title:
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech
  Synthesis

abstract:
  Denoising diffusion probabilistic models (DDPMs) have recently achieved
leading performances in many generative tasks. However, the inherited iterative
sampling process costs hindered their applications to speech synthesis. This
paper proposes FastDiff, a fast conditional diffusion model for high-quality
speech synthesis. FastDiff employs a stack of time-aware location-variable
convolutions of diverse receptive field patterns to efficiently model long-term
time dependencies with adaptive conditions. A noise schedule predictor is also
adopted to reduce the sampling steps without sacrificing the generation
quality. Based on FastDiff, we design an end-to-end text-to-speech synthesizer,
FastDiff-TTS, which generates high-fidelity speech waveforms without any
intermediate feature (e.g., Mel-spectrogram). Our evaluation of FastDiff
demonstrates the state-of-the-art results with higher-quality (MOS 4.28) speech
samples. Also, FastDiff enables a sampling speed of 58x faster than real-time
on a V100 GPU, making diffusion models practically applicable to speech
synthesis deployment for the first time. We further show that FastDiff
generalized well to the mel-spectrogram inversion of unseen speakers, and
FastDiff-TTS outperformed other competing methods in end-to-end text-to-speech
synthesis. Audio samples are available at \url{https://FastDiff.github.io/}.


title:
Dynamic Tomography Reconstruction by Projection-Domain Separable
  Modeling

abstract:
  In dynamic tomography the object undergoes changes while projections are
being acquired sequentially in time. The resulting inconsistent set of
projections cannot be used directly to reconstruct an object corresponding to a
time instant. Instead, the objective is to reconstruct a spatio-temporal
representation of the object, which can be displayed as a movie. We analyze
conditions for unique and stable solution of this ill-posed inverse problem,
and present a recovery algorithm, validating it experimentally. We compare our
approach to one based on the recently proposed GMLR variation on deep prior for
video, demonstrating the advantages of the proposed approach.


title: Profinite groupos with few conjugacy classes of $p$-elements

abstract:
  It is proved that a profinite group $G$ has fewer than $2^{\aleph_0}$
conjugacy classes of $p$-elements for an odd prime $p$ if and only if its
$p$-Sylow subgroups are finite. (Here, by a $p$-element one understands an
element that either has $p$-power order or topologically generates a group
isomorphic to ${\mathbb Z}_p$.) A weaker result is proved for $p=2$.


title: SPIKE: Secure and Private Investigation of the Kidney Exchange problem

abstract:
  Background: The kidney exchange problem (KEP) addresses the matching of
patients in need for a replacement organ with compatible living donors. Ideally
many medical institutions should participate in a matching program to increase
the chance for successful matches. However, to fulfill legal requirements
current systems use complicated policy-based data protection mechanisms that
effectively exclude smaller medical facilities to participate. Employing secure
multi-party computation (MPC) techniques provides a technical way to satisfy
data protection requirements for highly sensitive personal health information
while simultaneously reducing the regulatory burdens.
  Results: We have designed, implemented, and benchmarked SPIKE, a secure
MPC-based privacy-preserving KEP which computes a solution by finding matching
donor-recipient pairs in a graph structure. SPIKE matches 40 pairs in cycles of
length 2 in less than 4 minutes and outperforms the previous state-of-the-art
protocol by a factor of 400x in runtime while providing medically more robust
solutions.
  Conclusions: We show how to solve the KEP in a robust and privacy-preserving
manner achieving practical performance. The usage of MPC techniques fulfills
many data protection requirements on a technical level, allowing smaller health
care providers to directly participate in a kidney exchange with reduced legal
processes.


title: Ultra Marginal Feature Importance

abstract:
  Scientists frequently prioritize learning from data rather than training the
best possible model; however, research in machine learning often prioritizes
the latter. The development of marginal feature importance methods, such as
marginal contribution feature importance, attempts to break this trend by
providing a useful framework for explaining relationships in data in an
interpretable fashion. In this work, we generalize the framework of marginal
contribution feature importance to improve performance with regards to
detecting correlated interactions and reducing runtime. To do so, we consider
"information subsets" of the set of features $F$ and show that our importance
metric can be computed directly after applying fair representation learning
methods from the AI fairness literature. The methods of optimal transport and
linear regression are considered and explored experimentally for removing all
the information of our feature of interest $f$ from the feature set $F$. Given
these implementations, we show on real and simulated data that ultra marginal
feature importance performs at least as well as marginal contribution feature
importance, with substantially faster computation time and better performance
in the presence of correlated interactions and unrelated features.


title:
Relation between fluctuations and efficiency at maximum power for small
  heat engines

abstract:
  We study the ratio between the variances of work output and heat input,
$\eta^{(2)}$, for a class of four-stroke heat engines which covers various
typical cycles. Recent studies on the upper and lower bounds of $\eta^{(2)}$
are based on the quasistatic limit and the linear response regime,
respectively. We extend these relations to the finite-time regime within the
endoreversible approximation. We consider the ratio $\eta_{\text{MP}}^{(2)}$ at
maximum power and find that the square of the Curzon-Ahlborn efficiency,
$\eta_{\text{CA}}^2$, gives a good estimate of $\eta_{\text{MP}}^{(2)}$ for the
class of heat engines considered, i.e., $\eta_{\text{MP}}^{(2)} \simeq
\eta_{\text{CA}}^2$. This resembles the situation where the Curzon-Ahlborn
efficiency gives a good estimate of the efficiency at maximum power for various
kinds of finite-time heat engines. Taking an overdamped Brownian particle in a
harmonic potential as an example, we can realize such endoreversible small heat
engines and give an expression of the cumulants of work output and heat input.
The approximate relation $\eta_{\text{MP}}^{(2)} \simeq \eta_{\text{CA}}^2$ is
verified by numerical simulations. This relation also suggests a trade-off
between the efficiency and the stability of finite-time heat engines at maximum
power.


title:
On Quantum-Assisted LDPC Decoding Augmented with Classical
  Post-Processing

abstract:
  Utilizing present and futuristic Quantum Computers to solve difficult
problems in different domains has become one of the main endeavors at this
moment. Of course, in arriving at the requisite solution both quantum and
classical computers work in conjunction. With the continued popularity of Low
Density Parity Check (LDPC) codes and hence their decoding, this paper looks
into the latter as a Quadratic Unconstrained Binary Optimization (QUBO) and
utilized D-Wave 2000Q Quantum Annealer to solve it. The outputs from the
Annealer are classically post-processed using simple minimum distance decoding
to further improve the performance. We evaluated and compared this
implementation against the decoding performance obtained using Simulated
Annealing (SA) and belief propagation (BP) decoding with classical computers.
The results show that implementations of annealing (both simulated and quantum)
are superior to BP decoding and suggest that the advantage becomes more
prominent as block lengths increase. Reduced Bit Error Rate (BER) and Frame
Error Rate (FER) are observed for simulated annealing and quantum annealing, at
useful SNR range - a trend that persists for various codeword lengths.


title: Singularity-Avoiding Multi-Dimensional Root-Finder

abstract:
  We proposed in this paper a new method, which we named the W4 method, to
solve nonlinear equation systems. It may be regarded as an extension of the
Newton-Raphson~(NR) method to be used when the method fails. Indeed our method
can be applied not only to ordinary problems with non-singular Jacobian
matrices but also to problems with singular Jacobians, which essentially all
previous methods that employ the inversion of the Jacobian matrix have failed
to solve. In this article, we demonstrate that (i) our new scheme can define a
non-singular iteration map even for those problems by utilizing the singular
value decomposition, (ii) a series of vectors in the new iteration map
converges to the right solution under a certain condition, (iii) the standard
two-dimensional problems in the literature that no single method proposed so
far has been able to solve completely are all solved by our new method.


title:
Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial
  Sensor Networks

abstract:
  Industrial control systems (ICSs) are facing increasing cyber-physical
attacks that can cause catastrophes in the physical system. Efficient anomaly
detection models in the industrial sensor networks are essential for enhancing
ICS reliability and security, due to the sensor data is related to the
operational state of the ICS. Considering the limited availability of computing
resources, this paper proposes a hybrid anomaly detection approach in
cloud-edge collaboration industrial sensor networks. The hybrid approach
consists of sensor data detection models deployed at the edges and a sensor
data analysis model deployed in the cloud. The sensor data detection model
based on Gaussian and Bayesian algorithms can detect the anomalous sensor data
in real-time and upload them to the cloud for further analysis, filtering the
normal sensor data and reducing traffic load. The sensor data analysis model
based on Graph convolutional network, Residual algorithm and Long short-term
memory network (GCRL) can effectively extract the spatial and temporal features
and then identify the attack precisely. The proposed hybrid anomaly detection
approach is evaluated using a benchmark dataset and baseline anomaly detection
models. The experimental results show that the proposed approach can achieve an
overall 11.19% increase in Recall and an impressive 14.29% improvement in
F1-score, compared with the existing models.


title: A novel formulation for the evolution of relativistic rotating stars

abstract:
  We present a new formulation to construct numerically equilibrium
configurations of rotating stars in general relativity. Having in mind the
application to their quasi static evolutions, we adopt a Lagrangian formulation
of our own devising, in which we solve force balance equations to seek for the
positions of fluid elements assigned to the grid points, instead of the
ordinary Eulerian formulation. Unlike previous works in the literature, we do
not employ the first integral of the Euler equation, which is not obtained by
an analytic integration in general. We assign a mass, specific angular momentum
and entropy to each fluid element in contrast to the previous methods, in which
the spatial distribution of the angular velocity or angular momentum is
specified. Those distributions are determined after the positions of all fluid
elements (or grid points) are derived in our formulation. We solve the large
system of algebraic nonlinear equations that are obtained by discretizing the
time-independent Euler and Einstein equations in the finite-elements method by
using our new multi-dimensional root-finding scheme, named the W4 method. To
demonstrate the capability of our new formulation, we construct some rotational
configurations both barotropic and baroclinic. We also solve three evolutionary
sequences that mimic the cooling, mass-loss, and mass-accretion as simple toy
models.


