title:
Photon counting from the vacuum ultraviolet to the short wavelength
  infrared using semiconductor and superconducting technologies

abstract:
  In the last decade, several photon counting technologies have been developed
opening a new window for experiments in the low photon number regime. Several
ongoing and future projects in HEP benefit from these developments, which will
also have a large impact outside HEP. During the next decade there is a clear
technological opportunity to fully develop these sensors and produce a large
impact in HEP. In this white paper we discuss the need for photon counting
technologies in future projects, and present some technological opportunities
to address those needs.


title: One-loop tunnelling-induced energetics

abstract:
  Tunnelling between degenerate vacuua is allowed in finite-volume Quantum
Field Theory, and features remarkable energetic properties, which result from
the competition of different dominant configurations in the partition function.
We derive the one-loop effective potential based on two homogeneous vacuua of
the bare theory, and we discuss the resulting Null Energy Condition violation
in O(4)-symmetric Euclidean spacetime, as a result of a non-extensive effective
action.


title:
Highly sensitive and high throughput magnetic resonance thermometry
  using superparamagnetic nanoparticles

abstract:
  Magnetic resonance imaging (MRI) enables non-invasive 3D thermometry during
thermal ablation of cancerous tumors. While T1 or T2 contrast MRI are
relatively insensitive to temperature, techniques with greater temperature
sensitivity such as chemical shift or diffusion imaging suffer from motional
artifacts and long scan times. We describe an approach for highly sensitive and
high throughput MR thermometry that is not susceptible to motional artifacts.
We use superparamagnetic iron oxide nanoparticles (SPIONs) to spoil T2 of water
protons. Motional narrowing results in proportionality between T2 and the
diffusion constant, dependent only on the temperature in a specific
environment. Our results show, for pure water, the nuclear magnetic resonance
(NMR) linewidth and T2 follow the same temperature dependence as the
self-diffusion constant of water. Thus, T2 mapping is a diffusion mapping in
the presence of SPIONs, and T2 is a thermometer. For pure water, a T2 mapping
of a 64 x 64 image (voxel size = 0.5 mm x 0.5 mm x 3 mm) in a 9.4 T MRI scanner
resulted in a temperature resolution of 0.5 K for a scan time of 2 minutes.
This indicates a highly sensitive and high throughput MR thermometry technique
potentially useful for monitoring of biological tissues during thermal
therapies or for diagnosis.


title:
The Cauchy-Dirichlet Problem for Singular Nonlocal Diffusions on Bounded
  Domains

abstract:
  We study the homogeneous Cauchy-Dirichlet Problem (CDP) for a nonlinear and
nonlocal diffusion equation of singular type of the form $\partial_t u
=-\mathcal{L} u^m$ posed on a bounded Euclidean domain
$\Omega\subset\mathbb{R}^N$ with smooth boundary and $N\ge 1$. The linear
diffusion operator $\mathcal{L}$ is a sub-Markovian operator, allowed to be of
nonlocal type, while the nonlinearity is of singular type, namely
$u^m=|u|^{m-1}u$ with $0<m<1$. The prototype equation is the Fractional Fast
Diffusion Equation (FFDE), when $\mathcal{L}$ is one of the three possible
Dirichlet Fractional Laplacians on $\Omega$.
  Our main results shall provide a complete basic theory for solutions to
(CDP): existence and uniqueness in the biggest class of data known so far, both
for nonnegative and signed solutions; sharp smoothing estimates: besides the
classical $L^p-L^\infty$ smoothing effects, we provide new weighted estimates,
which represent a novelty also in well studied local case, i.e. for solutions
to the FDE $u_t=\Delta u^m$. We compare two strategies to prove smoothing
effects: Moser iteration VS Green function method.
  Due to the singular nonlinearity and to presence of nonlocal diffusion
operators, the question of how solutions satisfy the lateral boundary
conditions is delicate. We answer with quantitative upper boundary estimates
that show how boundary data are taken.
  Once solutions exists and are bounded we show that they extinguish in finite
time and we provide upper and lower estimates for the extinction time, together
with explicit sharp extinction rates in different norms.
  The methods of this paper are constructive, in the sense that all the
relevant constants involved in the estimates are computable.


title:
Constrained Clustering and Multiple Kernel Learning without Pairwise
  Constraint Relaxation

abstract:
  Clustering under pairwise constraints is an important knowledge discovery
tool that enables the learning of appropriate kernels or distance metrics to
improve clustering performance. These pairwise constraints, which come in the
form of must-link and cannot-link pairs, arise naturally in many applications
and are intuitive for users to provide. However, the common practice of
relaxing discrete constraints to a continuous domain to ease optimization when
learning kernels or metrics can harm generalization, as information which only
encodes linkage is transformed to informing distances. We introduce a new
constrained clustering algorithm that jointly clusters data and learns a kernel
in accordance with the available pairwise constraints. To generalize well, our
method is designed to maximize constraint satisfaction without relaxing
pairwise constraints to a continuous domain where they inform distances. We
show that the proposed method outperforms existing approaches on a large number
of diverse publicly available datasets, and we discuss how our method can scale
to handling large data.


