Abstract:
  This paper describes the AS-NU systems for two tracks in MultiSpeaker
Multi-Style Voice Cloning Challenge (M2VoC). The first track focuses on using a
small number of 100 target utterances for voice cloning, while the second track
focuses on using only 5 target utterances for voice cloning. Due to the serious
lack of data in the second track, we selected the speaker most similar to the
target speaker from the training data of the TTS system, and used the speaker's
utterances and the given 5 target utterances to fine-tune our model. The
evaluation results show that our systems on the two tracks perform similarly in
terms of quality, but there is still a clear gap between the similarity score
of the second track and the similarity score of the first track.


Abstract:
  When a bug manifests in a user-facing application, it is likely to be exposed
through the graphical user interface (GUI). Given the importance of visual
information to the process of identifying and understanding such bugs, users
are increasingly making use of screenshots and screen-recordings as a means to
report issues to developers. However, when such information is reported en
masse, such as during crowd-sourced testing, managing these artifacts can be a
time-consuming process. As the reporting of screen-recordings in particular
becomes more popular, developers are likely to face challenges related to
manually identifying videos that depict duplicate bugs. Due to their graphical
nature, screen-recordings present challenges for automated analysis that
preclude the use of current duplicate bug report detection techniques. To
overcome these challenges and aid developers in this task, this paper presents
Tango, a duplicate detection technique that operates purely on video-based bug
reports by leveraging both visual and textual information. Tango combines
tailored computer vision techniques, optical character recognition, and text
retrieval. We evaluated multiple configurations of Tango in a comprehensive
empirical evaluation on 4,860 duplicate detection tasks that involved a total
of 180 screen-recordings from six Android apps. Additionally, we conducted a
user study investigating the effort required for developers to manually detect
duplicate video-based bug reports and compared this to the effort required to
use Tango. The results reveal that Tango's optimal configuration is highly
effective at detecting duplicate video-based bug reports, accurately ranking
target duplicate videos in the top-2 returned results in 83% of the tasks.
Additionally, our user study shows that, on average, Tango can reduce developer
effort by over 60%, illustrating its practicality.


Abstract:
  Federated Learning (FL) is an emerging learning scheme that allows different
distributed clients to train deep neural networks together without data
sharing. Neural networks have become popular due to their unprecedented
success. To the best of our knowledge, the theoretical guarantees of FL
concerning neural networks with explicit forms and multi-step updates are
unexplored. Nevertheless, training analysis of neural networks in FL is
non-trivial for two reasons: first, the objective loss function we are
optimizing is non-smooth and non-convex, and second, we are even not updating
in the gradient direction. Existing convergence results for gradient
descent-based methods heavily rely on the fact that the gradient direction is
used for updating. This paper presents a new class of convergence analysis for
FL, Federated Learning Neural Tangent Kernel (FL-NTK), which corresponds to
overparamterized ReLU neural networks trained by gradient descent in FL and is
inspired by the analysis in Neural Tangent Kernel (NTK). Theoretically, FL-NTK
converges to a global-optimal solution at a linear rate with properly tuned
learning parameters. Furthermore, with proper distributional assumptions,
FL-NTK can also achieve good generalization.


Abstract:
  To provide spectroscopic data for lowly charged tungsten ions relevant to
fusion research, this work focuses on the W8+ ion. Six visible spectra lines in
the range of 420-660 nm are observed with a compact electron-beam ion trap in
Shanghai. These lines are assigned to W8+ based on their intensity variations
as increasing electron-beam energy and the M1 line from the ground
configuration in W7+. Furthermore, transition energies are calculated for the
30 lowest levels of the 4f14 5s2 5p4, 4f13 5s2 5p5 and 4f12 5s2 5p6
configurations of W8+ by using the flexible atomic code (FAC) and GRASP
package, respectively. Reasonably good agreement is found between our two
independent atomic-structure calculations. The resulting atomic parameters are
adopted to simulate the spectra based on the collisional-radiative model
implemented in the FAC code. This assists us with identification of six strong
M1 transitions in 4f13 5s2 5p5 and 4f12 5s2 5p6 configurations from our
experiments


Abstract:
  During the night between 9 and 10 September 2017, multiple flash floods
associated to a heavy-precipitation event affected the town of Livorno, located
in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours,
associated with a return period higher than 200 years, caused all the largest
streams of the Livorno municipality to flood several areas of the town. We used
the limited-area Weather Research and Forecasting (WRF) model, in a
convection-permitting setup, to reconstruct the extreme event leading to the
flash floods. We evaluated possible forecasting improvements emerging from the
assimilation of local ground stations and X- and S-band radar data into the
WRF, using the configuration operational at the meteorological center of
Tuscany region (LaMMA) at the time of the event. Simulations were verified
against weather station observations, through an innovative method aimed at
disentangling the positioning and intensity errors of precipitation forecasts.
By providing more accurate descriptions of the low-level flow and a better
assessment of the atmospheric water vapour, the results demonstrate that
assimilating radar data improved the quantitative precipitation forecasts.


Abstract:
  Particle swarm optimization (PSO) is an iterative search method that moves a
set of candidate solution around a search-space towards the best known global
and local solutions with randomized step lengths. PSO frequently accelerates
optimization in practical applications, where gradients are not available and
function evaluations expensive. Yet the traditional PSO algorithm ignores the
potential knowledge that could have been gained of the objective function from
the observations by individual particles. Hence, we draw upon concepts from
Bayesian optimization and introduce a stochastic surrogate model of the
objective function. That is, we fit a Gaussian process to past evaluations of
the objective function, forecast its shape and then adapt the particle
movements based on it. Our computational experiments demonstrate that baseline
implementations of PSO (i.e., SPSO2011) are outperformed. Furthermore, compared
to, state-of-art surrogate-assisted evolutionary algorithms, we achieve
substantial performance improvements on several popular benchmark functions.
Overall, we find that our algorithm attains desirable properties for
exploratory and exploitative behavior.


Abstract:
  Two-point time-series data, characterized by baseline and follow-up
observations, are frequently encountered in health research. We study a novel
two-point time series structure without a control group, which is driven by an
observational routine clinical dataset collected to monitor key risk markers of
type-$2$ diabetes (T2D) and cardiovascular disease (CVD). We propose a
resampling approach called 'I-Rand' for independently sampling one of the two
time points for each individual and making inference on the estimated causal
effects based on matching methods. The proposed method is illustrated with data
from a service-based dietary intervention to promote a low-carbohydrate diet
(LCD), designed to impact risk of T2D and CVD. Baseline data contain a
pre-intervention health record of study participants, and health data after LCD
intervention are recorded at the follow-up visit, providing a two-point
time-series pattern without a parallel control group. Using this approach we
find that obesity is a significant risk factor of T2D and CVD, and an LCD
approach can significantly mitigate the risks of T2D and CVD. We provide code
that implements our method.


Abstract:
  In theory, the Lanczos algorithm generates an orthogonal basis of the
corresponding Krylov subspace. However, in finite precision arithmetic, the
orthogonality and linear independence of the computed Lanczos vectors is
usually lost quickly. In this paper we study a class of matrices and starting
vectors having a special nonzero structure that guarantees exact computations
of the Lanczos algorithm whenever floating point arithmetic satisfying the IEEE
754 standard is used. Analogous results are formulated also for a variant of
the conjugate gradient method that produces then almost exact results. The
results are extended to the Arnoldi algorithm, the nonsymmetric Lanczos
algorithm, the Golub-Kahan bidiagonalization, the block-Lanczos algorithm and
their counterparts for solving linear systems.


Abstract:
  We present a theoretical investigation of the resonant elastic scattering of
twisted light, carrying angular momentum, by partially stripped ions. Special
emphasis is placed on a question of whether the scattered radiation is also
twisted. In order to investigate such an "angular momentum transfer", we
develop an approach that allows us to find a quantum state of the final photon
without projecting it onto a detector state. A general expression for this
so--called \textit{evolved} state of outgoing radiation is derived and it can
be used to analyze the resonant scattering by any ion, independently of its
shell structure. Here, we illustrate our approach with the strong electric
dipole $n S_{0} \to n' P_{1} \to n S_{0}$ transitions, which play an important
role for the Gamma Factory project at CERN. For the incident Bessel light, the
scattered radiation is shown to be in a superposition of twisted modes with the
projections of the total angular momentum $m_f = 0, \pm 1$. The larger values
of $m_f$ can be efficiently generated by inducing transitions of higher
multiplicity. This angular momentum transfer, together with a remarkable cross
section that is many orders of magnitude larger than that of the backward
Compton scattering, makes the resonant photon scattering an effective tool for
the production of twisted x-- and even gamma--rays at the Gamma Factory
facility.


Abstract:
  We study double gamma ($\gamma\gamma$) decay nuclear matrix elements (NMEs)
for a wide range of nuclei from titanium to xenon, and explore their relation
to neutrinoless double-beta ($0\nu\beta\beta$) NMEs. To favor the comparison,
we focus on double-magnetic dipole transitions in the final $\beta\beta$
nuclei, in particular the $\gamma\gamma$ decay of the double isobaric analog of
the initial $\beta\beta$ state into the ground state. For the most probable
decay with equal-energy photons, our large-scale nuclear shell model results
show a good linear correlation between the $\gamma\gamma$ and $0\nu\beta\beta$
NMEs. Our analysis reveals that the correlation holds for $\gamma\gamma$
transitions driven by the spin or orbital angular momentum due to the dominance
of zero-coupled nucleon pairs, a feature common to $0\nu\beta\beta$ decay. Our
findings point out the potential of future $\gamma\gamma$ decay measurements to
constrain $0\nu\beta\beta$ NMEs, which are key to answer fundamental physics
questions based on $0\nu\beta\beta$ experiments.


