Abstract:
  This article describes an action rule induction algorithm based on a
sequential covering approach. Two variants of the algorithm are presented. The
algorithm allows the action rule induction from a source and a target decision
class point of view. The application of rule quality measures enables the
induction of action rules that meet various quality criteria. The article also
presents a method for recommendation induction. The recommendations indicate
the actions to be taken to move a given test example, representing the source
class, to the target one. The recommendation method is based on a set of
induced action rules. The experimental part of the article presents the results
of the algorithm operation on sixteen data sets. As a result of the conducted
research the Ac-Rules package was made available.


Abstract:
  We investigate information theoretic properties of low rank (less than or
equal to 3) quantum channels with $SU(2)$-symmetry, where we have a complete
description. We prove that PPT property coincides with entanglement-breaking
property and that degradability seldomly holds in this class. In connection
with these results we will demonstrate how we can compute Holevo and coherent
information of those channels. In particular, we exhibit a strong form of
additivity violation of coherent information, which resembles the
superactivation of coherent information of depolarizing channels.


Abstract:
  Variational auto-encoders (VAEs) are a powerful approach to unsupervised
learning. They enable scalable approximate posterior inference in
latent-variable models using variational inference (VI). A VAE posits a
variational family parameterized by a deep neural network called an encoder
that takes data as input. This encoder is shared across all the observations,
which amortizes the cost of inference. However the encoder of a VAE has the
undesirable property that it maps a given observation and a
semantics-preserving transformation of it to different latent representations.
This "inconsistency" of the encoder lowers the quality of the learned
representations, especially for downstream tasks, and also negatively affects
generalization. In this paper, we propose a regularization method to enforce
consistency in VAEs. The idea is to minimize the Kullback-Leibler (KL)
divergence between the variational distribution when conditioning on the
observation and the variational distribution when conditioning on a random
semantic-preserving transformation of this observation. This regularization is
applicable to any VAE. In our experiments we apply it to four different VAE
variants on several benchmark datasets and found it always improves the quality
of the learned representations but also leads to better generalization. In
particular, when applied to the Nouveau Variational Auto-Encoder (NVAE), our
regularization method yields state-of-the-art performance on MNIST and
CIFAR-10. We also applied our method to 3D data and found it learns
representations of superior quality as measured by accuracy on a downstream
classification task.


Abstract:
  We consider here a problem of population dynamics modeled on a logistic
equation with both classical and nonlocal diffusion, possibly in combination
with a pollination term.
  The environment considered is a niche with zero-flux, according to a new type
of Neumann condition.
  We discuss the situations that are more favorable for the survival of the
species, in terms of the first positive eigenvalue.
  Quite surprisingly, the eigenvalue analysis for the one dimensional case is
structurally different than the higher dimensional setting, and it sensibly
depends on the nonlocal character of the dispersal.
  The mathematical framework of this problem takes into consideration the
equation $$ -\alpha\Delta u +\beta(-\Delta)^su =(m-\mu u)u+\tau\;J\star u
\qquad{\mbox{in }}\; \Omega,$$ where $m$ can change sign.
  This equation is endowed with a set of Neumann condition that combines the
classical normal derivative prescription and the nonlocal condition introduced
in [S. Dipierro, X. Ros-Oton, E. Valdinoci, Rev. Mat. Iberoam. (2017)].
  We will establish the existence of a minimal solution for this problem and
provide a throughout discussion on whether it is possible to obtain non-trivial
solutions (corresponding to the survival of the population).
  The investigation will rely on a quantitative analysis of the first
eigenvalue of the associated problem and on precise asymptotics for large lower
and upper bounds of the resource.
  In this, we also analyze the role played by the optimization strategy in the
distribution of the resources, showing concrete examples that are unfavorable
for survival, in spite of the large resources that are available in the
environment.


Abstract:
  The Rosseland mean opacity of dust in protoplanetary disks is often
calculated assuming the interstellar medium (ISM) size distribution and a
constant dust-to-gas ratio. However, the dust size distribution and the
dust-to-gas ratio in protoplanetary disks are distinct from those of the ISM.
Here, we use simple dust evolution models that incorporate grain growth and
transport to calculate the time evolution of mean opacity of dust grains as a
function of distance from the star. Dust dynamics and size distribution are
sensitive to the assumed value of the turbulence strength $\alpha_{\rm t}$ and
the velocity at which grains fragment $v_{\rm frag}$. For moderate-to-low
turbulence strengths of $\alpha_{\rm t} \lesssim 10^{-3}$ and substantial
differences in $v_{\rm frag}$ for icy and ice-free grains, we find a spatially
non-uniform dust-to-gas ratio and grain size distribution that deviate
significantly from the ISM values, in agreement with previous studies. The
effect of a non-uniform dust-to-gas ratio on the Rosseland mean opacity
dominates over that of the size distribution. This spatially varying -- that is
non-monotonic -- dust-to-gas ratio creates a region in the protoplanetary disk
that is optimal for producing hydrogen-rich planets, potentially explaining the
apparent peak in gas giant planet occurrence rate at intermediate distances.
The enhanced dust-to-gas ratio within the ice line also suppresses gas
accretion rates onto sub-Neptune cores, thus stifling their tendency to undergo
runaway gas accretion within disk lifetimes. Finally, our work corroborates the
idea that low mass cores with large primordial gaseous envelopes
(`super-puffs') originate beyond the ice line.


Abstract:
  Demand response (DR) programs have gained much attention during the last
three decades to optimize the decisions of the electricity market participants
considering the demand-side management (DSM). It can potentially enhance system
reliability and manages price volatility by modifying the amount or time of
electricity consumption. This paper proposes a novel game-theoretical model
accounting for the relationship between retailers (leaders) and consumers
(followers) in a dynamic price environment where both players optimize their
respective economic goals under uncertainty. The model is solved under two
frameworks. First by considering retailer's market power and second by
accounting for an equilibrium setting based on a Cournot game. These are
formulated in terms of a mathematical program with equilibrium constraints
(MPEC) and with a mixed-integer linear program (MILP), respectively. In
particular, the retailers' market power model is first formulated as a bi-level
optimization problem, and the MPEC is subsequently derived by replacing the
consumers' problem (lower level) with its Karush-Kuhn-Tucker (KKT) optimality
conditions. In contrast, the Cournot equilibrium model is solved as a MILP by
concatenating the retailer's and consumers' KKT optimality conditions. The
solution sets, the practical approaches for solutions, the required techniques
to test and compare the performance of the model are undertaken with realistic
data. Numerical simulations confirm the applicability and effectiveness of the
proposed model to explore the interactions of markets power and DR programs.
The results confirm that consumers are better off in an equilibrium framework
while the retailer increases its expected profit when the market power is
considered. However, we show how these results are highly affected by the
levels of of consumers' flexibility.


Abstract:
  In a human-robot collaborative task where a robot helps its partner by
finding described objects, the depth dimension plays a critical role in
successful task completion. Existing studies have mostly focused on
comprehending the object descriptions using RGB images. However, 3-dimensional
space perception that includes depth information is fundamental in real-world
environments. In this work, we propose a method to identify the described
objects considering depth dimension data. Using depth features significantly
improves performance in scenes where depth data is critical to disambiguate the
objects and across our whole evaluation dataset that contains objects that can
be specified with and without the depth dimension.


Abstract:
  This paper introduces techniques to integrate WordNet into a Fuzzy Logic
Programming system. Since WordNet relates words but does not give graded
information on the relation between them, we have implemented standard
similarity measures and new directives allowing the proximity equations linking
two words to be generated with an approximation degree. Proximity equations are
the key syntactic structures which, in addition to a weak unification
algorithm, make a flexible query-answering process possible in this kind of
programming language. This addition widens the scope of Fuzzy Logic
Programming, allowing certain forms of lexical reasoning, and reinforcing
Natural Language Processing applications.
  [Under consideration in Theory and Practice of Logic Programming (TPLP)]


Abstract:
  The IoT ecosystem suffers from a variety of problems around security,
identity, access control, data flow and data storage that introduce friction
into interactions between various parties. In many respects, the situation is
similar to the early days of the Internet, where, prior to the establishment of
Internet Exchanges, routing between different BGP autonomous systems was often
point to point. We propose a similar solution, the IoT Exchange, where IoT
device owners can register their devices and offer data for sale or can upload
data into the IoT services of any of the big hyperscale cloud platforms for
further processing. The goal of the IoT Exchange is to break down the silos
within which device wireless connectivity types and cloud provider IoT systems
constrain users to operate. In addition, if the device owner needs to maintain
the data close to the edge to reduce access latency, the MillenniumDB service
running in an edge data center with minimal latency to the edge device,
provides a database with a variety of schema engines (SQL, noSQL, etc). The IoT
exchange uses decentralized identifiers for identity management and verifiable
credentials for authorizing software updates and to control access to the
devices, to avoid dependence on certificate authorities and other centralized
identity and authorization management systems. In addition, verifiable
credentials provide a way whereby privacy preserving processing can be applied
to traffic between a device and an end data or control customer, if some risk
of privacy compromise exists.


Abstract:
  In this work, Bernstein's concentration inequalities for squared integrable
matrix-valued discrete-time martingales are obtained. Based on Lieb's theory
and Bernstein's condition, a suitable supermartingale can be constructed. Our
proof is largely based on this new exponential supermartingale, Freedman's
method, and Doob's stopping theorem. Our result can be regarded as an extension
of Tropp's work (ECP, 2012).


Abstract:
  We study the interplay between properties of measures on a Boolean algebra A
and forcing names for ultrafilters on A. We show that several well known
measure theoretic properties of Boolean algebras (such as supporting a strictly
positive measure or carrying only separable measures) have quite natural
characterizations in the forcing language. We show some applications of this
approach. In particular, we reprove a theorem of Kunen saying that in the
classical random model there are no towers of height $\omega_2$.