title:
Optimal Differential Astrometry for Multiconjugate Adaptive Optics. I.
  Astrometric Distortion Mapping using On-sky GeMS Observations of NGC 6723

abstract:
  The Extremely Large Telescope and the Thirty Meter Telescope will use state
of the art multiconjugate adaptive optics (MCAO) systems to obtain the full D4
advantage that their apertures can provide. However, to reach the full
astrometric potential of these facilities for on-sky science requires
understanding any residual astrometric distortions from these systems and find
ways to measure and eliminate them. In this work, we use Gemini multiconjugate
adaptive optic system (GeMS) observations of the core of NGC 6723 to better
understand the on-sky astrometric performance of MCAO. We develop new methods
to measure the astrometric distortion fields of the observing system, which
probe the distortion at the highest possible spatial resolution. We also
describe methods for examining the time-variable and static components of the
astrometric distortion. When applied to the GeMS Gemini South Adaptive Optics
Imager (GSAOI) data, we are able to see the effect of the field rotator at the
subpixel level, and we are able to empirically derive the distortion due to the
optical design of GeMS-GSAOI. We argue that the resulting distortion maps are a
valuable tool to measure and monitor the on-sky astrometric performance of
future instrumentation. Our overall astrometry pipeline produces high-quality
proper motions with an uncertainty floor of 45 uas per year. We measure the
proper motion dispersion profile of NGC 6723 from a radius of 10 arcsec out to
1 arcmin based on 12000 stars. We also produce a high-quality optical-near
infrared color-magnitude diagram, which clearly shows the extreme horizontal
branch and main-sequence knee of this cluster.


title:
Reversible Dewetting of Thin Lubricating Films Underneath Aqueous Drops
  Using External Electric Field

abstract:
  The stability of thin liquid films on a surface depends on the excess free
energy of the system involving various short-range and long-range interactions.
In an unstable condition, thin liquid films may dewet into multiple small-sized
droplets via spinodal, homogeneous, or heterogeneous nucleation process.
However, if the total excess free energy of the system can be manipulated using
an external stimulus, one can control the stability of thin liquid films on
demand. Here, we study the reversible dewetting process of thin lubricating
films underneath aqueous drops on slippery surfaces using an external electric
field. Upon applying voltage, stable thin lubricating films dewet in a manner
identical to the spinodal dewetting of nanometer-thick liquid films. Upon
removing the applied voltage, the dewetted droplets spread, coalesce with
neighboring ones, and form a uniform film again, however the time taken during
rewetting is very different compared to dewetting. The characteristic features
of both the dewetting and rewetting processes are present over multiple cycles.
Due to the random nature of the spinodal dewetting process, the final dewetting
pattern does not show any correlation over multiple dewetting cycles.


title: Double circuits in bicircular matroids

abstract:
  The first non-trivial case of Hadwiger's conjecture for oriented matroids
reads as follows. If $\mathcal{O}$ is an $M(K_4)$-free oriented matroid, then
$\mathcal{O}$ admits a NZ $3$-coflow, i.e., it is $3$-colourable in the sense
of Hochst\"attler-Ne\v{s}et\v{r}il. The class of gammoids is a class of
$M(K_4)$-free orientable matroids and it is the minimal minor-closed class that
contains all transversal matroids. Towards proving the previous statement for
the class of gammoids, Goddyn, Hochst\"attler, and Neudauer conjectured that
every gammoid has a positive coline (equivalently, a positive double circuit),
which implies that all orientations of gammoids are $3$-colourable. In this
brief note we disprove Goddyn, Hochst\"attler, and Neudauers' conjecture by
exhibiting a large class of bicircular matroids that do not contain positive
double circuits.


title:
Optimization-Based Safe Stabilizing Feedback with Guaranteed Region of
  Attraction

abstract:
  This paper proposes an optimization with penalty-based feedback design
framework for safe stabilization of control affine systems. Our starting point
is the availability of a control Lyapunov function (CLF) and a control barrier
function (CBF) defining affine-in-the-input inequalities that certify,
respectively, the stability and safety objectives for the dynamics. Leveraging
ideas from penalty methods for constrained optimization, the proposed design
framework imposes one of the inequalities as a hard constraint and the other
one as a soft constraint. We study the properties of the closed-loop system
under the resulting feedback controller and identify conditions on the penalty
parameter to eliminate undesired equilibria that might arise. Going beyond the
local stability guarantees available in the literature, we are able to provide
an inner approximation of the region of attraction of the equilibrium, and
identify conditions under which the whole safe set belongs to it. Simulations
illustrate our results.