Abstract:
  Fix a square-free monomial $m \in S = \mathbb{K}[x_1,\ldots,x_n]$. The
square-free principal Borel ideal generated by $m$, denoted ${\rm sfBorel}(m)$,
is the ideal generated by all the square-free monomials that can be obtained
via Borel moves from the monomial $m$. We give upper and lower bounds for the
Waldschmidt constant of ${\rm sfBorel}(m)$ in terms of the support of $m$, and
in some cases, exact values. For any rational $\frac{a}{b} \geq 1$, we show
that there exists a square-free principal Borel ideal with Waldschmidt constant
equal to $\frac{a}{b}$.


Abstract:
  A major goal for reduced-order models of unsteady fluid flows is to uncover
and exploit latent low-dimensional structure. Proper orthogonal decomposition
(POD) provides an energy-optimal linear basis to represent the flow kinematics,
but converges slowly for advection-dominated flows and tends to overestimate
the number of dynamically relevant variables. We show that nonlinear
correlations in the temporal POD coefficients can be exploited to identify the
underlying attractor, characterized by a minimal set of driving modes and a
manifold equation for the remaining modes. By viewing these nonlinear
correlations as an invariant manifold reduction, this least-order
representation can be used to stabilize POD-Galerkin models or as a state space
for data-driven model identification. In the latter case, we use sparse
polynomial regression to learn a compact, interpretable dynamical system model
from the time series of the active modal coefficients. We demonstrate this
perspective on a quasiperiodic shear-driven cavity flow and show that the
dynamics evolve on a torus generated by two independent Stuart-Landau
oscillators. These results emphasize the importance of nonlinear dimensionality
reduction to reveal underlying structure in complex flows.


Abstract:
  In this paper, we study the market-oriented online bi-objective service
scheduling problem for pleasingly parallel jobs with variable resources in
cloud environments, from the perspective of SaaS (Software-as-as-Service)
providers who provide job-execution services. The main process of scheduling
SaaS services in clouds is: a SaaS provider purchases cloud instances from IaaS
providers to schedule end users' jobs and charges users accordingly. This
problem has several particular features, such as the job-oriented end users,
the pleasingly parallel jobs with soft deadline constraints, the online
settings, and the variable numbers of resources. For maximizing both the
revenue and the user satisfaction rate, we design an online algorithm for SaaS
providers to optimally purchase IaaS instances and schedule pleasingly parallel
jobs. The proposed algorithm can achieve competitive objectives in polynomial
run-time. The theoretical analysis and simulations based on real-world Google
job traces as well as synthetic datasets validate the effectiveness and
efficiency of our algorithm.


Abstract:
  The hadronic contribution to the muon anomalous magnetic moment
$a_\mu=(g_\mu-2)/2$ has to be determined at the per-mille level for the
Standard Model prediction to match the expected final uncertainty of the
ongoing E989 experiment. That is 3 times better than the current precision from
the dispersive approach, and 5-15 times smaller than the uncertainty based on
the purely theoretical determinations from lattice QCD. So far the
stumbling-block is the large statistical error in the Monte Carlo evaluation of
the required correlation functions which can hardly be tamed by brute force. In
this talk we present our proposal to solve this problem by multi-level Monte
Carlo integration, a technique which reduces the variance of correlators
exponentially in the distance of the fields. We report the results of our
feasibility tests for the computation of the Hadronic Vacuum Polarization on a
lattice with a linear extension of 3~fm, a spacing of 0.065 fm, and a pion mass
of 270 MeV. Indeed the two-level integration makes the contribution to the
statistical error from long-distances de-facto negligible by accelerating its
inverse scaling with the cost of the simulation. These findings establish
multi-level Monte Carlo as a solid and efficient method for a precise lattice
determination of the hadronic contribution to $a_\mu$.


Abstract:
  Representations in the form of Symmetric Positive Definite (SPD) matrices
have been popularized in a variety of visual learning applications due to their
demonstrated ability to capture rich second-order statistics of visual data.
There exist several similarity measures for comparing SPD matrices with
documented benefits. However, selecting an appropriate measure for a given
problem remains a challenge and in most cases, is the result of a
trial-and-error process. In this paper, we propose to learn similarity measures
in a data-driven manner. To this end, we capitalize on the \alpha\beta-log-det
divergence, which is a meta-divergence parametrized by scalars \alpha and
\beta, subsuming a wide family of popular information divergences on SPD
matrices for distinct and discrete values of these parameters. Our key idea is
to cast these parameters in a continuum and learn them from data. We
systematically extend this idea to learn vector-valued parameters, thereby
increasing the expressiveness of the underlying non-linear measure. We conjoin
the divergence learning problem with several standard tasks in machine
learning, including supervised discriminative dictionary learning and
unsupervised SPD matrix clustering. We present Riemannian gradient descent
schemes for optimizing our formulations efficiently, and show the usefulness of
our method on eight standard computer vision tasks.


Abstract:
  We propose a new method to estimate ion escape from unmagnetized planets that
combines observations and models. Assuming that upstream solar wind conditions
are known, a computer model of the interaction between the solar wind and the
planet is executed for different ionospheric ion production rates. This results
in different amounts of mass loading of the solar wind. Then we obtain the ion
escape rate from the model run that best fit observations of the bow shock
location. As an example of the method we estimate the heavy ion escape from
Mars on 2015-03-01 to be $2\cdot 10^{24}$ ions per second, using a hybrid
plasma model and observations by MAVEN and Mars Express. This method enables
studies of how escape depend on different parameters, and also escape rates
during extreme solar wind conditions, applicable to studies of escape in the
early solar system, and at exoplanets.


Abstract:
  Ontology-mediated query answering is a popular paradigm for enriching answers
to user queries with background knowledge. For querying the absence of
information, however, there exist only few ontology-based approaches. Moreover,
these proposals conflate the closed-domain and closed-world assumption, and
therefore are not suited to deal with the anonymous objects that are common in
ontological reasoning. Many real-world applications, like processing electronic
health records (EHRs), also contain a temporal dimension, and require efficient
reasoning algorithms. Moreover, since medical data is not recorded on a regular
basis, reasoners must deal with sparse data with potentially large temporal
gaps. Our contribution consists of two main parts: In the first part we
introduce a new closed-world semantics for answering conjunctive queries with
negation over ontologies formulated in the description logic ELHb, which is
based on the minimal canonical model. We propose a rewriting strategy for
dealing with negated query atoms, which shows that query answering is possible
in polynomial time in data complexity. In the second part, we extend this
minimal-world semantics for answering metric temporal conjunctive queries with
negation over the lightweight temporal logic TELHb and obtain similar
rewritability and complexity results. This paper is under consideration in
Theory and Practice of Logic Programming (TPLP).


Abstract:
  Since the earliest conceptualizations by Lee and Markus, and Propoi in the
1960s, Model Predictive Control (MPC) has become a major success story of
systems and control with respect to industrial impact and with respect to
continued and wide-spread research interest. The field has evolved from
conceptually simple linear-quadratic (convex) settings in discrete and
continuous time to nonlinear and distributed settings including hybrid,
stochastic, and infinite-dimensional systems. Put differently, essentially the
entire spectrum of dynamic systems can be considered in the MPC framework with
respect to both -- system theoretic analysis and tailored numerics. Moreover,
recent developments in machine learning also leverage MPC concepts and
learning-based and data-driven MPC have become highly active research areas.
  However, this evident and continued success renders it increasingly complex
to live up to industrial expectations while enabling graduate students for
state-of-the-art research in teaching MPC. Hence, this position paper attempts
to trigger a discussion on teaching MPC. To lay the basis for a fruitful
debate, we subsequently investigate the prospect of covering MPC in
undergraduate courses; we comment on teaching textbooks; and we discuss the
increasing complexity of research-oriented graduate teaching of~MPC.


Abstract:
  While supervised learning is widely used for perception modules in
conventional autonomous driving solutions, scalability is hindered by the huge
amount of data labeling needed. In contrast, while end-to-end architectures do
not require labeled data and are potentially more scalable, interpretability is
sacrificed. We introduce a novel architecture that is trained in a fully
self-supervised fashion for simultaneous multi-step prediction of space-time
cost map and road dynamics. Our solution replaces the manually designed cost
function for motion planning with a learned high dimensional cost map that is
naturally interpretable and allows diverse contextual information to be
integrated without manual data labeling. Experiments on real world driving data
show that our solution leads to lower number of collisions and road violations
in long planning horizons in comparison to baselines, demonstrating the
feasibility of fully self-supervised prediction without sacrificing either
scalability or interpretability.


Abstract:
  Autoencoders are among the earliest introduced nonlinear models for
unsupervised learning. Although they are widely adopted beyond research, it has
been a longstanding open problem to understand mathematically the feature
extraction mechanism that trained nonlinear autoencoders provide.
  In this work, we make progress in this problem by analyzing a class of
two-layer weight-tied nonlinear autoencoders in the mean field framework. Upon
a suitable scaling, in the regime of a large number of neurons, the models
trained with stochastic gradient descent are shown to admit a mean field
limiting dynamics. This limiting description reveals an asymptotically precise
picture of feature learning by these models: their training dynamics exhibit
different phases that correspond to the learning of different principal
subspaces of the data, with varying degrees of nonlinear shrinkage dependent on
the $\ell_{2}$-regularization and stopping time. While we prove these results
under an idealized assumption of (correlated) Gaussian data, experiments on
real-life data demonstrate an interesting match with the theory.
  The autoencoder setup of interests poses a nontrivial mathematical challenge
to proving these results. In this setup, the "Lipschitz" constants of the
models grow with the data dimension $d$. Consequently an adaptation of previous
analyses requires a number of neurons $N$ that is at least exponential in $d$.
Our main technical contribution is a new argument which proves that the
required $N$ is only polynomial in $d$. We conjecture that $N\gg d$ is
sufficient and that $N$ is necessarily larger than a data-dependent intrinsic
dimension, a behavior that is fundamentally different from previously studied
setups.


Abstract:
  We demonstrate the generation of a trapped ion oscillator having large
oscillation amplitude of $16.9~{\rm \mu m}$. Applying an offset voltage to the
ion trap electrode helped achieve a displacement of the trap center within the
time scale of 5 ns. The fluorescence dynamics of the ion were analyzed after
the displacement to estimate the oscillation amplitude. The realized trap
displacement is one order magnitude larger than that achieved in the previous
work. Thus, this result is an important step toward the realization of a
gyroscope using a single trapped ion.