Abstract:
  We show a connection between sampling and optimization on discrete domains.
For a family of distributions $\mu$ defined on size $k$ subsets of a ground set
of elements that is closed under external fields, we show that rapid mixing of
natural local random walks implies the existence of simple approximation
algorithms to find $\max \mu(\cdot)$. More precisely we show that if
(multi-step) down-up random walks have spectral gap at least inverse
polynomially large in $k$, then (multi-step) local search can find $\max
\mu(\cdot)$ within a factor of $k^{O(k)}$. As the main application of our
result, we show a simple nearly-optimal $k^{O(k)}$-factor approximation
algorithm for MAP inference on nonsymmetric DPPs. This is the first nontrivial
multiplicative approximation for finding the largest size $k$ principal minor
of a square (not-necessarily-symmetric) matrix $L$ with $L+L^\intercal\succeq
0$.
  We establish the connection between sampling and optimization by showing that
an exchange inequality, a concept rooted in discrete convex analysis, can be
derived from fast mixing of local random walks. We further connect exchange
inequalities with composable core-sets for optimization, generalizing recent
results on composable core-sets for DPP maximization to arbitrary distributions
that satisfy either the strongly Rayleigh property or that have a log-concave
generating polynomial.


Abstract:
  Given any equation and the integers $[n] = \{1,\dots,n\}$, one can ask: does
every 2-coloring of $[n]$ contain at least as many monochromatic solutions
(asymptotically) as a uniformly random 2-coloring? We show that the answer is
"no" for every 3-term linear equation with integer coefficients, i.e. there
always exist 2-colorings with asymptotically fewer monochromatic solutions than
uniformly random ones.


Abstract:
  The proliferation of social media platforms like Twitter has heightened the
consequences of the spread of misinformation. To understand and model the
spread of misinformation, in this paper, we leveraged the SEIZ (Susceptible,
Exposed, Infected, Skeptics) epidemiological model to describe the underlying
process that delineates the spread of misinformation on Twitter. Compared to
the other epidemiological models, this model produces broader results because
it includes the additional Skeptics (Z) compartment, wherein a user may be
exposed to an item of misinformation but not engage in any reaction to it, and
the additional Exposed (E) compartment, wherein the user may need some time
before deciding to spread a misinformation item. We analyzed misinformation
regarding the unrest in Washington, D.C. in the month of March 2020 which was
propagated by the use of the #DCblackout hashtag by different users across the
U.S. on Twitter. Our analysis shows that misinformation can be modeled using
the concept of epidemiology. To the best of our knowledge, this research is the
first to attempt to apply the SEIZ epidemiological model to the spread of a
specific item of misinformation, which is a category distinct from that of
rumor, and a hoax on online social media platforms. Applying a mathematical
model can help to understand the trends and dynamics of the spread of
misinformation on Twitter and ultimately help to develop techniques to quickly
identify and control it.


Abstract:
  Given a degeneration of compact projective complex manifolds $X$ over the
punctured disc, with meromorphic singularities, and a relatively ample line
bundle $L$ on $X$, we study spaces of plurisubharmonic metrics on $L$, with
particular focus on (relative) finite-energy conditions. We endow the space
$\hat \cE^1(L)$ of relatively maximal, relative finite-energy metrics with a
$d_1$-type distance given by the Lelong number at zero of the collection of
fibrewise Darvas $d_1$-distances. We show that this metric structure is
complete and geodesic. Seeing $X$ and $L$ as schemes $X_\K$, $L_\K$ over the
discretely-valued field $\K=\mathbb{C}((t))$ of complex Laurent series, we show
that the space $\cE^1(L_\K\an)$ of non-Archimedean finite-energy metrics over
$L_\K\an$ embeds isometrically and geodesically into $\hat \cE^1(L)$, and
characterize its image. This generalizes previous work of
Berman-Boucksom-Jonsson, treating the trivially-valued case. We investigate
consequences regarding convexity of non-Archimedean functionals.


Abstract:
  In this short paper, we will review the proposal of a correspondence between
the doubled geometry of Double Field Theory and the higher geometry of bundle
gerbes. Double Field Theory is T-duality covariant formulation of the
supergravity limit of String Theory, which generalises Kaluza-Klein theory by
unifying metric and Kalb-Ramond field on a doubled-dimensional space. In light
of the proposed correspondence, this doubled geometry is interpreted as an
atlas description of the higher geometry of bundle gerbes. In this sense,
Double Field Theory can be interpreted as a field theory living on the total
space of the bundle gerbe, just like Kaluza-Klein theory is set on the total
space of a principal bundle. This correspondence provides a higher geometric
interpretation for para-Hermitian geometry which opens the door to its
generalisation to the other Extended Field Theories. This review is based on,
but not limited to, my talk at the workshop Generalized Geometry and
Applications at Universit\"at Hamburg on 3rd of March 2020.


Abstract:
  Stagnation detection has been proposed as a mechanism for randomized search
heuristics to escape from local optima by automatically increasing the size of
the neighborhood to find the so-called gap size, i.e., the distance to the next
improvement. Its usefulness has mostly been considered in simple multimodal
landscapes with few local optima that could be crossed one after another. In
multimodal landscapes with a more complex location of optima of similar gap
size, stagnation detection suffers from the fact that the neighborhood size is
frequently reset to $1$ without using gap sizes that were promising in the
past.
  In this paper, we investigate a new mechanism called radius memory which can
be added to stagnation detection to control the search radius more carefully by
giving preference to values that were successful in the past. We implement this
idea in an algorithm called SD-RLS$^{\text{m}}$ and show compared to previous
variants of stagnation detection that it yields speed-ups for linear functions
under uniform constraints and the minimum spanning tree problem. Moreover, its
running time does not significantly deteriorate on unimodal functions and a
generalization of the Jump benchmark. Finally, we present experimental results
carried out to study SD-RLS$^{\text{m}}$ and compare it with other algorithms.


Abstract:
  In this paper we analyze a mixed displacement-pseudostress formulation for
the elasticity eigenvalue problem. We propose a finite element method to
approximate the pseudostress tensor with Raviart-Thomas elements and the
displacement with piecewise polynomials. With the aid of the classic theory for
compact operators, we prove that our method is convergent and does not
introduce spurious modes. Also, we obtain error estimates for the proposed
method. Finally, we report some numerical tests supporting the theoretical
results.


Abstract:
  An impurity hole observed in the Large Helical Device (LHD) is a hollow
density profile of an impurity ion species formed in the core plasma where the
negative (inward-pointing) ambipolar radial electric field ($E_r$) exists.
Although local neoclassical models have predicted the sign of $E_r$ in impurity
hole plasmas is negative for the entire minor radius, an experimental
measurement of an impurity hole plasma has shown that the $E_r$ changes the
sign from negative to positive along the minor radius. In the present work, we
investigate neoclassical impurity transport in an impurity hole plasma using a
global neoclassical simulation code FORTEC-3D. The variation of electrostatic
potential on flux surface ($\Phi_1$) is evaluated from the quasi-neutrality
condition in multi-ion-species plasma by the global simulation. The ambipolar
$E_r$ and neoclassical fluxes are determined in solving a global drift-kinetic
equation including the effect of $\Phi_1$. By the global simulation, we show
that an $E_r$ which changes the sign along the radius is obtained as a solution
of the ambipolar condition and with such an $E_r$, impurity carbon flux can be
outwardly directed even where $E_r<0$ and the carbon density profile is hollow
around the magnetic axis. Furthermore, it is found that the outward carbon flux
is only a factor 2-3 from balancing the modeled inward turbulent flux. Our
result indicates that we have moved one step closer to reproducing the impurity
transport in impurity hole plasmas by kinetic simulation.


Abstract:
  One key property in recommender systems is the long-tail distribution in
user-item interactions where most items only have few user feedback. Improving
the recommendation of tail items can promote novelty and bring positive effects
to both users and providers, and thus is a desirable property of recommender
systems. Current novel recommendation studies over-emphasize the importance of
tail items without differentiating the degree of users' intent on popularity
and often incur a sharp decline of accuracy. Moreover, none of existing methods
has ever taken the extreme case of tail items, i.e., cold-start items without
any interaction, into consideration.
  In this work, we first disclose the mechanism that drives a user's
interaction towards popular or niche items by disentangling her intent into
conformity influence (popularity) and personal interests (preference). We then
present a unified end-to-end framework to simultaneously optimize accuracy and
novelty targets based on the disentangled intent of popularity and that of
preference. We further develop a new paradigm for novel recommendation of
cold-start items which exploits the self-supervised learning technique to model
the correlation between collaborative features and content features. We conduct
extensive experimental results on three real-world datasets. The results
demonstrate that our proposed model yields significant improvements over the
state-of-the-art baselines in terms of accuracy, novelty, coverage, and
trade-off.


Abstract:
  One of the major open problems in noncommutative algebraic geometry is the
classification of noncommutative projective surfaces (or, slightly more
generally, of noetherian connected graded domains of Gelfand-Kirillov dimension
3). In a companion paper the authors described a noncommutative version of
blowing down and, for example, gave a noncommutative analogue of Castelnuovo's
classic theorem that lines of self-intersection (-1) on a smooth surface can be
contracted.
  In this paper we will use these techniques to construct explicit birational
transformations between various noncommutative surfaces containing an elliptic
curve. Notably we show that Van den Bergh's quadrics can be obtained from the
Sklyanin algebra by suitably blowing up and down, and we also provide a
noncommutative analogue of the classical Cremona transform. This extends and
amplifies earlier work of Presotto and Van den Bergh.


Abstract:
  Millions of people are affected by acute and chronic wounds yearly across the
world. Continuous wound monitoring is important for wound specialists to allow
more accurate diagnosis and optimization of management protocols. Machine
Learning-based classification approaches provide optimal care strategies
resulting in more reliable outcomes, cost savings, healing time reduction, and
improved patient satisfaction. In this study, we use a deep learning-based
method to classify burn wound images into two or three different categories
based on the wound conditions. A pre-trained deep convolutional neural network,
AlexNet, is fine-tuned using a burn wound image dataset and utilized as the
classifier. The classifier's performance is evaluated using classification
metrics such as accuracy, precision, and recall as well as confusion matrix. A
comparison with previous works that used the same dataset showed that our
designed classifier improved the classification accuracy by more than 8%.


