Abstract:
  The use of classical computers to simulate quantum computing has been
successful in aiding the study of quantum algorithms and circuits that are too
complex to examine analytically. Current implementations of quantum computing
simulators are limited to two-level quantum systems. Recent advances in
high-dimensional quantum computing systems have demonstrated the viability of
working with multi-level superposition and entanglement. These advances allow
an agile increase in the number of dimensions of the system while maintaining
quantum entanglement, achieving higher encoding of information and making
quantum algorithms less vulnerable to decoherence and computational errors. In
this paper, we introduce QuantumSkynet, a novel high-dimensional cloud-based
quantum computing simulator. This platform allows simulations of qudit-based
quantum algorithms. We also propose a unified generalization of
high-dimensional quantum gates, which are available for simulations in
QuantumSkynet. Finally, we report simulations and their results for qudit-based
versions of the Deutsch--Jozsa and quantum phase estimation algorithms using
QuantumSkynet.


Abstract:
  The two-field equations governing fully nonlinear dynamics of the drift wave
(DW) and geodesic acoustic mode (GAM) in the toroidal geometry are derived in
nonlinear gyrokinetic framework. Two stages with distinctive features are
identified and analyzed. In the linear growth stage, the set of nonlinear
equations can be reduced to the intensively studied parametric decay
instability (PDI), accounting for the spontaneous resonant excitation of GAM by
DW. The main results of previous works on spontaneous GAM excitation, e.g., the
much enhanced GAM group velocity and the nonlinear growth rate of GAM, are
reproduced from numerical solution of the two-field equations. In the fully
nonlinear stage, soliton structures are observed to form due to the balancing
of the self-trapping effect by the spontaneously excited GAM and kinetic
dispersiveness of DW. The soliton structures enhance turbulence spreading from
DW linearly unstable to stable region, exhibiting convective propagation
instead of typical linear dispersive process, and is thus, expected to induce
core-edge interaction and nonlocal transport.


Abstract:
  Due to the mobility and frequent disconnections, the correctness of mobile
interaction systems, such as mobile robot systems and mobile payment systems,
are often difficult to analyze. This paper introduces three critical properties
of systems, called system connectivity, interaction soundness and data
validity, and presents a related modeling and analysis method, based on a kind
of Petri nets called VPN. For a given system, a model including component nets
and interaction structure nets is constructed by using VPNs. The component net
describes the internal process of each component, while the interaction
structure net reflects the dynamic interaction between components. Based on
this model, three properties are defined and analyzed. The case study of a
practical mobile payment system shows the effectiveness of the proposed method.


Abstract:
  Point defects in insulators are considered promising candidates for quantum
technologies. In keeping with this, we present an extensive optically-detected
magnetic resonance (ODMR) study at room-temperature on individual TR12 centers
(ZPL at 471nm), which are known in the literature since 1956. In this work we
found TR12 centers to show a strong ODMR signal under optical saturation. These
observed defects were created in high-purity epitaxial layers of diamond by
standard irradiation and annealing processes. From the analysis of the ODMR
spectra along with antibunching measurements and coherent population trapping,
we proposed the energy level structure of TR12 center, consisting of ground
state and excited state singlets complemented by a metastable triplet in
between. Mapping the fluorescence dependence of the center on an external
magnetic field and on the polarization of laser excitation, allows us to
identify twelve inequivalent orientations for TR12 centers. This includes the
exact orientations of the dipole transition and the triplet axes in the diamond
lattice in full agreement with the results of modeling based on the proposed
level structure. Furthermore, a static Jahn-Teller effect was detected through
fluorescence switching between two levels at low optical excitation power,
directly observable in the real-time fluorescence signal for various
polarization of laser excitation. Based on these results we discuss the
prospects of the TR12 center in diamond for quantum sensing and quantum
information processing.


Abstract:
  The aims of this article is to generalize some useful Besicovitch-Morse type
covering lemmas in complete Riemannian manifolds and try to find more spaces
such that the so-called BCP and WBCP are equivalent while these two properties
are weaker and still useful. We also get interest in the best constants of
Besicovitch-type covering properties in Euclidean spaces and sorted out the
best results of related problems before giving a new proof of Besicovitch
covering theorem in the one-dimensional case.


Abstract:
  Since a rigorous microscopic treatment of a nematic fluid system based on a
pairwise interaction potential is immensely complex we had introduced a simple
mean field potential which was a modification of the Maier-Saupe potential in a
previous paper. Building up on that here we have modified that potential to
take into account the various aspects of a smectic A-nematic phase transition.
In particular we have studied the dependence of the phase transition on the
coupling coefficient between the nematic and smectic order parameters which in
turn depends on the length of alkyl chain, existence of tricritical point,
variation of entropy and specific heat as well as the dependence of the phase
transition on pressure.


Abstract:
  Learned networks in the domain of visual recognition and cognition impress in
part because even though they are trained with datasets many orders of
magnitude smaller than the full population of possible images, they exhibit
sufficient generalization to be applicable to new and previously unseen data.
Although many have examined issues regarding generalization from several
perspectives, we wondered If a network is trained with a biased dataset that
misses particular samples corresponding to some defining domain attribute, can
it generalize to the full domain from which that training dataset was
extracted? It is certainly true that in vision, no current training set fully
captures all visual information and this may lead to Selection Bias. Here, we
try a novel approach in the tradition of the Thought Experiment. We run this
thought experiment on a real domain of visual objects that we can fully
characterize and look at specific gaps in training data and their impact on
performance requirements. Our thought experiment points to three conclusions:
first, that generalization behavior is dependent on how sufficiently the
particular dimensions of the domain are represented during training; second,
that the utility of any generalization is completely dependent on the
acceptable system error; and third, that specific visual features of objects,
such as pose orientations out of the imaging plane or colours, may not be
recoverable if not represented sufficiently in a training set. Any currently
observed generalization in modern deep learning networks may be more the result
of coincidental alignments and whose utility needs to be confirmed with respect
to a system's performance specification. Our Thought Experiment Probe approach,
coupled with the resulting Bias Breakdown can be very informative towards
understanding the impact of biases.


Abstract:
  The expanding application in Micro-Air Vehicles has encouraged many
researchers to understand the unsteady flow around a flapping foil at a low
Reynolds number. We numerically investigate an incompressible unsteady flow
around a two-dimensional pitching airfoil (SD7003) at high reduced frequency in
the laminar regime. This study interrogates the effect of different unsteady
parameters, namely amplitude (A), reduced frequency (k), Reynolds number (Re),
and asymmetry parameter (S) for pitching motion on the force coefficients. The
inviscid theoretical model is utilized to calculate the lift coefficient for
sinusoidal motion in the viscous regime, and a comparison is made with the
numerical results. The theoretical analysis identifies the influence of the
non-circulatory lift over circulatory lift at a high reduced frequency.
Further, the results indicate that the reduced frequency (k) and asymmetry
parameter (S) have a significant impact on the instantaneous and time-averaged
force coefficients as well as on the vortex structure in the wake. Finally, the
Fast Fourier Transformation (FFT) analysis is carried out over a simulated case
with fixed amplitude and Reynolds number for distinct k and S values. The
findings confirm that the dominant frequency in the flow (k*) has a direct
correlation to the airfoil pitching frequency (k).


Abstract:
  Numerous efforts have been invested in improving the effectiveness of bug
localization techniques, whereas little attention is paid to making these tools
run more efficiently in continuously evolving software repositories. This paper
first analyzes the information retrieval model behind a classic bug
localization tool, BugLocator, and builds a mathematical foundation
illustrating that the model can be updated incrementally when codebase or bug
reports evolve. Then, we present IncBL, a tool for Incremental Bug Localization
in evolving software repositories. IncBL is evaluated on the Bugzbook dataset,
and the results show that IncBL can significantly reduce the running time by
77.79% on average compared with the re-computing the model, while maintaining
the same level of accuracy. We also implement IncBL as a Github App that can be
easily integrated into open-source projects on GitHub. Users can deploy and use
IncBL locally as well. The demo video for IncBL can be viewed at
https://youtu.be/G4gMuvlJSb0, and the source code can be found at
https://github.com/soarsmu/IncBL.


Abstract:
  Recently, DETR and Deformable DETR have been proposed to eliminate the need
for many hand-designed components in object detection while demonstrating good
performance as previous complex hand-crafted detectors. However, their
performance on Video Object Detection (VOD) has not been well explored. In this
paper, we present TransVOD, an end-to-end video object detection model based on
a spatial-temporal Transformer architecture. The goal of this paper is to
streamline the pipeline of VOD, effectively removing the need for many
hand-crafted components for feature aggregation, e.g., optical flow, recurrent
neural networks, relation networks. Besides, benefited from the object query
design in DETR, our method does not need complicated post-processing methods
such as Seq-NMS or Tubelet rescoring, which keeps the pipeline simple and
clean. In particular, we present temporal Transformer to aggregate both the
spatial object queries and the feature memories of each frame. Our temporal
Transformer consists of three components: Temporal Deformable Transformer
Encoder (TDTE) to encode the multiple frame spatial details, Temporal Query
Encoder (TQE) to fuse object queries, and Temporal Deformable Transformer
Decoder to obtain current frame detection results. These designs boost the
strong baseline deformable DETR by a significant margin (3%-4% mAP) on the
ImageNet VID dataset. TransVOD yields comparable results performance on the
benchmark of ImageNet VID. We hope our TransVOD can provide a new perspective
for video object detection. Code will be made publicly available at
https://github.com/SJTU-LuHe/TransVOD.