Abstract:
  Boundary Representations (B-Reps) are the industry standard in 3D Computer
Aided Design/Manufacturing (CAD/CAM) and industrial design due to their
fidelity in representing stylistic details. However, they have been ignored in
the 3D style research. Existing 3D style metrics typically operate on meshes or
pointclouds, and fail to account for end-user subjectivity by adopting fixed
definitions of style, either through crowd-sourcing for style labels or
hand-crafted features. We propose UVStyle-Net, a style similarity measure for
B-Reps that leverages the style signals in the second order statistics of the
activations in a pre-trained (unsupervised) 3D encoder, and learns their
relative importance to a subjective end-user through few-shot learning. Our
approach differs from all existing data-driven 3D style methods since it may be
used in completely unsupervised settings, which is desirable given the lack of
publicly available labelled B-Rep datasets. More importantly, the few-shot
learning accounts for the inherent subjectivity associated with style. We show
quantitatively that our proposed method with B-Reps is able to capture stronger
style signals than alternative methods on meshes and pointclouds despite its
significantly greater computational efficiency. We also show it is able to
generate meaningful style gradients with respect to the input shape, and that
few-shot learning with as few as two positive examples selected by an end-user
is sufficient to significantly improve the style measure. Finally, we
demonstrate its efficacy on a large unlabeled public dataset of CAD models.
Source code and data will be released in the future.


Abstract:
  In the absence of any hints of new physics in LHC, TeV dark matter candidates
interacting through electroweak force (EWDM) are still highly motivated. We
extend the Standard Model by adding an arbitrary SU(2) DM multiplet in
non-chiral representation. In addition to the well-known real representation
which has no coupling to the nuclei at tree level, the complex representation
can lead to a new DM candidate providing that one includes a higher dimensional
mass-splitting operator, which survives the current direct detection bounds.
Since the masses of gauge mediators are light compared to the dark particles,
Sommerfeld effect is dominant and affects the value of annihilation
cross-section in both the early universe and current time. We computed the
relic abundance through freeze-out mechanism in order to determine DM mass.
Gamma ray fluxes in our galaxy and dwarf satellites provide a promising tool to
probe EWDM theory. We confronted the four fermionic representations of the
model with the latest astrophysical observations. It can be concluded that the
model passes the current experimental constraints successfully, and it is
accessible to future observations.


Abstract:
  We propose a method to accurately obtain the ratio of tumor cells over an
entire histological slide. We use deep fully convolutional neural network
models trained to detect and classify cells on images of H&E-stained tissue
sections. Pathologists' labels consisting of exhaustive nuclei locations and
tumor regions were used to trained the model in a supervised fashion. We show
that combining two models, each working at a different magnification allows the
system to capture both cell-level details and surrounding context to enable
successful detection and classification of cells as either tumor-cell or
normal-cell. Indeed, by conditioning the classification of a single cell on a
multi-scale context information, our models mimic the process used by
pathologists who assess cell neoplasticity and tumor extent at different
microscope magnifications. The ratio of tumor cells can then be readily
obtained by counting the number of cells in each class. To analyze an entire
slide, we split it into multiple tiles that can be processed in parallel. The
overall tumor cell ratio can then be aggregated. We perform experiments on a
dataset of 100 slides with lung tumor specimens from both resection and tissue
micro-array (TMA). We train fully-convolutional models using heavy data
augmentation and batch normalization. On an unseen test set, we obtain an
average mean absolute error on predicting the tumor cell ratio of less than 6%,
which is significantly better than the human average of 20% and is key in
properly selecting tissue samples for recent genetic panel tests geared at
prescribing targeted cancer drugs. We perform ablation studies to show the
importance of training two models at different magnifications and to justify
the choice of some parameters, such as the size of the receptive field.


Abstract:
  From a physical/dynamical system perspective, the potential well represents
the proportional mass of points that escape the neighbourhood of a given point.
In the last 20 years, several works have shown the importance of this quantity
to obtain precise approximations for several recurrence time distributions in
mixing stochastic processes and dynamical systems. Besides providing a review
of the different scaling factors used in the literature in recurrence times,
the present work contributes with two new results: (1) for $\phi$-mixing and
$\psi$-mixing processes, we give a new exponential approximation for hitting
and return times using the potential well as scaling parameter. The error terms
are explicit and sharp. (2) We analyse the uniform positivity of the potential
well.


Abstract:
  This paper proposes a question-answering (QA) benchmark for spatial reasoning
on natural language text which contains more realistic spatial phenomena not
covered by prior work and is challenging for state-of-the-art language models
(LM). We propose a distant supervision method to improve on this task.
Specifically, we design grammar and reasoning rules to automatically generate a
spatial description of visual scenes and corresponding QA pairs. Experiments
show that further pretraining LMs on these automatically generated data
significantly improves LMs' capability on spatial understanding, which in turn
helps to better solve two external datasets, bAbI, and boolQ. We hope that this
work can foster investigations into more sophisticated models for spatial
reasoning over text.


Abstract:
  This work presents a method for designing the weighting parameter required by
Wiener-based binaural noise reduction methods. This parameter establishes the
desired tradeoff between noise reduction and binaural cue preservation in
hearing aid applications. The proposed strategy was specially derived for the
preservation of interaural level difference, interaural time difference and
interaural coherence binaural cues. It is defined as a function of the average
input noise power at the microphones, providing robustness against the
influence of joint changes in noise and speech power (Lombard effect), as well
as to signal to noise ratio (SNR) variations. A theoretical framework, based on
the mathematical definition of the homogeneity degree, is presented and applied
to a generic augmented Wiener-based cost function. The theoretical insights
obtained are supported bycomputational simulations and psychoacoustic
experiments using the multichannel Wiener filter with interaural transfer
function preservation technique (MWF-ITF), as a case study. Statistical
analysis indicates that the proposed dynamic structure for the weighting
parameter and the design method of its fixed part provide significant
robustness against changes in the original binaural cues of both speech and
residual noise, at the cost of a small decrease in the noise reduction
performance, as compared to the use of a purely fixed weighting parameter.


Abstract:
  Quantum networks are complex systems formed by the interaction among quantum
processors through quantum channels. Analogous to classical computer networks,
quantum networks allow for the distribution of quantum computation among
quantum computers. In this work, we describe a quantum walk protocol to perform
distributed quantum computing in a quantum network. The protocol uses a quantum
walk as a quantum control signal to perform distributed quantum operations. We
consider a generalization of the discrete-time coined quantum walk model that
accounts for the interaction between a quantum walker system in the network
graph with quantum registers inside the network nodes. The protocol logically
captures distributed quantum computing, abstracting hardware implementation and
the transmission of quantum information through channels. Control signal
transmission is mapped to the propagation of the walker system across the
network, while interactions between the control layer and the quantum registers
are embedded into the application of coin operators. We demonstrate how to use
the quantum walker system to perform a distributed CNOT operation, which shows
the universality of the protocol for distributed quantum computing.
Furthermore, we apply the protocol to the task of entanglement distribution in
a quantum network.


Abstract:
  Reweighting adversarial data during training has been recently shown to
improve adversarial robustness, where data closer to the current decision
boundaries are regarded as more critical and given larger weights. However,
existing methods measuring the closeness are not very reliable: they are
discrete and can take only a few values, and they are path-dependent, i.e.,
they may change given the same start and end points with different attack
paths. In this paper, we propose three types of probabilistic margin (PM),
which are continuous and path-independent, for measuring the aforementioned
closeness and reweighting adversarial data. Specifically, a PM is defined as
the difference between two estimated class-posterior probabilities, e.g., such
the probability of the true label minus the probability of the most confusing
label given some natural data. Though different PMs capture different geometric
properties, all three PMs share a negative correlation with the vulnerability
of data: data with larger/smaller PMs are safer/riskier and should have
smaller/larger weights. Experiments demonstrate that PMs are reliable
measurements and PM-based reweighting methods outperform state-of-the-art
methods.


Abstract:
  This is loosely a continuation of the author's previous paper
arXiv:1802.09496. In the first part, given a fibered variety, we pull back the
Leray filtration to the Chow group, and use this to give some criteria for the
Hodge and Tate conjectures to hold for such varieties. In the second part, we
show that the Hodge conjecture holds for a good desingularization of a self
fibre product of a non-isotrivial elliptic surface. We also show that the Hodge
and Tate conjectures hold for natural families of abelian varieties
parameterized by certain Shimura curves. This uses Zucker's description of the
mixed Hodge structure on the cohomology of a variation of Hodge structures on a
curve, along with appropriate "vanishing" theorems.


Abstract:
  The aim of this work is to investigate the occurrence of two different
spontaneous symmetry breakings in two levels of the description of
fermion-scalar field model by means of a set of gap equations and of a
background field effective action. For that, we consider the Yukawa model, as a
toy model for interactions between non-massive fermions intermediated by a
self-interacting real scalar field. This model has at stakes two symmetries at
the classical level that, as we know, might be spontaneously or dynamically
broken with mass generation for the particles. The auxiliary field method is
considered and it produces coupled renormalized gap equations. The effective
action is then written with quantum contributions by external background fields
method. We brought to light how the renormalization procedure affects the
physical gaps, investigate its proprieties, and discuss the connection of the
auxiliary fields not only to define composite states but also to compute the
effective action.