title:
A Quantitative version of Korovkin-type theorems on Banach Function
  Spaces

abstract:
  In 1953, P.P. Korovkin discovered a simple criterion to decide whether a
sequence of positive linear operators on C[0,1] is an approximation process or
not by only testing the convergence on the functions \{1, t, t^2\} (the test
set). This result is known as the first Korovkin theorem. This outstanding
result led to a new theory called Korovkin-type Approximation Theory. In 1968,
O. Shisha and B. Mond formulated and proved a quantitative form of this
theorem. This estimation theorem gives the convergence of a sequence of
positive linear operators to the identity operator in terms of the convergence
of the test functions and a quantity called modulus of continuity. Later in
1972, Ronald A DeVore proved an extension to this theorem. T. Nishishiraho
obtained extensions of such results in various settings in the 1980's. These
quantitative versions give inequalities that cover the approximation results
and provide the convergence rate depending on the smoothness of the function.
In a recent article, Yusuf Zeren, Migdad Ismailov and Cemil Karacam proved
Korovkin-type theorems on Banach function spaces. They also derived an analogue
of the Korovkin theorem for Kantorovich polynomials. These results are
applicable in various concrete examples such as Lebesgue spaces, grand-Lebesgue
spaces, Morrey-type spaces and their weighted versions, weak Lebesgue spaces,
Orlicz spaces, etc. This article discusses the quantitative versions of these
results, in line with the classical quantitative versions by O. Shisha, B. Mond
and Ronald A DeVore. We obtained estimation theorems in terms of the modulus of
continuity for functions in C[0,1] under weaker conditions. These quantitative
forms apply to the case of Kantorovich polynomials on several spaces such as
Lebesgue spaces, Grand Lebesgue spaces, Morrey-type spaces etc. We also discuss
the special forms of the inequalities in these various examples.


title:
Active Learning of Discriminative Subgraph Patterns for API Misuse
  Detection

abstract:
  A common cause of bugs and vulnerabilities are the violations of usage
constraints associated with Application Programming Interfaces (APIs). API
misuses are common in software projects, and while there have been techniques
proposed to detect such misuses, studies have shown that they fail to reliably
detect misuses while reporting many false positives. One limitation of prior
work is the inability to reliably identify correct patterns of usage. Many
approaches confuse a usage pattern's frequency for correctness. Due to the
variety of alternative usage patterns that may be uncommon but correct, anomaly
detection-based techniques have limited success in identifying misuses. We
address these challenges and propose ALP (Actively Learned Patterns),
reformulating API misuse detection as a classification problem. After
representing programs as graphs, ALP mines discriminative subgraphs. While
still incorporating frequency information, through limited human supervision,
we reduce the reliance on the assumption relating frequency and correctness.
The principles of active learning are incorporated to shift human attention
away from the most frequent patterns. Instead, ALP samples informative and
representative examples while minimizing labeling effort. In our empirical
evaluation, ALP substantially outperforms prior approaches on both MUBench, an
API Misuse benchmark, and a new dataset that we constructed from real-world
software projects.


title: Reduced atmospheres of post-impact worlds: The early Earth

abstract:
  Impacts may have had a significant effect on the atmospheric chemistry of the
early Earth. Reduced phases in the impactor (e.g., metallic iron) can reduce
the planet's H$_2$O inventory to produce massive atmospheres rich in H$_2$.
Whilst previous studies have focused on the interactions between the impactor
and atmosphere in such scenarios, we investigate two further effects, 1) the
distribution of the impactor's iron inventory during impact between the target
interior, target atmosphere, and escaping the target, and 2) interactions
between the post-impact atmosphere and the impact-generated melt phase. We find
that these two effects can potentially counterbalance each other, with the
melt-atmosphere interactions acting to restore reducing power to the atmosphere
that was initially accreted by the melt phase. For a $\sim10^{22}\,\mathrm{kg}$
impactor, when the iron accreted by the melt phase is fully available to reduce
this melt, we find an equilibrium atmosphere with H$_2$ column density
$\sim10^4\,\mathrm{moles\,cm^{-2}}$
($p\mathrm{H2}\sim120\,\mathrm{bars}\mathrm{,}~X_\mathrm{H2}\sim0.77$),
consistent with previous estimates. However, when the iron is not available to
reduce the melt (e.g., sinking out in large diameter blobs), we find
significantly less H$_2$ ($7\times10^2-5\times10^3\,\mathrm{moles\,cm^{-2}}$,
$p\mathrm{H2}\lesssim60\,\mathrm{bars}\mathrm{,}~X_\mathrm{H2}\lesssim0.41$).
These lower H$_2$ abundances are sufficiently high that species important to
prebiotic chemistry can form (e.g., NH3, HCN), but sufficiently low that the
greenhouse heating effects associated with highly reducing atmospheres, which
are problematic to such chemistry, are suppressed. The manner in which iron is
accreted by the impact-generated melt phase is critical in determining the
reducing power of the atmosphere and re-solidified melt pool in the aftermath
of impact.


title: Towards Reliable Neural Generative Modeling of Detectors

abstract:
  The increasing luminosities of future data taking at Large Hadron Collider
and next generation collider experiments require an unprecedented amount of
simulated events to be produced. Such large scale productions demand a
significant amount of valuable computing resources. This brings a demand to use
new approaches to event generation and simulation of detector responses. In
this paper, we discuss the application of generative adversarial networks
(GANs) to the simulation of the LHCb experiment events. We emphasize main
pitfalls in the application of GANs and study the systematic effects in detail.
The presented results are based on the Geant4 simulation of the LHCb Cherenkov
detector.


title: Reliable event detection for Taylor methods in astrodynamics

abstract:
  We present a novel approach for the detection of events in systems of
ordinary differential equations. The new method combines the unique features of
Taylor integrators with state-of-the-art polynomial root finding techniques to
yield a novel algorithm ensuring strong event detection guarantees at a modest
computational overhead. Detailed tests and benchmarks focused on problems in
astrodynamics and celestial mechanics (such as collisional N-body systems,
spacecraft dynamics around irregular bodies accounting for eclipses,
computation of Poincare' sections, etc.) show how our approach is superior in
both performance and detection accuracy to strategies commonly employed in
modern numerical integration works. The new algorithm is available in our open
source Taylor integration package heyoka.

categories: astro-ph.EP astro-ph.IM cs.CE physics.comp-ph


title: Faster Approximate Covering of Subcurves under the Fr\'echet Distance

abstract:
  Subtrajectory clustering is an important variant of the trajectory clustering
problem, where the start and endpoints of trajectory patterns within the
collected trajectory data are not known in advance. We study this problem in
the form of a set cover problem for a given polygonal curve: find the smallest
number $k$ of representative curves such that any point on the input curve is
contained in a subcurve that has Fr\'echet distance at most a given $\Delta$ to
a representative curve. We focus on the case where the representative curves
are line segments and approach this NP-hard problem with classical techniques
from the area of geometric set cover: we use a variant of the multiplicative
weights update method which was first suggested by Br\"onniman and Goodrich for
set cover instances with small VC-dimension. We obtain a
bicriteria-approximation algorithm that computes a set of $O(k\log(k))$ line
segments that cover a given polygonal curve of $n$ vertices under Fr\'echet
distance at most $O(\Delta)$. We show that the algorithm runs in
$\widetilde{O}(k^2 n + k n^3)$ time in expectation and uses $ \widetilde{O}(k n
+ n^3)$ space. For two dimensional input curves that are $c$-packed, we bound
the expected running time by $\widetilde{O}(k^2 c^2 n)$ and the space by $
\widetilde{O}(kn + c^2 n)$. In $\mathbb{R}^d$ the dependency on $n$ instead is
quadratic. In addition, we present a variant of the algorithm that uses
implicit weight updates on the candidate set and thereby achieves near-linear
running time in $n$ without any assumptions on the input curve, while keeping
the same approximation bounds. This comes at the expense of a small
(polylogarithmic) dependency on the relative arclength.


title:
Exchange bias in van der Waals MnBi$_2$Te$_4$/Cr$_2$Ge$_2$Te$_6$
  heterostructure

abstract:
  The layered van der Waals (vdW) material MnBi$_2$Te$_4$ is an intrinsic
magnetic topological insulator with various topological phases such as quantum
anomalous Hall effect (QAHE) and axion states. However, both the zero-field and
high-temperature QAHE are not easy to realize. It is theoretically proposed
that the exchange bias can be introduced in the MnBi2Te4/ferromagnetic (FM)
insulator heterostructures and thus opens the surface states gap, making it
easier to realize the zero-field or high-temperature QAHE. Here we report the
electrically tunable exchange bias in the van der Waals
MnBi$_2$Te$_4$/Cr$_2$Ge$_2$Te$_6$ heterostructure. The exchange bias emerges
over a critical magnetic field and reaches the maximum value near the magnetic
band gap. Moreover, the exchange bias was experienced by the antiferromagnetic
(AFM) MnBi$_2$Te$_4$ layer rather than the FM layer. Such van der Waals
heterostructure provides a promising platform to study the novel exchange bias
effect and explore the possible high-temperature QAHE.


title: Motif Cut Sparsifiers

abstract:
  A motif is a frequently occurring subgraph of a given directed or undirected
graph $G$. Motifs capture higher order organizational structure of $G$ beyond
edge relationships, and, therefore, have found wide applications such as in
graph clustering, community detection, and analysis of biological and physical
networks to name a few. In these applications, the cut structure of motifs
plays a crucial role as vertices are partitioned into clusters by cuts whose
conductance is based on the number of instances of a particular motif, as
opposed to just the number of edges, crossing the cuts.
  In this paper, we introduce the concept of a motif cut sparsifier. We show
that one can compute in polynomial time a sparse weighted subgraph $G'$ with
only $\widetilde{O}(n/\epsilon^2)$ edges such that for every cut, the weighted
number of copies of $M$ crossing the cut in $G'$ is within a $1+\epsilon$
factor of the number of copies of $M$ crossing the cut in $G$, for every
constant size motif $M$.
  Our work carefully combines the viewpoints of both graph sparsification and
hypergraph sparsification. We sample edges which requires us to extend and
strengthen the concept of cut sparsifiers introduced in the seminal work of to
the motif setting. We adapt the importance sampling framework through the
viewpoint of hypergraph sparsification by deriving the edge sampling
probabilities from the strong connectivity values of a hypergraph whose
hyperedges represent motif instances. Finally, an iterative sparsification
primitive inspired by both viewpoints is used to reduce the number of edges in
$G$ to nearly linear.
  In addition, we present a strong lower bound ruling out a similar result for
sparsification with respect to induced occurrences of motifs.