Abstract:
  Video question answering (Video QA) presents a powerful testbed for
human-like intelligent behaviors. The task demands new capabilities to
integrate video processing, language understanding, binding abstract linguistic
concepts to concrete visual artifacts, and deliberative reasoning over
spacetime. Neural networks offer a promising approach to reach this potential
through learning from examples rather than handcrafting features and rules.
However, neural networks are predominantly feature-based - they map data to
unstructured vectorial representation and thus can fall into the trap of
exploiting shortcuts through surface statistics instead of true systematic
reasoning seen in symbolic systems. To tackle this issue, we advocate for
object-centric representation as a basis for constructing spatio-temporal
structures from videos, essentially bridging the semantic gap between low-level
pattern recognition and high-level symbolic algebra. To this end, we propose a
new query-guided representation framework to turn a video into an evolving
relational graph of objects, whose features and interactions are dynamically
and conditionally inferred. The object lives are then summarized into resumes,
lending naturally for deliberative relational reasoning that produces an answer
to the query. The framework is evaluated on major Video QA datasets,
demonstrating clear benefits of the object-centric approach to video reasoning.


Abstract:
  With the recent developments in neural networks, there has been a resurgence
in algorithms for the automatic generation of simulation ready electronic
circuits from hand-drawn circuits. However, most of the approaches in
literature were confined to classify different types of electrical components
and only a few of those methods have shown a way to rebuild the circuit
schematic from the scanned image, which is extremely important for further
automation of netlist generation. This paper proposes a real-time algorithm for
the automatic recognition of hand-drawn electrical circuits based on object
detection and circuit node recognition. The proposed approach employs You Only
Look Once version 5 (YOLOv5) for detection of circuit components and a novel
Hough transform based approach for node recognition. Using YOLOv5 object
detection algorithm, a mean average precision (mAP0.5) of 98.2% is achieved in
detecting the components. The proposed method is also able to rebuild the
circuit schematic with 80% accuracy with a near-real time performance of 0.33s
per schematic generation.


Abstract:
  We present a novel deep neural network (DNN) architecture for compressing an
image when a correlated image is available as side information only at the
decoder. This problem is known as distributed source coding (DSC) in
information theory. In particular, we consider a pair of stereo images, which
generally have high correlation with each other due to overlapping fields of
view, and assume that one image of the pair is to be compressed and
transmitted, while the other image is available only at the decoder. In the
proposed architecture, the encoder maps the input image to a latent space,
quantizes the latent representation, and compresses it using entropy coding.
The decoder is trained to extract the common information between the input
image and the correlated image, using only the latter. The received latent
representation and the locally generated common information are passed through
a decoder network to obtain an enhanced reconstruction of the input image. The
common information provides a succinct representation of the relevant
information at the receiver. We train and demonstrate the effectiveness of the
proposed approach on the KITTI and Cityscape datasets of stereo image pairs.
Our results show that the proposed architecture is capable of exploiting the
decoder-only side information, and outperforms previous work on stereo image
compression with decoder side information.


Abstract:
  Ly-alpha emitting galaxies and giant Ly-alpha blobs (LABs) have been
extensively observed to study the formation history of galaxies. However, the
origin of their extended Ly-alpha emission, especially of LABs, remains
controversial. Polarization signals from some LABs have been discovered, and
this is commonly interpreted as strong evidence supporting that the extended
Ly-alpha emission originates from the resonance scattering. The Monte Carlo
Ly-alpha radiative transfer code LaRT is updated to investigate the
polarization of Ly-alpha using the Stokes vector formalism. We apply LaRT to a
few models to explore the fundamental polarization properties of Ly-alpha.
Interestingly, individual Ly-alpha photon packets are found to be almost
completely polarized by a sufficient number of scatterings (N_scatt > 10^4-10^5
in a static medium) or Doppler shifts induced by gas motion, even starting from
unpolarized light. It is also found that the polarization pattern can exhibit a
non-monotonically increasing pattern in some cases, besides the commonly-known
trend that the polarization monotonically increases with radius. The
polarization properties are primarily determined by the degree of polarization
of individual photon packets and the anisotropy of the Ly-alpha radiation
field, which are eventually controlled by the medium's optical depth and
velocity field. If once Ly-alpha photon packets achieve ~100% polarization, the
radial profile of polarization appears to correlate with the surface brightness
profile. A steep surface brightness profile tends to yield a rapid increase of
the linear polarization near the Ly-alpha source location. In contrast, a
shallow surface brightness profile gives rise to a slowly increasing
polarization pattern.


Abstract:
  In this work we elaborate on holographic description of the path-integral
optimization in conformal field theories (CFT) using Hartle-Hawking wave
functions in Anti-de Sitter spacetimes. We argue that the maximization of the
Hartle-Hawking wave function is equivalent to the path-integral optimization
procedure in CFT. In particular, we show that metrics that maximize gravity
wave functions computed in particular holographic geometries, precisely match
those derived in the path-integral optimization procedure for their dual CFT
states. The present work is a detailed version of \cite{Boruch:2020wax} and
contains many new results such as analysis of excited states in various
dimensions including JT gravity, and a new way of estimating holographic
path-integral complexity from Hartle-Hawking wave functions. Finally, we
generalize the analysis to Lorentzian Anti-de Sitter and de Sitter geometries
and use it to shed light on path-integral optimization in Lorentzian CFTs.


Abstract:
  The possibility of long-baseline quantum experiments in space makes it
necessary to better understand the time evolution of relativistic quantum
particles in a weakly varying gravitational field. We explain why conventional
treatments by traditional quantum optics and atomic physics based on quantum
mechanics may become inadequate when faced with issues related to locality,
simultaneity, signaling, causality, etc. Quantum field theory is needed. Adding
the effects of gravitation, we are led to Quantum Field Theory in Curved
Spacetime (QFTCST). This well-established theory should serve as the canonical
reference theory to a large class of proposed space experiments testing the
foundations of gravitation and quantum theory, and the basic notions of quantum
information theory in relativistic settings.
  This is the first in a series of papers treating near-term quantum optics and
matter waves experiments in space from the perspective of QFTCST. We analyze
the quantum motion of photons and of scalar massive particles using QFTCST with
application to interferometer experiments. Our main result is that, for
photons, the weak gravitational field is to leading order completely equivalent
to an inhomogeneous dielectric, thus allowing for a description of quantum
optics experiments in curved space using familiar notions from the theory of
optical media. We also discuss interference experiments that probe first-order
quantum coherence, the importance of a covariant particle detection theory, and
the relevance of time of arrival measurements. For massive particles with
internal structure, we identify a novel gravity-induced phase shift that
originates from the different gravitational masses attributed to the excited
internal states. This phase shift can in principle be measured in space
experiments.


Abstract:
  Different AdS-Rindler wedges can be mapped to each other using bulk
isometries. In this paper we address how the boundary representations
corresponding to the AdS-Rindler wedges transform under such isometries. We
show that when a bulk wedge is mapped to another using a bulk isometry, their
boundary representations are mapped by the conformal transformation
corresponding to the isometry. We comment on the import of this result on the
relation between AdS/CFT and quantum error correction.


Abstract:
  Markov Population Models are a widespread formalism used to model the
dynamics of complex systems, with applications in Systems Biology and many
other fields. The associated Markov stochastic process in continuous time is
often analyzed by simulation, which can be costly for large or stiff systems,
particularly when a massive number of simulations has to be performed (e.g. in
a multi-scale model). A strategy to reduce computational load is to abstract
the population model, replacing it with a simpler stochastic model, faster to
simulate. Here we pursue this idea, building on previous works and constructing
a generator capable of producing stochastic trajectories in continuous space
and discrete time. This generator is learned automatically from simulations of
the original model in a Generative Adversarial setting. Compared to previous
works, which rely on deep neural networks and Dirichlet processes, we explore
the use of state of the art generative models, which are flexible enough to
learn a full trajectory rather than a single transition kernel.


Abstract:
  In this paper, we exploit the capability of multi-agent deep reinforcement
learning (MA-DRL) technique to generate a transmit power pool (PP) for Internet
of things (IoT) networks with semi-grant-free non-orthogonal multiple access
(SGF-NOMA). The PP is mapped with each resource block (RB) to achieve
distributed transmit power control (DPC). We first formulate the resource
(sub-channel and transmit power) selection problem as stochastic Markov game,
and then solve it using two competitive MA-DRL algorithms, namely double deep Q
network (DDQN) and Dueling DDQN. Each GF user as an agent tries to find out the
optimal transmit power level and RB to form the desired PP. With the aid of
dueling processes, the learning process can be enhanced by evaluating the
valuable state without considering the effect of each action at each state.
Therefore, DDQN is designed for communication scenarios with a small-size
action-state space, while Dueling DDQN is for a large-size case. Our results
show that the proposed MA-Dueling DDQN based SGF-NOMA with DPC outperforms the
SGF-NOMA system with the fixed-power-control mechanism and networks with pure
GF protocols with 17.5% and 22.2% gain in terms of the system throughput,
respectively. Moreover, to decrease the training time, we eliminate invalid
actions (high transmit power levels) to reduce the action space. We show that
our proposed algorithm is computationally scalable to massive IoT networks.
Finally, to control the interference and guarantee the quality-of-service
requirements of grant-based users, we find the optimal number of GF users for
each sub-channel.


Abstract:
  We propose in this paper a new nonlinear mathematical model of an oscillating
water column. The one-dimensional shallow water equations in the presence of
this device are essentially reformulated as two transmission problems: the
first one is associated with a step in front of the device and the second one
is related to the interaction between waves and a fixed partially-immersed
structure. By taking advantage of free surface Bernoulli's equation, we close
the system by deriving a transmission condition that involves a time-dependent
air pressure inside the chamber of the device, instead of a constant
atmospheric pressure as in the previous work \cite{bocchihevergara2021}. We
then show that the second transmission problem can be reduced to a quasilinear
hyperbolic initial boundary value problem with a semilinear boundary condition
determined by an ODE depending on the trace of the solution to the PDE at the
boundary. Local well-posedness for general problems of this type is established
via an iterative scheme by using linear estimates for the PDE and nonlinear
estimates for the ODE. Finally, the well-posedness of the transmission problem
related to the wave-structure interaction in the oscillating water column is
obtained as an application of the general theory.