Abstract:
  Since the first experimental observation of all-optical switching phenomena,
intensive research has been focused on finding suitable magnetic systems that
can be integrated as storage elements within spintronic devices and whose
magnetization can be controlled through ultra-short single laser pulses. We
report here atomistic spin simulations of all-optical switching in multilayered
structures alternating n monolayers of Tb and m monolayers of Co. By using a
two temperature model, we numerically calculate the thermal variation of the
magnetization of each sublattice as well as the magnetization dynamics of
[Tbn/Com] multilayers upon incidence of a single laser pulse. In particular,
the condition to observe thermally-induced magnetization switching is
investigated upon varying systematically both the composition of the sample
(n,m) and the laser fluence. The samples with one monolayer of Tb as [Tb1/Co2]
and [Tb1/Co3] are showing thermally induced magnetization switching above a
fluence threshold. The reversal mechanism is mediated by the residual
magnetization of the Tb lattice while the Co is fully demagnetized in agreement
with the models developed for ferrimagnetic alloys. The switching is however
not fully deterministic but the error rate can be tuned by the damping
parameter. Increasing the number of monolayers the switching becomes completely
stochastic. The intermixing at the Tb/Co interfaces appears to be a promising
way to reduce the stochasticity. These results predict for the first time the
possibility of TIMS in [Tb/Co] multilayers and suggest the occurrence of
sub-picosecond magnetization reversal using single laser pulses.


Abstract:
  In completely generic four-dimensional gauge-Yukawa theories, the
renormalization group $ \beta $-functions are known to the 3-2-2 loop order in
gauge, Yukawa, and quartic couplings, respectively. It does, however, remain
difficult to apply these results to specific models without the use of
dedicated computer tools. We describe a procedure for extracting $ \beta
$-functions using the general results and introduce RGBeta, a dedicated
Mathematica package for extracting the $ \overline{\text{MS}} $ $ \beta
$-functions in broad classes of models. The package and example notebooks are
available from the GitHub repository at https://github.com/aethomsen/RGBeta .


Abstract:
  The cosmological implications of the Covariant Canonical Gauge Theory of
Gravity (CCGG) are investigated. CCGG is a Palatini theory derived from first
principles using the canonical transformation formalism in the covariant
Hamiltonian formulation. The Einstein-Hilbert theory is thereby extended by a
quadratic Riemann-Cartan term in the Lagrangian. Moreover, the requirement of
covariant conservation of the stress-energy tensor leads to necessary presence
of torsion. In the Friedman universe that promotes the cosmological constant to
a time-dependent function, and gives rise to a geometrical correction with the
EOS of dark radiation. The resulting cosmology, compatible with the
$\Lambda$CDM parameter set, encompasses bounce and bang scenarios with graceful
exits into the late dark energy era. Testing those scenarios against low-z
observations shows that CCGG is a viable theory.


Abstract:
  Manual and computer aided methods to perform semen analysis are
time-consuming, requires extensive training and prone to human error. The use
of classical machine learning and deep learning based methods using videos to
perform semen analysis have yielded good results. The state-of-the-art method
uses regular convolutional neural networks to perform quality assessments on a
video of the provided sample. In this paper we propose an improved deep
learning based approach using three-dimensional convolutional neural networks
to predict sperm motility from microscopic videos of the semen sample. We make
use of the VISEM dataset that consists of video and tabular data of semen
samples collected from 85 participants. We were able to achieve good results
from significantly less data points. Our models indicate that deep learning
based automatic semen analysis may become a valuable and effective tool in
fertility and IVF labs.


Abstract:
  In this work, we investigate differential chaos shift keying (DCSK), a
communication-based waveform, in the context of wireless power transfer (WPT).
Particularly, we present a DCSK-based WPT architecture, that employs an analog
correlator at the receiver in order to boost the energy harvesting (EH)
performance. By taking into account the nonlinearities of the EH process, we
derive closed-form analytical expressions for the peak-to-average-power-ratio
of the received signal as well as the harvested power. Nontrivial design
insights are provided, where it is shown how the parameters of the transmitted
waveform affects the EH performance. Furthermore, it is demonstrated that the
employment of a correlator at the receiver achieves significant EH gains in
DCSK-based WPT systems.


Abstract:
  We study primordial non-gaussianity in supersolid inflation. The dynamics of
supersolid is formulated in terms of an effective field theory based on four
scalar fields with a shift symmetric action minimally coupled with gravity. In
the scalar sector, there are two phonon-like excitations with a kinetic mixing
stemming from the completely spontaneous breaking of diffeomorphism. In a
squeezed configuration, $f_{\text{NL}}$ of scalar perturbations is angle
dependent and not proportional to slow-roll parameters showing a blunt
violation of the Maldacena consistency relation. Contrary to solid inflation,
the violation persists even after an angular average and generically the amount
of non-gaussianity is significant. During inflation, non-gaussianity in the TSS
and TTS sector is enhanced in the same region of the parameters space where the
secondary production of gravitational waves is sizeable enough to enter in the
sensitivity region of LISA, while the scalar $f_{\text{NL}}$ is still within
the current experimental limits.


Abstract:
  Research in the field of malware classification often relies on machine
learning models that are trained on high-level features, such as opcodes,
function calls, and control flow graphs. Extracting such features is costly,
since disassembly or code execution is generally required. In this paper, we
conduct experiments to train and evaluate machine learning models for malware
classification, based on features that can be obtained without disassembly or
execution of code. Specifically, we visualize malware samples as images and
employ image analysis techniques. In this context, we focus on two machine
learning models, namely, Convolutional Neural Networks (CNN) and Extreme
Learning Machines (ELM). Surprisingly, we find that ELMs can achieve accuracies
on par with CNNs, yet ELM training requires less than~2\%\ of the time needed
to train a comparable CNN.


Abstract:
  A model is developed, based on the density functional perturbation theory and
the inverse Kohn-Sham method, that can be used to improve relativistic nuclear
energy density functionals towards an exact but unknown Kohn-Sham
exchange-correlation functional. The improved functional is determined by
empirical exact ground-state densities of finite systems. A test of the model
and an illustrative calculation are performed, starting from two different
approximate functionals, to reproduce the parameters and density dependence of
a target functional, using exact ground-state densities of symmetric N=Z
systems.


Abstract:
  We consider the problem of finite resistance $R$ in superconducting films
with geometry of a strip of width $W$ near zero temperature. The resistance is
generated by vortex configurations of the phase field. In the first type of
process, quantum phase slip, the vortex worldline in 2+1 dimensional space-time
is space-like (i.e., the superconducting phase winds in time and space). In the
second type, vortex tunneling, the worldline is time-like (i.e., the phase
winds in the two spatial directions) and connects opposite edges of the film.
For moderately disordered samples, processes of second type favor a train of
vortices, each of which tunnels only across a fraction of the sample.
Optimization with respect to the number of vortices yields a tunneling distance
of the order of the coherence length $\xi$, and the train of vortices becomes
equivalent to a quantum phase slip. Based on this theory, we find the
resistance $\ln R \sim -g W/\xi$, where $g$ is the dimensionless normal-state
conductance. Incorporation of quantum fluctuations indicates a quantum phase
transition to an insulating state for $g \lesssim 1$.


Abstract:
  We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging.
Previous solutions have sought to explicitly recover the 3D geometry (e.g., as
point clouds) or voxel density (e.g., within a pre-defined volume) of the
hidden scene. In contrast, inspired by the recent Neural Radiance Field (NeRF)
approach, we use a multi-layer perceptron (MLP) to represent the neural
transient field or NeTF. However, NeTF measures the transient over spherical
wavefronts rather than the radiance along lines. We therefore formulate a
spherical volume NeTF reconstruction pipeline, applicable to both confocal and
non-confocal setups. Compared with NeRF, NeTF samples a much sparser set of
viewpoints (scanning spots) and the sampling is highly uneven. We thus
introduce a Monte Carlo technique to improve the robustness in the
reconstruction. Comprehensive experiments on synthetic and real datasets
demonstrate NeTF provides higher quality reconstruction and preserves fine
details largely missing in the state-of-the-art.


Abstract:
  The horizontal orientation angle and vertical inclination angle of an
elongated subsurface object are key parameters for object identification and
imaging in ground penetrating radar (GPR) applications. Conventional methods
can only extract the horizontal orientation angle or estimate both angles in
narrow ranges due to limited polarimetric information and detection capability.
To address these issues, this letter, for the first time, explores the
possibility of leveraging neural networks with multi-polarimetric GPR data to
estimate both angles of an elongated subsurface object in the entire spatial
range. Based on the polarization-sensitive characteristic of an elongated
object, we propose a multi-polarization aggregation and selection neural
network (MASNet), which takes the multi-polarimetric radargrams as inputs,
integrates their characteristics in the feature space, and selects
discriminative features of reflected signal patterns for accurate orientation
estimation. Numerical results show that our proposed MASNet achieves high
estimation accuracy with an angle estimation error of less than 5{\deg}. The
promising results obtained by the proposed method encourages one to think of
new solutions for GPR-related tasks by integrating multi-polarization
information with deep learning techniques.


Abstract:
  Most existing metric learning methods focus on learning a similarity or
distance measure relying on similar and dissimilar relations between sample
pairs. However, pairs of samples cannot be simply identified as similar or
dissimilar in many real-world applications, e.g., multi-label learning, label
distribution learning. To this end, relation alignment metric learning (RAML)
framework is proposed to handle the metric learning problem in those scenarios.
But RAML learn a linear metric, which can't model complex datasets. Combining
with deep learning and RAML framework, we propose a hierarchical relationship
alignment metric leaning model HRAML, which uses the concept of relationship
alignment to model metric learning problems under multiple learning tasks, and
makes full use of the consistency between the sample pair relationship in the
feature space and the sample pair relationship in the label space. Further we
organize several experiment divided by learning tasks, and verified the better
performance of HRAML against many popular methods and RAML framework.


Abstract:
  The chiral anomaly underlies a broad number of phenomena, from enhanced
electronic transport in topological metals to anomalous currents in the
quark-gluon plasma. The discovery of topological states of matter in
non-Hermitian systems -- effective descriptions of dissipative systems --
raises the question of whether there are anomalous conservation laws that
remain unaccounted for. To answer this question, we consider both $1+1$ and
$3+1$ dimensions, presenting a unified formulation to calculate anomalous
responses in Hermitianized, anti-Hermitianized and non-Hermitian systems of
massless electrons with complex Fermi velocities coupled to non-Hermitian gauge
fields. Our results indicate that the quantum conservation laws of chiral
currents of non-Hermitian systems are not related to those in Hermitianized and
anti-Hermitianized systems, as would be expected classically, due to novel
anomalous terms that we derive. These may have implications for a broad class
of emerging experimental systems that realize non-Hermitian Hamiltonians.