title: A Possible Subclassification of Fast Radio Bursts

abstract:
  Although fast radio bursts (FRBs) have been an active field in astronomy and
cosmology, their origin is still unknown to date. One of the interesting topics
is the classification of FRBs, which is closely related to the origin of FRBs.
Different physical mechanisms are required by different classes of FRBs. In the
literature, they usually could be classified into non-repeating and repeating
FRBs. Well motivated by the observations, here we are interested in the
possible subclassification of FRBs. By using the first CHIME/FRB catalog, we
propose to subclassify non-repeating (type I) FRBs into type Ia and Ib FRBs.
The distribution of type Ia FRBs is delayed with respect to the cosmic star
formation history (SFH), and hence they are probably associated with old
stellar populations, while the distribution of type Ib FRBs tracks SFH, and
hence they are probably associated with young stellar populations. Accordingly,
the physical criteria for this subclassification of type I FRBs have been
clearly determined. We find that there are some tight empirical correlations
for type Ia FRBs but not for type Ib FRBs, and vice versa. These make them
different in physical properties. Similarly, we suggest that repeating (type
II) FRBs could also be subclassified into type IIa and IIb FRBs. A universal
subclassification scheme is given at the end. This subclassification of FRBs
might help us to reveal quite different physical mechanisms behind them, and
improve their applications in astronomy and cosmology.

categories: astro-ph.HE astro-ph.CO astro-ph.IM gr-qc


title: Organic log-domain integrator synapse

abstract:
  Synapses play a critical role in memory, learning, and cognition. Their main
functions include converting pre-synaptic voltage spikes to post-synaptic
currents, as well as scaling the input signal. Several brain-inspired
architectures have been proposed to emulate the behavior of biological
synapses. While these are useful to explore the properties of nervous systems,
the challenge of making biocompatible and flexible circuits with biologically
plausible time constants and tunable gain remains. Here, a physically flexible
organic log-domain integrator synaptic circuit is shown to address this
challenge. In particular, the circuit is fabricated using organic-based
materials that are electrically active, offer flexibility and biocompatibility,
as well as time constants (critical in learning neural codes and encoding
spatiotemporal patterns) that are biologically plausible. Using a 10 nF
synaptic capacitor, the time constant reached 126 ms and 221 ms before and
during bending, respectively. The flexible synaptic circuit is characterized
before and during bending, followed by studies on the effects of weighting
voltage, synaptic capacitance, and disparity in pre-synaptic signals on the
time constant.


title:
DSRC & C-V2X Comparison for Connected and Automated Vehicles in
  Different Traffic Scenarios

abstract:
  Researches have been devoted to making connected and automated vehicles
(CAVs) faster in different traffic scenarios. By using C-V2X or DSRC
communication protocol, CAVs can work more effectively. In this paper, we
compare these two communication protocols on CAVs in three different traffic
scenarios including ramp merging, intersection, and platoon brake. It shows
there is a trade-off between communication range and interval when leveraging
C-V2X or DSRC for CAVs. The result can help support further application designs
for CAV autonomously choosing communication protocols in different traffic
scenarios.


title:
Temperature mapping of stacked silicon dies from x-ray diffraction
  intensities

abstract:
  Increasing power densities in integrated circuits has led to an increased
prevalence of thermal hotspots in integrated circuits. Tracking these thermal
hotspots is imperative to prevent circuit failures. In 3D integrated circuits,
conventional surface techniques like infrared thermometry are unable to measure
3D temperature distribution and optical and magnetic resonance techniques are
difficult to apply due to the presence of metals and large current densities.
X-rays offer high penetration depth and can be used to probe 3D structures. We
report a method utilizing the temperature dependence of x-rays diffraction
intensity via the Debye-Waller factor to simultaneously map the temperature of
an individual silicon die that is a part of a stack of dies. Utilizing beamline
1-ID-E at the Advanced Photon Source (Argonne), we demonstrate for each
individual silicon die, a temperature resolution of 3 K, a spatial resolution
of 100 um x 400 um and a temporal resolution of 20 s. Utilizing a sufficiently
high intensity laboratory source, e.g., from a liquid anode source, this method
can be scaled down to laboratories for non-invasive temperature mapping of 3D
integrated circuits.