Abstract:
  Low light conditions in aerial images adversely affect the performance of
several vision based applications. There is a need for methods that can
efficiently remove the low light attributes and assist in the performance of
key vision tasks. In this work, we propose a new method that is capable of
enhancing the low light image in a self-supervised fashion, and sequentially
apply detection and segmentation tasks in an end-to-end manner. The proposed
method occupies a very small overhead in terms of memory and computational
power over the original algorithm and delivers superior results. Additionally,
we propose the generation of a new low light aerial dataset using GANs, which
can be used to evaluate vision based networks for similar adverse conditions.


Abstract:
  Automated machine learning (AutoML) systems aim to enable training machine
learning (ML) models for non-ML experts. A shortcoming of these systems is that
when they fail to produce a model with high accuracy, the user has no path to
improve the model other than hiring a data scientist or learning ML -- this
defeats the purpose of AutoML and limits its adoption. We introduce an
interpretable data feedback solution for AutoML. Our solution suggests new data
points for the user to label (without requiring a pool of unlabeled data) to
improve the model's accuracy. Our solution analyzes how features influence the
prediction among all ML models in an AutoML ensemble, and we suggest more data
samples from feature ranges that have high variance in such analysis. Our
evaluation shows that our solution can improve the accuracy of AutoML by 7-8%
and significantly outperforms popular active learning solutions in data
efficiency, all the while providing the added benefit of being interpretable.


Abstract:
  Over the past several years, the electrocardiogram (ECG) has been
investigated for its uniqueness and potential to discriminate between
individuals. This paper discusses how this discriminatory information can help
in continuous user authentication by a wearable chest strap which uses dry
electrodes to obtain a single lead ECG signal. To the best of the authors'
knowledge, this is the first such work which deals with continuous
authentication using a genuine wearable device as most prior works have either
used medical equipment employing gel electrodes to obtain an ECG signal or have
obtained an ECG signal through electrode positions that would not be feasible
using a wearable device. Prior works have also mainly dealt with using the ECG
signal for identification rather than verification, or dealt with using the ECG
signal for discrete authentication. This paper presents a novel algorithm which
uses QRS detection, weighted averaging, Discrete Cosine Transform (DCT), and a
Support Vector Machine (SVM) classifier to determine whether the wearer of the
device should be positively verified or not. Zero intrusion attempts were
successful when tested on a database consisting of 33 subjects.


Abstract:
  Electrochemistry experiments have established that the capacitance of
electrode-electrolyte interfaces is much larger for good metals such as gold
and platinum than for carbon-based materials. Despite the development of
elaborate electrode interaction potentials, to date molecular dynamics
simulations were not able to capture this effect. Here we show that changing
the width of the Gaussian charge distribution used to represent the atomic
charges in gold is an effective way to tune its metallicity. Larger Gaussian
widths lead to a capacitance of aqueous solutions (pure water and 1 molar NaCl)
in good agreement with recent ab initio molecular dynamics results. For pure
water, the increase in the capacitance is not accompanied with structural
changes, while in the presence of salt the Na$^+$ cations tend to adsorb
significantly on the surface. For a strongly metallic gold electrode, these
ions can even form inner sphere complexes on hollow sites of the surface.


Abstract:
  We propose a new kind of coherent state for the general $SO(D+1)$ formulation
of loop quantum gravity in the $(1+D)$-dimensional space-time. Instead of
Thiemann's coherent state for $SO(D+1)$ gauge theory, our coherent spin-network
state is given by constructing proper superposition over quantum numbers of the
spin-networks with vertices labelled by the coherent intertwiners. Such
superposition type coherent states are labelled by the so-called generalized
twisted geometric variables which capture the geometric meaning of discretized
general relativity. We study the basic properties of this kind of coherent
states, i.e., the completeness and peakedness property. Moreover, we show that
the superposition type coherent states are consistent with Thiemann's coherent
state for $SO(D+1)$ gauge theory in large $\eta$ limit.


Abstract:
  The combination of core/shell geometry and band gap engineering in nanowire
heterostructures can be employed to realize systems with novel transport and
optical properties. Here, we report on the growth of InAs/InP/GaAsSb
core-dual-shell nanowires by catalyst-free chemical beam epitaxy on Si(111)
substrates. Detailed morphological, structural, and compositional analyses of
the nanowires as a function of growth parameters were carried out by scanning
and transmission electron microscopy and by energy-dispersive X-ray
spectroscopy. Furthermore, by combining the scanning transmission electron
microscopy-Moire technique with geometric phase analysis, we studied the
residual strain and the relaxation mechanisms in this system. We found that InP
shell facets are well-developed along all the crystallographic directions only
when the nominal thickness is above 1 nm, suggesting an island-growth mode.
Moreover, the crystallographic analysis indicates that both InP and GaAsSb
shells grow almost coherently to the InAs core along the 112 direction and
elastically compressed along the 110 direction. For InP shell thickness above 8
nm, some dislocations and roughening occur at the interfaces. This study
provides useful general guidelines for the fabrication of high-quality devices
based on these core-dual-shell nanowires.


Abstract:
  Self-adjusting computation is an approach for automatically producing dynamic
algorithms from static ones. The approach works by tracking control and data
dependencies, and propagating changes through the dependencies when making an
update. Extensively studied in the sequential setting, some results on parallel
self-adjusting computation exist, but are either only applicable to limited
classes of computations, such as map-reduce, or are ad-hoc systems with no
theoretical analysis of their performance.
  In this paper, we present the first system for parallel self-adjusting
computation that applies to a wide class of nested parallel algorithms and
provides theoretical bounds on the work and span of the resulting dynamic
algorithms. As with bounds in the sequential setting, our bounds relate a
"distance" measure between computations on different inputs to the cost of
propagating an update. However, here we also consider parallelism in the
propagation cost. The main innovation in the paper is in using Series-Parallel
trees (SP trees) to track sequential and parallel control dependencies to allow
propagation of changes to be applied safely in parallel. We show both
theoretically and through experiments that our system allows algorithms to
produce updated results over large datasets significantly faster than
from-scratch execution. We demonstrate our system with several example
applications, including algorithms for dynamic sequences and dynamic trees. In
all cases studied, we show that parallel self-adjusting computation can provide
a significant benefit in both work savings and parallel time.


Abstract:
  We study over-the-air model aggregation in federated edge learning (FEEL)
systems, where channel state information at the transmitters (CSIT) is assumed
to be unavailable. We leverage the reconfigurable intelligent surface (RIS)
technology to align the cascaded channel coefficients for CSIT-free model
aggregation. To this end, we jointly optimize the RIS and the receiver by
minimizing the aggregation error under the channel alignment constraint. We
then develop a difference-of-convex algorithm for the resulting non-convex
optimization. Numerical experiments on image classification show that the
proposed method is able to achieve a similar learning accuracy as the
state-of-the-art CSIT-based solution, demonstrating the efficiency of our
approach in combating the lack of CSIT.


Abstract:
  Voice authentication is drawing increasing attention and becomes an
attractive alternative to passwords for mobile authentication. Recent advances
in mobile technology further accelerate the adoption of voice biometrics in an
array of diverse mobile applications. However, recent studies show that voice
authentication is vulnerable to replay attacks, where an adversary can spoof a
voice authentication system using a pre-recorded voice sample collected from
the victim. In this paper, we propose VoiceLive, a liveness detection system
for both text-dependent and text-independent voice authentication on
smartphones. VoiceLive detects a live user by leveraging the user's unique
vocal system and the stereo recording of smartphones. In particular, utilizing
the built-in gyroscope, loudspeaker, and microphone, VoiceLive first measures
the smartphone's distance and angle from the user, then it captures the
position-specific time-difference-of-arrival (TDoA) changes in a sequence of
phoneme sounds to the two microphones of the phone, and uses such unique TDoA
dynamic which doesn't exist under replay attacks for liveness detection.
VoiceLive is practical as it doesn't require additional hardware but
two-channel stereo recording that is supported by virtually all smartphones.
Our experimental evaluation with 12 participants and different types of phones
shows that VoiceLive achieves over 99% detection accuracy at around 1% Equal
Error Rate (EER) on the text-dependent system and around 99% accuracy and 2%
EER on the text-independent one. Results also show that VoiceLive is robust to
different phone positions, i.e. the user is free to hold the smartphone with
distinct distances and angles.


Abstract:
  This paper investigates how Natural Language Understanding (NLU) could be
applied in Emotion Recognition, a specific task in affective computing. We
finetuned different transformers language models (BERT, DistilBERT, RoBERTa,
XLNet, and ELECTRA) using a fine-grained emotion dataset and evaluating them in
terms of performance (f1-score) and time to complete.


Abstract:
  In this paper we revise the theory of turnpikes in discounted Markov decision
processes, prove the turnpike theorem for the undiscounted model and apply the
results to the specific random walk.


Abstract:
  Deep neural networks are easily misled by adversarial examples. Although lots
of defense methods are proposed, many of them are demonstrated to lose
effectiveness when against properly performed adaptive attacks. How to evaluate
the adversarial robustness effectively is important for the realistic
deployment of deep models, but yet still unclear. To provide a reasonable
solution, one of the primary things is to understand the error (or gap) between
the true adversarial robustness and the evaluated one, what is it and why it
exists. Several works are done in this paper to make it clear. Firstly, we
introduce an interesting phenomenon named gradient traps, which lead to
incompetent adversaries and are demonstrated to be a manifestation of
evaluation error. Then, we analyze the error and identify that there are three
components. Each of them is caused by a specific compromise. Moreover, based on
the above analysis, we present our evaluation suggestions. Experiments on
adversarial training and its variations indicate that: (1) the error does exist
empirically, and (2) these defenses are still vulnerable. We hope these
analyses and results will help the community to develop more powerful defenses.