Abstract:
  Scene text image super-resolution (STISR) aims to improve the resolution and
visual quality of low-resolution (LR) scene text images, and consequently boost
the performance of text recognition. However, most of existing STISR methods
regard text images as natural scene images, ignoring the categorical
information of text. In this paper, we make an inspiring attempt to embed
categorical text prior into STISR model training. Specifically, we adopt the
character probability sequence as the text prior, which can be obtained
conveniently from a text recognition model. The text prior provides categorical
guidance to recover high-resolution (HR) text images. On the other hand, the
reconstructed HR image can refine the text prior in return. Finally, we present
a multi-stage text prior guided super-resolution (TPGSR) framework for STISR.
Our experiments on the benchmark TextZoom dataset show that TPGSR can not only
effectively improve the visual quality of scene text images, but also
significantly improve the text recognition accuracy over existing STISR
methods. Our model trained on TextZoom also demonstrates certain generalization
capability to the LR images in other datasets.


Abstract:
  The main goal of this paper is to discuss how to integrate the possibilities
of crowdsourcing platforms with systems supporting workflow to enable the
engagement and interaction with business tasks of a wider group of people.
Thus, this work is an attempt to expand the functional capabilities of typical
business systems by allowing selected process tasks to be performed by
unlimited human resources. Opening business tasks to crowdsourcing, within
established Business Process Management Systems (BPMS) will improve the
flexibility of company processes and allow for lower work-load and greater
specialization among the staff employed on-site. The presented conceptual work
is based on the current international standards in this field, promoted by
Workflows Management Coalition. To this end, the functioning of business
platforms was analysed and their functionality was presented visually, followed
by a proposal and a discussion of how to implement crowdsourcing into workflow
systems.


Abstract:
  The beautiful structures of single and multi-domain proteins are clearly
ordered in some fashion but cannot be readily classified using group theory
methods that are successfully used to describe periodic crystals. For this
reason, protein structures are considered to be aperiodic, and may have evolved
this way for functional purposes, especially in instances that require a
combination of softness and rigidity within the same molecule. By analyzing the
solved protein structures, we show that orientational symmetry is broken in the
aperiodic arrangement of the secondary structural elements (SSEs), which we
deduce by calculating the nematic order parameter, $P_{2}$. We find that the
folded structures are nematic droplets with a broad distribution of $P_{2}$. We
argue that non-zero values of $P_{2}$, leads to an arrangement of the SSEs that
can resist mechanical forces, which is a requirement for allosteric proteins.
Such proteins, which resist mechanical forces in some regions while being
flexible in others, transmit signals from one region of the protein to another
(action at a distance) in response to binding of ligands (oxygen, ATP or other
small molecules).


Abstract:
  Both single-laser and two-laser experiments were conducted to look into the
ion-imaging of Br*(2P1/2) and Br(2P3/2) photo-fragmented from
1-bromo-2-methylbutane in the range 232-240 nm via a detection scheme of (2+1)
resonance-enhanced multiphoton ionization. The angular analysis of these
photofragment distributions yields the anisotropy parameter beta = 1.88 +/-
0.06 for the Br* excited state which arises from a parallel transition, while
beta = 0.63 +/- 0.09 for the Br ground state indicates the contribution from
both a perpendicular transition and a non-adiabatic transition. When a hexapole
coupled with an orienting field was implemented, the parent molecules are
spatially oriented to yield an orientation efficiency |<cos theta>| of 0.15.
Besides the chi angle between the recoil velocity v and the transition dipole
moment mu, orienting molecules allows for the evaluation of the angle alpha
between v and the permanent molecular dipole moment d. The angular analysis of
Br* photofragment distribution yields chi to be 11.5 degrees and alpha in the
range from 160 degrees to 180 degrees with weak dependency. In the two-laser
experiments, the angular anisotropy of Br photofragment distribution was found
to be smaller (0.38 +/- 0.10) when the photolysis wavelength was red-shifted to
240 nm, suggesting the increasing contributions from perpendicular transitions.


Abstract:
  Particularly important to hurricane risk assessment for coastal regions is
finding accurate approximations of return probabilities of maximum windspeeds.
Since extremes in maximum windspeed have a direct relationship to minimums in
the central pressure, accurate windspeed return estimates rely heavily on
proper modeling of the central pressure minima. Using the HURDAT2 database, we
show that the central pressure minima of hurricane events can be appropriately
modeled by a nonstationary extreme value distribution. We also provide and
validate a Poisson distribution with a nonstationary rate parameter to model
returns of hurricane events. Using our nonstationary models and numerical
simulation techniques from established literature, we perform a simulation
study to model returns of maximum windspeeds of hurricane events along the
North Atlantic Coast. We show that our revised model agrees with current data
and results in an expectation of higher maximum windspeeds for all regions
along the coast with the highest maximum windspeeds occurring in the northern
part of the coast.


Abstract:
  In this paper, I generalize the Naszodi-Mendonca method in order to identify
changes in marital preferences over multiple dimensions, such as the partners'
race and education level. Similar to the original Naszodi-Mendonca method,
preferences are identified by the generalized method through estimating their
effects on marriage patterns, in particular, on the share of inter-racial
couples, and the share of educationally homogamous couples. This is not a
simple task because marriage patterns are shaped not only by marital
preferences, but also by the distribution of marriageable males and females by
traits. The generalized Naszodi-Mendonca method is designed for constructing
counterfactuals to perform the decomposition. I illustrate the application of
the generalized Naszodi-Mendonca method by decomposing changes in the
prevalence of racial and educational homogamy in the 1980s using US data from
IPUMS.


Abstract:
  To enable multiple missiles to attack a maneuvering target simultaneously,
fixed-time distributed cooperative guidance laws are proposed in this paper.
Here, we present a novel fixed-time fast nonsingular terminal sliding mode
surface (FNTSMS). In particular, the sliding mode surface not only avoids
singularities but also has the characteristic of a settling time boundary
regardless of the initial conditions. Based on the FNTSMS, we have developed a
distributed guidance law, which has the characteristic of fixed-time
convergence. The guidance law achieves the consensus of range-to-go, relative
velocity along and perpendicular to the line of sight (LOS) direction to
realize the simultaneous attack. In addition, a saturation function is
introduced to avoid the chattering problem caused by the commonly used sign
function. Furthermore, the distributed cooperative guidance law with
communication failure is considered and proved theoretically, which shows that
the proposed guidance law has excellent performance. Finally, the simulation
results verify the performance of the distributed guidance law and its
robustness against communication topology mutations, and explain the phenomenon
in detail.


Abstract:
  For better clustering performance, appropriate representations are critical.
Although many neural network-based metric learning methods have been proposed,
they do not directly train neural networks to improve clustering performance.
We propose a meta-learning method that train neural networks for obtaining
representations such that clustering performance improves when the
representations are clustered by the variational Bayesian (VB) inference with
an infinite Gaussian mixture model. The proposed method can cluster unseen
unlabeled data using knowledge meta-learned with labeled data that are
different from the unlabeled data. For the objective function, we propose a
continuous approximation of the adjusted Rand index (ARI), by which we can
evaluate the clustering performance from soft clustering assignments. Since the
approximated ARI and the VB inference procedure are differentiable, we can
backpropagate the objective function through the VB inference procedure to
train the neural networks. With experiments using text and image data sets, we
demonstrate that our proposed method has a higher adjusted Rand index than
existing methods do.


Abstract:
  Deployed machine learning models are confronted with the problem of changing
data over time, a phenomenon also called concept drift. While existing
approaches of concept drift detection already show convincing results, they
require true labels as a prerequisite for successful drift detection.
Especially in many real-world application scenarios-like the ones covered in
this work-true labels are scarce, and their acquisition is expensive.
Therefore, we introduce a new algorithm for drift detection, Uncertainty Drift
Detection (UDD), which is able to detect drifts without access to true labels.
Our approach is based on the uncertainty estimates provided by a deep neural
network in combination with Monte Carlo Dropout. Structural changes over time
are detected by applying the ADWIN technique on the uncertainty estimates, and
detected drifts trigger a retraining of the prediction model. In contrast to
input data-based drift detection, our approach considers the effects of the
current input data on the properties of the prediction model rather than
detecting change on the input data only (which can lead to unnecessary
retrainings). We show that UDD outperforms other state-of-the-art strategies on
two synthetic as well as ten real-world data sets for both regression and
classification tasks.


Abstract:
  The present study examined 4698 Indian Coronary Artery Disease research
publications, as indexed in Web of Science database during 1990-2019, with a
view to understand their growth rate, global share, citation impact,
international collaborative papers, distribution of publications by broad
subjects, productivity and citation profile of top organizations and authors,
and preferred media of communication. The Indian publications registered an
annual average growth rate of 11.47%, global share of 1.14%, international
collaborative publications share of 38.89% and its citation impact averaged to
25.58 citations per paper. Among broad subjects, Cardiovascular System &
Cardiology contributed the largest publications share of 19.14% in Indian
coronary artery disease output, followed by Neurosciences & Neurology (14.94%),
Pharmacology & Pharmacy (8.51%), etc. during 1990-2019. Among various
organizations and authors contributing to Indian coronary artery disease
research, the top 20 organizations and top 30 authors together contributed
40.70% and 37.29% respectively as their share of Indian publication output and
38.36% and 33.13% respectively as their share of Indian citation output during
1990-2019. Among 1222 contributing journals in Indian coronary artery disease
research, the top 30 journals registered 30.80% share during 1990-2019. There
is an urgent need to increase the publication output, improve research quality
and improve international collaboration. Indian government also needs to come
up with a policy for identification, screening, diagnosis and treatment of
coronary artery disease patients, besides curriculum reform in teaching,
capacity building, patient education and political support are badly needed.


Abstract:
  Fix $R>1$ and let $A_R=\{1/R\le |z|\le R \}$ be an annulus. Also, let $K(R)$
denote the smallest constant such that $A_R$ is a $K(R)$-spectral set for the
bounded linear operator $T\in \mathcal{B}(H)$ whenever $||T||\le R$ and
$||T^{-1}||\le R.$ We show that $K(R)\ge 2, \text{ for all } R>1. $ This
improves on previous results by Badea, Beckermann and Crouzeix.