title: GriTS: Grid table similarity metric for table structure recognition

abstract:
  In this paper, we propose a new class of evaluation metric for table
structure recognition, grid table similarity (GriTS). Unlike prior metrics,
GriTS evaluates the correctness of a predicted table directly in its natural
form as a matrix. To create a similarity measure between matrices, we
generalize the two-dimensional largest common substructure (2D-LCS) problem,
which is NP-hard, to the 2D most similar substructures (2D-MSS) problem and
propose a polynomial-time heuristic for solving it. We validate empirically
using the PubTables-1M dataset that comparison between matrices exhibits more
desirable behavior than alternatives for table structure recognition
evaluation. GriTS also unifies all three subtasks of cell topology recognition,
cell location recognition, and cell content recognition within the same
framework, which simplifies the evaluation and enables more meaningful
comparisons across different types of structure recognition approaches. Code
will be released at https://github.com/microsoft/table-transformer.


title:
Symmetry Resolved Entanglement of Excited States in Quantum Field Theory
  I: Free Theories, Twist Fields and Qubits

abstract:
  The excess entanglement resulting from exciting a finite number of
quasiparticles above the ground state of a free integrable quantum field theory
has been investigated quite extensively in the literature. It has been found
that it takes a very simple form, depending only on the number of excitations
and their statistics. There is now mounting evidence that such formulae also
apply to interacting and even higher-dimensional theories. In this paper we
study the entanglement content of such zero-density excited states focusing on
the symmetry resolved entanglement, that is on quantum field theories that
possess an internal symmetry. The ratio of charged moments between the excited
and grounds states, from which the symmetry resolved entanglement entropy can
be obtained, takes a very simple and universal form, which in addition to the
number and statistics of the excitations, now depends also on the symmetry
charge. Using form factor techniques, we obtain both the ratio of moments and
the symmetry resolved entanglement entropies in complex free theories which
possess U(1) symmetry. The same formulae are found for simple qubit states.


title: Initial state QED radiation aspects for future $e^+e^-$ colliders

abstract:
  This white paper concerns theoretical and phenomenological aspects relevant
to the physics of future $e^+e^-$ colliders, in particular regarding
initial-state QED radiation. The contributions each contain key technical
aspects, and are formulated in a pedagogical manner so as to render them
accessible also to those who are not directly working on these and
immediately-related topics. This should help both experts and non-experts
understand the theoretical challenges that we shall face at future $e^+e^-$
colliders. Specifically, this paper contains descriptions of the treatment of
initial state radiation from several Monte Carlo collaborations, as well as
contributions that explain a number of more theoretical developments with
promise of future phenomenological impact.


title: A Tikhonov approach to level set curvature computation

abstract:
  In numerical simulations of two-phase flows, the computation of the curvature
of the interface is a crucial ingredient. Using a finite element and level set
discretization, the discrete interface is typically the level set of a low
order polynomial, which often results in a poor approximation of the interface
curvature. We present an approach to curvature computation using an approximate
inversion of the $L^2$ projection operator from the Sobolev space $H^2$ or
$H^3$. For finite element computation of the approximate inverse, the resulting
higher order equation is reformulated as a system of second order equations.
Due to the Tikhonov regularization, the method is demonstrated to be stable
against discretization irregularities. Numerical examples are shown for
interior interfaces as well as interfaces intersecting the boundary of the
domain.


title:
A Scalable Model Specialization Framework for Training and Inference
  using Submodels and its Application to Speech Model Personalization

abstract:
  Model fine-tuning and adaptation have become a common approach for model
specialization for downstream tasks or domains. Fine-tuning the entire model or
a subset of the parameters using light-weight adaptation has shown considerable
success across different specialization tasks. Fine-tuning a model for a large
number of domains typically requires starting a new training job for every
domain posing scaling limitations. Once these models are trained, deploying
them also poses significant scalability challenges for inference for real-time
applications. In this paper, building upon prior light-weight adaptation
techniques, we propose a modular framework that enables us to substantially
improve scalability for model training and inference. We introduce Submodels
that can be quickly and dynamically loaded for on-the-fly inference. We also
propose multiple approaches for training those Submodels in parallel using an
embedding space in the same training job. We test our framework on an extreme
use-case which is speech model personalization for atypical speech, requiring a
Submodel for each user. We obtain 128x Submodel throughput with a fixed
computation budget without a loss of accuracy. We also show that learning a
speaker-embedding space can scale further and reduce the amount of
personalization training data required per speaker.


title:
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic
  Change Segmentation

abstract:
  Earth observation is a fundamental tool for monitoring the evolution of land
use in specific areas of interest. Observing and precisely defining change, in
this context, requires both time-series data and pixel-wise segmentations. To
that end, we propose the DynamicEarthNet dataset that consists of daily,
multi-spectral satellite observations of 75 selected areas of interest
distributed over the globe with imagery from Planet Labs. These observations
are paired with pixel-wise monthly semantic segmentation labels of 7 land use
and land cover (LULC) classes. DynamicEarthNet is the first dataset that
provides this unique combination of daily measurements and high-quality labels.
In our experiments, we compare several established baselines that either
utilize the daily observations as additional training data (semi-supervised
learning) or multiple observations at once (spatio-temporal learning) as a
point of reference for future research. Finally, we propose a new evaluation
metric SCS that addresses the specific challenges associated with time-series
semantic change segmentation. The data is available at:
https://mediatum.ub.tum.de/1650201.