Abstract:
  We consider the classical Yaglom limit theorem for the Neutron Branching
Process (NBP) in the setting that the mean semigroup is critical, i.e. its
leading eigenvalue is zero. We show that the law of the process conditioned on
survival is asymptotically equivalent to an exponential distribution. As part
of the proof, we also show that the probability of survival decays inversely
proportionally to time. Although Yaglom limit theorems have recently been
handled in the setting of spatial branching processes and superprocesses, [22,
23], as well as in the setting of isotropic homogeneous Neutron Branching
Processes, [21], our approach and the main novelty of this work is based around
a precise result for the scaled asymptotics for the k-th martingale moments of
the NBP (rather than the Yaglom limit itself). Our proof of the asymptotic
martingale moments turns out to offer a general approach to asymptotic
martingale moments of critical branching Markov processes with a non-local
branching mechanism. Indeed this is the context in which we give both our
moment proofs and the Yaglom limit.


Abstract:
  The field of mathematical morphology offers well-studied techniques for image
processing. In this work, we view morphological operations through the lens of
persistent homology, a tool at the heart of the field of topological data
analysis. We demonstrate that morphological operations naturally form a
multiparameter filtration and that persistent homology can then be used to
extract information about both topology and geometry in the images as well as
to automate methods for optimizing the study and rendering of structure in
images. For illustration, we apply this framework to analyze noisy binary,
grayscale, and color images.


Abstract:
  Artificial intelligence (AI) algorithms using deep learning have advanced the
classification of skin disease images; however these algorithms have been
mostly applied "in silico" and not validated clinically. Most dermatology AI
algorithms perform binary classification tasks (e.g. malignancy versus benign
lesions), but this task is not representative of dermatologists' diagnostic
range. The American Academy of Dermatology Task Force on Augmented Intelligence
published a position statement emphasizing the importance of clinical
validation to create human-computer synergy, termed augmented intelligence
(AuI). Liu et al's recent paper, "A deep learning system for differential
diagnosis of skin diseases" represents a significant advancement of AI in
dermatology, bringing it closer to clinical impact. However, significant issues
must be addressed before this algorithm can be integrated into clinical
workflow. These issues include accurate and equitable model development,
defining and assessing appropriate clinical outcomes, and real-world
integration.


Abstract:
  Complex A is a high-velocity cloud that is traversing through the Galactic
halo toward the Milky Way's disk. We combine both new and archival Green Bank
Telescope observations to construct a spectroscopically resolved HI~21-cm map
of this entire complex at a $17.1\lesssim\log{\left({N_{\rm HI},\,1\sigma}/{\rm
cm}^{-2}\right)}\lesssim17.9$ sensitivity for a ${\rm FWHM}=20~{\rm km}\,{\rm
s}^{-1}$ line and $\Delta\theta=9.1\,{\rm arcmins}$ or $17\lesssim\Delta
d_{\theta}\lesssim30~\rm pc$ spatial resolution. We find that that Complex A is
has a Galactic standard of rest frame velocity gradient of $\Delta\rm
v_{GSR}/\Delta L=25~{\rm km}\,{\rm s}^{-1}/{\rm kpc}$ along its length, that it
is decelerating at a rate of $\langle a\rangle_{\rm GSR}=55~{\rm km}/{\rm
yr}^2$, and that it will reach the Galactic plane in $\Delta t\lesssim70~{\rm
Myrs}$ if it can survive the journey. We have identify numerous signatures of
gas disruption. The elongated and multi-core structure of Complex A indicates
that either thermodynamic instabilities or shock-cascade processes have
fragmented this stream. We find Rayleigh-Taylor fingers on the low-latitude
edge of this HVC; many have been pushed backward by ram-pressure stripping. On
the high-latitude side of the complex, Kelvin-Helmholtz instabilities have
generated two large wings that extend tangentially off Complex A. The tips of
these wings curve slightly forward in the direction of motion and have an
elevated \hi\ column density, indicating that these wings are forming
Rayleigh-Taylor globules at their tips and that this gas is becoming entangled
with unseen vortices in the surrounding coronal gas. These observations provide
new insights on the survivability of low-metallicity gas streams that are
accreting onto $L_\star$ galaxies.


Abstract:
  In many applications, it is desirable that a classifier not only makes
accurate predictions, but also outputs calibrated posterior probabilities.
However, many existing classifiers, especially deep neural network classifiers,
tend to be uncalibrated. Post-hoc calibration is a technique to recalibrate a
model by learning a calibration map. Existing approaches mostly focus on
constructing calibration maps with low calibration errors, however, this
quality is inadequate for a calibrator being useful. In this paper, we
introduce two constraints that are worth consideration in designing a
calibration map for post-hoc calibration. Then we present Meta-Cal, which is
built from a base calibrator and a ranking model. Under some mild assumptions,
two high-probability bounds are given with respect to these constraints.
Empirical results on CIFAR-10, CIFAR-100 and ImageNet and a range of popular
network architectures show our proposed method significantly outperforms the
current state of the art for post-hoc multi-class classification calibration.


Abstract:
  In matrix theory and numerical analysis there are two very famous and
important results. One is Gersgorin circle theorem, the other is strictly
diagonally dominant theorem. They have important application and research
value, and have been widely used and studied. In this paper, we investigate
generalized diagonally dominant matrices and matrix eigenvalue inclusion
regions. A class of G-function pairs is proposed, which extends the concept of
G-functions. Thirteen kind of G-function pairs are established. Their
properties and characteristics are studied. By using these special G-function
pairs, we construct a large number of sufficient and necessary conditions for
strictly diagonally dominant matrices and matrix eigenvalue inclusion regions.
These conditions and regions are composed of different combinations of
G-function pairs, deleted absolute row sums and column sums of matrices. The
results extend, include and are better than some classical results.


Abstract:
  In this paper, first we propose a novel hybrid renewable energy-based
solution for frost prevention in horticulture applications involving active
heaters. Then, we develop a multi-objective robust optimization-based
formulation to optimize the distribution of a given number of active heaters in
a given large-scale orchard. The objectives are to optimally heat the orchard
by the proposed frost prevention system and to minimize the total length of the
energy distribution pipe network (which is directly related to the installation
cost and the cost of energy losses during energy transfer). Next, the resulting
optimization problem is approximated using a discretization scheme. A case
study is provided to give an idea of the potential savings using the proposed
optimization method compared to the result from a heuristic-based design, which
showed a 24.13% reduction in the total pipe length and a 54.29% increase in
frost prevention.


Abstract:
  Machine learning (ML) is an important part of modern data science
applications. Data scientists today have to manage the end-to-end ML life cycle
that includes both model training and model serving, the latter of which is
essential, as it makes their works available to end-users. Systems for model
serving require high performance, low cost, and ease of management. Cloud
providers are already offering model serving options, including managed
services and self-rented servers. Recently, serverless computing, whose
advantages include high elasticity and fine-grained cost model, brings another
possibility for model serving.
  In this paper, we study the viability of serverless as a mainstream model
serving platform for data science applications. We conduct a comprehensive
evaluation of the performance and cost of serverless against other model
serving systems on two clouds: Amazon Web Service (AWS) and Google Cloud
Platform (GCP). We find that serverless outperforms many cloud-based
alternatives with respect to cost and performance. More interestingly, under
some circumstances, it can even outperform GPU-based systems for both average
latency and cost. These results are different from previous works' claim that
serverless is not suitable for model serving, and are contrary to the
conventional wisdom that GPU-based systems are better for ML workloads than
CPU-based systems. Other findings include a large gap in cold start time
between AWS and GCP serverless functions, and serverless' low sensitivity to
changes in workloads or models. Our evaluation results indicate that serverless
is a viable option for model serving. Finally, we present several practical
recommendations for data scientists on how to use serverless for scalable and
cost-effective model serving.


Abstract:
  We propose a nonlinear reduced basis method for the efficient approximation
of parametrized partial differential equations (PDEs), exploiting kernel proper
orthogonal decomposition (KPOD) for the generation of a reduced-order space and
neural networks for the evaluation of the reduced-order approximation. In
particular, we use KPOD in place of the more classical POD, on a set of
high-fidelity solutions of the problem at hand to extract a reduced basis. This
method provides a more accurate approximation of the snapshots' set featuring a
lower dimension, while maintaining the same efficiency as POD. A neural network
(NN) is then used to find the coefficients of the reduced basis by following a
supervised learning paradigm and shown to be effective in learning the map
between the time/parameter values and the projection of the high-fidelity
snapshots onto the reduced space. In this NN, both the number of hidden layers
and the number of neurons vary according to the intrinsic dimension of the
differential problem at hand and the size of the reduced space. This adaptively
built NN attains good performances in both the learning and the testing phases.
Our approach is then tested on two benchmark problems, a one-dimensional wave
equation and a two-dimensional nonlinear lid-driven cavity problem. We finally
compare the proposed KPOD-NN technique with a POD-NN strategy, showing that
KPOD allows a reduction of the number of modes that must be retained to reach a
given accuracy in the reduced basis approximation. For this reason, the NN
built to find the coefficients of the KPOD expansion is smaller, easier and
less computationally demanding to train than the one used in the POD-NN
strategy.


Abstract:
  As software systems are becoming more pervasive, they are also becoming more
susceptible to failures, resulting in potentially lethal combinations. Software
testing is critical to preventing software failures but is, arguably, the least
understood part of the software life cycle and the toughest to perform
correctly. Adequate research has been carried out in both the process and
technology dimensions of testing, but not in the human dimensions. This paper
attempts to fill in the gap by exploring the human dimension, i.e., trying to
understand the motivation of software professionals to take up and sustain
testing careers. Towards that end, a survey was conducted in four countries -
India, Canada, Cuba, and China - to try to understand how professional software
testers perceive and value work-related factors that could influence their
motivation to take up and sustain testing careers. With a sample of 220
software professionals, we observed that very few professionals are keen to
take up testing careers. Some aspects of software testing, such as the learning
opportunities, appear to be a common motivator across the four countries;
whereas the treatment meted out to testers as second-class citizens and the
complexity of the job appeared to be the most important de-motivators. This
comparative study offers useful insights that can help global software industry
leaders to come up with an action plan to put the software testing profession
under a new light. That could increase the number of software engineers
choosing testing careers, which would facilitate quality testing.