title:
Recovering Patient Journeys: A Corpus of Biomedical Entities and
  Relations on Twitter (BEAR)

abstract:
  Text mining and information extraction for the medical domain has focused on
scientific text generated by researchers. However, their direct access to
individual patient experiences or patient-doctor interactions can be limited.
Information provided on social media, e.g., by patients and their relatives,
complements the knowledge in scientific text. It reflects the patient's journey
and their subjective perspective on the process of developing symptoms, being
diagnosed and offered a treatment, being cured or learning to live with a
medical condition. The value of this type of data is therefore twofold:
Firstly, it offers direct access to people's perspectives. Secondly, it might
cover information that is not available elsewhere, including self-treatment or
self-diagnoses. Named entity recognition and relation extraction are methods to
structure information that is available in unstructured text. However, existing
medical social media corpora focused on a comparably small set of entities and
relations and particular domains, rather than putting the patient into the
center of analyses. With this paper we contribute a corpus with a rich set of
annotation layers following the motivation to uncover and model patients'
journeys and experiences in more detail. We label 14 entity classes (incl.
environmental factors, diagnostics, biochemical processes, patients'
quality-of-life descriptions, pathogens, medical conditions, and treatments)
and 20 relation classes (e.g., prevents, influences, interactions, causes) most
of which have not been considered before for social media data. The publicly
available dataset consists of 2,100 tweets with approx. 6,000 entity and 3,000
relation annotations. In a corpus analysis we find that over 80 % of documents
contain relevant entities. Over 50 % of tweets express relations which we
consider essential for uncovering patients' narratives about their journeys.


title:
Heat transfer enhancement in turbulent boundary layers with a pulsed
  slot jet in crossflow

abstract:
  The convective heat transfer enhancement in a turbulent boundary layer (TBL)
employing a pulsed, slot jet in crossflow is investigated experimentally. A
parametric study on actuation frequencies and duty cycles is performed. The
actuator is a flush-mounted slot jet that injects fluid into a well-behaved
zero-pressure-gradient TBL over a flat plate. A heated-thin-foil sensor is
employed to measure the time-averaged convective heat transfer coefficient
downstream of the actuator location. The results show that both the jet
penetration in the streamwise direction and the overall Nusselt number increase
with increasing duty cycle. The frequency at which the Nusselt number is
maximized is independent of the duty cycle. It is noted that such frequency is
strikingly equal to the inverse of the characteristic travel time of
large-scale turbulent structures convected within the buffer layer. Eventually,
a simplified model is proposed to decouple the effect of pulsation frequency
and duty cycle in the overall heat transfer enhancement, with a good agreement
with experimental data. The cost of actuation is also quantified in terms of
the amount of injected fluid during the actuation, leading to conclude that the
lowest duty cycle is the most efficient for heat transfer enhancement.


title:
Domain Invariant Model with Graph Convolutional Network for Mammogram
  Classification

abstract:
  Due to its safety-critical property, the image-based diagnosis is desired to
achieve robustness on out-of-distribution (OOD) samples. A natural way towards
this goal is capturing only clinically disease-related features, which is
composed of macroscopic attributes (e.g., margins, shapes) and microscopic
image-based features (e.g., textures) of lesion-related areas. However, such
disease-related features are often interweaved with data-dependent (but disease
irrelevant) biases during learning, disabling the OOD generalization. To
resolve this problem, we propose a novel framework, namely Domain Invariant
Model with Graph Convolutional Network (DIM-GCN), which only exploits invariant
disease-related features from multiple domains. Specifically, we first propose
a Bayesian network, which explicitly decomposes the latent variables into
disease-related and other disease-irrelevant parts that are provable to be
disentangled from each other. Guided by this, we reformulate the objective
function based on Variational Auto-Encoder, in which the encoder in each domain
has two branches: the domain-independent and -dependent ones, which
respectively encode disease-related and -irrelevant features. To better capture
the macroscopic features, we leverage the observed clinical attributes as a
goal for reconstruction, via Graph Convolutional Network (GCN). Finally, we
only implement the disease-related features for prediction. The effectiveness
and utility of our method are demonstrated by the superior OOD generalization
performance over others on mammogram benign/malignant diagnosis.


title: Interior estimates for the Virtual Element Method

abstract:
  We analyze the local accuracy of the virtual element method. More precisely,
we prove an error bound similar to the one holding for the finite element
method, namely, that the local $H^1$ error in a interior subdomain is bounded
by a term behaving like the best approximation allowed by the local smoothness
of the solution in a larger interior subdomain plus the global error measured
in a negative norm.


title:
Counting and equidistribution of reciprocal geodesics and dihedral
  groups

abstract:
  We study the growth of the number of conjugacy classes of infinite dihedral
subgroups of lattices in PSL(2,R), generalizing earlier work of Sarnak and
Bourgain-Kontorovich on the growth of the number of reciprocal geodesics on the
modular surface. We also prove that reciprocal geodesics are equidistributed in
the unit tangent bundle.


title: Referring Expression Comprehension via Cross-Level Multi-Modal Fusion

abstract:
  As an important and challenging problem in vision-language tasks, referring
expression comprehension (REC) aims to localize the target object specified by
a given referring expression. Recently, most of the state-of-the-art REC
methods mainly focus on multi-modal fusion while overlooking the inherent
hierarchical information contained in visual and language encoders. Considering
that REC requires visual and textual hierarchical information for accurate
target localization, and encoders inherently extract features in a hierarchical
fashion, we propose to effectively utilize the rich hierarchical information
contained in different layers of visual and language encoders. To this end, we
design a Cross-level Multi-modal Fusion (CMF) framework, which gradually
integrates visual and textual features of multi-layer through intra- and
inter-modal. Experimental results on RefCOCO, RefCOCO+, RefCOCOg, and
ReferItGame datasets demonstrate the proposed framework achieves significant
performance improvements over state-of-the-art methods.


title:
RIS-Assisted Vehicular Network with Direct Transmission over
  Double-Generalized Gamma Fading Channels

abstract:
  Reconfigurable intelligent surface (RIS) can provide stable connectivity for
vehicular communications when direct transmission becomes significantly weaker
with dynamic channel conditions between an access point and a moving vehicle.
In this paper, we analyze the performance of a RIS-assisted vehicular network
by coherently combining received signals reflected by RIS elements and direct
transmissions from the source terminal over double generalized Gamma (dGG)
fading channels. We present analytical expressions on the outage probability
and average bit-error rate (BER) performance of the considered system by
deriving exact density and distribution functions for the end-to-end
signal-to-noise ratio (SNR) resulted from the finite sum of the direct link and
product of channel coefficients each distributed according to the dGG. We also
develop asymptotic analysis on the outage probability and average BER to derive
diversity order for a better insight into the system performance at high SNR.
We validate the derived analytical expressions through numerical and simulation
results and demonstrate scaling of the system performance with RIS elements and
a comparison to the conventional relaying techniques and direct transmissions
considering various practically relevant scenarios.


title: Why we should respect analysis results as data

abstract:
  The development and approval of new treatments generates large volumes of
results, such as summaries of efficacy and safety. However, it is commonly
overlooked that analyzing clinical study data also produces data in the form of
results. For example, descriptive statistics and model predictions are data.
Although integrating and putting findings into context is a cornerstone of
scientific work, analysis results are often neglected as a data source. Results
end up stored as "data products" such as PDF documents that are not machine
readable or amenable to future analysis. We propose a solution to "calculate
once, use many times" by combining analysis results standards with a common
data model. This analysis results data model re-frames the target of analyses
from static representations of the results (e.g., tables and figures) to a data
model with applications in various contexts, including knowledge discovery.
Further, we provide a working proof of concept detailing how to approach
analyses standardization and construct a schema to store and query analysis
results.


title:
Planning for Temporally Extended Goals in Pure-Past Linear Temporal
  Logic: A Polynomial Reduction to Standard Planning

abstract:
  We study temporally extended goals expressed in Pure-Past LTL (PPLTL). PPLTL
is particularly interesting for expressing goals since it allows to express
sophisticated tasks as in the Formal Methods literature, while the worst-case
computational complexity of Planning in both deterministic and nondeterministic
domains (FOND) remains the same as for classical reachability goals. However,
while the theory of planning for PPLTL goals is well understood, practical
tools have not been specifically investigated. In this paper, we make a
significant leap forward in the construction of actual tools to handle PPLTL
goals. We devise a technique to polynomially translate planning for PPLTL goals
into standard planning. We show the formal correctness of the translation, its
complexity, and its practical effectiveness through some comparative
experiments. As a result, our translation enables state-of-the-art tools, such
as FD or MyND, to handle PPLTL goals seamlessly, maintaining the impressive
performances they have for classical reachability goals.


title:
Double parton scattering via photon-proton interactions: a new light on
  the transverse proton structure

abstract:
  In this contribution we present the main results of the investigation about
double parton scattering (DPS) in quasi-real photon-proton interactions. We
show the first evaluation of the DPS cross-section at leading-order for the
four-jet photo-production observed at HERA. To this aim the $\gamma-p$
effective cross section has been computed for the first time. One of the main
outcomes of this analysis is that the DPS contribution is not negligible and
potentially measurable. Furthermore, possible future data could be used to get
new information on the transverse proton structure not accessible in other
processes.


title:
ChildPredictor: A Child Face Prediction Framework with Disentangled
  Learning

abstract:
  The appearances of children are inherited from their parents, which makes it
feasible to predict them. Predicting realistic children's faces may help settle
many social problems, such as age-invariant face recognition, kinship
verification, and missing child identification. It can be regarded as an
image-to-image translation task. Existing approaches usually assume domain
information in the image-to-image translation can be interpreted by "style",
i.e., the separation of image content and style. However, such separation is
improper for the child face prediction, because the facial contours between
children and parents are not the same. To address this issue, we propose a new
disentangled learning strategy for children's face prediction. We assume that
children's faces are determined by genetic factors (compact family features,
e.g., face contour), external factors (facial attributes irrelevant to
prediction, such as moustaches and glasses), and variety factors (individual
properties for each child). On this basis, we formulate predictions as a
mapping from parents' genetic factors to children's genetic factors, and
disentangle them from external and variety factors. In order to obtain accurate
genetic factors and perform the mapping, we propose a ChildPredictor framework.
It transfers human faces to genetic factors by encoders and back by generators.
Then, it learns the relationship between the genetic factors of parents and
children through a mapping function. To ensure the generated faces are
realistic, we collect a large Family Face Database to train ChildPredictor and
evaluate it on the FF-Database validation set. Experimental results demonstrate
that ChildPredictor is superior to other well-known image-to-image translation
methods in predicting realistic and diverse child faces. Implementation codes
can be found at https://github.com/zhaoyuzhi/ChildPredictor.