Abstract:
  Differential dynamic microscopy (DDM) is increasingly used in the fields of
soft matter physics and biophysics to extract the dynamics of microscopic
objects across a range of wavevectors using optical microscopy. Standard DDM is
limited to detecting dynamics no faster than the camera frame rate. We report
on an extension to DDM where we sequentially illuminate the sample with
spectrally-distinct light and image with a color camera. By pulsing blue and
then red light separated by a lag time much smaller than the camera's exposure
time we are able to use this two-color DDM method to measure dynamics occurring
much faster than the camera frame rate.


Abstract:
  In this paper we make a detailed analysis of conservation principles in the
context of a family of fourth-order gravitational theories generated via a
quadratic Lagrangian. In particular, we focus on the associated notion of
energy and start a program related to its study. We also exhibit examples of
solutions which provide intuitions about this notion of energy which allows us
to interpret it, and introduce several study cases where its analysis seems
tractable. Finally, positive energy theorems are presented in restricted
situations.


Abstract:
  We study automorphic categories of nilpotent sheaves under degenerations of
smooth curves to nodal Deligne-Mumford curves. Our constructions realize affine
Hecke operators as the result of bubbling projective lines from marked points.
We use this to construct a "gluing functor" from the automorphic category of a
nodal Deligne-Mumford curve to the automorphic category of a smoothing.


Abstract:
  It has been predicted that the momentum and radiation pressure of light in
negative-index metamaterials (NIMs) reverse their direction from what is
observed for normal materials. The negative refraction and inverse Doppler
effect of light in NIMs have been experimentally observed, but the equally
fundamental phenomenon, the negative radiation pressure of light, still lacks
experimental verification. Here we show that the negative momentum and
radiation pressure of light in NIMs are caused by the sum of the positive
momentum density of the electromagnetic (EM) field and the negative momentum
density of the atoms due to the optical force, which drives the atoms backward
and reduces the local density of atoms at the site of the light field. In
contrast to earlier works, light in NIMs has both negative total momentum and
energy. For the experimental discovery of the negative radiation pressure, one
must record the reflection of both momentum components.


Abstract:
  We attribute deviations of the muon and electron magnetic moments from the
theoretical predictions to the presence of an additional $U(1)^\prime$
supersymmetric model. We interpret the discrepancies between the muon and
electron anomalous magnetic moments to be due to the presence of non-universal
$U(1)^\prime$ charges. In a minimally extended model, we show that requiring
both deviations to be satisfied imposes constraints on the spectrum of the
model, in particular on dark matter candidates and slepton masses and ordering.
Choosing three benchmarks with distinct dark matter features, we study
implications of the model at colliders, concentrating on variables that can
distinguish our non-universal scenario from other $U(1)^\prime$
implementations.


Abstract:
  Differential equations in general and neural ODEs in particular are an
essential technique in continuous-time system identification. While many
deterministic learning algorithms have been designed based on numerical
integration via the adjoint method, many downstream tasks such as active
learning, exploration in reinforcement learning, robust control, or filtering
require accurate estimates of predictive uncertainties. In this work, we
propose a novel approach towards estimating epistemically uncertain neural
ODEs, avoiding the numerical integration bottleneck. Instead of modeling
uncertainty in the ODE parameters, we directly model uncertainties in the state
space. Our algorithm - distributional gradient matching (DGM) - jointly trains
a smoother and a dynamics model and matches their gradients via minimizing a
Wasserstein loss. Our experiments show that, compared to traditional
approximate inference methods based on numerical integration, our approach is
faster to train, faster at predicting previously unseen trajectories, and in
the context of neural ODEs, significantly more accurate.


Abstract:
  Localization based on received signal strength (RSS) has drawn great interest
in the wireless sensor network (WSN). In this paper, we investigate the
RSS-based multi-sources localization problem with unknown transmitted power
under shadow fading. The log-normal shadowing effect is approximated through
Fenton-Wilkinson (F-W) method and maximum likelihood estimation is adopted to
optimize the RSS-based multiple sources localization problem. Moreover, we
exploit a sparse recovery and weighted average of candidates (SR-WAC) based
method to set up an initiation, which can efficiently approach a superior local
optimal solution. It is shown from the simulation results that the proposed
method has a much higher localization accuracy and outperforms the other


Abstract:
  We present an optimization framework based on Lagrange duality and the
scattering $\mathbb{T}$ operator of electromagnetism to construct limits on the
possible features that may be imparted to a collection of output fields from a
collection of input fields, i.e., constraints on achievable optical
transformations and the characteristics of structured materials as
communication channels. Implications of these bounds on the performance of
representative optical devices having multi-wavelength or multiport
functionalities are examined in the context of electromagnetic shielding,
focusing, near-field resolution, and linear computing.


Abstract:
  A common strategy to video understanding is to incorporate spatial and motion
information by fusing features derived from RGB frames and optical flow. In
this work, we introduce a new way to leverage semantic segmentation as an
intermediate representation for video understanding and use it in a way that
requires no additional labeling.
  Second, we propose a general framework which learns the intermediate
representations (optical flow and semantic segmentation) jointly with the final
video understanding task and allows the adaptation of the representations to
the end goal. Despite the use of intermediate representations within the
network, during inference, no additional data beyond RGB sequences is needed,
enabling efficient recognition with a single network.
  Finally, we present a way to find the optimal learning configuration by
searching the best loss weighting via evolution. We obtain more powerful visual
representations for videos which lead to performance gains over the
state-of-the-art.


Abstract:
  Automatic readability assessment (ARA) is the task of evaluating the level of
ease or difficulty of text documents for a target audience. For researchers,
one of the many open problems in the field is to make such models trained for
the task show efficacy even for low-resource languages. In this study, we
propose an alternative way of utilizing the information-rich embeddings of BERT
models with handcrafted linguistic features through a combined method for
readability assessment. Results show that the proposed method outperforms
classical approaches in readability assessment using English and Filipino
datasets, obtaining as high as 12.4% increase in F1 performance. We also show
that the general information encoded in BERT embeddings can be used as a
substitute feature set for low-resource languages like Filipino with limited
semantic and syntactic NLP tools to explicitly extract feature values for the
task.


Abstract:
  The oxytocin effects on large-scale brain networks such as Default Mode
Network (DMN) and Frontoparietal Network (FPN) have been largely studied using
fMRI data. However, these studies are mainly based on the statistical
correlation or Bayesian causality inference, lacking interpretability at
physical and neuroscience level. Here, we propose a physics-based framework of
Kuramoto model to investigate oxytocin effects on the phase dynamic neural
coupling in DMN and FPN. Testing on fMRI data of 59 participants administrated
with either oxytocin or placebo, we demonstrate that oxytocin changes the
topology of brain communities in DMN and FPN, leading to higher synchronization
in the FPN and lower synchronization in the DMN, as well as a higher variance
of the coupling strength within the DMN and more flexible coupling patterns
across time. These results together indicate that oxytocin may increase the
ability to overcome the corresponding internal oscillation dispersion and
support the flexibility in neural synchrony in various social contexts,
providing new evidence for explaining the oxytocin modulated social behaviors.
Our proposed Kuramoto model-based framework can be a potential tool in network
neuroscience and offers physical and neural insights into phase dynamics of the
brain.


Abstract:
  Cell structures were introduced by W. Debski and E. Tymchatyn as a way to
study some classes of topological spaces and their continuous functions by
means of discrete approximations. In this work we weaken the notion of cell
structure and prove that the resulting class of topological space admitting
such a generalized cell structure includes non-regular spaces.


Abstract:
  Many crowdsourced NLP datasets contain systematic gaps and biases that are
identified only after data collection is complete. Identifying these issues
from early data samples during crowdsourcing should make mitigation more
efficient, especially when done iteratively. We take natural language inference
as a test case and ask whether it is beneficial to put a linguist `in the loop'
during data collection to dynamically identify and address gaps in the data by
introducing novel constraints on the task. We directly compare three data
collection protocols: (i) a baseline protocol, (ii) a linguist-in-the-loop
intervention with iteratively-updated constraints on the task, and (iii) an
extension of linguist-in-the-loop that provides direct interaction between
linguists and crowdworkers via a chatroom. The datasets collected with linguist
involvement are more reliably challenging than baseline, without loss of
quality. But we see no evidence that using this data in training leads to
better out-of-domain model performance, and the addition of a chat platform has
no measurable effect on the resulting dataset. We suggest integrating expert
analysis \textit{during} data collection so that the expert can dynamically
address gaps and biases in the dataset.


Abstract:
  The shapes and morphology of the organs and tissues are important prior
knowledge in medical imaging recognition and segmentation. The morphological
operation is a well-known method for morphological feature extraction. As the
morphological operation is performed well in hand-crafted image segmentation
techniques, it is also promising to design an approach to approximate
morphological operation in the convolutional networks. However, using the
traditional convolutional neural network as a black-box is usually hard to
specify the morphological operation action. Here, we introduced a 3D
morphological operation residual block to extract morphological features in
end-to-end deep learning models for semantic segmentation. This study proposed
a novel network block architecture that embedded the morphological operation as
an infinitely strong prior in the convolutional neural network. Several 3D deep
learning models with the proposed morphological operation block were built and
compared in different medical imaging segmentation tasks. Experimental results
showed the proposed network achieved a relatively higher performance in the
segmentation tasks comparing with the conventional approach. In conclusion, the
novel network block could be easily embedded in traditional networks and
efficiently reinforce the deep learning models for medical imaging
segmentation.


Abstract:
  Let XNLP be the class of parameterized problems such that an instance of size
$n$ with parameter $k$ can be solved nondeterministically in time
$f(k)n^{O(1)}$ and space $f(k)\log(n)$ (for some computable function $f$). We
give a wide variety of XNLP-complete problems, such as {\sc List Coloring} and
{\sc Precoloring Extension} with pathwidth as parameter, {\sc Scheduling of
Jobs with Precedence Constraints}, with both number of machines and partial
order width as parameter, {\sc Bandwidth} and variants of {\sc Weighted
CNF-Satisfiability} and reconfiguration problems. In particular, this implies
that all these problems are $W[t]$-hard for all $t$. This also answers a long
standing question on the parameterized complexity of the {\sc Bandwidth}
problem.