Abstract:
  We describe an improvement on the magnetic scalar potential approach to the
design of an electromagnet, which incorporates the need to wind the coil as a
helix. Any magnetic field that can be described by a magnetic scalar potential
is produced with high fidelity within a Target region; all fields are confined
within a larger Return. The helical winding only affects the field in the
Return.


Abstract:
  We introduce the use of conditional generative adversarial networks
forgeneralised gravitational wave burst generation in the time
domain.Generativeadversarial networks are generative machine learning models
that produce new databased on the features of the training data set. We
condition the network on fiveclasses of time-series signals that are often used
to characterise gravitational waveburst searches: sine-Gaussian, ringdown,
white noise burst, Gaussian pulse and binaryblack hole merger. We show that the
model can replicate the features of these standardsignal classes and, in
addition, produce generalised burst signals through interpolationand class
mixing. We also present an example application where a convolutional
neuralnetwork classifier is trained on burst signals generated by our
conditional generativeadversarial network. We show that a convolutional neural
network classifier trainedonly on the standard five signal classes has a poorer
detection efficiency than aconvolutional neural network classifier trained on a
population of generalised burstsignals drawn from the combined signal class
space.


Abstract:
  The analogy between self-similar time series with given Hurst exponent H and
Markovian, Gaussian stochastic processes with multiplicative noise and entropic
index q (Borland, PRE 57, 6, 6634-6642, 1998) allows us to explain the
empirical results reported in (Pavithran et al., EPL, 129 2020 24004) and
(Pavithran et al. Sci. Reports 10.1 (2020) 1-8) with the help of the properties
of the nonextensive entropy Sq of index q: a dominant oscillating mode arises
as H goes to zero in many different systems and its amplitude is proportional
to 1/ H^2 . Thus, a decrease of H acts as precursor of large oscillations of
the state variable, which corresponds to catastrophic events in many problems
of practical interest. In contrast, if H goes to 1 then the time series is
strongly intermittent, fluctuations of the state variable follow a power law
whose exponent depends on H, and exceedingly large event are basically
unpredictable. These predictions agree with observations in problems of
aeroacoustics, aeroelasticity, electric engineering, hydrology, laser physics,
meteorology, plasma physics, plasticity, polemology, seismology and
thermoacoustics.


Abstract:
  We discuss relations between the initial boundary value problem (IBVP) and
quasi-local Hamiltonians in GR. The latter have traditionally been based on
Dirichlet boundary conditions, which however are shown here to be ill-posed for
the IBVP. We present and analyse several other choices of boundary conditions
which are better behaved with respect to the IBVP and carry out a corresponding
Hamiltonian analysis, using the framework of the covariant phase space method.


Abstract:
  Providing personalized explanations for recommendations can help users to
understand the underlying insight of the recommendation results, which is
helpful to the effectiveness, transparency, persuasiveness and trustworthiness
of recommender systems. Current explainable recommendation models mostly
generate textual explanations based on pre-defined sentence templates. However,
the expressiveness power of template-based explanation sentences are limited to
the pre-defined expressions, and manually defining the expressions require
significant human efforts. Motivated by this problem, we propose to generate
free-text natural language explanations for personalized recommendation. In
particular, we propose a hierarchical sequence-to-sequence model (HSS) for
personalized explanation generation. Different from conventional sentence
generation in NLP research, a great challenge of explanation generation in
e-commerce recommendation is that not all sentences in user reviews are of
explanation purpose. To solve the problem, we further propose an auto-denoising
mechanism based on topical item feature words for sentence generation.
Experiments on various e-commerce product domains show that our approach can
not only improve the recommendation accuracy, but also the explanation quality
in terms of the offline measures and feature words coverage. This research is
one of the initial steps to grant intelligent agents with the ability to
explain itself based on natural language sentences.


Abstract:
  We consider the single image super-resolution (SISR) problem, where a
high-resolution (HR) image is generated based on a low-resolution (LR) input.
Recently, generative adversarial networks (GANs) become popular to hallucinate
details. Most methods along this line rely on a predefined single-LR-single-HR
mapping, which is not flexible enough for the SISR task. Also, GAN-generated
fake details may often undermine the realism of the whole image. We address
these issues by proposing best-buddy GANs (Beby-GAN) for rich-detail SISR.
Relaxing the immutable one-to-one constraint, we allow the estimated patches to
dynamically seek the best supervision during training, which is beneficial to
producing more reasonable details. Besides, we propose a region-aware
adversarial learning strategy that directs our model to focus on generating
details for textured areas adaptively. Extensive experiments justify the
effectiveness of our method. An ultra-high-resolution 4K dataset is also
constructed to facilitate future super-resolution research.


Abstract:
  A long-standing challenge in artificial intelligence is lifelong learning. In
lifelong learning, many tasks are presented in sequence and learners must
efficiently transfer knowledge between tasks while avoiding catastrophic
forgetting over long lifetimes. On these problems, policy reuse and other
multi-policy reinforcement learning techniques can learn many tasks. However,
they can generate many temporary or permanent policies, resulting in memory
issues. Consequently, there is a need for lifetime-scalable methods that
continually refine a policy library of a pre-defined size. This paper presents
a first approach to lifetime-scalable policy reuse. To pre-select the number of
policies, a notion of task capacity, the maximal number of tasks that a policy
can accurately solve, is proposed. To evaluate lifetime policy reuse using this
method, two state-of-the-art single-actor base-learners are compared: 1) a
value-based reinforcement learner, Deep Q-Network (DQN) or Deep Recurrent
Q-Network (DRQN); and 2) an actor-critic reinforcement learner, Proximal Policy
Optimisation (PPO) with or without Long Short-Term Memory layer. By selecting
the number of policies based on task capacity, D(R)QN achieves near-optimal
performance with 6 policies in a 27-task MDP domain and 9 policies in an
18-task POMDP domain; with fewer policies, catastrophic forgetting and negative
transfer are observed. Due to slow, monotonic improvement, PPO requires fewer
policies, 1 policy for the 27-task domain and 4 policies for the 18-task
domain, but it learns the tasks with lower accuracy than D(R)QN. These findings
validate lifetime-scalable policy reuse and suggest using D(R)QN for larger and
PPO for smaller library sizes.


Abstract:
  We prove that the general linear groups of the integers, Gaussian integers,
and Eisenstein integers satisfy homological stability of slope 1 when using
$\mathbb{Z}[1/2]$-coefficients and of slope $2/3$ when using
$\mathbb{Z}$-coefficients.


Abstract:
  The sources of reliable, code-level information about vulnerabilities that
affect open-source software (OSS) are scarce, which hinders a broad adoption of
advanced tools that provide code-level detection and assessment of vulnerable
OSS dependencies.
  In this paper, we study the extent to which the output of off-the-shelf
static code analyzers can be used as a source of features to represent commits
in Machine Learning (ML) applications. In particular, we investigate how such
features can be used to construct embeddings and train ML models to
automatically identify source code commits that contain vulnerability fixes.
  We analyze such embeddings for security-relevant and non-security-relevant
commits, and we show that, although in isolation they are not different in a
statistically significant manner, it is possible to use them to construct a ML
pipeline that achieves results comparable with the state of the art.
  We also found that the combination of our method with commit2vec represents a
tangible improvement over the state of the art in the automatic identification
of commits that fix vulnerabilities: the ML models we construct and commit2vec
are complementary, the former being more generally applicable, albeit not as
accurate.


Abstract:
  We review a combinatoric approach to the Hodge Conjecture for Fermat
Varieties and announce new cases where the conjecture is true.


Abstract:
  This article studies the impact of online news on social and economic
consumer perceptions through the application of semantic network analysis.
Using almost 1.3 million online articles on Italian media covering a period of
four years, we assessed the incremental predictive power of economic-related
keywords on the Consumer Confidence Index. We transformed news into networks of
co-occurring words and calculated the semantic importance of specific keywords,
to see if words appearing in the articles could anticipate consumers'
judgements about the economic situation. Results show that economic-related
keywords have a stronger predictive power if we consider the current households
and national situation, while their predictive power is less significant with
regards to expectations about the future. Our indicator of semantic importance
offers a complementary approach to estimate consumer confidence, lessening the
limitations of traditional survey-based methods.


Abstract:
  We establish a direct connection between general tensor networks and deep
feed-forward artificial neural networks. The core of our results is the
construction of neural-network layers that efficiently perform tensor
contractions, and that use commonly adopted non-linear activation functions.
The resulting deep networks feature a number of edges that closely matches the
contraction complexity of the tensor networks to be approximated. In the
context of many-body quantum states, this result establishes that
neural-network states have strictly the same or higher expressive power than
practically usable variational tensor networks. As an example, we show that all
matrix product states can be efficiently written as neural-network states with
a number of edges polynomial in the bond dimension and depth logarithmic in the
system size. The opposite instead does not hold true, and our results imply
that there exist quantum states that are not efficiently expressible in terms
of matrix product states or practically usable PEPS, but that are instead
efficiently expressible with neural network states.


Abstract:
  Moir\'e super-potentials in two-dimensional materials allow unprecedented
control of the ratio between kinetic and interaction energy. By this, they pave
the way to study a wide variety of strongly correlated physics under a new
light. In particular, the transition metal dichalcogenides (TMDs) are promising
candidate "quantum simulators" of the Hubbard model on a triangular lattice.
Indeed, Mott and generalized Wigner crystals have been observed in such
devices. Here we theoretically propose to extend this model into the
multi-orbital regime by focusing on electron doped systems at filling higher
than 2. As opposed to hole bands, the electronic bands in TMD materials include
two, nearly degenerate species, which can be viewed as two orbitals with
different effective mass and binding energy. Using realistic band-structure
parameters and a slave-rotor mean-field theory, we find that an orbitally
selective Mott (OSM) phase can be stabilized over a wide range of fillings,
where one band is locked in a commensurate Mott state, while the other remains
itinerant with variable density. This scenario thus, realizes the basic
ingredients in the Kondo lattice model: A periodic lattice of localized
magnetic moments interacting with metallic states. We also discuss possible
experimental signatures of the OSM state.