Abstract:
  We give a possible splitting method to a Hamiltonian for the description of
charged particles moving around the Reissner-Nordstrom-(anti)-de Sitter black
hole with an external magnetic field. This Hamiltonian can be separated into
six analytical solvable pieces, whose solutions are explicit functions of
proper time. In this case, second- and fourth-order explicit symplectic
integrators are easily available. They exhibit excellent long-term behavior in
maintaining the boundness of Hamiltonian errors regardless of ordered or
chaotic orbits if appropriate step-sizes are chosen. Under some circumstances,
an increase of positive cosmological constant gives rise to strengthening the
extent of chaos from the global phase space; namely, chaos of charged particles
occurs easily for the accelerated expansion of the universe. However, an
increase of the magnitude of negative cosmological constant does not. The
different contributions on chaos are because the cosmological constant acts as
a repulsive force in the Reissner-Nordstrom-de Sitter black hole, but an
attractive force in the Reissner-Nordstrom-anti-de Sitter black hole.


Abstract:
  Weakly supervised temporal action localization aims to detect and localize
actions in untrimmed videos with only video-level labels during training.
However, without frame-level annotations, it is challenging to achieve
localization completeness and relieve background interference. In this paper,
we present an Action Unit Memory Network (AUMN) for weakly supervised temporal
action localization, which can mitigate the above two challenges by learning an
action unit memory bank. In the proposed AUMN, two attention modules are
designed to update the memory bank adaptively and learn action units specific
classifiers. Furthermore, three effective mechanisms (diversity, homogeneity
and sparsity) are designed to guide the updating of the memory network. To the
best of our knowledge, this is the first work to explicitly model the action
units with a memory network. Extensive experimental results on two standard
benchmarks (THUMOS14 and ActivityNet) demonstrate that our AUMN performs
favorably against state-of-the-art methods. Specifically, the average mAP of
IoU thresholds from 0.1 to 0.5 on the THUMOS14 dataset is significantly
improved from 47.0% to 52.1%.


Abstract:
  Determining when two individuals are within close distance is key to contain
a pandemic, e.g., to alert individuals in real-time and trace their social
contacts. Common approaches rely on either Bluetooth Low Energy (BLE) or
ultra-wideband (UWB) radios, that nonetheless strike opposite tradeoffs for
energy efficiency vs. accuracy of distance estimates.
  Janus reconciles these dimensions with a dual-radio protocol enabling
efficient and accurate social contact detection. Measurements show that Janus
achieves weeks to months of autonomous operation, depending on the
configuration. Several large-scale campaigns in real-world contexts confirm its
reliability and practical usefulness in enabling insightful analysis of contact
data.


Abstract:
  In this work, an optic fiber based $\textit{in situ}$ illumination system
integrated into an aberration-corrected environmental transmission electron
microscope (ETEM) is designed, built, characterized and applied. With this
illumination system, the dynamic responses of photoactive materials to photons
can be directly observed at the atomic level, and other stimuli including
heating and various gases can also be applied simultaneously. Either a
broadband light source or a high power laser source aiming to expedite
photoreactions can be utilized, fitting different application needs. The optic
fiber enters the ETEM through the objective aperture port, with a carefully
designed curvature and a 30{\deg} cut at the tip to orient the emitted light
upwards onto the TEM specimen. The intensity distributions striking the sample
from the broadband and laser sources are both measured, and due to the
non-uniform distributions, an alignment procedure has been developed to align
the bright spot with the electron optical axis of the TEM. The imaging and
spectroscopy performances of the ETEM are proved to be maintained after
incorporating this illumination system. Furthermore, Langmuir evaporation is
observed when in situ laser light is applied to GaAs, demonstrating the
phenomenon of optical heating on suitable semiconductor materials.


Abstract:
  Repeated local measurements of quantum many body systems can induce a phase
transition in their entanglement structure. These measurement-induced phase
transitions (MIPTs) have been studied for various types of dynamics, yet most
cases yield quantitatively similar values of the critical exponents, making it
unclear if there is only one underlying universality class. Here, we directly
probe the properties of the conformal field theories governing these MIPTs
using a numerical transfer-matrix method, which allows us to extract the
effective central charge, as well as the first few low-lying scaling dimensions
of operators at these critical points. Our results provide convincing evidence
that the generic and Clifford MIPTs for qubits lie in different universality
classes and that both are distinct from the percolation transition for qudits
in the limit of large onsite Hilbert space dimension. For the generic case, we
find strong evidence of multifractal scaling of correlation functions at the
critical point, reflected in a continuous spectrum of scaling dimensions.


Abstract:
  In this paper, we give a brief survey of recent results on axially symmetric
Navier-Stokes equations (ASNS) in the following categories: regularity
criterion, Liouville property for ancient solutions, decay and vanishing of
stationary solutions. Some discussions also touch on the full 3 dimensional
equations. Two results, closing of the scaling gap for ASNS and vanishing of
homogeneous D solutions in 3 dimensional slabs will be described in more
detail. In the addendum, two new results in the 3rd category will also be
presented, which are generalizations of recently published results by the
author and coauthors.


Abstract:
  DNA-driven self-assembly enables precise positioning of the colloidal
nanoparticles owing to specific Watson-Crick interactions. Another important
feature of this self-assembly method is its reversibility by controlling the
temperature of the medium. In this work, we study the potential of another
mechanism to control binding/unbinding process of the DNA-functionalized gold
nanoparticles. We employ the laser radiation that can be absorbed by the gold
nanoparticles to heat their network and disassociate it. Here, we show that we
can actively control the optical properties of the nanoparticle network by an
external optical excitation. We find out that by irradiating the structure with
a green hand-held laser the total transmittance can increase by ~30% compared
to the transmittance of the sample not irradiated by the laser. Similarly, the
optical microscopy images indicate the transformation of the nanoparticle
network from opaque to transparent while the nanoparticles formed a network
again after the laser irradiation stopped. Our results prove that the optical
excitation can be used to tailor the structure and thus the optical properties
of the DNA-self-assembled nanoparticle networks.


Abstract:
  Aerial scene recognition is a fundamental visual task and has attracted an
increasing research interest in the last few years. Most of current researches
mainly deploy efforts to categorize an aerial image into one scene-level label,
while in real-world scenarios, there often exist multiple scenes in a single
image. Therefore, in this paper, we propose to take a step forward to a more
practical and challenging task, namely multi-scene recognition in single
images. Moreover, we note that manually yielding annotations for such a task is
extraordinarily time- and labor-consuming. To address this, we propose a
prototype-based memory network to recognize multiple scenes in a single image
by leveraging massive well-annotated single-scene images. The proposed network
consists of three key components: 1) a prototype learning module, 2) a
prototype-inhabiting external memory, and 3) a multi-head attention-based
memory retrieval module. To be more specific, we first learn the prototype
representation of each aerial scene from single-scene aerial image datasets and
store it in an external memory. Afterwards, a multi-head attention-based memory
retrieval module is devised to retrieve scene prototypes relevant to query
multi-scene images for final predictions. Notably, only a limited number of
annotated multi-scene images are needed in the training phase. To facilitate
the progress of aerial scene recognition, we produce a new multi-scene aerial
image (MAI) dataset. Experimental results on variant dataset configurations
demonstrate the effectiveness of our network. Our dataset and codes are
publicly available.


Abstract:
  Code review is a key development practice that contributes to improve
software quality and to foster knowledge sharing among developers. However,
code review usually takes time and demands detailed and time-consuming analysis
of textual diffs. Particularly, detecting refactorings during code reviews is
not a trivial task, since they are not explicitly represented in diffs. For
example, a Move Function refactoring is represented by deleted (-) and added
lines (+) of code which can be located in different and distant source code
files. To tackle this problem, we introduce RAID, a refactoring-aware and
intelligent diff tool. Besides proposing an architecture for RAID, we
implemented a Chrome browser plug-in that supports our solution. Then, we
conducted a field experiment with eight professional developers who used RAID
for three months. We concluded that RAID can reduce the cognitive effort
required for detecting and reviewing refactorings in textual diff. Besides
documenting refactorings in diffs, RAID reduces the number of lines required
for reviewing such operations. For example, the median number of lines to be
reviewed decreases from 14.5 to 2 lines in the case of move refactorings and
from 113 to 55 lines in the case of extractions.


Abstract:
  We present basic constructions and properties in arithmetic Chern-Simons
theory with finite gauge group along the line of topological quantum field
theory. For a finite set $S$ of finite primes of a number field $k$, we
construct arithmetic analogues of the Chern-Simons 1-cocycle, the
prequantization bundle for a surface and the Chern-Simons functional for a
$3$-manifold. We then construct arithmetic analogues for $k$ and $S$ of the
quantum Hilbert space (space of conformal blocks) and the Dijkgraaf-Witten
partition function in (2+1)-dimensional Chern-Simons TQFT. We show some basic
and functorial properties of those arithmetic analogues. Finally we show
decomposition and gluing formulas for arithmetic Chern-Simons invariants and
arithmetic Dijkgraaf-Witten partition functions.