title: Scatter Ptychography

abstract:
  Coherent illumination reflected by a remote target may be secondarily
scattered by intermediate objects or materials. Here we show that phase
retrieval on remotely observed images of such scattered fields enables imaging
of the illuminated object at resolution proportional to $\lambda R_s/A_s$,
where $R_s$ is the range between the scatterer and the target and $A_s$ is the
diameter of the observed scatter. This resolution may exceed the resolution of
directly viewing the target by the factor $R_cA_s/R_sA_c$, where $R_c$ is the
range between the observer and the target and $A_c$ is the observing aperture.
Here we use this technique to demonstrate $\approx 32\times$ resolution
improvement relative to direct imaging.


title:
CRIRES high-resolution near-infrared spectroscopy of diffuse
  interstellar band profiles. Detection of 12 new DIBs in the YJ band and the
  introduction of a combined ISM sight line and stellar analysis approach

abstract:
  A high spectral resolution investigation of diffuse interstellar bands (DIBs)
in the near-infrared ($YJ$ band) is conducted to test new methods, to confirm
and improve existing parameters, and to search for new DIBs.
  Methods: The CRyogenic high-resolution InfraRed Echelle Spectrograph (CRIRES)
on the European Southern Observatory's Very Large Telescope was employed to
obtain spectra of four reddened background supergiant stars (HD 183143, HD
165784, HD 92207, HD 111613) and an unreddened comparison star (HD 87737) at
the highest resolution of $R \approx 100000$ currently achievable at
near-infrared wavelengths. The correction for telluric absorption was performed
by a modelling approach. Non-local thermodynamic equilibrium spectral modelling
of available optical and the new near-infrared spectra facilitated a
comprehensive characterisation of the atmospheric properties of the background
stars. A more precise and accurate determination of the reddening law along the
sight lines could be achieved than feasible before by comparison of the
observed and model spectral energy distributions. For DIBs that overlap with
stellar lines the DIB profile shapes could be recovered.
  Results: Seventeen known near-infrared DIBs were confirmed, and 12 previously
unknown and generally weaker DIBs were identified in the $YJ$ band. Three DIBs
that show uniform profiles along all sight lines were identified, possibly
connected to transitions from a common lower state of the same carrier. The
divergent extinction curve towards the frequently discussed DIB standard star
HD 183143 could be reproduced for the first time, requiring extra absorption by
$\sim$3.5 mag due to polycyclic aromatic hydrocarbons (PAHs) to match the
ultraviolet extinction bump. This extra absorption probably stems from a
circumstellar bubble lying in front of the star which is intersected
tangentially by the line of sight.


title:
Classifying phases protected by matrix product operator symmetries using
  matrix product states

abstract:
  We classify the different ways in which matrix product states (MPS) can
transform under the action of matrix product operator (MPO) symmetries. We give
a local characterization of MPS tensors that generate ground spaces which
remain invariant under a global MPO symmetry and generally correspond to
symmetry breaking phases. This allows us to derive a set of quantities,
satisfying the so-called coupled pentagon equations, invariant under continuous
deformations of the MPS tensor, which provides the invariants to detect
different gapped phases protected by MPO symmetries. Our result matches the
TQFT classification of gapped boundaries in terms of module categories of
fusion categories. However, our techniques extend the classification beyond
renormalization fixed points and facilitates the numerical study of these
systems. When the MPO symmetries form a global onsite representation of a
group, we recover the symmetry protected topological order classification for
unique and degenerate ground states. For MPO symmetries that are non-onsite
representations of groups, we obtain restrictions on the possible ground state
degeneracies depending both on the group and on the concrete form of the MPO
representation, as opposed to depend just on the group as in the onsite case.
Moreover, we study the interplay between time reversal symmetry and an MPO
symmetry and we also provide examples of our classification together with
explicit constructions of MPO and MPS based on groups. Finally, we elaborate on
the connection between our setup and gapped boundaries of two-dimensional
topological systems, where MPO symmetries also play a key role.