Abstract:
  Mitchell Feigenbaum discovered an intriguing property of viewing images
through cylindrical mirrors or looking into water. Because the eye is a lens
with an opening of about 5mm, many different rays of reflected images reach the
eye, and need to be interpreted by the visual system. This has the surprising
effect that what one perceives depends on the orientation of the head, whether
it is tilted or not. I explain and illustrate this phenomenon on the example of
a human eye looking at a ruler immersed in water.


Abstract:
  The restoration lemma by Afek, Bremler-Barr, Kaplan, Cohen, and Merritt
[Dist. Comp. '02] proves that, in an undirected unweighted graph, any
replacement shortest path avoiding a failing edge can be expressed as the
concatenation of two original shortest paths. However, the lemma is
tiebreaking-sensitive: if one selects a particular canonical shortest path for
each node pair, it is no longer guaranteed that one can build replacement paths
by concatenating two selected shortest paths. They left as an open problem
whether a method of shortest path tiebreaking with this desirable property is
generally possible.
  We settle this question affirmatively with the first general construction of
restorable tiebreaking schemes. We then show applications to various problems
in fault-tolerant network design. These include a faster algorithm for subset
replacement paths, more efficient fault-tolerant (exact) distance labeling
schemes, fault-tolerant subset distance preservers and $+4$ additive spanners
with improved sparsity, and fast distributed algorithms that construct these
objects. For example, an almost immediate corollary of our restorable
tiebreaking scheme is the first nontrivial distributed construction of sparse
fault-tolerant distance preservers resilient to three faults.


Abstract:
  We give a new proof for the central limit theorem in probability for the
directed polymer model in a bounded environment with bond disorder in the
interior of the weak disorder phase. In the same setup, we also show that the
large deviation rate function agrees with that of the underlying random walk.
In addition, for the Brownian polymer model, we show that the central limit
theorem holds almost surely in the whole weak disorder phase.
  The results are proved using the moment bound from [20] and a new tool
introduced in this paper, which allows a quantitative comparison between the
associated martingales at different inverse temperatures. This comparison is
made precise using the noise operator $T_\rho$ acting on the environment by
independent resampling.


Abstract:
  A splinter is a notion of singularity that has seen numerous recent
applications, especially in connection with the direct summand theorem, the
mixed characteristic minimal model program, Cohen-Macaulayness of absolute
integral closures and cohomology vanishing theorems. Nevertheless, many basic
questions about these singularities remain elusive. One outstanding problem is
whether the splinter property spreads from a point to an open neighborhood of a
noetherian scheme. Our paper addresses this problem in prime characteristic,
where we show that a locally noetherian scheme that has finite Frobenius or
that is locally essentially of finite type over a quasi-excellent local ring
has an open splinter locus. In particular, all varieties over fields of
positive characteristic have open splinter loci. Intimate connections are
established between the openness of splinter loci and $F$-compatible ideals,
which are prime characteristic analogues of log canonical centers. We prove the
surprising fact that for a large class of noetherian rings with pure (aka
universally injective) Frobenius, the splinter condition is detected by the
splitting of a single generically \'etale finite extension. We also show that
for a noetherian $\textbf{N}$-graded ring over a field, the homogeneous maximal
ideal detects the splinter property.


Abstract:
  We consider accelerated black hole horizons with and without defects. These
horizons appear in the $C$-metric solution to Einstein equations and in its
generalization to the case where external fields are present. These solutions
realize a variety of physical processes, from the decay of a cosmic string by a
black hole pair nucleation to the creation of a black hole pair by an external
electromagnetic field. Here, we show that such geometries exhibit an infinite
set of symmetries in their near horizon region, generalizing in this way
previous results for smooth isolated horizons. By considering the limit close
to both the black hole and the acceleration horizons, we show that a sensible
set of asymptotic boundary conditions gets preserved by supertranslation and
superrotation transformations. By acting on the geometry with such
transformations, we derive the superrotated, supertranslated version of the
$C$-metric and compute the associated conserved charges.


Abstract:
  Multi-access coded caching schemes from cross resolvable designs (CRD) have
been reported recently \cite{KNRarXiv}. To be able to compare coded caching
schemes with different number of users and possibly with different number of
caches a new metric called rate-per-user was introduced and it was shown that
under this new metric the schemes from CRDs perform better than the
Maddah-Ali-Niesen scheme in the large memory regime. In this paper a new class
of CRDs is presented and it is shown that the multi-access coded caching
schemes derived from these CRDs perform better than the Maddah-Ali-Niesen
scheme in the entire memory regime. Comparison with other known multi-access
coding schemes is also presented.


Abstract:
  The security of the Internet rests on a small number of open-source
cryptographic libraries: a vulnerability in any one of them threatens to
compromise a significant percentage of web traffic. Despite this potential for
security impact, the characteristics and causes of vulnerabilities in
cryptographic software are not well understood. In this work, we conduct the
first comprehensive analysis of cryptographic libraries and the vulnerabilities
affecting them. We collect data from the National Vulnerability Database,
individual project repositories and mailing lists, and other relevant sources
for eight widely used cryptographic libraries.
  Among our most interesting findings is that only 27.2% of vulnerabilities in
cryptographic libraries are cryptographic issues while 37.2% of vulnerabilities
are memory safety issues, indicating that systems-level bugs are a greater
security concern than the actual cryptographic procedures. In our investigation
of the causes of these vulnerabilities, we find evidence of a strong
correlation between the complexity of these libraries and their (in)security,
empirically demonstrating the potential risks of bloated cryptographic
codebases. We further compare our findings with non-cryptographic systems,
observing that these systems are, indeed, more complex than similar
counterparts, and that this excess complexity appears to produce significantly
more vulnerabilities in cryptographic libraries than in non-cryptographic
software.


Abstract:
  Recent advances in quantum engineering have given us the ability to design
hybrid systems with novel properties normally not present in the regime they
operate in. The coupling of spin ensembles and magnons to microwave resonators
has for instance lead to a much richer understanding of collective effects in
these systems and their potential quantum applications. We can also hybridize
electron and nuclear spin ensembles together in the solid-state regime to
investigate collective effects normally only observed in the atomic, molecular
and optical world. Here we explore in the solid state regime the dynamics of a
double domain nuclear spin ensemble coupled to the Nambu-Goldstone boson in
GaAs semiconductors and show it exhibits both collective and individual
relaxation (thermalization) on very different time scales. Further the
collective relaxation of the nuclear spin ensemble is what one would expect
from superradiant decay. This opens up the possibility for the exploration of
novel collective behaviour in solid state systems where the natural energies
associated with those spins are much less than the thermal energy.


Abstract:
  In this paper, we develop a computational multiscale to solve the parabolic
wave approximation with heterogeneous and variable media. Parabolic wave
approximation is a technique to approximate the full wave equation. One benefit
of the method is that: one wave propagation direction can be taken as an
evolution direction, and we then can discretize it using a classical scheme
like Backward Euler. Consequently, we obtain a set of quasi-gas-dynamic (QGD)
models with different heterogeneous permeability fields. Then, we employ
constraint energy minimization generalized multiscale finite element method
(CEM-GMsFEM) to perform spatial discretization for the problem. The resulting
system can be solved by combining the central difference in time evolution. Due
to the variable media, we apply the technique of proper orthogonal
decomposition (POD) to further the dimension of the problem and solve the
corresponding model problem in the POD space instead of in the whole multiscale
space spanned by all possible multiscale basis functions. We prove the
stability of the full discretization scheme and give the convergence analysis
of the proposed approximation scheme. Numerical results verify the
effectiveness of the proposed method.


Abstract:
  Nickel-based complex oxides have served as a playground for decades in the
quest for a copper-oxide analog of the high-temperature (high-Tc)
superconductivity. They may provide key points towards understanding the
mechanism of the high-Tc and an alternative route for a room-temperature
superconductor. The recent discovery of superconductivity in the infinite-layer
nickelate thin films has put this pursuit to an end. Having complete control in
material preparation and a full understanding of the properties and electronic
structures becomes the center of gravity of current research in nickelates.
Thus far, material synthesis remains challenging. The demonstration of perfect
diamagnetism is still missing, and understanding the role of the interface and
bulk to the superconducting properties is still lacking. Here, we synthesized
high-quality Nd0.8Sr0.2NiO2 thin films with different thicknesses and
investigated the interface and strain effects on the electrical, magnetic and
optical properties. The perfect diamagnetism is demonstrated, confirming the
occurrence of superconductivity in the thin films. Unlike the thick films in
which the normal state Hall coefficient (RH) changes signs from negative to
positive as the temperature decreases, the RH of the films thinner than 6.1-nm
remains negative at the whole temperature range below 300 K, suggesting a
thickness-driven band structure modification. The X-ray spectroscopy reveals
the Ni-O hybridization nature in doped finite-layer nickelates, and the
hybridization is enhanced as the thickness decreases. Consistent with band
structure calculations on nickelate/SrTiO3 interfaces, the interface and strain
effect induce the dominating electron-like band in the ultrathin film, thus
causing the sign-change of the RH.


Abstract:
  Recommending medications for patients using electronic health records (EHRs)
is a crucial data mining task for an intelligent healthcare system. It can
assist doctors in making clinical decisions more efficiently. However, the
inherent complexity of the EHR data renders it as a challenging task: (1)
Multilevel structures: the EHR data typically contains multilevel structures
which are closely related with the decision-making pathways, e.g., laboratory
results lead to disease diagnoses, and then contribute to the prescribed
medications; (2) Multiple sequences interactions: multiple sequences in EHR
data are usually closely correlated with each other; (3) Abundant noise: lots
of task-unrelated features or noise information within EHR data generally
result in suboptimal performance. To tackle the above challenges, we propose a
multilevel selective and interactive network (MeSIN) for medication
recommendation. Specifically, MeSIN is designed with three components. First,
an attentional selective module (ASM) is applied to assign flexible attention
scores to different medical codes embeddings by their relevance to the
recommended medications in every admission. Second, we incorporate a novel
interactive long-short term memory network (InLSTM) to reinforce the
interactions of multilevel medical sequences in EHR data with the help of the
calibrated memory-augmented cell and an enhanced input gate. Finally, we employ
a global selective fusion module (GSFM) to infuse the multi-sourced information
embeddings into final patient representations for medications recommendation.
To validate our method, extensive experiments have been conducted on a
real-world clinical dataset. The results demonstrate a consistent superiority
of our framework over several baselines and testify the effectiveness of our
proposed approach.