Abstract:
  Fluctuation-dissipation relations or "theorems" (FDTs) are fundamental for
statistical physics and can be rigorously derived for equilibrium systems.
Their applicability to non-equilibrium systems is, however, debated. Here, we
simulate an active microrheology experiment, in which a spherical colloid is
pulled with a constant external force through a fluid, creating
near-equilibrium and far-from-equilibrium systems. We characterize the
structural and dynamical properties of these systems, and reconstruct an
effective generalized Langevin equation (GLE) for the colloid dynamics.
Specifically, we test the validity of two FDTs: The first FDT relates the
non-equilibrium response of a system to equilibrium correlation functions, and
the second FDT relates the memory friction kernel in the GLE to the stochastic
force. We find that the validity of the first FDT depends strongly on the
strength of the external driving: it is fulfilled close to equilibrium and
breaks down far from it. In contrast, we observe that the second FDT is always
fulfilled. We provide a mathematical argument why this generally holds for
memory kernels reconstructed from a deterministic Volterra equation for
correlation functions, even for non-stationary non-equilibrium systems.
  Motivated by the Mori-Zwanzig formalism, we therefore suggest to impose an
orthogonality constraint on the stochastic force, which is in fact equivalent
to the validity of this Volterra equation. Such GLEs automatically satisfy the
second FDT and are unique, which is desirable when using GLEs for
coarse-grained modeling.


Abstract:
  A simple closed-form solution to the discrete Lyapunov equation (DLE) is
established for certain families of matrices. This solution is expressed in
terms of the eigen decomposition (ED) for which closed-form solutions are known
for all 2x2; 3x3 and certain families of matrices. For general matrices, the
proposed ED based closed-form solution can be used as an efficient numerical
solution when the ED can be computed. The result is then extended to give
closed-form solutions for a generalization of the DLE, called the Stein
equation. The proposed explicit solution's complexity is of the same order as
iterative solutions and significantly smaller than known closed-form solutions.
These solutions may prove convenient for analysis and synthesis problems
related to these equations due to their compact form.


Abstract:
  We develop an algorithm for the concurrent (on-the-fly) estimation of
parameters for a system of evolutionary dissipative partial differential
equations in which the state is partially observed. The intuitive nature of the
algorithm makes its extension to several different systems immediate, and it
allows for recovery of multiple parameters simultaneously. We test this
algorithm on the Kuramoto-Sivashinsky equation in one dimension and demonstrate
its efficacy in this context.


Abstract:
  This short paper introduces a recently patented line based numbering system.
The last allows a best concordance with decimal digits values, and open up new
opportunities, which are not possible with the classical decimal numeration
system. Proposed OILU symbolic allows generating a new type of number series,
based on multi facets numbers splitting process. On the other hand, this new
symbolic is used in the development of new visual markers, highly required in
augmented reality and UAV's navigation applications.


Abstract:
  We develop a simple toy model for polarized images of synchrotron emission
from an equatorial source around a Kerr black hole by using a semi-analytic
solution of the null geodesic equation and conservation of the Penrose-Walker
constant. Our model is an extension of Narayan et al. (2021), which presented
results for a Schwarzschild black hole, including a fully analytic
approximation. Our model includes an arbitrary observer inclination, black hole
spin, local boost, and local magnetic field configuration. We study the
geometric effects of black hole spin on photon parallel transport and isolate
these effects from the complicated combination of relativistic, gravitational,
and electromagnetic processes in the emission region. We find an analytic
approximation, consistent with previous work, for the subleading geometric
effect of spin on observed face-on polarization rotation in the direct image:
$\Delta {\rm EVPA} \sim -2a/r_{\rm s}^2$, where $a$ is the black hole spin and
$r_{\rm s}$ is the emission radius. We further show that spin introduces an
order unity effect on face-on subimages: $\Delta {\rm EVPA} \sim \pm
a/\sqrt{27}$. We also use our toy model to analyze polarization "loops"
observed during flares of orbiting hotspots. Our model provides insight into
polarimetric simulations and observations of black holes such as those made by
the EHT and GRAVITY.


Abstract:
  Current and future optical and near-infrared wide-field surveys have the
potential of finding kilonovae, the optical and infrared counterparts to
neutron star mergers, independently of gravitational-wave or high-energy
gamma-ray burst triggers. The ability to discover fast and faint transients
such as kilonovae largely depends on the area observed, the depth of those
observations, the number of re-visits per field in a given time frame, and the
filters adopted by the survey; it also depends on the ability to perform rapid
follow-up observations to confirm the nature of the transients. In this work,
we assess kilonova detectability in existing simulations of the LSST strategy
for the Vera C. Rubin Wide Fast Deep survey, with focus on comparing rolling to
baseline cadences. Although currently available cadences can enable the
detection of more than 300 kilonovae out to 1400 Mpc over the ten-year survey,
we can expect only 3-32 kilonovae similar to GW170817 to be recognizable as
fast-evolving transients. We also explore the detectability of kilonovae over
the plausible parameter space, focusing on viewing angle and ejecta masses. We
find that observations in redder izy bands are crucial for identification of
nearby (within 300 Mpc) kilonovae that could be spectroscopically classified
more easily than more distant sources. Rubin's potential for serendipitous
kilonova discovery could be increased by gain of efficiency with the employment
of individual 30s exposures (as opposed to 2x15s snap pairs), with the addition
of red-band observations coupled with same-night observations in g- or r-bands,
and possibly with further development of a new rolling-cadence strategy.


Abstract:
  Metal trihalides (MX$_3$) are the most important family of 2D magnetic
materials, being the chromium trihalides the most studied 2D magnets. The
discovery of CrI$_3$, the recent obtention of CrCl$_3$ in-plane magnetic
monolayer and the role of CrBr$_3$ inducing topological superconductivity in
NbSe$_2$, may serve to showcase the active research being done in this family
nowadays. Despite the high impact of these materials, most of the members in
the MX$_3$ family are still unexplored and constitute an untapped source of
interesting physical properties. Stimulated by the most recent advances in
straintronics and aware of the crucial role of the dielectric screening, we
present here a high throughput methodology to automatize the exploration of 2D
materials. Employing this methodology, we studied the MX$_3$ family (M= Cr, Fe,
V, Ti; X= Cl, Br, I) with the goal of advancing towards the solution of the
most problematic issue in these materials, namely the Curie temperature. We use
a particular case to show how this methodology allows us to obtain a complete
description of the magnetic interaction picture (J$_{iso}$, J$_{xx}$, J$_{yy}$,
J$_{zz}$) up to third neighbors condensed in a single effective equation per
parameter, describing magnetic interaction in terms of the strain and the
Hubbard U parameter. Additionally, and because of the important role of the
Hubbard U in MX$_3$ materials, we provide a library of self-consistent
calculated Hubbard U for the principal pseudopotential families. The work
presented herein advances in the description of the still unexplored MX$_3$
materials, opening the door to a rational design of 2D magnetic materials.


Abstract:
  The paper presents a two-stage stochastic program to model a routing problem
involving an Unmanned Aerial Vehicle (UAV) in the context of patrolling
missions. In particular, given a set of targets and a set of supplemental
targets corresponding to each target, the first stage decisions involve finding
the sequence in which the vehicle has to visit the set of targets. Upon
reaching each target, the UAV collects information and if the operator of the
UAV deems that the information collected is not of sufficient fidelity, then
the UAV has to visit all the supplemental targets corresponding to that target
to collect additional information before proceeding to visit the next target.
The problem is solved using a progressive hedging algorithm and extensive
computational results corroborating the effectiveness of the proposed model and
the solution methodology is presented.


Abstract:
  This study examines the influence of grandchildren's gender on grandparents'
voting behavior using independently collected individual-level data. The survey
was conducted immediately after the House of Councilors election in Japan. I
observed that individuals with a granddaughter were more likely to vote for
female candidates by around 10 % than those without. However, having a daughter
did not affect the parents' voting behavior. Furthermore, having a son or a
grandson did not influence grandparents' voting behavior. This implies that
grandparents voted for their granddaughter's future benefit because
granddaughters may be too young vote in a male-dominated and aging society.


Abstract:
  The \textit{Collatz's conjecture} is an unsolved problem in mathematics. It
is named after Lothar Collatz in 1973. The conjecture also known as Syrucuse
conjecture or problem.
  Take any positive integer $ n $. If $ n $ is even then divide it by $ 2 $,
else do "triple plus one" and get $ 3n+1 $. The conjecture is that for all
numbers, this process converges to one.
  In the modular arithmetic notation, define a function $ f $ as follows:
\[f(x)= \left\{ \begin{array}{lll} \frac{n}{2} &if & n\equiv 0 \pmod 2\\ 3n+1&
if& n\equiv 1 \pmod 2. \end{array}\right. \] In this paper, we present the
proof of the Collatz conjecture for many types of sets defined by the remainder
theorem of arithmetic. These sets are defined in mods
  $6, 12, 24, 36, 48, 60, 72, 84, 96, 108$ and we took only odd positive
remainders to work with. It is not difficult to prove that the same results are
true for any mod $12m, $ for positive integers $m$.


Abstract:
  Embedding learning (EL) and feature synthesizing (FS) are two of the popular
categories of fine-grained GZSL methods. EL or FS using global features cannot
discriminate fine details in the absence of local features. On the other hand,
EL or FS methods exploiting local features either neglect direct attribute
guidance or global information. Consequently, neither method performs well. In
this paper, we propose to explore global and direct attribute-supervised local
visual features for both EL and FS categories in an integrated manner for
fine-grained GZSL. The proposed integrated network has an EL sub-network and a
FS sub-network. Consequently, the proposed integrated network can be tested in
two ways. We propose a novel two-step dense attention mechanism to discover
attribute-guided local visual features. We introduce new mutual learning
between the sub-networks to exploit mutually beneficial information for
optimization. Moreover, we propose to compute source-target class similarity
based on mutual information and transfer-learn the target classes to reduce
bias towards the source domain during testing. We demonstrate that our proposed
method outperforms contemporary methods on benchmark datasets.


Abstract:
  The stringent constraints from the direct searches for exotic scalars at the
LHC as well as indirect bounds from flavor physics measurements have imposed
severe restrictions on the parameter space of new physics models featuring
extended Higgs sectors. In the Type-II 2HDM, this implies a lower bound on the
charged Higgs masses of $\cal O$(600 GeV). In this work we analyze the
phenomenology of a Z3HDM in the alignment limit focusing on the impact of
flavor physics constraints on its parameter space. We show that the couplings
of the two charged Higgs bosons in this model feature an additional suppression
factor compared to Type-II 2HDM. This gives rise to a significant relaxation of
the flavor physics constraints in this model, allowing the charged Higgs masses
to be as low as $\cal O$(200 GeV). We also consider the constraints coming from
precision electroweak observables and the observed diphoton decay rate of the
125 GeV Higgs boson at the LHC. The bounds coming from the direct searches of
nonstandard Higgs bosons at the LHC, particularly those from resonance searches
in the ditau channel, prove to be very effective in constraining this scenario
further.


