors are a common feature of almost all beyond Standard
Model (SM) scenarios which, in fact, can address many of the SM shortcomings
solely on their own. While many beyond SM scenarios have lost their appeal due
to the non-observation of their predicted particles or are experimentally
inaccessible, scalar extensions are well within the reach of many current and
upcoming experiments. Here, we discuss the novel phenomenon of dark
CP-violation which was introduced for the first time in the context of
non-minimal Higgs frameworks with an extended dark sector and point out its
experimental probes.


Abstract:
  Electrically addressing spin systems is predicted to be a key component in
developing scalable semiconductor-based quantum processing architectures, to
enable fast spin qubit manipulation and long-distance entanglement via
microwave photons. However, single spins have no electric dipole, and therefore
a spin-orbit mechanism must be integrated in the qubit design. Here, we propose
to couple microwave photons to atomically precise donor spin qubit devices in
silicon using the hyperfine interaction intrinsic to donor systems and an
electrically-induced spin-orbit coupling. We characterise a one-electron system
bound to a tunnel-coupled donor pair (1P-1P) using the tight-binding method,
and then estimate the spin-photon coupling achievable under realistic
assumptions. We address the recent experiments on double quantum dots (DQDs) in
silicon and indicate the differences between DQD and 1P-1P systems. Our
analysis shows that it is possible to achieve strong spin-photon coupling in
1P-1P systems in realistic device conditions without the need for an external
magnetic field gradient.


Abstract:
  In the past years, EUV lithography scanner systems have entered High-Volume
Manufacturing for state-of-the-art Integrated Circuits (IC), with critical
dimensions down to 10 nm. This technology uses 13.5 nm EUV radiation, which is
shaped and transmitted through a near-vacuum H2 background gas. This gas is
excited into a low-density H2 plasma by the EUV radiation, as generated in
pulsed mode operation by the Laser-Produced Plasma (LPP) in the EUV Source.
Thus, in the confinement created by the walls and mirrors within the scanner
system, a reductive plasma environment is created that must be understood in
detail to maximize mirror transmission over lifetime and to minimize molecular
and particle contamination in the scanner. Besides the irradiated mirrors,
reticle and wafer, also the plasma and radical load to the surrounding
construction materials must be considered. This paper will provide an overview
of the EUV-induced plasma in scanner context. Special attention will be given
to the plasma parameters in a confined geometry, such as may be found in the
scanner area near the reticle. Also, the translation of these specific plasma
parameters to off-line setups will be discussed.


Abstract:
  There has been much interest in novel models of dark matter that exhibit
interesting behavior on galactic scales. A primary motivation is the observed
Baryonic Tully-Fisher Relation in which the mass of galaxies increases as the
quartic power of rotation speed. This scaling is not obviously accounted for by
standard cold dark matter. This has prompted the development of dark matter
models that exhibit some form of so-called MONDian phenomenology to account for
this galactic scaling, while also recovering the success of cold dark matter on
large scales. A beautiful example of this are the so-called superfluid dark
matter models, in which a complex bosonic field undergoes spontaneous symmetry
breaking on galactic scales, entering a superfluid phase with a 3/2 kinetic
scaling in the low energy effective theory, that mediates a long-ranged MONDian
force. In this work we examine the causality and locality properties of these
and other related models. We show that the Lorentz invariant completions of the
superfluid models exhibit high energy perturbations that violate global
hyperbolicity of the equations of motion in the MOND regime and can be
superluminal in other parts of phase space. We also examine a range of
alternate models, finding that they also exhibit forms of non-locality.


Abstract:
  In this paper, we propose a class of monitoring statistics for a mean shift
in a sequence of high-dimensional observations. Inspired by the recent
U-statistic based retrospective tests developed by Wang et al.(2019) and Zhang
et al.(2020), we advance the U-statistic based approach to the sequential
monitoring problem by developing a new adaptive monitoring procedure that can
detect both dense and sparse changes in real-time. Unlike Wang et al.(2019) and
Zhang et al.(2020), where self-normalization was used in their tests, we
instead introduce a class of estimators for $q$-norm of the covariance matrix
and prove their ratio consistency. To facilitate fast computation, we further
develop recursive algorithms to improve the computational efficiency of the
monitoring procedure. The advantage of the proposed methodology is demonstrated
via simulation studies and real data illustrations.


Abstract:
  We examine the elements of the balance equation of entropy in open quantum
evolutions, and their response as we go over from a Markovian to a
non-Markovian situation. In particular, we look at the heat current and entropy
production rate in the non-Markovian reduced evolution, and a Markovian limit
of the same, experienced by one of two interacting systems immersed in a
Markovian bath. The analysis naturally leads us to define a heat current
deficit and an entropy production rate deficit, being differences between the
global and local versions of the corresponding quantities. The investigation
brings us, in certain cases, to a complementarity of the timeintegrated heat
current deficit with the relative entropy of entanglement between the two
systems.


Abstract:
  In this thesis we discuss various classical problems in enumerative geometry.
We are focused on ideas and methods which can be used explicitly for practical
computations. Our approach is based on studying the limits of elliptic stable
envelopes with shifted equivariant or Kahler variables from elliptic cohomology
to K-theory.
  We prove that for a variety X we can obtain K-theoretic stable envelopes for
the variety X^G of the G-fixed points of X, where G is a cyclic group acting on
X preserving the symplectic form.
  We formalize the notion of symplectic duality, also known as 3-dimensional
mirror symmetry. We obtain a factorization theorem about the limit of elliptic
stable envelopes to a wall, which generalizes the result of M.Aganagic and
A.Okounkov. This approach allows us to extend the action of quantum groups,
quantum Weyl groups, R-matrices etc., to actions on the K-theory of the
symplectic dual variety. In the case of the Hilbert scheme of points in plane,
our results imply the conjectures of E.Gorsky and A.Negut.
  We propose a new approach to K-theoretic quantum difference equations.


Abstract:
  This paper deals with the output regulation problem of a linear
time-invariant system in the presence of sporadically available measurement
streams. A regulator with a continuous intersample injection term is proposed,
where the intersample injection is provided by a linear dynamical system and
the state of which is reset with the arrival of every new measurement updates.
The resulting system is augmented with a timer triggering an instantaneous
update of the new measurement and the overall system is then analyzed in a
hybrid system framework. With the Lyapunov based stability analysis, we offer
sufficient conditions to ensure the objectives of the output regulation problem
are achieved under intermittency of the measurement streams. Then, from the
solution to linear matrix inequalities, a numerically tractable regulator
design procedure is presented. Finally, with the help of an illustrative
example, the effectiveness of the theoretical results are validated.