title:
A Fisher information-based incompatibility criterion for quantum
  channels

abstract:
  We introduce a new incompatibility criterion for quantum channels, based on
the notion of (quantum) Fisher information. Our construction is based on a
similar criterion for quantum measurements put forward by H.~Zhu. We then study
the power of the incompatibility criterion in different scenarios. Firstly, we
prove the first analytical conditions for the incompatibility of two Schur
channels. Then, we study the incompatibility structure of a tuple of
depolarizing channels, comparing the newly introduced criterion with the known
results from asymmetric quantum cloning.


title:
TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation
  and ensemble to recognize complex Named Entities in Bangla

abstract:
  Many areas, such as the biological and healthcare domain, artistic works, and
organization names, have nested, overlapping, discontinuous entity mentions
that may even be syntactically or semantically ambiguous in practice.
Traditional sequence tagging algorithms are unable to recognize these complex
mentions because they may violate the assumptions upon which sequence tagging
schemes are founded. In this paper, we describe our contribution to SemEval
2022 Task 11 on identifying such complex Named Entities. We have leveraged the
ensemble of multiple ELECTRA-based models that were exclusively pretrained on
the Bangla language with the performance of ELECTRA-based models pretrained on
English to achieve competitive performance on the Track-11. Besides providing a
system description, we will also present the outcomes of our experiments on
architectural decisions, dataset augmentations, and post-competition findings.


title:
Exact Master Equation for Quantum Brownian Motion with Generalization to
  Momentum-Dependent System-Environment Couplings

abstract:
  In this paper, we generalize the quantum Brownian motion to include
momentum-dependent system-environment couplings. The conventional QBM model
corresponds to the spacial case $W_k = V_k$. The generalized QBM is more
complicated but the generalization is necessary. This is because the particle
transition and the pair production between the system and the environment
represent two very different physical processes, and usually cannot have the
same coupling strengths. Thus, the conventional QBM model, which is
well-defined at classical level, is hardly realized in real quantum physical
world. We discuss the physical realizations of the generalized QBM in different
physical systems, and derive its exact master equation for both the initial
decoupled states and initial correlated states. The Hu-Paz-Zhang master
equation of the conventional QBM model is reproduced as a special case. We find
that the renormalized Brownian particle Hamiltonian after traced out all the
environmental states induced naturally a momentum-dependent potential, which
also shows the necessity of including the momentum-dependent coupling in the
QBM Hamiltonian. In the Hu-Paz-Zhang master equation, such a renormalized
potential is misplaced so that the correct renormalization Hamiltonian has not
been found. With the exact master equation for both the initial decoupled and
and initial correlated states, the issues about the initial jolt which is a
long-stand problem in the Hu-Paz-Zhang master equation is also re-examined. We
find that the so-called "initial jolt", which has been thought to be an
artificial effect due to the use of the initial decoupled system-environment
states, has nothing do to with the initial decoupled state. The new exact
master equation for the generalized QBM also has the potential applications to
photonics quantum computing.


title:
General Time-Dependent Configuration-Interaction Singles I: The
  Molecular Case

abstract:
  We present a grid-based implementation of the time-dependent
configuration-interaction singles method suitable for computing the
strong-field ionization of small gas-phase molecules. After outlining the
general equations of motion used in our treatment of this method, we present
example calculations of strong-field ionization of helium, lithium hydride,
water, and ethylene that demonstrate the utility of our implementation. The
following companion paper specializes to the case of spherical symmetry, which
is applied to various atoms.


title:
Transformer-Guided Convolutional Neural Network for Cross-View
  Geolocalization

abstract:
  Ground-to-aerial geolocalization refers to localizing a ground-level query
image by matching it to a reference database of geo-tagged aerial imagery. This
is very challenging due to the huge perspective differences in visual
appearances and geometric configurations between these two views. In this work,
we propose a novel Transformer-guided convolutional neural network (TransGCNN)
architecture, which couples CNN-based local features with Transformer-based
global representations for enhanced representation learning. Specifically, our
TransGCNN consists of a CNN backbone extracting feature map from an input image
and a Transformer head modeling global context from the CNN map. In particular,
our Transformer head acts as a spatial-aware importance generator to select
salient CNN features as the final feature representation. Such a coupling
procedure allows us to leverage a lightweight Transformer network to greatly
enhance the discriminative capability of the embedded features. Furthermore, we
design a dual-branch Transformer head network to combine image features from
multi-scale windows in order to improve details of the global feature
representation. Extensive experiments on popular benchmark datasets demonstrate
that our model achieves top-1 accuracy of 94.12\% and 84.92\% on CVUSA and
CVACT_val, respectively, which outperforms the second-performing baseline with
less than 50% parameters and almost 2x higher frame rate, therefore achieving a
preferable accuracy-efficiency tradeoff.


title: A Swanson-like Hamiltonian and the inverted harmonic oscillator

abstract:
  We deduce the eigenvalues and the eigenvectors of a parameter-dependent
Hamiltonian $H_\theta$ which is closely related to the Swanson Hamiltonian, and
we construct bi-coherent states for it. After that, we show how and in which
sense the eigensystem of the Hamiltonian $H$ of the inverted quantum harmonic
oscillator can be deduced from that of $H_\theta$. We show that there is no
need to introduce a different scalar product using some ad hoc metric operator,
as suggested by other authors. Indeed we prove that a distributional approach
is sufficient to deal with the Hamiltonian $H$ of the inverted oscillator.


title:
Using consumer feedback from location-based services in PoI recommender
  systems for people with autism

abstract:
  When suggesting Points of Interest (PoIs) to people with autism spectrum
disorders, we must take into account that they have idiosyncratic sensory
aversions to noise, brightness and other features that influence the way they
perceive places. Therefore, recommender systems must deal with these aspects.
However, the retrieval of sensory data about PoIs is a real challenge because
most geographical information servers fail to provide this data. Moreover,
ad-hoc crowdsourcing campaigns do not guarantee to cover large geographical
areas and lack sustainability. Thus, we investigate the extraction of sensory
data about places from the consumer feedback collected by location-based
services, on which people spontaneously post reviews from all over the world.
Specifically, we propose a model for the extraction of sensory data from the
reviews about PoIs, and its integration in recommender systems to predict item
ratings by considering both user preferences and compatibility information. We
tested our approach with autistic and neurotypical people by integrating it
into diverse recommendation algorithms. For the test, we used a dataset built
in a crowdsourcing campaign and another one extracted from TripAdvisor reviews.
The results show that the algorithms obtain the highest accuracy and ranking
capability when using TripAdvisor data. Moreover, by jointly using these two
datasets, the algorithms further improve their performance. These results
encourage the use of consumer feedback as a reliable source of information
about places in the development of inclusive recommender systems.


title: Hochschild cohomology of symmetric groups in low degrees

abstract:
  We compute the dimensions of the Hochschild cohomology of symmetric groups
over prime fields in low degrees. This involves us in studying some partition
identities and generating functions of the dimensions in any fixed degree of
the Hochschild cohomology of symmetric groups.


title: Dynamic Interlining in Bus Operations

abstract:
  The paper introduces and evaluates the concept of the dynamic interlining of
buses. Dynamic interlining is an operational strategy for routes that have a
terminal station at a common hub, that allows a portion of (or all) the fleet
to be shared among the routes belonging to the hub (shared fleet) as needed.
The shared fleet is dispatched on an on-demand basis to serve scheduled trips
on any route to avoid delays and regulate services. The paper examines
systematically the impacts of dynamic interlining on service reliability. It
formulates the dispatching problem as an optimization problem and uses
simulation to evaluate the dynamic interlining strategy under a variety of
operating conditions. Using bus routes in Boston Massachusetts Bay
Transportation Authority (MBTA) as a case study, the feasibility of the
strategy, as well as factors that affect its performance are investigated.
Results show that dynamic interlining can improve service reliability
(increases on-time departures and decreases departure headways variability at
the hub). The fraction of the fleet that is shared has the most dominant impact
on performance. In the case where all buses are dynamically interlined, the
performance improves as route frequency increases and more routes participate
in the strategy.


title:
Matrix-analytic methods for solving Poisson's equation with applications
  to Markov chains of GI/G/1-type

abstract:
  In this paper, we are devoted to developing matrix-analytic methods for
solving Poisson's equation for irreducible and positive recurrent discrete-time
Markov chains (DTMCs). Two special solutions, including the deviation matrix D
and the expected additive-type functional matrix K, will be considered. The
results are applied to Markov chains of GI/G/1-type and MAP/G/1 queues with
negative customers. Further extensions to continuous-time Markov chains (CTMCs)
are also investigated.


title: Merging of neural networks

abstract:
  We propose a simple scheme for merging two neural networks trained with
different starting initialization into a single one with the same size as the
original ones. We do this by carefully selecting channels from each input
network. Our procedure might be used as a finalization step after one tries
multiple starting seeds to avoid an unlucky one. We also show that training two
networks and merging them leads to better performance than training a single
network for an extended period of time.
  Availability: https://github.com/fmfi-compbio/neural-network-merging


title: The pion in the graviton soft-wall model: phenomenological applications

abstract:
  The holographic graviton soft-wall model, introduced to describe the spectrum
of scalar and tensor glueballs, is improved to incorporate the realization of
chiral-symmetry as in QCD. Such a goal is achieved by including the
longitudinal dynamics of QCD into the scheme. Using the relation between
AdS/QCD and Light-Front dynamics, we construct the appropriate wave function
for the pion which is used to calculate several pion observables. The
comparison of our results with phenomenology is remarkably successful.


title:
Eliminating Backdoor Triggers for Deep Neural Networks Using Attention
  Relation Graph Distillation

abstract:
  Due to the prosperity of Artificial Intelligence (AI) techniques, more and
more backdoors are designed by adversaries to attack Deep Neural Networks
(DNNs).Although the state-of-the-art method Neural Attention Distillation (NAD)
can effectively erase backdoor triggers from DNNs, it still suffers from
non-negligible Attack Success Rate (ASR) together with lowered classification
ACCuracy (ACC), since NAD focuses on backdoor defense using attention features
(i.e., attention maps) of the same order. In this paper, we introduce a novel
backdoor defense framework named Attention Relation Graph Distillation (ARGD),
which fully explores the correlation among attention features with different
orders using our proposed Attention Relation Graphs (ARGs). Based on the
alignment of ARGs between both teacher and student models during knowledge
distillation, ARGD can eradicate more backdoor triggers than NAD. Comprehensive
experimental results show that, against six latest backdoor attacks, ARGD
outperforms NAD by up to 94.85% reduction in ASR, while ACC can be improved by
up to 3.23%.