Abstract:
  Quantum information is typically encoded in the state of a qubit that is
decoupled from the environment. In contrast, waveguide quantum electrodynamics
studies qubits coupled to a mode continuum, exposing them to a loss channel and
causing quantum information to be lost before coherent operations can be
performed. Here we restore coherence by realizing a dark state that exploits
symmetry properties and interactions between four qubits. Dark states decouple
from the waveguide and are thus a valuable resource for quantum information but
also come with a challenge: they cannot be controlled by the waveguide drive.
We overcome this problem by designing a drive that utilizes the symmetry
properties of the collective state manifold allowing us to selectively drive
both bright and dark states. The decay time of the dark state exceeds that of
the waveguide-limited single qubit by more than two orders of magnitude.
Spectroscopy on the second excitation manifold provides further insight into
the level structure of the hybridized system. Our experiment paves the way for
implementations of quantum many-body physics in waveguides and the realization
of quantum information protocols using decoherence-free subspaces.


Abstract:
  Sparse neural networks have been widely applied to reduce the computational
demands of training and deploying over-parameterized deep neural networks. For
inference acceleration, methods that discover a sparse network from a
pre-trained dense network (dense-to-sparse training) work effectively.
Recently, dynamic sparse training (DST) has been proposed to train sparse
neural networks without pre-training a dense model (sparse-to-sparse training),
so that the training process can also be accelerated. However, previous
sparse-to-sparse methods mainly focus on Multilayer Perceptron Networks (MLPs)
and Convolutional Neural Networks (CNNs), failing to match the performance of
dense-to-sparse methods in the Recurrent Neural Networks (RNNs) setting. In
this paper, we propose an approach to train intrinsically sparse RNNs with a
fixed parameter count in one single run, without compromising performance.
During training, we allow RNN layers to have a non-uniform redistribution
across cell gates for better regularization. Further, we propose SNT-ASGD, a
novel variant of the averaged stochastic gradient optimizer, which
significantly improves the performance of all sparse training methods for RNNs.
Using these strategies, we achieve state-of-the-art sparse training results,
better than the dense-to-sparse methods, with various types of RNNs on Penn
TreeBank and Wikitext-2 datasets. Our codes are available at
https://github.com/Shiweiliuiiiiiii/Selfish-RNN.


Abstract:
  Attention-based pre-trained language models such as GPT-2 brought
considerable progress to end-to-end dialogue modelling. However, they also
present considerable risks for task-oriented dialogue, such as lack of
knowledge grounding or diversity. To address these issues, we introduce
modified training objectives for language model finetuning, and we employ
massive data augmentation via back-translation to increase the diversity of the
training data. We further examine the possibilities of combining data from
multiples sources to improve performance on the target dataset. We carefully
evaluate our contributions with both human and automatic methods. Our model
substantially outperforms the baseline on the MultiWOZ data and shows
competitive performance with state of the art in both automatic and human
evaluation.


Abstract:
  The incremental poses computed through odometry can be integrated over time
to calculate the pose of a device with respect to an initial location. The
resulting global pose may be used to formulate a second, consistency based,
loss term in a deep odometry setting. In such cases where multiple losses are
imposed on a network, the uncertainty over each output can be derived to weigh
the different loss terms in a maximum likelihood setting. However, when
imposing a constraint on the integrated transformation, due to how only
odometry is estimated at each iteration of the algorithm, there is no
information about the uncertainty associated with the global pose to weigh the
global loss term. In this paper, we associate uncertainties with the output
poses of a deep odometry network and propagate the uncertainties through each
iteration. Our goal is to use the estimated covariance matrix at each
incremental step to weigh the loss at the corresponding step while weighting
the global loss term using the compounded uncertainty. This formulation
provides an adaptive method to weigh the incremental and integrated loss terms
against each other, noting the increase in uncertainty as new estimates arrive.
We provide quantitative and qualitative analysis of pose estimates and show
that our method surpasses the accuracy of the state-of-the-art Visual Odometry
approaches. Then, uncertainty estimates are evaluated and comparisons against
fixed baselines are provided. Finally, the uncertainty values are used in a
realistic example to show the effectiveness of uncertainty quantification for
localization.


Abstract:
  Learning to model and reconstruct humans in clothing is challenging due to
articulation, non-rigid deformation, and varying clothing types and topologies.
To enable learning, the choice of representation is the key. Recent work uses
neural networks to parameterize local surface elements. This approach captures
locally coherent geometry and non-planar details, can deal with varying
topology, and does not require registered training data. However, naively using
such methods to model 3D clothed humans fails to capture fine-grained local
deformations and generalizes poorly. To address this, we present three key
innovations: First, we deform surface elements based on a human body model such
that large-scale deformations caused by articulation are explicitly separated
from topological changes and local clothing deformations. Second, we address
the limitations of existing neural surface elements by regressing local
geometry from local features, significantly improving the expressiveness.
Third, we learn a pose embedding on a 2D parameterization space that encodes
posed body geometry, improving generalization to unseen poses by reducing
non-local spurious correlations. We demonstrate the efficacy of our surface
representation by learning models of complex clothing from point clouds. The
clothing can change topology and deviate from the topology of the body. Once
learned, we can animate previously unseen motions, producing high-quality point
clouds, from which we generate realistic images with neural rendering. We
assess the importance of each technical contribution and show that our approach
outperforms the state-of-the-art methods in terms of reconstruction accuracy
and inference time. The code is available for research purposes at
https://qianlim.github.io/SCALE .


Abstract:
  The X and Gamma Imaging Spectrometer instrument on-board the THESEUS mission
(selected by ESA in the framework of the Cosmic Vision M5 launch opportunity,
currently in phase A) is based on a detection plane composed of several
thousands of single active elements. Each element comprises a 4.5x4.5x30 mm 3
CsI(Tl) scintillator bar, optically coupled at both ends to Silicon Drift
Detectors (SDDs). The SDDs acts both as photodetectors for the scintillation
light and as direct X-ray sensors. In this paper the design of the XGIS
detection plane is reviewed, outlining the strategic choices in terms of
modularity and redundancy of the system. Results on detector-electronics
prototypes are also described. Moreover, the design and development of the
low-noise front-end electronics is presented, emphasizing the innovative
architectural design based on custom-designed Application-Specific Integrated
Circuits (ASICs).


Abstract:
  In the last few years, significant advances have been made in understanding
the distributions of exoplanet populations and the architecture of planetary
systems. We review the recent progress of planet statistics, with a focus on
the inner <~ 1 AU region of the planetary system that has been fairly
thoroughly surveyed by the Kepler mission. We also discuss the theoretical
implications of these statistical results for planet formation and dynamical
evolution.


Abstract:
  Let $G=(V(G),E(G))$ be a simple graph with vertex set $V(G)$ and edge set
$E(G)$. Let $S$ be a subset of $V(G)$, and let $B(S)$ be the set of neighbours
of $S$ in $V(G) \setminus S$. The differential $\partial(S)$ of $S$ is defined
as $|B(S)|-|S|$. The maximum value of $\partial(S)$ taken over all subsets
$S\subseteq V$ is the differential $\partial(G)$ of $G$. A graph operator is a
mapping $F: G\rightarrow G'$, where $G$ and $G'$ are families of graphs.The
graph $\S{G}$ is defined as the graph obtained from $G$ con bipartici\'on de
v\'ertices $V(G)\cup E(G)$, donde hay tantas aristas entre $v \in V(G)$ y $e
\in E(G)$, como veces $e$ sea incidente con $v$ en $G$. In this paper we study
the relationship between $\partial(G)$ and $\partial(\S{G})$. Besides, we
relate the differential of a graph with known parameters of a graph, namely,
its domination and independence number.


Abstract:
  Recently, heatmap regression models have become the mainstream in locating
facial landmarks. To keep computation affordable and reduce memory usage, the
whole procedure involves downsampling from the raw image to the output heatmap.
However, how much impact will the quantization error introduced by downsampling
bring? The problem is hardly systematically investigated among previous works.
This work fills the blank and we are the first to quantitatively analyze the
negative gain. The statistical results show the NME generated by quantization
error is even larger than 1/3 of the SOTA item, which is a serious obstacle for
making a new breakthrough in face alignment. To compensate the impact of
quantization effect, we propose a novel method, called Heatmap In Heatmap(HIH),
which leverages two categories of heatmaps as label representation to encode
coordinate. And in HIH, the range of one heatmap represents a pixel of the
other category of heatmap. Also, we even combine the face alignment with
solutions of other fields to make a comparison. Extensive experiments on
various benchmarks show the feasibility of HIH and the superior performance
than other solutions. Moreover, the mean error reaches to 4.18 on WFLW, which
exceeds SOTA a lot. Our source code are made publicly available at
supplementary material.


Abstract:
  Increased levels of digitalization in society expose companies to new
security threats, requiring them to establish adequate security and privacy
measures. Additionally, the presence of exogenous forces like new regulations,
e.g., GDPR and the global COVID-19 pandemic, pose new challenges for companies
that should preserve an adequate level of security while having to adapt to
change. In this paper, we investigate such challenges through a two-phase study
in companies located in Denmark -- a country characterized by a high level of
digitalization and trust -- focusing on software development and tech-related
companies. Our results show a number of issues, most notably i) a misalignment
between software developers and management when it comes to the implementation
of security and privacy measures, ii) difficulties in adapting company
practices in light of implementing GDPR compliance, and iii) different views on
the need to adapt security measures to cope with the COVID-19 pandemic.