Abstract:
  While the Rydberg-blockade regime provides the natural setting for creating
$W$-type entanglement with cold neutral atoms, it is demonstrated here that a
scalable entanglement resource of this type can even be obtained under
completely different physical circumstances. To be more precise, a special
instance of twisted $W$ states -- namely, $\pi$-twisted ones -- can be
engineered in one-dimensional arrays of cold neutral atoms with Rydberg-dressed
resonant dipole-dipole interaction. In particular, it is shown here that this
is possible even when a (dressed) Rydberg excitation is coupled to the motional
degrees of freedom of atoms in their respective, nearly-harmonic optical-dipole
microtraps, which are quantized into dispersionless (zero-dimensional) bosons.
For a specially chosen ("sweet-spot") detuning of the off-resonant dressing
lasers from the relevant internal atomic transitions, the desired $\pi$-twisted
$W$ state of Rydberg-dressed qubits is the ground state of the effective
excitation -- boson Hamiltonian of the system in a broad window of the relevant
parameters. Being at the same time separated from the other eigenstates by a
gap equal to the single-boson energy, this $W$ state can be prepared using a
Rabi-type driving protocol. The corresponding preparation times are independent
of the system size and several orders of magnitude shorter than the effective
lifetimes of the relevant atomic states.


Abstract:
  Recently, neural networks have been successfully employed to improve upon
state-of-the-art performance in ad-hoc retrieval tasks via machine-learned
ranking functions. While neural retrieval models grow in complexity and impact,
little is understood about their correspondence with well-studied IR
principles. Recent work on interpretability in machine learning has provided
tools and techniques to understand neural models in general, yet there has been
little progress towards explaining ranking models.
  We investigate whether one can explain the behavior of neural ranking models
in terms of their congruence with well understood principles of document
ranking by using established theories from axiomatic IR. Axiomatic analysis of
information retrieval models has formalized a set of constraints on ranking
decisions that reasonable retrieval models should fulfill. We operationalize
this axiomatic thinking to reproduce rankings based on combinations of
elementary constraints. This allows us to investigate to what extent the
ranking decisions of neural rankers can be explained in terms of retrieval
axioms, and which axioms apply in which situations. Our experimental study
considers a comprehensive set of axioms over several representative neural
rankers. While the existing axioms can already explain the particularly
confident ranking decisions rather well, future work should extend the axiom
set to also cover the other still "unexplainable" neural IR rank decisions.


Abstract:
  We introduce a natural temporal analogue of Eulerian circuits and prove that,
in contrast with the static case, it is NP-hard to determine whether a given
temporal graph is temporally Eulerian even if strong restrictions are placed on
the structure of the underlying graph and each edge is active at only three
times. However, we do obtain an FPT-algorithm with respect to a new parameter
called interval-membership-width which restricts the times assigned to
different edges; we believe that this parameter will be of independent interest
for other temporal graph problems. Our techniques also allow us to resolve two
open question of Akrida, Mertzios and Spirakis [CIAC 2019] concerning a related
problem of exploring temporal stars. Furthermore, we introduce a vertex-variant
of interval-membership-width (which can be arbitrarily larger than its
edge-counterpart) and use it to obtain an FPT-time algorithm for a natural
vertex-exploration problem that remains hard even when
interval-membership-width is bounded.


Abstract:
  This paper introduces a new benchmarking dataset for marine snow removal of
underwater images. Marine snow is one of the main degradation sources of
underwater images that are caused by small particles, e.g., organic matter and
sand, between the underwater scene and photosensors. We mathematically model
two typical types of marine snow from the observations of real underwater
images. The modeled artifacts are synthesized with underwater images to
construct large-scale pairs of ground-truth and degraded images to calculate
objective qualities for marine snow removal and to train a deep neural network.
We propose two marine snow removal tasks using the dataset and show the first
benchmarking results of marine snow removal. The Marine Snow Removal
Benchmarking Dataset is publicly available online.


Abstract:
  We present a study of undoped AlGaN/GaN separate confinement heterostructures
designed to operate as electron beam pumped ultraviolet lasers. We discuss the
effect of spontaneous and piezoelectric polarization on carrier diffusion,
comparing the results of cathodoluminescence with electronic simulations of the
band structure and Monte Carlo calculations of the electron trajectories.
Carrier collection is significantly improved using an asymmetric graded-index
separate confinement heterostructure (GRINSCH). The graded layers avoid
potential barriers induced by polarization differences in the heterostructure
and serve as strain transition buffers which reduce the mosaicity of the active
region and the linewidth of spontaneous emission.


Abstract:
  We study Property (T) in the $\Gamma(n,k,d)$ model of random groups: as $k$
tends to infinity this gives the Gromov density model, introduced in [Gro93].
We provide bounds for Property (T) in the $k$-angular model of random groups,
i.e. the $ \Gamma (n,k,d)$ model where $k$ is fixed and $n$ tends to infinity.
We also prove that for $d>1\slash 3$, a random group in the $\Gamma(n,k,d)$
model has Property (T) with probability tending to $1$ as $k$ tends to
infinity, strengthening the results of \.{Z}uk and Kotowski--Kotowski, who
consider only groups in the $\Gamma (n,3k,d)$ model.


Abstract:
  The characterisation of small conducting inclusions with low conductivities
in an otherwise uniform background from low-frequency electrical field
measurements has important applications in medical imaging using electrical
impedance tomography as well as in geological imaging using electrical
resistivity tomography. It is known that such objects can be characterised by a
P\'oyla-Szeg\"o polarizability tensor. The coefficients of this tensor for
different shapes and contrasts involves the solution of a transmission problem,
which in general must be done numerically. With a dictionary classification
algorithm in mind, we provide a series of accurate tensor characterisations of
objects that can be used as benchmarks for developers of software tools for
solving the transmission problem. To this end, we apply adaptive the boundary
element method to achieve our accurate solutions.


Abstract:
  Circular dichroism is the differential rate of absorption of right- and
left-handed circularly polarized light by chiral particles. Optical vortices
which convey orbital angular momentum (OAM) possess a chirality associated with
the clockwise or anti-clockwise twisting of their wavefront. Here it is
highlighted that both oriented and randomly oriented chiral particles absorb
photons from twisted beams at different rates depending on whether the vortex
twists to the right or the left through a dipole coupling scheme. This is in
contrast to previous studies that investigated dipole couplings with vortex
modes in the paraxial approximation and showed no such chiral sensitivity to
the vortex handedness: only in oriented media where electric quadrupole
coupling contributes to optical activity effects due to absorption does such a
mechanism exist for paraxial vortices. The distinct difference in the scheme
highlighted in this work is that longitudinal fields are taken account of and
these allow for OAM transfer even during dipole interactions. Due to the vortex
dichroism persisting in randomly oriented collections of chiral particles, the
mechanism has a distinct advantage in its potential applicability in chemical
and biochemical applications where the systems under study are invariably in
the liquid phase. Additionally, the result is put into context in terms of the
quantifiable optical chirality, highlighting that optical OAM can in fact
increase the optical chirality density of an electromagnetic field.


Abstract:
  We consider more general framework than the corresponding one considered in
our previous work on the Hodge-Iwasawa theory. In our current consideration we
consider the corresponding more general base spaces, namely the analytic adic
spaces and analytic perfectoid spaces in Kedlaya's AWS Lecture notes. We hope
our discussion will also shed some light on further generalization to even more
general spaces such as those considered by Gabber-Ramero namely one just
considers certain topological rings which satisfy the Fontaine-Wintenberger
idempotent correspondence and calls them perfectoid generalizing the notions
from Scholze, Fontaine, Kedlaya-Liu and Kedlaya (AWS Lecture notes). Actually
some of the discussion we presented here is already in some more general form
for this purpose (although we have not made enough efforts to write all the
things).


Abstract:
  The onset of rigidity in interacting liquids, as they undergo a transition to
a disordered solid, is associated with a dramatic rearrangement of the
low-frequency vibrational spectrum. In this letter, we derive scaling forms for
the singular dynamical response of disordered viscoelastic networks near both
jamming and rigidity percolation. Using effective-medium theory, we extract
critical exponents, invariant scaling combinations and analytical formulas for
universal scaling functions near these transitions. Our scaling forms describe
the behavior in space and time near the various onsets of rigidity, for rigid
and floppy phases and the crossover region, including diverging length and time
scales at the transitions. We expect that these behaviors can be measured in
systems ranging from colloidal suspensions to anomalous charge-density
fluctuations of "strange" metals.


Abstract:
  The scattering of multispectral incoherent light is a common and unfavorable
signal scrambling in natural scenes. However, the blurred light spot due to
scattering still holds lots of information remaining to be explored. Former
methods failed to recover the polarized hyperspectral information from
scattered incoherent light or relied on additional dispersion elements. Here we
put forward the transmission matrix (TM) approach for extended objects under
incoherent illumination by speculating the unknown TM through experimentally
calibrated or digitally emulated ways. Employing a fiber bundle as a powerful
imaging and dispersion element, we recover the spatial information in 252
polarized-spectral channels from a single speckle, thus achieving single-shot,
high-resolution, broadband hyperspectral imaging for two polarization states
with the cheap, compact, fiber-bundle-only system. Based on the scattering
principle itself, our method not only greatly improves the robustness of the TM
approach to retrieve the input spectral information, but also reveals the
feasibility to explore the polarized spatio-spectral information from blurry
speckles only with the help of simple optical setups.


Abstract:
  We present a BRST analysis of supersymmetry anomalies of $\mathcal{N} = 1$
supersymmetric quantum field theories with anomalous R symmetry. To this end,
we consider the coupling of the matter theory to classical $\mathcal{N} = 1$
new minimal supergravity. We point out that a supersymmetry anomaly cocycle
associated to the $\mathrm{U}(1){}_\mathrm{R}$ field does exist for this
theory. It is non-trivial in the space of supergravity fields (and ghosts), but
it becomes BRST-exact in the functional space that includes antifields.
Equivalently, the $\mathrm{U}(1){}_\mathrm{R}$ supersymmetry anomaly cocycle
vanishes "on-shell". It is therefore removable. However, to remove it --
precisely because it is not trivial in the smaller space of fields -- one needs
to deform the supergravity BRST operator. This deformation is triggered, at
first order in the anomaly coefficient, by a local operator $S_1$ of ghost
number 1. We give a cohomological characterization of $S_1$ and compute it in
full detail. At higher orders in the anomaly coefficient, we expect a priori
that further deformations of the BRST rules are necessary.