title:
Baseline Systems for the First Spoofing-Aware Speaker Verification
  Challenge: Score and Embedding Fusion

abstract:
  Deep learning has brought impressive progress in the study of both automatic
speaker verification (ASV) and spoofing countermeasures (CM). Although
solutions are mutually dependent, they have typically evolved as standalone
sub-systems whereby CM solutions are usually designed for a fixed ASV system.
The work reported in this paper aims to gauge the improvements in reliability
that can be gained from their closer integration. Results derived using the
popular ASVspoof2019 dataset indicate that the equal error rate (EER) of a
state-of-the-art ASV system degrades from 1.63% to 23.83% when the evaluation
protocol is extended with spoofed trials.%subjected to spoofing attacks.
However, even the straightforward integration of ASV and CM systems in the form
of score-sum and deep neural network-based fusion strategies reduce the EER to
1.71% and 6.37%, respectively. The new Spoofing-Aware Speaker Verification
(SASV) challenge has been formed to encourage greater attention to the
integration of ASV and CM systems as well as to provide a means to benchmark
different solutions.


title: Fermi surface and nested magnetic breakdown in WTe2

abstract:
  We report a detailed Shubnikov-de Haas (SdH) study on the Weyl type-II
semimetal WTe2 in magnetic fields up to 29 T. By using the SdH results to guide
our density functional theory calculations, we are able to accurately determine
its Fermi surface by employing a moderate Hubbard U term, which is an essential
step in explaining the unusual electronic properties of this much studied
material. In addition to the fundamental orbits, we observe magnetic breakdown,
which can consistently be explained within the model of a Russian-doll-nested
Fermi surface of electron and hole pockets. The onset of magnetic breakdown in
WTe2 is solely determined by impurity damping in contrast to magnetic breakdown
scenarios in other metallic systems.


title:
Implication of the dark axion portal for the EDM of fermions and dark
  matter probing with NA64$e$, NA64$\mu$, LDMX, $\mbox{M}^3$, and BaBar

abstract:
  The link between ordinary Standard model (SM) photon and both dark photon and
axion like particle (ALP) can be introduced through the dark axion portal
coupling. Given the dark axion portal setup, in the present paper we refer the
dark photon as the mediator between SM and dark matter (DM) particles, implying
that it decays predominantly into pair of DM fermions. Furthermore, we discuss
in detail the implication of the dark axion portal scenario for the lepton
(electron and muon) fixed target experiments. In particular, for the specific
fixed target facility we study the missing energy signatures of the dark photon
production followed by its invisible decay into stable DM particles. We
investigated the potential to probe dark axion portal vertices with regarding
signatures and derive the expected sensitivities of NA64$e$, NA64$\mu$, LDMX
and $\mbox{M}^3$. Moreover, we estimated the expected reach of NA64$e$ from the
projected statistics of the $J/\psi$ vector meson invisible decays. We also
recasted BaBar monophoton bounds for the specific dark axion portal scenario.
In addition, we modified the dark axion portal setup by including in the model
both the hadron and lepton specific ALP couplings. As the result, we obtain the
bounds on the combination of fermion-specific couplings of ALP from the fixed
target experiments. We discuss the implication of the modified dark axion
portal scenario for the electric dipole moments (EDM) of SM fermions. In
addition, we derived the novel constraints on the combination of the
$CP$-violating neutron-specific ALP couplings from the existing bounds on
neutron EDM by taking into account the neutron anomalous magnetic moment.


title: Agile data management in NAV: A Case Study

abstract:
  To satisfy the need for analytical data in the development of digital
services, many organizations use data warehouse, and, more recently, data lake
architectures. These architectures have traditionally been accompanied by
centralized organizational models, where a single team or department has been
responsible for gathering, transforming, and giving access to analytical data.
However, such centralized models presuppose stability and are incompatible with
agile software development where applications and databases are continuously
updated. To achieve more agile forms of data management, some organizations
have therefore begun to experiment with distributed data management models such
as 'data meshes'. Research on this topic is however limited. In this paper, we
report findings from a case study of a public sector organization in Norway
that has begun the transition from centralized to distributed data management,
outlining both the benefits and challenges of a distributed approach.


title:
Spectral functions of the half-filled 1D Hubbard chain within the
  exchange-correlation potential formalism

abstract:
  The spectral functions of the one-band half-filled 1D Hubbard chain are
calculated using the exchange-correlation potential formalism developed
recently. The exchange-correlation potential is adopted from the exact
potential derived from the Hubbard dimer. Within an approximation in which the
full Green function is replaced by a non-interacting one, the spectral
functions can be calculated analytically. Despite the simplicity of the
approximation, the resulting spectra are in favorable agreement with the more
accurate results obtained from the dynamic density-matrix renormalization group
method. In particular, the calculated band gap as a function of $U$ is in close
agreement with the exact gap obtained from the Bethe ansatz. In addition, the
formal general solution to the equation of motion of the Green function is
presented and the difference between the traditional self-energy approach and
the exchange-correlation potential formalism is also discussed and elaborated.
A simplified Holstein Hamiltonian is considered to further illustrate the
general form of the exchange-correlation potential.


title:
Method and new tabulations for flux-weighted line-opacity and radiation
  line-force in supersonic media

abstract:
  In accelerating and supersonic media, the interaction of photons with
spectral lines can be of ultimate importance. However, fully accounting for
such line forces currently can only be done by specialised codes in 1-D
steady-state flows. More general cases and higher dimensions require
alternative approaches. We presented a comprehensive and fast method for
computing the radiation line-force using tables of spectral line-strength
distribution parameters, which can be applied in arbitrary (multi-D,
time-dependent) simulations, including those accounting for the
line-deshadowing instability, to compute the appropriate opacities. We assumed
local thermodynamic equilibrium to compute a flux-weighted line opacity from
$>4$ million spectral lines. We derived the spectral line strength and
tabulated the corresponding line-distribution parameters for a range of input
densities $\rho\in[10^{-20},10^{-10}]gcm^{-3}$ and temperatures
$T\in[10^4,10^{4.7}]K$. We found that the variation of the line distribution
parameters plays an essential role in setting the wind dynamics in our models.
In our benchmark study, we also found a good overall agreement between the
O-star mass-loss rates of our models and those derived from steady-state
studies using more detailed radiative transfer. Our models reinforce that
self-consistent variation of the line-distribution parameters is important for
the dynamics of line-driven flows. Within a well-calibrated O-star regime, our
results support the proposed methodology. In practice, utilising the provided
tables, yielded a factor $>100$ speed-up in computational time compared to
specialised 1-D model-atmosphere codes of line-driven winds, which constitutes
an important step towards efficient multi-D simulations. We conclude that our
method and tables are ready to be exploited in various radiation-hydrodynamic
simulations where the line force is important.


title: Impact of heavy quark and quarkonium data on nuclear gluon PDFs

abstract:
  A clear understanding of nuclear parton distribution functions (nPDFs) plays
a crucial role in the interpretation of collider data taken at the Relativistic
Heavy Ion Collider (RHIC), the Large Hadron Collider (LHC) and in the near
future at the Electron-Ion Collider (EIC). Even with the recent inclusions of
vector boson and light meson production data, the uncertainty of the gluon PDF
remains substantial and limits the interpretation of heavy ion collision data.
To obtain new constraints on the nuclear gluon PDF, we extend our recent
nCTEQ15WZ+SIH analysis to inclusive quarkonium and open heavy-flavor meson
production data from the LHC. This vast new data set covers a wide kinematic
range and puts strong constraints on the nuclear gluon PDF down to $x\lesssim
10^{-5}$. The theoretical predictions for these data sets are obtained from a
data-driven approach, where proton-proton data are used to determine effective
scattering matrix elements. This approach is validated with detailed
comparisons to existing next-to-leading order (NLO) calculations in
non-relativistic QCD (NRQCD) for quarkonia and in the general-mass
variable-flavor-number scheme (GMVFNS) for the open heavy-flavored mesons. In
addition, the uncertainties from the data-driven approach are determined using
the Hessian method and accounted for in the PDF fits. This extension of our
previous analyses represents an important step toward the next generation of
PDFs not only by including new data sets, but also by exploring new methods for
future analyses.


title:
DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose
  Estimation

abstract:
  Monocular 6D pose estimation is a fundamental task in computer vision.
Existing works often adopt a two-stage pipeline by establishing correspondences
and utilizing a RANSAC algorithm to calculate 6 degrees-of-freedom (6DoF) pose.
Recent works try to integrate differentiable RANSAC algorithms to achieve an
end-to-end 6D pose estimation. However, most of them hardly consider the
geometric features in 3D space, and ignore the topology cues when performing
differentiable RANSAC algorithms. To this end, we proposed a Depth-Guided Edge
Convolutional Network (DGECN) for 6D pose estimation task. We have made efforts
from the following three aspects: 1) We take advantages ofestimated depth
information to guide both the correspondences-extraction process and the
cascaded differentiable RANSAC algorithm with geometric information. 2)We
leverage the uncertainty ofthe estimated depth map to improve accuracy and
robustness ofthe output 6D pose. 3) We propose a differentiable
Perspective-n-Point(PnP) algorithm via edge convolution to explore the topology
relations between 2D-3D correspondences. Experiments demonstrate that our
proposed network outperforms current works on both effectiveness and
efficiency.


title:
Convergence analysis of a Local Discontinuous Galerkin approximation for
  nonlinear systems with Orlicz-structure

abstract:
  In this paper, we investigate a Local Discontinuous Galerkin (LDG)
approximation for systems with Orlicz-structure. We propose a new numerical
flux, which yields optimal convergence rates for linear ansatz functions. In
particular, for problems with $(p,\delta)$-structure for arbitrary $p \in
(1,\infty)$ and $\delta\ge 0$, our approach yields a unified treatment.