title: Time and Quantum Clocks: a review of recent developments

abstract:
  In this review we present the problem of time in quantum physics, including a
short history of the problem and the known objections about considering time a
quantum observable. The need to deal with time as an observable is elaborated
through some unresolved problems. The lack of a consistent theory of time is
currently hindering the formulation of a full-fledged theory of quantum
gravity. It is argued that the proposal set forth by several authors of
considering an intrinsic measurement of quantum time, besides having the
conventional external time, is compelling. Recently several suggestions have
been put forward to revive the proposal of Page and Wootters (1983),
elaborating and resolving some of the main ambiguities of the original proposal
and opening new scope for understanding its content. The approach followed in
these new contributions exposes the need to go beyond the limitations enforced
by the conventional approach of quantum physics. The attitude of covariant loop
quantum gravity, in which it is called to completely ignore time, is also
discussed. This review could be a step forward in an endeavour to reform our
outlook of the unification of the theory of relativity and quantum physics by
furnishing the conceptual ground needed for this goal. Intentionally, some
technical details are avoided since we aim to present the approaches to resolve
the problem in a simple way with the clearest possible outlook. These can be
looked up in the original references provided.


title: Design of Customized Adaptive Radar Detectors in the CFAR Feature Plane

abstract:
  The paper addresses the design of adaptive radar detectors having desired
behavior, in Gaussian disturbance with unknown statistics. Specifically, given
detection probability specifications for chosen signal-to-noise ratios and
steering vector mismatch levels, a methodology for the optimal design of
customized CFAR detectors is devised in a suitable feature plane based on
maximal invariant statistics. To overcome the analytical and numerical
intractability of the resulting optimization problem, a novel general
reduced-complexity algorithm is developed, which is shown to be effective in
providing a close approximation of the desired detector. The proposed approach
solves the open problem of ensuring a prefixed false alarm probability while
controlling the behavior under both matched and mismatched conditions, so
enabling the design of fully customized adaptive CFAR detectors.


title: Winternitz stack protocols

abstract:
  This paper proposes and evaluates a new bipartite post-quantum digital
signature protocol based on Winternitz chains and the HORS oracle. Mutually
mistrustful Alice and Bob are able to agree and sign a series of documents in a
way that makes it impossible (within the assumed security model) to repudiate
their signatures. The number of signatures supported by a single public key is
limited by a large number but the security of the signature scheme is not
diminished by repeated application. A single public key supports both parties.
Some ramifications are discussed, security parameters evaluated and an
application area delineated for the proposed concept.


title: Linearization for difference equations with infinite delay

abstract:
  In this article, we construct a conjugacy map for a linear difference
equation with infinite delay and corresponding nonlinear perturbation. We also
prove that the conjugacy map is one-one with some additional conditions. As an
application of our result, we show that the cases of (uniform) exponential
dichotomy follow from our result.


title: A Kinetic Study of the Saturation of the Bell Instability

abstract:
  The nonresonant cosmic ray instability, predicted by Bell (2004), is thought
to play an important role in the acceleration and confinement of cosmic rays
(CR) close to supernova remnants. Despite its importance, the exact mechanism
responsible for the saturation of the instability has not been determined, and
there is no first-principle prediction for the amplitude of the saturated
magnetic field. Using a survey of self-consistent hybrid simulations (with
kinetic ions and fluid electrons), we study the non-linear evolution of the
Bell instability as a function of the parameters of the CR population. We find
that saturation is achieved when the magnetic pressure in the amplified field
is comparable to the initial CR momentum flux.


title:
A Top-down Supervised Learning Approach to Hierarchical Multi-label
  Classification in Networks

abstract:
  Node classification is the task of inferring or predicting missing node
attributes from information available for other nodes in a network. This paper
presents a general prediction model to hierarchical multi-label classification
(HMC), where the attributes to be inferred can be specified as a strict poset.
It is based on a top-down classification approach that addresses hierarchical
multi-label classification with supervised learning by building a local
classifier per class. The proposed model is showcased with a case study on the
prediction of gene functions for Oryza sativa Japonica, a variety of rice. It
is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model,
by evaluating both approaches in terms of prediction performance and
computational cost. The results in this work support the working hypothesis
that the proposed model can achieve good levels of prediction efficiency, while
scaling up in relation to the state of the art.


title:
Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel
  Attention for Facial Action Analysis

abstract:
  Visual attention has been extensively studied for learning fine-grained
features in both facial expression recognition (FER) and Action Unit (AU)
detection. A broad range of previous research has explored how to use attention
modules to localize detailed facial parts (e,g. facial action units), learn
discriminative features, and learn inter-class correlation. However, few
related works pay attention to the robustness of the attention module itself.
Through experiments, we found neural attention maps initialized with different
feature maps yield diverse representations when learning to attend the
identical Region of Interest (ROI). In other words, similar to general feature
learning, the representational quality of attention maps also greatly affects
the performance of a model, which means unconstrained attention learning has
lots of randomnesses. This uncertainty lets conventional attention learning
fall into sub-optimal. In this paper, we propose a compact model to enhance the
representational and focusing power of neural attention maps and learn the
"inter-attention" correlation for refined attention maps, which we term the
"Self-Diversified Multi-Channel Attention Network (SMA-Net)". The proposed
method is evaluated on two benchmark databases (BP4D and DISFA) for AU
detection and four databases (CK+, MMI, BU-3DFE, and BP4D+) for facial
expression recognition. It achieves superior performance compared to the
state-of-the-art methods.