Abstract:
  The recent advances of Augmented Reality (AR) in healthcare have shown that
technology is a significant part of the current healthcare system. In recent
days, augmented reality has proposed numerous smart applications in healthcare
domain including wearable access, telemedicine, remote surgery, diagnosis of
medical reports, emergency medicine, etc. The aim of the developed augmented
healthcare application is to improve patient care, increase efficiency, and
decrease costs. This article puts on an effort to review the advances in
AR-based healthcare technologies and goes to peek into the strategies that are
being taken to further this branch of technology. This article explores the
important services of augmented-based healthcare solutions and throws light on
recently invented ones as well as their respective platforms. It also addresses
concurrent concerns and their relevant future challenges. In addition, this
paper analyzes distinct AR security and privacy including security requirements
and attack terminologies. Furthermore, this paper proposes a security model to
minimize security risks. Augmented reality advantages in healthcare, especially
for operating surgery, emergency diagnosis, and medical training is being
demonstrated here thorough proper analysis. To say the least, the article
illustrates a complete overview of augmented reality technology in the modern
healthcare sector by demonstrating its impacts, advancements, current
vulnerabilities; future challenges, and concludes with recommendations to a new
direction for further research.


Abstract:
  We establish the twisted functoriality in nonabelian Hodge theory in positive
characteristic. As an application, we obtain a purely algebraic proof of the
fact that the pullback of a semistable Higgs bundle with vanishing Chern
classes is again semistable.


Abstract:
  The semantic segmentation (SS) task aims to create a dense classification by
labeling at the pixel level each object present on images. Convolutional neural
network (CNN) approaches have been widely used, and exhibited the best results
in this task. However, the loss of spatial precision on the results is a main
drawback that has not been solved. In this work, we propose to use a multi-task
approach by complementing the semantic segmentation task with edge detection,
semantic contour, and distance transform tasks. We propose that by sharing a
common latent space, the complementary tasks can produce more robust
representations that can enhance the semantic labels. We explore the influence
of contour-based tasks on latent space, as well as their impact on the final
results of SS. We demonstrate the effectiveness of learning in a multi-task
setting for hourglass models in the Cityscapes, CamVid, and Freiburg Forest
datasets by improving the state-of-the-art without any refinement
post-processing.


Abstract:
  Lenses are a category theoretic construct and are used in a wide variety of
applications. Symmetric lenses compose to, of course, form new symmetric
lenses. Symmetric lenses are usually represented as spans of asymmetric lenses.
In many applications, the fact that a symmetric lens might also be represented
as a cospan of asymmetric lenses is important, especially for implementation
purposes. However, the composition of symmetric lenses does not preserve the
property that the lenses can be represented by cospans -- two such symmetric
lenses may (and frequently do) compose to form a symmetric lens which cannot be
represented as a cospan of asymmetric lenses. Thus preserving the factorisation
to show how cospans of asymmetric lenses might be used in implementations
becomes important. In 2018, the first work on multilenses was begun.
Multilenses can be represented as multi-spans of asymmetric lenses (often
called 'wide spans', these are spans with an arbitrary finite number of legs).
In this paper we analyse a small but realistic example of a supply chain in
which the cospan representations would be 'composed away' by ordinary symmetric
lens composition, and introduce a new kind of composition which we call
'fusion' in which two ordinary symmetric lenses (spans with two legs) fuse to
form a multilens with three legs preserving the cospan representations, and
more generally, two symmetric multilenses, spans with say m and n legs, fuse to
form a symmetric multilens with m+n-1 legs, again preserving cospan
representations.


Abstract:
  Privacy considerations and bias in datasets are quickly becoming
high-priority issues that the computer vision community needs to face. So far,
little attention has been given to practical solutions that do not involve
collection of new datasets. In this work, we show that for object detection on
COCO, both anonymizing the dataset by blurring faces, as well as swapping faces
in a balanced manner along the gender and skin tone dimension, can retain
object detection performances while preserving privacy and partially balancing
bias.


Abstract:
  Fine-grained estimation of galaxy merger stages from observations is a key
problem useful for validation of our current theoretical understanding of
galaxy formation. To this end, we demonstrate a CNN-based regression model that
is able to predict, for the first time, using a single image, the merger stage
relative to the first perigee passage with a median error of 38.3 million years
(Myrs) over a period of 400 Myrs. This model uses no specific dynamical
modeling and learns only from simulated merger events. We show that our model
provides reasonable estimates on real observations, approximately matching
prior estimates provided by detailed dynamical modeling. We provide a
preliminary interpretability analysis of our models, and demonstrate first
steps toward calibrated uncertainty estimation.


Abstract:
  We present a simple algorithm that perfectly samples configurations from the
unique Gibbs measure of a spin system on a potentially infinite graph $G$. The
sampling algorithm assumes strong spatial mixing together with subexponential
growth of $G$. It produces a finite window onto a perfect sample from the Gibbs
distribution. The run-time is linear in the size of the window.


Abstract:
  In recent years, federated learning has been embraced as an approach for
bringing about collaboration across large populations of learning agents.
However, little is known about how collaboration protocols should take agents'
incentives into account when allocating individual resources for communal
learning in order to maintain such collaborations. Inspired by game theoretic
notions, this paper introduces a framework for incentive-aware learning and
data sharing in federated learning. Our stable and envy-free equilibria capture
notions of collaboration in the presence of agents interested in meeting their
learning objectives while keeping their own sample collection burden low. For
example, in an envy-free equilibrium, no agent would wish to swap their
sampling burden with any other agent and in a stable equilibrium, no agent
would wish to unilaterally reduce their sampling burden.
  In addition to formalizing this framework, our contributions include
characterizing the structural properties of such equilibria, proving when they
exist, and showing how they can be computed. Furthermore, we compare the sample
complexity of incentive-aware collaboration with that of optimal collaboration
when one ignores agents' incentives.


Abstract:
  With the advent of gravitational wave astronomy and first pictures of the
"shadow" of the central black hole of our milky way, theoretical analyses of
black holes (and compact objects mimicking them sufficiently closely) have
become more important than ever. The near future promises more and more
detailed information about the observable black holes and black hole
candidates. This information could lead to important advances on constraints on
or evidence for modifications of general relativity. More precisely, we are
studying the influence of weak teleparallel perturbations on general
relativistic vacuum spacetime geometries in spherical symmetry. We find the
most general family of spherically symmetric, static vacuum solutions of the
theory, which are candidates for describing teleparallel black holes which
emerge as perturbations to the Schwarzschild black hole. We compare our
findings to results on black hole or static, spherically symmetric solutions in
teleparallel gravity discussed in the literature, by comparing the predictions
for classical observables such as the photon sphere, the perihelion shift, the
light deflection, and the Shapiro delay. On the basis of these observables, we
demonstrate that among the solutions we found, there exist spacetime geometries
that lead to much weaker bounds on teleparallel gravity than those found
earlier. Finally, we move on to a discussion of how the teleparallel
perturbations influence the Hawking evaporation in these spacetimes.


Abstract:
  Single channel speech separation has experienced great progress in the last
few years. However, training neural speech separation for a large number of
speakers (e.g., more than 10 speakers) is out of reach for the current methods,
which rely on the Permutation Invariant Loss (PIT). In this work, we present a
permutation invariant training that employs the Hungarian algorithm in order to
train with an $O(C^3)$ time complexity, where $C$ is the number of speakers, in
comparison to $O(C!)$ of PIT based methods. Furthermore, we present a modified
architecture that can handle the increased number of speakers. Our approach
separates up to $20$ speakers and improves the previous results for large $C$
by a wide margin.


Abstract:
  (Partial) ranking loss is a commonly used evaluation measure for multi-label
classification, which is usually optimized with convex surrogates for
computational efficiency. Prior theoretical work on multi-label ranking mainly
focuses on (Fisher) consistency analyses. However, there is a gap between
existing theory and practice -- some pairwise losses can lead to promising
performance but lack consistency, while some univariate losses are consistent
but usually have no clear superiority in practice. In this paper, we attempt to
fill this gap through a systematic study from two complementary perspectives of
consistency and generalization error bounds of learning algorithms. Our results
show that learning algorithms with the consistent univariate loss have an error
bound of $O(c)$ ($c$ is the number of labels), while algorithms with the
inconsistent pairwise loss depend on $O(\sqrt{c})$ as shown in prior work. This
explains that the latter can achieve better performance than the former in
practice. Moreover, we present an inconsistent reweighted univariate loss-based
learning algorithm that enjoys an error bound of $O(\sqrt{c})$ for promising
performance as well as the computational efficiency of univariate losses.
Finally, experimental results validate our theoretical analyses.


Abstract:
  Existing rotated object detectors are mostly inherited from the horizontal
detection paradigm, as the latter has evolved into a well-developed area.
However, these detectors are difficult to perform prominently in high-precision
detection due to the limitation of current regression loss design, especially
for objects with large aspect ratios. Taking the perspective that horizontal
detection is a special case for rotated object detection, in this paper, we are
motivated to change the design of rotation regression loss from induction
paradigm to deduction methodology, in terms of the relation between rotation
and horizontal detection. We show that one essential challenge is how to
modulate the coupled parameters in the rotation regression loss, as such the
estimated parameters can influence to each other during the dynamic joint
optimization, in an adaptive and synergetic way. Specifically, we first convert
the rotated bounding box into a 2-D Gaussian distribution, and then calculate
the Kullback-Leibler Divergence (KLD) between the Gaussian distributions as the
regression loss. By analyzing the gradient of each parameter, we show that KLD
(and its derivatives) can dynamically adjust the parameter gradients according
to the characteristics of the object. It will adjust the importance (gradient
weight) of the angle parameter according to the aspect ratio. This mechanism
can be vital for high-precision detection as a slight angle error would cause a
serious accuracy drop for large aspect ratios objects. More importantly, we
have proved that KLD is scale invariant. We further show that the KLD loss can
be degenerated into the popular $l_{n}$-norm loss for horizontal detection.
Experimental results on seven datasets using different detectors show its
consistent superiority, and codes are available at
https://github.com/yangxue0827/RotationDetection.