title: Revisiting initial sets in abstract argumentation

abstract:
  We revisit the notion of initial sets by Xu and Cayrol, i.e., non-empty
minimal admissible sets in abstract argumentation frameworks. Initial sets are
a simple concept for analysing conflicts in an abstract argumentation framework
and to explain why certain arguments can be accepted. We contribute with new
insights on the structure of initial sets and devise a simple non-deterministic
construction principle for any admissible set, based on iterative selection of
initial sets of the original framework and its induced reducts. In particular,
we characterise many existing admissibility-based semantics via this
construction principle, thus providing a constructive explanation on the
structure of extensions. We also investigate certain problems related to
initial sets with respect to their computational complexity.


title:
Engineering bound states in the continuum at telecom wavelengths with
  non-Bravais lattices

abstract:
  Various optical phenomena can be induced in periodic arrays of nanoparticles
by the radiative coupling of the local dipoles in each particle. Probably the
most impressive example is bound states in the continuum (BICs), which are
electromagnetic modes with a dispersion inside the light cone but infinite
lifetime, i.e., modes that cannot leak to the continuum. Symmetry-protected
BICs appear at highly symmetric points in the dispersion of periodic systems.
Although the addition of nonequivalent lattice points in a unit cell is an easy
and straightforward way of tuning the symmetry, BICs in such particle lattice,
i.e., non-Bravais lattice, are less explored among periodic systems. Starting
from a periodic square lattice of Si nanodisks, we have prepared three
non-Bravais lattices by detuning size and position of the second disk in the
unit cell. Diffraction-induced coupling excites magnetic/electric dipoles in
each nanodisk, producing two surface lattice resonances at the $\Gamma$ point
with a band gap in between. %of $\sim$ 41 meV.
  The high/low energy branch becomes a BIC for the size/position-detuned array,
respectively, while both branches are bright (or leaky) when both size and
position are detuned simultaneously. The role of magnetic and electric
resonances in dielectric nanoparticles and the change of BIC to bright
character of the modes is explained by the two different origins of BICs in the
detuned arrays, which is further discussed with the aid of a coupled electric
and magnetic dipole model. This study gives a simple way of tuning BICs at
telecom wavelengths in non-Bravais lattices, including both plasmonic and
dielectric systems, thus scalable to a wide range of frequencies.


title:
Characterizing the $\gamma$-Ray Variability of Active Galactic Nuclei
  with Stochastic Process Method

abstract:
  The $\gamma$-ray astronomy in time domain has been by now progressed further
as the variabilities of Active Galactic Nuclei (AGNs) on different timescales
have been reported a lot. We study the $\gamma$-ray variabilities of 23 jetted
AGNs through applying a stochastic process method to the ~12.7 yr long-term
light curve (LC) obtained by Fermi-Large Area Telescope (Fermi-LAT). In this
method, the stochastically driven damped simple harmonic oscillator (SHO) and
the damped random walk (DRW) models are used to model the long-term LCs. Our
results show that the long-term variabilities of 23 AGNs can be characterized
well by both SHO and DRW models. However, the SHO model is restricted in the
over-damped mode and the parameters are poorly constrained. The SHO power
spectral densities (PSDs) are same as the typical DRW PSD. In the plot of the
rest-frame timescale that corresponds to the broken frequency in the PSD versus
black hole mass, the intrinsic $\gamma$-ray characteristic timescales of 23
AGNs occupy almost the same space with the optical variability timescales
obtained from the accretion disk emission. This suggests a connection between
the jet and the accretion disk. Same as the optical variability of AGN
accretion disk, the $\gamma$-ray timescale is also consistent with the thermal
timescale caused by the thermal instability in the standard accretion disk of
AGN.


title: Remaining loads in a PH/M/c queue with impatient customers

abstract:
  There are two contributions to the literature in this note. Firstly, we point
out that under some weak conditions the result in Swensen (1986) on the
remaining loads in a GI/M/$c$ queue with impatient customers, derived under the
assumption that the distribution of inter-arrival times is Coxian, is also
valid for the more general phase type distribution. From this result the
distributions of the virtual and the actual waiting time can easily be
obtained. Secondly, the relation to an alternative expression for the
distribution of the virtual waiting time derived by Kawanishi and Takine (2015)
is also discussed.


title:
NAND-SPIN-Based Processing-in-MRAM Architecture for Convolutional Neural
  Network Acceleration

abstract:
  The performance and efficiency of running large-scale datasets on traditional
computing systems exhibit critical bottlenecks due to the existing "power wall"
and "memory wall" problems. To resolve those problems, processing-in-memory
(PIM) architectures are developed to bring computation logic in or near memory
to alleviate the bandwidth limitations during data transmission. NAND-like
spintronics memory (NAND-SPIN) is one kind of promising magnetoresistive
random-access memory (MRAM) with low write energy and high integration density,
and it can be employed to perform efficient in-memory computation operations.
In this work, we propose a NAND-SPIN-based PIM architecture for efficient
convolutional neural network (CNN) acceleration. A straightforward data mapping
scheme is exploited to improve the parallelism while reducing data movements.
Benefiting from the excellent characteristics of NAND-SPIN and in-memory
processing architecture, experimental results show that the proposed approach
can achieve $\sim$2.6$\times$ speedup and $\sim$1.4$\times$ improvement in
energy efficiency over state-of-the-art PIM solutions.


title:
Non-collapsing condition and Sobolev embeddings for Haj{\l}asz-Besov
  spaces

abstract:
  In this paper we will focus on understanding the relation between Sobolev
embedding theorems for Haj{\l}asz-Besov spaces defined on a doubling metric
measure space $(\Omega,d,\mu)$ and the non-collapsing condition of the measure,
i.e. \[ \inf_{x\in\Omega}\mu(B(x,1))>0. \] We will also obtain embedding
results for Haj{\l}asz-Besov spaces whose modulus of smoothness is generated by
a rearrangement invariant quasi-norm.


title:
Wurtzite quantum wires with strong spatial confinement: polarization
  anisotropies in single wire spectroscopy

abstract:
  We report GaAs/AlGaAs nanowires in the one-dimensional (1D) quantum limit.
The ultrathin wurtzite GaAs cores between 20-40\,nm induce large confinement
energies of several tens of meV, allowing us to experimentally resolve up to
four well separated subband excitations in microphotoluminescence spectroscopy.
Our detailed experimental and theoretical polarization-resolved study reveals a
strong diameter-dependent anisotropy of these transitions: We demonstrate that
the polarization of the detected photoluminescence is governed by the symmetry
of the wurtzite 1D quantum wire subbands on the one hand, but also by the
dielectric mismatch of the wires with the surrounding material on the other
hand. The latter effect leads to a strong attenuation of perpendicularly
polarized light in thin dielectric wires, making the thickness of the AlGaAs
shell an important factor in the observed polarization behavior. Including the
dielectric mismatch to our k.p-based simulated polarization-resolved spectra of
purely wurtzite GaAs quantum wires, we find an excellent agreement between
experiment and theory.


title:
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and
  Adaptive Inference Approach

abstract:
  Conventional model quantization methods use a fixed quantization scheme to
different data samples, which ignores the inherent "recognition difficulty"
differences between various samples. We propose to feed different data samples
with varying quantization schemes to achieve a data-dependent dynamic
inference, at a fine-grained layer level. However, enabling this adaptive
inference with changeable layer-wise quantization schemes is challenging
because the combination of bit-widths and layers is growing exponentially,
making it extremely difficult to train a single model in such a vast searching
space and use it in practice. To solve this problem, we present the Arbitrary
Bit-width Network (ABN), where the bit-widths of a single deep network can
change at runtime for different data samples, with a layer-wise granularity.
Specifically, first we build a weight-shared layer-wise quantizable
"super-network" in which each layer can be allocated with multiple bit-widths
and thus quantized differently on demand. The super-network provides a
considerably large number of combinations of bit-widths and layers, each of
which can be used during inference without retraining or storing myriad models.
Second, based on the well-trained super-network, each layer's runtime bit-width
selection decision is modeled as a Markov Decision Process (MDP) and solved by
an adaptive inference strategy accordingly. Experiments show that the
super-network can be built without accuracy degradation, and the bit-widths
allocation of each layer can be adjusted to deal with various inputs on the
fly. On ImageNet classification, we achieve 1.1% top1 accuracy improvement
while saving 36.2% BitOps.


title: Type IIB S-folds: flat deformations, holography and stability

abstract:
  We review recent progress in the study of S-folds in light of the
gauge/gravity duality and the AdS swampland conjecture. S-folds correspond to
non-geometric backgrounds of type IIB supergravity of the form
$\,\textrm{AdS}_4 \, \times \, \textrm{S}^1 \, \times \, \mathcal{M}\,$ that
involve a non-trivial $\,\textrm{SL}(2,\,\mathbb{Z})\,$ (S-duality) monodromy
for the type IIB fields when moving around the $\,\textrm{S}^1$. We present
four such solutions with $\,\mathcal{M}=\textrm{S}^{5}\,$ that preserve
$\,\mathcal{N}=4,2,1,0\,$ supersymmetries. Via the AdS/CFT correspondence,
these solutions are conjectured to describe new strongly coupled
three-dimensional CFT's on a localised interface of SYM. We discuss the
existence of flat deformations in the gravity side dual to marginal
deformations of the conjectured S-fold CFT's. From a geometrical perspective,
the flat deformations induce a monodromy $\,h\,$ on $\,\mathcal{M}\,$ and
replace $\,\textrm{S}^1 \,\times\, \mathcal{M}\,$ by the so-called mapping
torus $\,T(\mathcal{M})_h$. Interestingly, the flat deformations provide a
controlled mechanism of supersymmetry breaking for $\,\mathcal{N} \ge 2\,$
S-folds. We present a class of such non-supersymmetric S-folds obtained by
flat-deforming the $\,\mathcal{N}=4\,$ S-fold and discuss their
(non-)perturbative stability.


title:
A data filling methodology for time series based on CNN and (Bi)LSTM
  neural networks

abstract:
  In the process of collecting data from sensors, several circumstances can
affect their continuity and validity, resulting in alterations of the data or
loss of information. Although classical methods of statistics, such as
interpolation-like techniques, can be used to approximate the missing data in a
time series, the recent developments in Deep Learning (DL) have given impetus
to innovative and much more accurate forecasting techniques. In the present
paper, we develop two DL models aimed at filling data gaps, for the specific
case of internal temperature time series obtained from monitored apartments
located in Bolzano, Italy. The DL models developed in the present work are
based on the combination of Convolutional Neural Networks (CNNs), Long
Short-Term Memory Neural Networks (LSTMs), and Bidirectional LSTMs (BiLSTMs).
Two key features of our models are the use of both pre- and post-gap data, and
the exploitation of a correlated time series (the external temperature) in
order to predict the target one (the internal temperature). Our approach
manages to capture the fluctuating nature of the data and shows good accuracy
in reconstructing the target time series. In addition, our models significantly
improve the already good results from another DL architecture that is used as a
baseline for the present work.