title:
Existence and uniqueness for the inhomogeneous 1-Laplace evolution
  equation revisited

abstract:
  In this paper we deal with an inhomogeneous parabolic Dirichlet problem
involving the 1-Laplacian operator. We show the existence of a unique solution
when data belong to $L^1(0,T;L^2(\Omega))$ for every $T>0$. As a consequence,
global existence and uniqueness for data in $L^1_{loc}(0,+\infty;L^2(\Omega))$
is obtained. Our analysis retrieves the results of \cite{SW} in a correct and
complete way.


title: Post-selection inference for e-value based confidence intervals

abstract:
  Suppose that one can construct a valid $(1-\delta)$-CI for each of $K$
parameters of potential interest. If a data analyst uses an arbitrary
data-dependent criterion to select some subset $S$ of parameters, then the
aforementioned confidence intervals for the selected parameters are no longer
valid due to selection bias. We design a new method to adjust the intervals in
order to control the false coverage rate (FCR). The main established method is
the "BY procedure" by Benjamini and Yekutieli (JASA, 2005). Unfortunately, the
BY guarantees require certain restrictions on the the selection criterion and
on the dependence between the CIs. We propose a natural and much simpler method
-- both in implementation, and in proof -- which is valid under any dependence
structure between the original CIs, and any (unknown) selection criterion, but
which only applies to a special, yet broad, class of CIs. Our procedure reports
$(1-\delta|S|/K)$-CIs for the selected parameters, and we prove that it
controls the FCR at $\delta$ for confidence intervals that implicitly invert
e-values; examples include those constructed via supermartingale methods, or
via universal inference, or via Chernoff-style bounds on the moment generating
function, among others. The e-BY procedure is proved to be admissible, and it
recovers the BY procedure as a special case via calibration. Our work also has
implications for multiple testing in sequential settings, since it applies at
stopping times, to continuously-monitored confidence sequences with bandit
sampling.


title:
SerialTrack: ScalE and Rotation Invariant Augmented Lagrangian Particle
  Tracking

abstract:
  We present a new particle tracking algorithm to accurately resolve large
deformation and rotational motion fields, which takes advantage of both local
and global particle tracking algorithms. We call this method the ScalE and
Rotation Invariant Augmented Lagrangian Particle Tracking (SerialTrack). This
method builds an iterative scale and rotation invariant topology-based feature
for each particle within a multi-scale tracking algorithm. The global kinematic
compatibility condition is applied as a global augmented Lagrangian constraint
to enhance the tracking accuracy. An open source software package implementing
this numerical approach to track both 2D and 3D, incremental and cumulative
deformation fields is provided.


title:
Mitigating Gender Bias in Distilled Language Models via Counterfactual
  Role Reversal

abstract:
  Language models excel at generating coherent text, and model compression
techniques such as knowledge distillation have enabled their use in
resource-constrained settings. However, these models can be biased in multiple
ways, including the unfounded association of male and female genders with
gender-neutral professions. Therefore, knowledge distillation without any
fairness constraints may preserve or exaggerate the teacher model's biases onto
the distilled model. To this end, we present a novel approach to mitigate
gender disparity in text generation by learning a fair model during knowledge
distillation. We propose two modifications to the base knowledge distillation
based on counterfactual role reversal$\unicode{x2014}$modifying teacher
probabilities and augmenting the training set. We evaluate gender polarity
across professions in open-ended text generated from the resulting distilled
and finetuned GPT$\unicode{x2012}$2 models and demonstrate a substantial
reduction in gender disparity with only a minor compromise in utility. Finally,
we observe that language models that reduce gender polarity in language
generation do not improve embedding fairness or downstream classification
fairness.


title: NeuMan: Neural Human Radiance Field from a Single Video

abstract:
  Photorealistic rendering and reposing of humans is important for enabling
augmented reality experiences. We propose a novel framework to reconstruct the
human and the scene that can be rendered with novel human poses and views from
just a single in-the-wild video. Given a video captured by a moving camera, we
train two NeRF models: a human NeRF model and a scene NeRF model. To train
these models, we rely on existing methods to estimate the rough geometry of the
human and the scene. Those rough geometry estimates allow us to create a
warping field from the observation space to the canonical pose-independent
space, where we train the human model in. Our method is able to learn subject
specific details, including cloth wrinkles and accessories, from just a 10
seconds video clip, and to provide high quality renderings of the human under
novel poses, from novel views, together with the background.