Abstract:
  We analyze the unparticle effects on the lepton flavor universality violating
ratios in $b\to s$ transition, $R(K^{(*)})$. We concentrate on the vector
unparticles which contribute to the relevant Wilson coefficients $C_{9,10}$.
New effects appear in the form of some powers of the momentum transfer squared,
which is a different feature from other new physics models like leptoquarks or
$Z'$. Constraints from $B_s$-${\bar B}_s$ mixing, $B_s\to\mu^+\mu^-$, and the
electron/muon anomalous magnetic moments are considered. It is found that the
vector unparticles successfully explain $R(K^{(*)})$ with moderate values of
model parameters.


Abstract:
  This article proposes a new population-based optimization algorithm called
the Tangent Search Algorithm (TSA) to solve optimization problems. The TSA uses
a mathematical model based on the tangent function to move a given solution
toward a better solution. The tangent flight function has the advantage to
balance between the exploitation and the exploration search. Moreover, a novel
escape procedure is used to avoid to be trapped in local minima. Besides, an
adaptive variable step size is also integrated in this algorithm to enhance the
convergence capacity. The performance of TSA is assessed in three classes of
tests: classical tests, CEC benchmarks, and engineering optimization problems.
Moreover, several studies and metrics have been used to observe the behavior of
the proposed TSA. The experimental results show that TSA algorithm is capable
to provide very promising and competitive results on most benchmark functions
thanks to better balance between exploration and exploitation of the search
space. The main characteristics of this new optimization algorithm is its
simplicity and efficiency and it requires only a small number of user-defined
parameters.


Abstract:
  Recently Hachem et al. formulated a multiaccess coded caching model which
consists of a central server connected to $K$ users via an error-free shared
link, and $K$ cache-nodes. Each cache-node is equipped with a local cache and
each user can access $L$ neighbouring cache-nodes with a cyclic wrap-around
fashion. In this paper, we take the privacy of the users' demands into
consideration, i.e., each user can only get its required file and can not get
any information about the demands of other users. By storing some private keys
at the cache-nodes, we propose a novel transformation approach to transform a
non-private multiaccess coded caching scheme into a private multiaccess coded
caching scheme.


Abstract:
  Superconductivity in graphene-based systems has recently attracted much
attention, as either intrinsic behavior or induced by proximity to a
superconductor may lead to interesting topological phases and symmetries of the
pairing function. A prominent system considers the pairing to have chiral
symmetry. The question arises as to the effect of possible spin-orbit coupling
on the resulting superconducting quasiparticle spectrum. Utilizing a
Bogolyubov-de Gennes (BdG) Hamiltonian, we explore the interplay of different
interaction terms in the system, and their role in generating complex Berry
curvatures in the quasiparticle spectrum, as well as non-trivial topological
behavior. We demonstrate that the topology of the BdG Hamiltonian in these
systems may result in the appearance of edge states along the zigzag edges of
nanoribbons in the appropriate regime.


Abstract:
  Post-processing of static embedding has beenshown to improve their
performance on both lexical and sequence-level tasks. However, post-processing
for contextualized embeddings is an under-studied problem. In this work, we
question the usefulness of post-processing for contextualized embeddings
obtained from different layers of pre-trained language models. More
specifically, we standardize individual neuron activations using z-score,
min-max normalization, and by removing top principle components using the
all-but-the-top method. Additionally, we apply unit length normalization to
word representations. On a diverse set of pre-trained models, we show that
post-processing unwraps vital information present in the representations for
both lexical tasks (such as word similarity and analogy)and sequence
classification tasks. Our findings raise interesting points in relation to
theresearch studies that use contextualized representations, and suggest
z-score normalization as an essential step to consider when using them in an
application.


Abstract:
  Let $W_a$ be an affine Weyl group. In 1987 Jian Yi Shi gave a
characterization of the elements $w \in W_a$ in terms of $\Phi^+$-tuples
$(k(w,\alpha))_{\alpha \in \Phi^+}$ called the Shi vectors. Using these
coefficients, a formula is provided to compute the standard length of $W_a$. In
this note we express the twisted affine length function of $W_a$ in terms of
the Shi coefficients.


Abstract:
  We present lattice calculations of the low-lying spectrum of $^{12}$C using a
simple nucleon-nucleon interaction that is independent of spin and isospin and
therefore invariant under Wigner's SU(4) symmetry. We find strong signals for
all excited states up to $\sim 15$~MeV above the ground state, and explore the
structure of each state using a large variety of $\alpha$ cluster and harmonic
oscillator trial states, projected onto given irreducible representations of
the cubic group. We are able to verify earlier findings for the $\alpha$
clustering in the Hoyle state and the second $2^+$ state of $^{12}$C. The
success of these calculations to describe the full low-lying energy spectrum
using spin-independent interactions suggest that either the spin-orbit
interactions are somewhat weak in the $^{12}$C system, or the effects of
$\alpha$ clustering are diminishing their influence. This is in agreement with
previous findings from {\it ab initio} shell model calculations.


Abstract:
  Motivated by an energy efficient building application, we want to optimize a
quadratic cost functional subject to the Boussinesq approximation of the
Navier-Stokes equations and to bilateral state and control constraints. Since
the computation of such an optimal solution is numerically costly, we design an
efficient strategy to compute a sub-optimal (but applicationally acceptable)
solution with significantly reduced computational effort. We employ an economic
Model Predictive Control (MPC) strategy to obtain a feedback control. The MPC
sub-problems are based on a linear-quadratic optimal control problem subjected
to mixed control and state constraints and a convection-diffusion equation,
reduced with proper orthogonal decomposition. To solve each sub-problem, we
apply a primal-dual active set strategy. The method can be fully parallelized,
which enables the solution of large problems with real-world parameters.


Abstract:
  In this paper, we introduce PeerGAN, a generative adversarial network (GAN)
solution to improve the stability of the generated samples and to mitigate mode
collapse. Built upon the Vanilla GAN's two-player game between the
discriminator $D_1$ and the generator $G$, we introduce a peer discriminator
$D_2$ to the min-max game. Similar to previous work using two discriminators,
the first role of both $D_1$, $D_2$ is to distinguish between generated samples
and real ones, while the generator tries to generate high-quality samples which
are able to fool both discriminators. Different from existing methods, we
introduce another game between $D_1$ and $D_2$ to discourage their agreement
and therefore increase the level of diversity of the generated samples. This
property alleviates the issue of early mode collapse by preventing $D_1$ and
$D_2$ from converging too fast. We provide theoretical analysis for the
equilibrium of the min-max game formed among $G, D_1, D_2$. We offer
convergence behavior of PeerGAN as well as stability of the min-max game. It's
worth mentioning that PeerGAN operates in the unsupervised setting, and the
additional game between $D_1$ and $D_2$ does not need any label supervision.
Experiments results on a synthetic dataset and on real-world image datasets
(MNIST, Fashion MNIST, CIFAR-10, STL-10, CelebA, VGG) demonstrate that PeerGAN
outperforms competitive baseline work in generating diverse and high-quality
samples, while only introduces negligible computation cost.


Abstract:
  We demarcate the unstable regimes of superfluids in a Rabi-coupled
two-component Bose-Einstein condensate in the presence of optical lattices. We
find that the Rabi coupling can stabilize superfluids. A significant separation
between Landau and dynamical instabilities is presented in a Rabi-coupled
Zeeman lattice.


Abstract:
  Searches for counterparts to multimessenger events with optical imagers use
difference imaging to detect new transient sources. However, even with existing
artifact detection algorithms, this process simultaneously returns several
classes of false positives: false detections from poor quality image
subtractions, false detections from low signal-to-noise images, and detections
of pre-existing variable sources. Currently, human visual inspection to remove
the false positives is a central part of multimessenger follow-up observations,
but when next generation gravitational wave and neutrino detectors come online
and increase the rate of multimessenger events, the visual inspection process
will be prohibitively expensive. We approach this problem with two
convolutional neural networks operating on the difference imaging outputs. The
first network focuses on removing false detections and demonstrates an accuracy
of 92 percent on our dataset. The second network focuses on sorting all real
detections by the probability of being a transient source within a host galaxy
and distinguishes between various classes of images that previously required
additional human inspection. We find the number of images requiring human
inspection will decrease by a factor of 1.5 using our approach alone and a
factor of 3.6 using our approach in combination with existing algorithms,
facilitating rapid multimessenger counterpart identification by the
astronomical community.


Abstract:
  Cumulative hardness comparisons are a simple but statistically powerful test
for the presence of gravitational lensing in gamma-ray bursts (GRBs). Since
gravitational lensing does not change photon energies, all source images should
have the same spectra -- and hence hardness. Applied to the recent claim that
the two pulses in GRB 950830 are lensed images of the same pulse, the measured
flux ratio between the two main pulses should be the same at all energies.
After summing up all the counts in both of GRB 950830's two pulses in all four
BATSE energy bands, it was found that in energy channel 3, the second pulse
appears somewhat weak. In comparison with the other BATSE energy channels, the
difference was statistically significant at above 90\%. This model-independent
test indicates that the case for GRB 950830 involving a gravitational lens may
be intriguing -- but should not be considered proven.