Abstract:
  In our paper, we present Deep Learning models with a layer differentiated
training method which were used for the SHARED TASK@ CONSTRAINT 2021 sub-tasks
COVID19 Fake News Detection in English and Hostile Post Detection in Hindi. We
propose a Layer Differentiated training procedure for training a pre-trained
ULMFiT arXiv:1801.06146 model. We used special tokens to annotate specific
parts of the tweets to improve language understanding and gain insights on the
model making the tweets more interpretable. The other two submissions included
a modified RoBERTa model and a simple Random Forest Classifier. The proposed
approach scored a precision and f1 score of 0.96728972 and 0.967324832
respectively for sub-task "COVID19 Fake News Detection in English". Also,
Coarse-Grained Hostility f1 Score and Weighted FineGrained f1 score of 0.908648
and 0.533907 respectively for sub-task Hostile Post Detection in Hindi. The
proposed approach ranked 61st out of 164 in the sub-task "COVID19 Fake News
Detection in English and 18th out of 45 in the sub-task Hostile Post Detection
in Hindi".


Abstract:
  The angular-averaged differential cross section (dcs) of the elastic electron
proton (ep) scattering, covering Q^2 < 1.0GeV^2, was fitted via a combined
modified eq-scatterings where q is a point particle. The modifications
represent the cloud-covering effects to q. An energy-decaying ratio (edr) was
derived by inspecting the generated dcs ep from the form factor data gathered
at Mainz Microtron (A1-Collaboration) and Continuous Electron Beam Accelerator
Facility (Jefferson Laboratory) when compared to the dcs eq with modified
relativistic recoil factor. The diminishing cloud layer, edr, has a decay rate
of -2.8 for the data sets under investigation. The formulated SBM and SEM
fitting models use the bare and effective u and d-quark masses, respectively,
while SCBM and SCEM integrate other considerations. Three comparison methods
were used and all of them favor the models with other additional
considerations. SCEM was the most favored model in general.


Abstract:
  The periodic microphases that self-assemble in systems with competing
short-range attractive and long-range repulsive interactions are structurally
both rich and elegant. Significant theoretical and computational efforts have
thus been dedicated to untangling their properties. By contrast, disordered
microphases, which are structurally just as rich but nowhere near as elegant,
have not been as carefully considered. Part of the difficulty is that simple
mean-field descriptions make a homogeneity assumption that washes away all of
their structural features. Here, we study disordered microphases by exactly
solving a SALR model on the Bethe lattice. By sidestepping the homogenization
assumption, this treatment recapitulates many of the key structural regimes of
disordered microphases, including particle and void cluster fluids as well as
gelation. This analysis also provides physical insight into the relationship
between various structural and thermal observables, between criticality and
physical percolation, as well as between glassiness and microphase ordering.


Abstract:
  In order to formalize Distributed Ledger Technologies and their
interconnections, a recent line of research work has formulated the notion of
Distributed Ledger Object (DLO), which is a concurrent object that maintains a
totally ordered sequence of records, abstracting blockchains and distributed
ledgers. Through DLO, the Atomic Appends problem, intended as the need of a
primitive able to append multiple records to distinct ledgers in an atomic way,
is studied as a basic interconnection problem among ledgers.
  In this work, we propose the Distributed Grow-only Set object (DSO), which
instead of maintaining a sequence of records, as in a DLO, maintains a set of
records in an immutable way: only Add and Get operations are provided. This
object is inspired by the Grow-only Set (G-Set) data type which is part of the
Conflict-free Replicated Data Types. We formally specify the object and we
provide a consensus-free Byzantine-tolerant implementation that guarantees
eventual consistency. We then use our Byzantine-tolerant DSO (BDSO)
implementation to provide consensus-free algorithmic solutions to the Atomic
Appends and Atomic Adds (the analogous problem of atomic appends applied on
G-Sets) problems, as well as to construct consensus-free Single-Writer BDLOs.
We believe that the BDSO has applications beyond the above-mentioned problems.


Abstract:
  We study the network pricing problem where the leader maximizes their revenue
by determining the optimal amounts of tolls to charge on a set of arcs, under
the assumption that the followers will react rationally and choose the shortest
paths to travel. Many distinct single-level reformulations to this bilevel
optimization program have been proposed, however, their relationship has not
been established. In this paper, we aim to build a connection between those
reformulations and explore the combination of the path representation with
various modeling options, allowing us to generate 12 different reformulations
of the problem. Moreover, we propose a new path enumeration scheme, path-based
preprocessing, and hybrid framework to further improve performance and
robustness when solving the final model. We provide numerical results,
comparing all the derived reformulations and confirming the efficiency of the
novel dimensionality reduction procedures.


Abstract:
  The rapidly evolving field of Artificial Intelligence necessitates automated
approaches to co-design neural network architecture and neural accelerators to
maximize system efficiency and address productivity challenges. To enable joint
optimization of this vast space, there has been growing interest in
differentiable NN-HW co-design. Fully differentiable co-design has reduced the
resource requirements for discovering optimized NN-HW configurations, but fail
to adapt to general hardware accelerator search spaces. This is due to the
existence of non-synthesizable (invalid) designs in the search space of many
hardware accelerators. To enable efficient and realizable co-design of
configurable hardware accelerators with arbitrary neural network search spaces,
we introduce RHNAS. RHNAS is a method that combines reinforcement learning for
hardware optimization with differentiable neural architecture search. RHNAS
discovers realizable NN-HW designs with 1.84x lower latency and 1.86x lower
energy-delay product (EDP) on ImageNet and 2.81x lower latency and 3.30x lower
EDP on CIFAR-10 over the default hardware accelerator design.


Abstract:
  We review Hodge structures, relating filtrations, Galois Theory and
Jordan-Holder structures. The prototypical case of periods of Riemann surfaces
is compared with the Galois-Artin framework of algebraic numbers.


Abstract:
  We consider the $L^\infty$-optimal mass transportation problem \[
\min_{\Pi(\mu, \nu)} \gamma-\mathrm{ess\,sup\,} c(x,y), \] for a new class of
costs $c(x,y)$ for which we introduce a tentative notion of twist condition. In
particular we study the conditions under which the infinitely-motonone
minimizers are induced by a transportation map. We also state a uniqueness
result for infinitely cyclically monotone Monge minimizers that corresponds to
this class of cost functions. We compare the results to previous works.


Abstract:
  Aims: Our Gulf War Illness (GWI) study conducts combinatorial screening of
many interactive neural and humoral biomarkers in order to establish
predictive, diagnostic, and therapeutic targets. We encounter obstacles at
every stage of the biomarker discovery process, from sample acquisition,
bio-marker extraction to multi-aspect, multi-way interaction analysis, due to
the study complexity and lack of support for complex data problem solutions. We
introduce a novel data platform, named ROSALIND, to overcome the challenges,
foster healthy and vital collaborations and advance scientific inquiries.
  Main methods: ROSALIND is a researcher-centered, study-specific data
platform. It provides vital support of individual creativity and effort in
collaborative research. We follow the principles etched in the platform name -
ROSALIND stands for resource organisms with self-governed accessibility,
linkability, integrability, neutrality, and dependability. We translate, encode
and implement the principles in the platform with novel use of advanced
concepts and techniques to ensure and protect data integrity and research
integrity. From a researcher's vantage point, ROSALIND embodies nuance
utilities and advanced functionalities in one system, beyond conventional
storage, archive and data management.
  Key findings: The deployment of ROSALIND in our GWI study in recent 12 months
has accelerated the pace of data experiment and analysis, removed numerous
error sources, and increased research quality and productivity.
  Significance: ROSALIND seems the first to address data integrity and research
integrity in tandem with digital measures and means. It also promises a new
type of distributed research networks with individualized data platforms
connected in various self-organized collaboration configurations.


Abstract:
  We analyse the behaviour of the exponential sampling series $S_{w}^{\chi}f$
at jump discontinuity of the bounded signal $f.$ We obtain a representation
lemma that is used for analysing the series $S_{w}^{\chi}f$ and we establish
approximation of jump discontinuity functions by the series $S_{w}^{\chi}f.$
The rate of approximation of the exponential sampling series $S_{w}^{\chi}f$ is
obtained in terms of logarithmic modulus of continuity of functions and the
round-off and time-jitter errors are also studied. Finally we give some
graphical representation of approximation of discontinuous functions by
$S_{w}^{\chi}f$ using suitable kernels.


Abstract:
  The Minho Quotation Resource was originally released in 2012. It provided
approximately 500,000 quotes from business leaders, analysts and politicians
that spanned the period from 2008 to 2012. The original resource had several
failings which include a large number of missing job titles and affiliations as
well as unnormalised job titles which produced a large variation in spellings
and formats of the same employment position. Also, there were numerous
duplicate posts. This update has standardised the job title text as well as the
imputation of missing job titles and affiliations. Duplicate quotes have been
deleted. This update also provides some metaphor and simile extraction as well
as an emotion distribution of the quotes. This update has also replaced an
antiquated version of Lucene index with a JSONL format as well as a rudimentary
interface that can query the data supplied with the resource. It is hoped that
this update will encourage the study of business communication in a time of a
financial crisis.


Abstract:
  We present the development of a machine learning based pipeline to fully
automate the calibration of the frequency comb used to read out optical/IR
Microwave Kinetic Inductance Detector (MKID) arrays. This process involves
determining the resonant frequency and optimal drive power of every pixel (i.e.
resonator) in the array, which is typically done manually. Modern optical/IR
MKID arrays, such as DARKNESS (DARK-speckle Near-infrared Energy-resolving
Superconducting Spectrophotometer) and MEC (MKID Exoplanet Camera), contain
10-20,000 pixels, making the calibration process extremely time consuming; each
2000 pixel feedline requires 4-6 hours of manual tuning. Here we present a
pipeline which uses a single convolutional neural network (CNN) to perform both
resonator identification and tuning simultaneously. We find that our pipeline
has performance equal to that of the manual tuning process, and requires just
twelve minutes of computational time per feedline.