Abstract:
  Slowly changing variables in a continuous state space constitute an important
category of reinforcement learning and see its application in many domains,
such as modeling a climate control system where temperature, humidity, etc.
change slowly over time. However, this subject is less addressed in recent
studies. Classical methods with certain variants, such as Dynamic Programming
with Tile Coding which discretizes the state space, fail to handle slowly
changing variables because those methods cannot capture the tiny changes in
each transition step, as it is computationally expensive or impossible to
establish an extremely granular grid system. In this paper, we introduce a
Hyperspace Neighbor Penetration (HNP) approach that solves the problem. HNP
captures in each transition step the state's partial "penetration" into its
neighboring hyper-tiles in the gridded hyperspace, thus does not require the
transition to be inter-tile in order for the change to be captured. Therefore,
HNP allows for a very coarse grid system, which makes the computation feasible.
HNP assumes near linearity of the transition function in a local space, which
is commonly satisfied. In summary, HNP can be orders of magnitude more
efficient than classical method in handling slowly changing variables in
reinforcement learning. We have made an industrial implementation of NHP with a
great success.


Abstract:
  While pre-trained language models (e.g., BERT) have achieved impressive
results on different natural language processing tasks, they have large numbers
of parameters and suffer from big computational and memory costs, which make
them difficult for real-world deployment. Therefore, model compression is
necessary to reduce the computation and memory cost of pre-trained models. In
this work, we aim to compress BERT and address the following two challenging
practical issues: (1) The compression algorithm should be able to output
multiple compressed models with different sizes and latencies, in order to
support devices with different memory and latency limitations; (2) The
algorithm should be downstream task agnostic, so that the compressed models are
generally applicable for different downstream tasks. We leverage techniques in
neural architecture search (NAS) and propose NAS-BERT, an efficient method for
BERT compression. NAS-BERT trains a big supernet on a search space containing a
variety of architectures and outputs multiple compressed models with adaptive
sizes and latency. Furthermore, the training of NAS-BERT is conducted on
standard self-supervised pre-training tasks (e.g., masked language model) and
does not depend on specific downstream tasks. Thus, the compressed models can
be used across various downstream tasks. The technical challenge of NAS-BERT is
that training a big supernet on the pre-training task is extremely costly. We
employ several techniques including block-wise search, search space pruning,
and performance approximation to improve search efficiency and accuracy.
Extensive experiments on GLUE and SQuAD benchmark datasets demonstrate that
NAS-BERT can find lightweight models with better accuracy than previous
approaches, and can be directly applied to different downstream tasks with
adaptive model sizes for different requirements of memory or latency.


Abstract:
  We present results on the number of linear regions of the functions that can
be represented by artificial feedforward neural networks with maxout units. A
rank-k maxout unit is a function computing the maximum of $k$ linear functions.
For networks with a single layer of maxout units, the linear regions correspond
to the upper vertices of a Minkowski sum of polytopes. We obtain face counting
formulas in terms of the intersection posets of tropical hypersurfaces or the
number of upper faces of partial Minkowski sums, along with explicit sharp
upper bounds for the number of regions for any input dimension, any number of
units, and any ranks, in the cases with and without biases. Based on these
results we also obtain asymptotically sharp upper bounds for networks with
multiple layers.


Abstract:
  A new formulation for light propagation in geometric optics by means of the
Bi-local Geodesic Operators is considered. We develop the BiGONLight
Mathematica package, uniquely designed to apply this framework to compute
optical observables in Numerical Relativity. Our package can be used for light
propagation on a wide range of scales and redshifts and accepts numerical as
well as analytical input for the spacetime metric. In this paper we focus on
two cosmological observables, the redshift and the angular diameter distance,
specializing our analysis to a wall universe modeled within the post-Newtonian
approximation. With this choice and the input metric in analytical form, we are
able to estimate non-linearities of light propagation by comparing and
isolating the contributions coming from Newtonian and post-Newtonian
approximations as opposed to linear perturbation theory. We also clarify the
role of the dominant post-Newtonian contribution represented by the linear
initial seed which, strictly speaking, is absent in the Newtonian treatment. We
found that post-Newtonian non-linear corrections are below $1\%$, in agreement
with previous results in the literature.


Abstract:
  In this article we prove that, over complete manifolds of dimension $n$ with
vanishing curvature at infinity, the essential spectrum of the Hodge Laplacian
on differential $k$-forms is a connected interval for $0\leq k\leq n$. We also
prove that, under a weaker condition where the Ricci curvature is
asymptotically nonnegative, the essential spectrum on $k$-forms is
$[0,\infty)$, but only for $0\leq k\leq q$ and $n-q \leq k \leq n$ for some
integer $ q\geq 1$ which depends the structure of the manifolds at infinity. We
achieve these general results through a novel method that combines collapsing
theory and a generalized Weyl criterion. More specifically, we use
Cheeger-Fukaya-Gromov and Cheeger-Colding theory to describe large balls of
these manifolds at infinity which capture their spectrum, and obtain very rough
test-forms. These rough test-forms are then shown to satisfy a new generalized
version of the classical Weyl criterion which is also proved in this article.


Abstract:
  We point out that fractionalized bosonic charge excitations can explain the
recently discovered photo-induced superconducting-like response in
$\kappa\text{-(ET})_2\text{Cu}[\text{N(CN)}_2]\text{Br}$, an organic metal
close to the Mott transition. The pump laser exerts a periodic drive on the
fractionalized field, creating a non-equilibrium condensate, which gives a
Drude peak much narrower than the equilibrium scattering rate, hence
superconducting-like response. Our proposal illuminates new possibilities of
detecting fractionalization and can be readily tested in spin liquid candidates
and in cold atom systems.


Abstract:
  Deploying Reinforcement Learning (RL) agents in the real-world require that
the agents satisfy safety constraints. Current RL agents explore the
environment without considering these constraints, which can lead to damage to
the hardware or even other agents in the environment. We propose a new method,
LBPO, that uses a Lyapunov-based barrier function to restrict the policy update
to a safe set for each training iteration. Our method also allows the user to
control the conservativeness of the agent with respect to the constraints in
the environment. LBPO significantly outperforms state-of-the-art baselines in
terms of the number of constraint violations during training while being
competitive in terms of performance. Further, our analysis reveals that
baselines like CPO and SDDPG rely mostly on backtracking to ensure safety
rather than safe projection, which provides insight into why previous methods
might not have effectively limit the number of constraint violations.


Abstract:
  The key to realizing fault-tolerant quantum computation for singlet-triplet
(ST) qubits in semiconductor double quantum dot (DQD) is to operate both the
single- and two-qubit gates with high fidelity. The feasible way includes
operating the qubit near the transverse sweet spot (TSS) to reduce the leading
order of the noise, as well as adopting the proper pulse sequences which are
immune to noise. The single-qubit gates can be achieved by introducing an AC
drive on the detuning near the TSS. The large dipole moment of the DQDs at the
TSS has enabled strong coupling between the qubits and the cavity resonator,
which leads to a two-qubit entangling gates. When operating in the proper
region and applying modest pulse sequences, both single- and two-qubit gates
are having fidelity higher than 99%. Our results suggest that taking advantage
of the appropriate pulse sequences near the TSS can be effective to obtain
high-fidelity ST qubits.


Abstract:
  In this paper, we present algorithms for synthesizing controllers to
distribute a group (possibly swarms) of homogeneous robots (agents) over
heterogeneous tasks which are operated in parallel. We present algorithms as
well as analysis for global and local-feedback-based controller for the swarms.
Using ergodicity property of irreducible Markov chains, we design a controller
for global swarm control. Furthermore, to provide some degree of autonomy to
the agents, we augment this global controller by a local feedback-based
controller using Language measure theory. We provide analysis of the proposed
algorithms to show their correctness. Numerical experiments are shown to
illustrate the performance of the proposed algorithms.


Abstract:
  The goal of this PhD thesis is to study a diophantine approximation problem
stated by Schmidt in 1967. The problem aim to study the approximation of a
subspace of $\mathbb{R}^n$ by rational subspaces, not necessarily of the same
dimension, by determining the exponents of approximation associated to the
various angles between the subspaces.


Abstract:
  In this work, we study the dynamics of a single active Brownian particle, as
well as the collective behavior of interacting active Brownian particles, in a
fluctuating heterogeneous environment. We employ a variant of the diffusing
diffusivity model where the equation of motion of the active particle involves
a time-dependent motility and diffusivities. Within our model, those
fluctuations are coupled to each other. Using analytical methods, we obtain the
probability distribution function of particle displacement and its moments for
a single particle. We then investigate the impact of the environmental
fluctuations on the collective behavior of the active Brownian particles by
means of extensive numerical simulations. Our results show that the
fluctuations hinder the motility-induced phase separation, accompanied by a
significant change of the density dependence of particle velocities. These
effects are interpreted using our analytical results for the dynamics of a
single particle.


Abstract:
  Recently demonstrated physical-world adversarial attacks have exposed
vulnerabilities in perception systems that pose severe risks for
safety-critical applications such as autonomous driving. These attacks place
adversarial artifacts in the physical world that indirectly cause the addition
of a universal patch to inputs of a model that can fool it in a variety of
contexts. Adversarial training is the most effective defense against
image-dependent adversarial attacks. However, tailoring adversarial training to
universal patches is computationally expensive since the optimal universal
patch depends on the model weights which change during training. We propose
meta adversarial training (MAT), a novel combination of adversarial training
with meta-learning, which overcomes this challenge by meta-learning universal
patches along with model training. MAT requires little extra computation while
continuously adapting a large set of patches to the current model. MAT
considerably increases robustness against universal patch attacks on image
classification and traffic-light detection.