Abstract:
  We introduce a methodology to study the possible matter flows of an ecosystem
defined by observational biomass data and realistic biological constraints. The
flows belong to a polyhedron in a multi dimensional space making statistical
exploration difficult in practice; instead, we propose to solve a convex
optimization problem. Five criteria corresponding to ecological network indices
have been selected to be used as convex goal functions. Numerical results show
that the method is fast and can be used for large systems. Minimum flow
solutions are analyzed using flow decomposition in paths and circuits. Their
consistency is also tested by introducing a system of differential equations
for the biomasses and examining the stability of the biomass fixed point. The
method is illustrated and explained throughout the text on an ecosystem toy
model. It is also applied to realistic food models.


Abstract:
  We study spin excitations and Kondo effect in open-shell nanographenes,
motivated by recent scanning tunneling inelastic spectroscopy experiments.
Specifically, we consider three systems, the triangulene, the extended
triangulene with rocket shape, both with an $S=1$ ground state, and a
triangulene dimer with $S=0$ on account of intermolecular exchange. We focus on
the consequences of hybridization of the nanographene zero-modes with a
conducting substrate on the $dI/dV$ lineshapes associated with spin
excitations. The partially filled nanographene zero-modes coupled to the
conduction electrons in the substrate constitute multi-orbital Anderson
impurity models that we solve in the one-crossing approximation which treats
the coupling to the substrate to infinite order. We find that the coupling to
the substrate leads to (i) renormalization of the spin flip excitation energies
of the bare molecule, (ii) broadening of the spectral features and (iii) the
emergence of zero bias Kondo peaks for the $S=1$ ground states. The calculated
substrate induced shift of the spin excitation energies is found to be
significantly larger than their broadening, which implies that this effect has
to be considered when comparing experimental results and theory.


Abstract:
  This paper studies the performance of a model-free LQR based PID (i-LQR-PID)
controller designed for tracking control problem of a 2-DoF laboratory
helicopter. The control problem addressed in 2-DoF helicopter system aims to
track the desired pitch and yaw axes trajectories despite disturbed operating
conditions. In addition to the unpredictable variations, the 2-DoF helicopter
dynamic is highly nonlinear with having strong cross-couplings in their models
as well as being open loop unstable system. Thus, we propose a model-free LQR
based PID control strategy in order to achieve better trajectory tracking
control objectives. Robustness tests are performed experimentally to show the
effectiveness of the model-free control.


Abstract:
  We present a scalable post-processing algorithm for debiasing trained models,
including deep neural networks (DNNs), which we prove to be near-optimal by
bounding its excess Bayes risk. We empirically validate its advantages on
standard benchmark datasets across both classical algorithms as well as modern
DNN architectures and demonstrate that it outperforms previous post-processing
methods while performing on par with in-processing. In addition, we show that
the proposed algorithm is particularly effective for models trained at scale
where post-processing is a natural and practical choice.


Abstract:
  In this paper, we propose a hybrid technique for semantic question matching.
It uses our proposed two-layered taxonomy for English questions by augmenting
state-of-the-art deep learning models with question classes obtained from a
deep learning based question classifier. Experiments performed on three
open-domain datasets demonstrate the effectiveness of our proposed approach. We
achieve state-of-the-art results on partial ordering question ranking (POQR)
benchmark dataset. Our empirical analysis shows that coupling standard
distributional features (provided by the question encoder) with knowledge from
taxonomy is more effective than either deep learning (DL) or taxonomy-based
knowledge alone.


Abstract:
  Much of the structure of cosmological correlators is controlled by their
singularities, which in turn are fixed in terms of flat-space scattering
amplitudes. An important challenge is to interpolate between the singular
limits to determine the full correlators at arbitrary kinematics. This is
particularly relevant because the singularities of correlators are not directly
observable, but can only be accessed by analytic continuation. In this paper,
we study rational correlators, including those of gauge fields, gravitons, and
the inflaton, whose only singularities at tree level are poles and whose
behavior away from these poles is strongly constrained by unitarity and
locality. We describe how unitarity translates into a set of cutting rules that
consistent correlators must satisfy, and explain how this can be used to
bootstrap correlators given information about their singularities. We also
derive recursion relations that allow the iterative construction of more
complicated correlators from simpler building blocks. In flat space, all energy
singularities are simple poles, so that the combination of unitarity
constraints and recursion relations provides an efficient way to bootstrap the
full correlators. In many cases, these flat-space correlators can then be
transformed into their more complex de Sitter counterparts. As an example of
this procedure, we derive the correlator associated to graviton Compton
scattering in de Sitter space, though the methods are much more widely
applicable.


Abstract:
  In this paper we explore audiovisual emotion recognition under noisy acoustic
conditions with a focus on speech features. We attempt to answer the following
research questions: (i) How does speech emotion recognition perform on noisy
data? and (ii) To what extend does a multimodal approach improve the accuracy
and compensate for potential performance degradation at different noise levels?
We present an analytical investigation on two emotion datasets with
superimposed noise at different signal-to-noise ratios, comparing three types
of acoustic features. Visual features are incorporated with a hybrid fusion
approach: The first neural network layers are separate modality-specific ones,
followed by at least one shared layer before the final prediction. The results
show a significant performance decrease when a model trained on clean audio is
applied to noisy data and that the addition of visual features alleviates this
effect.


Abstract:
  Smartphones contain information that is more sensitive and personal than
those found on computers and laptops. With an increase in the versatility of
smartphone functionality, more data has become vulnerable and exposed to
attackers. Successful mobile malware attacks could steal a user's location,
photos, or even banking information. Due to a lack of post-attack strategies
firms also risk going out of business due to data theft. Thus, there is a need
besides just detecting malware intrusion in smartphones but to also identify
the data that has been stolen to assess, aid in recovery and prevent future
attacks. In this paper, we propose an accessible, non-intrusive machine
learning solution to not only detect malware intrusion but also identify the
type of data stolen for any app under supervision. We do this with Android
usage data obtained by utilising publicly available data collection framework-
SherLock. We test the performance of our architecture for multiple users on
real-world data collected using the same framework. Our architecture exhibits
less than 9% inaccuracy in detecting malware and can classify with 83%
certainty on the type of data that is being stolen.


Abstract:
  A scenario of non-Hermitian bulk--boundary correspondence proposed for
one-dimensional topological insulators is adapted to a non-Hermitian Chern
insulator to examine its applicability to two-dimensional systems. This
scenario employs bulk geometry under a modified periodic boundary condition and
boundary geometry under an open boundary condition. The bulk geometry is used
to define a topological number, whereas the boundary geometry is used to
observe the presence or absence of a topological boundary state. It is
demonstrated that the bulk--boundary correspondence holds in a two-dimensional
Chern insulator with gain/loss-type non-Hermiticity; a nontrivial Chern number
calculated in the bulk geometry is in one-to-one correspondence with the
presence of a topological boundary state in the boundary geometry. This
approach enables us to determine a phase diagram in the boundary geometry.


Abstract:
  In this paper we present the first deterministic polynomial time algorithm
for determining the existence of a Hamiltonian cycle and finding a Hamiltonian
cycle in general graphs. Our algorithm can also resolve the Hamiltonian path
problem in the traceable graphs. The space complexity of our algorithm is
O(n^4). The time complexity are theoretically O(n^5*d^2) on average and
O(n^6*d^2) in the worst case respectively, where d is the maximum degree of
vertex. With parallel computing, the space complexity can be improved to O(n^3)
and the time complexity to O(n^3*d^2) on average and O(n^4*d^2) in the worst
case. We construct the corresponding path hologram transformed from the
original graph and compute the path set, which is a collection of segment sets
consisting of all the vertices located on the same segment layer among all the
longest basic paths, of every vertex with dynamic programming. The path
hologram is a multi-segment graph with the vertex <u, k> where u is a vertex
and k is the segment layer of u in the path hologram. To ensure each path
stored in the path set is legal and each segment set of the path set contains
only valid vertices, the key strategy of our method is the "consecutive"
deleting-replenishing operations recursively on the left/right action field of
a vertex, respectively. The greatest contribution of our method is the path set
in which all the legal paths can be stored in O(n^2) space for a complete graph
of order n. In fact, our algorithm can be directly applied to the original
graph. Besides, our algorithm can deal with the finite general graphs including
undirected, directed, and mixed. As a result, the well-known problem HCP in NPC
can be now resolved practically in deterministic polynomial time for general
graphs in the worst case.


Abstract:
  The paper proves existence of renormalized stationary solutions for a dense
class of discrete velocity Boltzmann equations in the plane with given ingoing
boundary values. The proof is based on the construction of a sequence of
approximations with L1 compactness for the integrated collision frequency and
gain term. Compactness is obtained using the Kolmogorov-Riesz theorem.


Abstract:
  Fine time-resolved analysis of matter - i.e. spectroscopy and photon
scattering - in the linear response regime requires a fs-scale pulsed, high
repetition rate, fully coherent X-ray source. A seeded Free-Electron Laser
(FEL) driven by a Super-Conducting Linac, generating $10^{8}$-$10^{10}$
coherent photons at 2-5 keV with abou 0.5 MHz of repetition rate, can address
this need. The seeding scheme proposed is the Echo-Enabled Harmonic Generation,
alimented by a FEL Oscillator working at 13.6 nm with a cavity based on Mo-Si
mirrors. The whole chain of the X-ray generation is here described by means of
start-to-end simulations. Comparisons with the Self Amplified Spontaneus
Emission and a fresh-bunch harmonic cascade performed with similar electron
beams show the validity of this scheme.


Abstract:
  For sequential data, change points are moments of abrupt regime switches.
Such changes appear in different scenarios, including complex video
surveillance, and we need to detect them as fast as possible. Classic
approaches for change point detection (CPD) perform poorly for semi-structured
sequential data because of the absence of adequate data representation learning
procedure. We propose a principled loss function that approximates classic
rigorous solutions but is differentiable and makes possible representation
learning. This loss function balances change detection delay and time to false
alarm to provide a successful model for CPD. In experiments, we consider simple
series and more complex real-world image sequences and videos with change
points. For more complex problems, we show that we need more meaningful
representations tailored for the specificity of the CPD task. Taking this into
account, the proposed approach InDiD improves baseline results of CPD for
various data types. For explosion detection, F1 score for our method is $0.54$
compared to baseline scores $0.46$ and $0.30$.