Abstract:
  Artificial spin ice systems have seen burgeoning interest due to their
intriguing physics and potential applications in reprogrammable memory, logic
and magnonics. Integration of artificial spin ice with functional magnonics is
a relatively recent research direction, with a host of promising results. As
the field progresses, direct in-depth comparisons of distinct artificial spin
systems are crucial to advancing the field. While studies have investigated the
effects of different lattice geometries, little comparison exists between
systems comprising continuously connected nanostructures, where spin-waves
propagate via dipole-exchange interaction, and systems with nanobars
disconnected at vertices where spin-wave propagation occurs via stray
dipolar-field. Gaining understanding of how these very different coupling
methods affect both spin-wave dynamics and magnetic reversal is key for the
field to progress and provides crucial system-design information including for
future systems containing combinations of connected and disconnected elements.
Here, we study the magnonic response of two kagome spin ices via Brillouin
light scattering, a continuously connected system and a disconnected system
with vertex gaps. We observe distinct high-frequency dynamics and magnetization
reversal regimes between the systems, with key distinctions in spin-wave
localization and mode quantization, microstate-trajectory during reversal and
internal field-profiles. These observations are pertinent for the fundamental
understanding of artificial spin systems and broader design and engineering of
reconfigurable functional magnonic crystals.


Abstract:
  Studies using asteroseismic ages and rotation rates from star-spot rotation
have indicated that standard age-rotation relations may break down roughly
half-way through the main sequence lifetime, a phenomenon referred to as
weakened magnetic braking. While rotation rates from spots can be difficult to
determine for older, less active stars, rotational splitting of asteroseismic
oscillation frequencies can provide rotation rates for both active and
quiescent stars, and so can confirm whether this effect really takes place on
the main sequence.
  We obtained asteroseismic rotation rates of 91 main sequence stars showing
high signal-to-noise modes of oscillation. Using these new rotation rates,
along with effective temperatures, metallicities and seismic masses and ages,
we built a hierarchical Bayesian mixture model to determine whether the
ensemble more closely agreed with a standard rotational evolution scenario, or
one where weakened magnetic braking takes place. The weakened magnetic braking
scenario was found to be 98.4% more likely for our stellar ensemble, adding to
the growing body of evidence for this stage of stellar rotational evolution.
This work represents the largest catalogue of seismic rotation on the main
sequence to date, opening up possibilities for more detailed ensemble analysis
of rotational evolution with Kepler.


Abstract:
  We establish the existence and uniqueness of solutions to stochastic 2D
Navier-Stokes equations in a time-dependent domain driven by Brownian motion. A
martingale solution is constructed through domain transformation and
appropriate Galerkin approximations on time-dependent spaces. The probabilistic
strong solution follows from the pathwise uniqueness and the Yamada-Watanable
theorem.


Abstract:
  Visible-infrared cross-modality person re-identification (VI-ReID), whose aim
is to match person images between visible and infrared modality, is a
challenging cross-modality image retrieval task. Most existing works integrate
batch normalization layers into their neural network, but we found out that
batch normalization layers would lead to two types of distribution gap: 1)
inter-mini-batch distribution gap -- the distribution gap of the same modality
between each mini-batch; 2) intra-mini-batch modality distribution gap -- the
distribution gap of different modality within the same mini-batch. To address
these problems, we propose a new batch normalization layer called Modality
Batch Normalization (MBN), which normalizes each modality sub-mini-batch
respectively instead of the whole mini-batch, and can reduce these distribution
gap significantly. Extensive experiments show that our MBN is able to boost the
performance of VI-ReID models, even with different datasets, backbones and
losses.


Abstract:
  Defect detection in the manufacturing industry is of utmost importance for
product quality inspection. Recently, optical defect detection has been
investigated as an anomaly detection using different deep learning methods.
However, the recent works do not explore the use of point pattern features,
such as SIFT for anomaly detection using the recently developed set-based
methods. In this paper, we present an evaluation of different point pattern
feature detectors and descriptors for defect detection application. The
evaluation is performed within the random finite set framework. Handcrafted
point pattern features, such as SIFT as well as deep features are used in this
evaluation. Random finite set-based defect detection is compared with
state-of-the-arts anomaly detection methods. The results show that using point
pattern features, such as SIFT as data points for random finite set-based
anomaly detection achieves the most consistent defect detection accuracy on the
MVTec-AD dataset.


Abstract:
  We compute the sphere and disk partition functions in semiclassical Liouville
and analogous quantities in double-scaled matrix integrals. The quantity
sphere/disk^2 is unambiguous and we find a precise numerical match between the
Liouville answer and the matrix integral answer. An application is to show that
the sphere partition function in JT gravity is infinite.


Abstract:
  2019 is the bicentenary of George Gabriel Stokes, who in 1851 described the
drag - Stokes drag - on a body moving immersed in a fluid, and 2020 is the
centenary of Christopher Robin Milne, for whom the game of poohsticks was
invented; his father A. A. Milne's "The House at Pooh Corner", in which it was
first described in print, appeared in 1928. So this is an apt moment to review
the state of the art of the fluid mechanics of a solid body in a complex fluid
flow, and one floating at the interface between two fluids in motion.
Poohsticks pertains to the latter category, when the two fluids are water and
air.


Abstract:
  We prove that all Gibbs measures of the $q$-state Potts model on
$\mathbb{Z}^2$ are linear combinations of the extremal measures obtained as
thermodynamic limits under free or monochromatic boundary conditions. In
particular all Gibbs measures are invariant under translations. This statement
is new at points of first-order phase transition, that is at $T=T_{c}(q)$ when
$q>4$. In this case the structure of Gibbs measures is the most complex in the
sense that there exist $q+1$ distinct extremal measures.
  Most of the work is devoted to the FK-percolation model on $\mathbb{Z}^{2}$
with $q\geq 1$, where we prove that every Gibbs measure is a linear combination
of the free and wired ones. The arguments are non-quantitative and follow the
spirit of the seminal works of Aizenman and Higuchi, which established the
Gibbs structure for the two-dimensional Ising model. Infinite-range
dependencies in FK-percolation (i.e., a weaker spatial Markov property) pose
serious additional difficulties compared to the case of the Ising model. For
example, it is not automatic, albeit true, that thermodynamic limits are Gibbs.
The result for the Potts model is then derived using the Edwards-Sokal coupling
and auto-duality. The latter ingredient is necessary since applying the
Edwards-Sokal procedure to a Gibbs measure for the Potts model does not
automatically produce a Gibbs measure for FK-percolation.
  Finally, the proof is generic enough to adapt to the FK-percolation and Potts
models on the triangular and hexagonal lattices and to the loop $O(n)$ model in
the range of parameters for which its spin representation is positively
associated.


Abstract:
  Label Smoothing (LS) improves model generalization through penalizing models
from generating overconfident output distributions. For each training sample
the LS strategy smooths the one-hot encoded training signal by distributing its
distribution mass over the non-ground truth classes. We extend this technique
by considering example pairs, coined PLS. PLS first creates midpoint samples by
averaging random sample pairs and then learns a smoothing distribution during
training for each of these midpoint samples, resulting in midpoints with high
uncertainty labels for training. We empirically show that PLS significantly
outperforms LS, achieving up to 30% of relative classification error reduction.
We also visualize that PLS produces very low winning softmax scores for both in
and out of distribution samples.


Abstract:
  Mobile phones enable the collection of a wealth of private information, from
unique identifiers (e.g., email addresses), to a user's location, to their text
messages. This information can be harvested by apps and sent to third parties,
which can use it for a variety of purposes. In this paper we perform the
largest study of private information collection (PIC) on Android to date.
Leveraging an anonymized dataset collected from the customers of a popular
mobile security product, we analyze the flows of sensitive information
generated by 2.1M unique apps installed by 17.3M users over a period of 21
months between 2018 and 2019. We find that 87.2% of all devices send private
information to at least five different domains, and that actors active in
different regions (e.g., Asia compared to Europe) are interested in collecting
different types of information. The United States (62% of the total) and China
(7% of total flows) are the countries that collect most private information.
Our findings raise issues regarding data regulation, and would encourage
policymakers to further regulate how private information is used by and shared
among the companies and how accountability can be truly guaranteed.


Abstract:
  We consider the problem of absence of backscattering in the transport of
Manakov solitons on a line. The concept of transparent boundary conditions is
used for modeling the reflectionless propagation of Manakov vector solitons in
a one-dimensional domain. Artificial boundary conditions that ensure the
absence of backscattering are derived and their numerical implementation is
demonstrated.


Abstract:
  The concept of social trust has attracted an attention of information
processors/data scientists and information consumers / business firms. One of
the main reasons for acquiring the value of SBD is to provide frameworks and
methodologies using which the credibility of online social services users can
be evaluated. These approaches should be scalable to accommodate large-scale
social data. Hence, there is a need for well comprehending of social trust to
improve and expand the analysis process and inferring credibility of social big
data. Given the exposed environment's settings and fewer limitations related to
online social services, the medium allows legitimate and genuine users as well
as spammers and other low trustworthy users to publish and spread their
content. This chapter presents an overview of the notion of credibility in the
context of SBD. It also list an array of approaches to measure and evaluate the
trustworthiness of users and their contents. Finally, a case study is presented
that incorporates semantic analysis and machine learning modules to measure and
predict users' trustworthiness in numerous domains in different time periods.
The evaluation of the conducted experiment validates the applicability of the
incorporated machine learning techniques to predict highly trustworthy
domain-based users.


Abstract:
  This paper studies a single-machine scheduling problem with a non-renewable
resource (NR-SSP) and total weighted completion time criterion. The
non-renewable resource is consumed when the machine starts processing a job. We
consider the case where each job's weight in the objective function is
proportional to its resource consumption amount. The problem is known to be
NP-hard in this case. We propose a 3-approximation list scheduling algorithm
for this problem. Besides, we show that the approximation ratio 3 is tight for
the algorithm.