title: Micromasers as Quantum Batteries

abstract:
  We show that a micromaser is an excellent model of quantum battery. A highly
excited, pure, and effectively steady state of the cavity mode, charged by
coherent qubits, can be achieved, also in the ultrastrong coupling regime of
field-matter interaction. Stability of these appealing features against loss of
coherence of the qubits and the effect of counter-rotating terms in the
interaction Hamiltonian are also discussed.

categories: quant-ph cond-mat.mes-hall cond-mat.stat-mech cond-mat.str-el


title: Computational Design of Kinesthetic Garments

abstract:
  Kinesthetic garments provide physical feedback on body posture and motion
through tailored distributions of reinforced material. Their ability to
selectively stiffen a garment's response to specific motions makes them
appealing for rehabilitation, sports, robotics, and many other application
fields. However, finding designs that distribute a given amount of
reinforcement material to maximally stiffen the response to specified motions
is a challenging problem. In this work, we propose an optimization-driven
approach for automated design of reinforcement patterns for kinesthetic
garments. Our main contribution is to cast this design task as an on-body
topology optimization problem. Our method allows designers to explore a
continuous range of designs corresponding to various amounts of reinforcement
coverage. Our model captures both tight contact and lift-off separation between
cloth and body. We demonstrate our method on a variety of reinforcement design
problems for different body sites and motions. Optimal designs lead to a two-
to threefold improvement in performance in terms of energy density. A set of
manufactured designs were consistently rated as providing more resistance than
baselines in a comparative user study


title: Computational ghost imaging for transmission electron microscopy

abstract:
  While transmission electron microscopes (TEM) can achieve a much higher
resolution than optical microscopes, they face challenges of damage to samples
during the high energy processes involved. Here, we explore using computational
ghost imaging techniques in electron microscopy to reduce the total required
intensity. The technological lack of the equivalent high-resolution, optical
spatial light modulator for electrons means that a different approach needs to
be pursued. To this end, we show a beam shaping technique based on the use of a
distribution of electrically charged metal needles to structure the beam,
alongside a novel reconstruction method to handle the resulting highly
non-orthogonal patterns. Second, we illustrate the application of this ghost
imaging approach in electron microscopy. To test the full extent of the
capabilities of this technique, we realised an analogous optical setup method.
In both regimes, the ability to reduce the amount of total illumination
intensity is evident in comparison to raster scanning.

categories: physics.optics cond-mat.mes-hall physics.bio-ph physics.ins-det quant-ph


title:
SYK model with an extra diagonal perturbation: phase transition in the
  eigenvalue spectrum

abstract:
  We study the SYK model with an extra constant source, \.i.e. a constant
matrix or equivalently a diagonal matrix with only one non-zero entry
$\lambda_1$. By using methods from analytic combinatorics, we find exact
expressions for the moments of this model. We further prove that the spectrum
of this model can have a gap when $\lambda_1>\lambda^c_1$, thus exhibiting a
phase transition in $\lambda_1$. In this case, a single isolated eigenvalue
splits off from SYK's eigenvalues distribution. We located this single
eigenvalue by analyzing the singular behavior of a supercritical functional
composition scheme. In certain limit our results recover the ones of random
matrices with non-zero mean entries.

categories: math-ph cond-mat.stat-mech hep-th math.CO math.MP


title: Ferrous Metal Matrix Composites Status Scope and Challenges

abstract:
  The present paper is an effort to culminate the status, scopes and challenges
in the development of ferrous metal matrix composites (FMMCs). The FMMCs are
old but less in use than the non-ferrous metal matrix composites (NFMMCs), as
far as literature and actual applications are concerned. Therefore, this
stimulates the exploration of the reasons behind the scarcity of literature and
field applications of the FMMCs, which must be investigated scientifically. The
powder metallurgy route is the most used process for fabricating iron and steel
based FMMCs by reinforcing particulates. At the same time, the in-situ method
has been used for the fabrication and cast iron-based FMMCs. The main
characteristics being considered during the designing and fabrication of FMMCs
are wear resistance and improved specific mechanical properties. To fabricate
cheaper and eco-friendly FMMCs, traditionally used costly reinforcements such
as SiC, WC, TiC, SiO2, TiO2, TiB2 are required to be replaced by inexpensive
industrial wastes like red-mud, fly-ashes and grinding swarf. The data
extracted from the web of science exhibited that the FMMCs have been researched
less than the NFMMCs. The increasing number of research papers on FMMCs
indicates a bright future. FMMCs are going to be a favourite topic among
researchers and manufacturers. Higher strengths, wear resistance, dimensional
stability at elevated temperatures, and, most importantly, the lower cost will
put forward the FMMCs as a stiff competitor of NFMMCs. In developing and mass
production of FMMCs for field applications, challenges like oxidation and
higher weight still require special research efforts.


title:
Adaptive isogeometric methods with $C^1$ (truncated) hierarchical
  splines on planar multi-patch domains

abstract:
  Isogeometric analysis is a powerful paradigm which exploits the high
smoothness of splines for the numerical solution of high order partial
differential equations. However, the tensor-product structure of standard
multivariate B-spline models is not well suited for the representation of
complex geometries, and to maintain high continuity on general domains special
constructions on multi-patch geometries must be used. In this paper we focus on
adaptive isogeometric methods with hierarchical splines, and extend the
construction of $C^1$ isogeometric spline spaces on multi-patch planar domains
to the hierarchical setting. We introduce a new abstract framework for the
definition of hierarchical splines, which replaces the hypothesis of local
linear independence for the basis of each level by a weaker assumption. We also
develop a refinement algorithm that guarantees that the assumption is fulfilled
by $C^1$ splines on certain suitably graded hierarchical multi-patch mesh
configurations, and prove that it has linear complexity. The performance of the
adaptive method is tested by solving the Poisson and the biharmonic problems.


title:
On Learning the Invisible in Photoacoustic Tomography with Flat
  Directionally Sensitive Detector

abstract:
  In photoacoustic tomography (PAT) with flat sensor, we routinely encounter
two types of limited data. The first is due to using a finite sensor and is
especially perceptible if the region of interest is large relatively to the
sensor or located farther away from the sensor. In this paper, we focus on the
second type caused by a varying sensitivity of the sensor to the incoming
wavefront direction which can be modelled as binary i.e. by a cone of
sensitivity. Such visibility conditions result, in Fourier domain, in a
restriction of both the image and the data to a bowtie, akin to the one
corresponding to the range of the forward operator. The visible ranges, in
image and data domains, are related by the wavefront direction mapping. We
adapt the wedge restricted Curvelet decomposition, we previously proposed for
the representation of the full PAT data, to separate the visible and invisible
wavefronts in the image. We optimally combine fast approximate operators with
tailored deep neural network architectures into efficient learned
reconstruction methods which perform reconstruction of the visible coefficients
and the invisible coefficients are learned from a training set of similar data.


title:
The coherent differential imaging on speckle area nulling (CDI-SAN)
  method for high-contrast imaging under speckle variation

abstract:
  Differential imaging is a postprocessing method to obtain high contrast,
often used for exoplanet searches. The coherent differential imaging on speckle
area nulling (CDI-SAN) method was developed to detect a faint exoplanet lying
beneath residual speckles of a host star. It utilizes image acquisitions faster
than the stellar speckle variation synchronized with five shapes of a
deformable mirror repeatedly. By using the only the integrated values of each
of the five images and square differences for a long interval of observations,
the light of the exoplanet could be separated from the stellar light. The
achievable contrast would reach to almost the photon-noise limit of the
residual speckle intensities under appropriate conditions. The CDI-SAN can be
applied to both ground-based and space telescopes.


title:
Few-body clusters in a multiband Hubbard model: Tetramers, pentamers,
  and beyond

abstract:
  We start with a variational approach and derive a set of coupled integral
equations for the exact solutions of the bound states of $N$ identical
spin-$\uparrow$ fermions and a single spin-$\downarrow$ fermion in a generic
multiband Hubbard Hamiltonian with onsite attractive interactions. As an
illustration we apply the integral equations to the so-called sawtooth lattice
up to $N = 3$, i.e., to the $(3+1)$-body problem, due in part to its flat
one-body band and one-dimensional simplicity, and most importantly to our
benchmarking capacity with the DMRG simulations and exact diagonalization. Our
numerical results reveal not only the presence of tetramer states in this
two-band model but also their quasi-flat dispersion when formed in a flat
one-body band. For $N = \{4, 5, \cdots, 10 \}$ our DMRG simulations and exact
diagonalization suggest the presence of larger and larger multimers with lower
and lower binding energies, conceivably without an upper bound on $N$ and with
a quasi-flat dispersion when formed in a flat one-body band. These peculiar
$(N+1)$-body clusters are in sharp contrast with the exact results on the
single-band linear-chain model where none of the $N \ge 2$ multimers appear.


title: An algorithm to calculate generalized Seifert matrices

abstract:
  We develop an algorithm for computing generalized Seifert matrices for
colored links given as closures of colored braids. The algorithm has been
implemented by the second author as a computer program called Clasper. Clasper
also outputs the Conway potential function, the multivariable Alexander
polynomial and the Cimasoni-Florens signatures of a link, and displays a
visualization of the C-complex used for producing the generalized Seifert
matrices.


title:
In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark
  Data Sets for in-silico Model Validation

abstract:
  A deeper understanding of the influence of common cardiovascular diseases
like stenosis, aneurysm or atherosclerosis on the circulatory mechanism is
required, to establish new methods for early diagnosis. Different types of
simulators were developed in the past to simulate healthy and pathological
conditions of blood flow, often based on computational models, which allow to
generate large data sets. However, since computational models often lack some
aspects of real world data, hardware simulators are used to close this gap and
generate data for model validation. The aim of this study is the development
and validation of a hardware simulator to generate benchmark data sets of
healthy and pathological conditions. The in-vitro hardware simulator in this
study includes the major 33 arteries and is driven by a ventricular assist
device generating a parametrised input condition at the heart node. Physiologic
flow conditions including heart rate, systolic/diastolic pressure, peripheral
resistance and compliance are adjustable in a wide range. The pressure and flow
waves at 17+1 different locations are measured by inverted fluid resistant
pressure transducers and one ultrasound flow transducer supporting a detailed
analysis of the measurement data. The pressure and flow waves show
physiological conditions. Furthermore, the influence of stenoses degree and
location on blood pressure and flow was investigated. The results indicate
decreasing translesional pressure and flow with increasing degree of stenosis,
as expected. The benchmark data set is made available to the research
community, with the purpose to validate and compare in-silico models of
different type.


title: M3triCity: Visualizing Evolving Software & Data Cities

abstract:
  The city metaphor for visualizing software systems in 3D has been widely
explored and has led to many diverse implementations and approaches. Common
among all approaches is a focus on the software artifacts, while the aspects
pertaining to the data and information (stored both in databases and files)
used by a system are seldom taken into account.
  We present M3triCity, an interactive web application whose goal is to
visualize object-oriented software systems, their evolution, and the way they
access data and information. We illustrate how it can be used for program
comprehension and evolution analysis of data-intensive software systems.
  Demo video URL: https://youtu.be/uBMvZFIlWtk


title: Fluctuation-based Outlier Detection

abstract:
  Outlier detection is an important topic in machine learning and has been used
in a wide range of applications. Outliers are objects that are few in number
and deviate from the majority of objects. As a result of these two properties,
we show that outliers are susceptible to a mechanism called fluctuation. This
article proposes a method called fluctuation-based outlier detection (FBOD)
that achieves a low linear time complexity and detects outliers purely based on
the concept of fluctuation without employing any distance, density or isolation
measure. Fundamentally different from all existing methods. FBOD first converts
the Euclidean structure datasets into graphs by using random links, then
propagates the feature value according to the connection of the graph. Finally,
by comparing the difference between the fluctuation of an object and its
neighbors, FBOD determines the object with a larger difference as an outlier.
The results of experiments comparing FBOD with seven state-of-the-art
algorithms on eight real-world tabular datasets and three video datasets show
that FBOD outperforms its competitors in the majority of cases and that FBOD
has only 5% of the execution time of the fastest algorithm. The experiment
codes are available at:
https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection.


title:
Towards Fewer Labels: Support Pair Active Learning for Person
  Re-identification

abstract:
  Supervised-learning based person re-identification (re-id) require a large
amount of manual labeled data, which is not applicable in practical re-id
deployment. In this work, we propose a Support Pair Active Learning (SPAL)
framework to lower the manual labeling cost for large-scale person
reidentification. The support pairs can provide the most informative
relationships and support the discriminative feature learning. Specifically, we
firstly design a dual uncertainty selection strategy to iteratively discover
support pairs and require human annotations. Afterwards, we introduce a
constrained clustering algorithm to propagate the relationships of labeled
support pairs to other unlabeled samples. Moreover, a hybrid learning strategy
consisting of an unsupervised contrastive loss and a supervised support pair
loss is proposed to learn the discriminative re-id feature representation. The
proposed overall framework can effectively lower the labeling cost by mining
and leveraging the critical support pairs. Extensive experiments demonstrate
the superiority of the proposed method over state-of-the-art active learning
methods on large-scale person re-id benchmarks.


title: A subgradient method with non-monotone line search

abstract:
  In this paper we present a subgradient method with non-monotone line search
for the minimization of convex functions with simple convex constraints.
Different from the standard subgradient method with prefixed step sizes, the
new method selects the step sizes in an adaptive way. Under mild conditions
asymptotic convergence results and iteration-complexity bounds are obtained.
Preliminary numerical results illustrate the relative efficiency of the
proposed method.


title: Probing magnetism in exfoliated VI3 layers with magnetotransport

abstract:
  We perform magnetotransport experiments on exfoliated multilayers of
$\rm{VI_3}$, a 2D magnetic semiconductor reported to exhibit Ising
ferromagnetism both in bulk and 2D form. Measurements of the magnetoconductance
performed on field-effect transistors and tunnel barriers consistently show
that the Curie temperature is $T_C~=~57~K$ (significantly larger than in the
bulk; $T_C^{\rm bulk}~=~50~K$), irrespective of thickness (between 7 and
20~nm), in agreement with recent magneto-optical experiments. Measurements on
tunnel barriers allow magnetism to be probed down to temperatures much lower
than $T_C$. They show that below $T~\simeq~40$ K the magnetoconductance in a
perpendicular magnetic field becomes negative, and that a pronounced hysteresis
appears when the field is applied parallel to the planes. These phenomena,
which have no observable counterpart in magneto-optical measurements, indicate
an evolution of the magnetic state that deviates from that expected from a
simple Ising ferromagnet. We attribute the effect to the large in-plane
component of the magnetization, which we suggest to point in different
directions in different $\rm{VI_3}$ layers. Our results provide new information
about magnetism in exfoliated $\rm{VI_3}$ crystals and illustrate the
complementarity of magnetotransport and magneto-optical measurements to probe
the state of atomically thin 2D magnets.


title:
MedFACT: Modeling Medical Feature Correlations in Patient Health
  Representation Learning via Feature Clustering

abstract:
  In healthcare prediction tasks, it is essential to exploit the correlations
between medical features and learn better patient health representations.
Existing methods try to estimate feature correlations only from data, or
increase the quality of estimation by introducing task-specific medical
knowledge. However, such methods either are difficult to estimate the feature
correlations due to insufficient training samples, or cannot be generalized to
other tasks due to reliance on specific knowledge. There are medical research
revealing that not all the medical features are strongly correlated. Thus, to
address the issues, we expect to group up strongly correlated features and
learn feature correlations in a group-wise manner to reduce the learning
complexity without losing generality. In this paper, we propose a general
patient health representation learning framework MedFACT. We estimate
correlations via measuring similarity between temporal patterns of medical
features with kernel methods, and cluster features with strong correlations
into groups. The feature group is further formulated as a correlation graph,
and we employ graph convolutional networks to conduct group-wise feature
interactions for better representation learning. Experiments on two real-world
datasets demonstrate the superiority of MedFACT. The discovered medical
findings are also confirmed by literature, providing valuable medical insights
and explanations.


title:
Probing for the host galaxies of the fast X-ray transients XRT 000519
  and XRT 110103

abstract:
  Over the past few years, $\sim$30 extragalactic fast X-ray transients (FXRTs)
have been discovered, mainly in Chandra and XMM-Newton data. Their nature
remains unclear, with proposed origins including a double neutron star merger,
a tidal disruption event involving an intermediate-mass black hole and a white
dwarf, or a supernova shock breakout. A decisive differentiation between these
three promising mechanisms for their origin requires an understanding of the
FXRT energetics, environments, and/or host properties. We present optical
observations obtained with the Very Large Telescope for the FXRTs XRT 000519
and XRT 110103 and Gran Telescopio Canarias observations for XRT 000519
designed to search for host galaxies of these FXRTs. In the $g_s$, $r_s$ and
$R$-band images, we detect an extended source on the North-West side of the
$\sim$ $1^{\prime\prime}$ (68% confidence) error circle of the X-ray position
of XRT 000519 with a Kron magnitude of $g_s=$26.29$\pm$0.09 (AB magnitude). We
discuss the XRT 000519 association with the probable host candidate for various
possible distances, and we conclude that if XRT 000519 is associated with the
host candidate a supernova shock breakout scenario is likely excluded. No host
galaxy is found near XRT 110103 down to a limiting magnitude of $R>25.8$.


title:
Studies of certain classes of functions and its connection with
  $S$-embeddedness

abstract:
  We call a function $f$ in $C(X)$ to be hard-bounded if $f$ is bounded on
every hard subset, a special kind of closed subset, of $X$. We call a subset
$T$ of $X$ to be $S$-embedded if every hard-bounded continuous function of $T$
can be continuously extended upto $X$. Every $S$-embedded subset is
$C^*$-embedded. In this paper we have given a characterization of the converse
part. To get the converse, we came across a type of function which are bounded
away from zero on every hard subset of a subset. We further studied few
properties of this type of functions and also of hard-bounded functions.


title:
Symplectic Grassmannians, dual conformal symmetry and 4-point amplitudes
  in 6D

abstract:
  We investigate a new algebra-based approach of finding Grassmannian formulas
for scattering amplitudes. Our prime motivation is massive amplitudes of 4D
$\mathcal{N}=4$ SYM, and therefore we consider a 6D Grassmannian formula, where
we can take advantage of massless kinematics. We next use symmetry arguments,
and in particular, 6D dual conformal symmetry generalized to arbitrary dual
conformal weights. Assuming a rational ansatz in terms of Pl\"{u}cker
coordinates (i.e. minors) for the integrand, this approach leads to a set of
algebraic equations. As an example, we explicitly find the solution for 4-point
scattering amplitudes up to proportionality constants.


title: On the category of quotient Banach spaces after Wegner

abstract:
  We study Waelbroeck's category of Banach quotients after Wegner, focusing on
its basic homological and functional analytic properties.


title: Hardy spaces and quasiconformal maps in the Heisenberg group

abstract:
  We define Hardy spaces $H^p$, $0<p<\infty$, for quasiconformal mappings on
the Kor\'{a}nyi unit ball $B$ in the first Heisenberg group $\mathbb{H}^1$. Our
definition is stated in terms of the Heisenberg polar coordinates introduced by
Kor\'{a}nyi and Reimann, and Balogh and Tyson. First, we prove the existence of
$p_0(K)>0$ such that every $K$-quasiconformal map $f:B \to f(B) \subset
\mathbb{H}^1$ belongs to $H^p$ for all $0<p<p_0(K)$. Second, we give two
equivalent conditions for the $H^p$ membership of a quasiconformal map $f$, one
in terms of the radial limits of $f$, and one using a nontangential maximal
function of $f$. As an application, we characterize Carleson measures on $B$
via integral inequalities for quasiconformal mappings on $B$ and their radial
limits. Our paper thus extends results by Astala and Koskela, Jerison and
Weitsman, Nolder, and Zinsmeister, from $\mathbb{R}^n$ to $\mathbb{H}^1$. A
crucial difference between the proofs in $\mathbb{R}^n$ and $\mathbb{H}^1$ is
caused by the nonisotropic nature of the Kor\'{a}nyi unit sphere with its two
characteristic points.