title: Stability Against the Odds: the Case of Chromonic Liquid Crystals

abstract:
  The ground state of chromonic liquid crystals, as revealed by a number of
recent experiments, is quite different from that of ordinary nematic liquid
crystals: it is twisted instead of uniform. The common explanation provided for
this state within the classical elastic theory of Frank demands that one
Ericksen's inequality is violated. Since in general such a violation makes
Frank's elastic free-energy functional unbounded below, the question arises as
to whether the twisted ground state can be locally stable. We answer this
question in the affirmative. In reaching this conclusion, a central role is
played by the specific boundary conditions imposed in the experiments on the
boundary of rigid containers and by a general formula that we derive here for
the second variation of Frank's elastic free energy.


title: Minimax Regret for Cascading Bandits

abstract:
  Cascading bandits model the task of learning to rank $K$ out of $L$ items
over $n$ rounds of partial feedback. For this model, the minimax (i.e.,
gap-free) regret is poorly understood; in particular, the best known lower and
upper bounds are $\Omega(\sqrt{nL/K})$ and $\tilde{O}(\sqrt{nLK})$,
respectively. We improve the lower bound to $\Omega(\sqrt{nL})$ and show
CascadeKL-UCB (which ranks items by their KL-UCB indices) attains it up to log
terms. Surprisingly, we also show CascadeUCB1 (which ranks via UCB1) can suffer
suboptimal $\Omega(\sqrt{nLK})$ regret. This sharply contrasts with standard
$L$-armed bandits, where the corresponding algorithms both achieve the minimax
regret $\sqrt{nL}$ (up to log terms), and the main advantage of KL-UCB is only
to improve constants in the gap-dependent bounds. In essence, this contrast
occurs because Pinsker's inequality is tight for hard problems in the $L$-armed
case but loose (by a factor of $K$) in the cascading case.


title: Stability properties for a class of inverse problems

abstract:
  We establish Lipschitz stability properties for a class of inverse problems.
In that class, the associated direct problem is formulated by an integral
operator Am depending non-linearly on a parameter m and operating on a function
u. In the inversion step both u and m are unknown but we are only interested in
recovering m. We discuss examples of such inverse problems for the elasticity
equation with applications to seismology and for the inverse scattering problem
in electromagnetic theory. Assuming a few injectivity and regularity properties
for Am, we prove that the inverse problem with a finite number of data points
is solvable and that the solution is Lipschitz stable in the data. We show a
reconstruction example illustrating the use of neural networks.


title: Phase Factors in Singular Value Decomposition and Schmidt Decomposition

abstract:
  In singular value decomposition (SVD) of a complex matrix A, the singular
vectors or the eigenvectors of AA{\dag} and A{\dag}A are unique up to complex
phase factors. Thus, the two unitary matrices in SVD are unique up to diagonal
matrices of phase factors, the phase-factor matrices. Also, the product of
these two phase-factor matrices, or the product of phase factors of the
corresponding singular vectors with the same singular value, is unique. In the
Schmidt decomposition, a phase-factor matrix is a phase rotation operator
acting on a subsystem alone. We summarize here three simple steps to
consistently carry out the SVD and the Schmidt decomposition including the
phase factors.


title: Maximum entropy quantum state distributions

abstract:
  We propose an approach to the realization of many-body quantum state
distributions inspired by combined principles of thermodynamics and mesoscopic
physics. Its essence is a maximum entropy principle conditioned by conservation
laws. We go beyond traditional thermodynamics and condition on the full
distribution of the conserved quantities. The result are quantum state
distributions whose deviations from `thermal states' get more pronounced in the
limit of wide input distributions. We describe their properties in terms of
entanglement measures and discuss strategies for state engineering by methods
of current date experimentation.


title:
Cross-correlations between mm-wave line-intensity mapping and weak
  lensing surveys: preliminary consideration of long-term prospects

abstract:
  The field of millimetre-wave line-intensity mapping (LIM) is seeing increased
experimental activity with pathfinder surveys already deployed or deploying in
the next few years, making spectroscopic measurements of unresolved atomic and
molecular line emission tracing the large-scale structure of the Universe. The
next decade will also see the Rubin Observatory Legacy Survey of Space and Time
(LSST) undertake a photometric galaxy survey programme of unprecedented scope,
including measurements of cosmic shear exploiting weak gravitational lensing
(WL) of background galaxies to map projected large-scale structure. We consider
prospects for detecting angular cross power spectra between non-tomographic
cosmic shear and mm-wave LIM surveys that measure emission from CO lines at
$z=0.5$-$1$. We forecast that once the LSST Year 10 WL dataset is available, a
future LIM experiment, conceivably deployed in the next 10-15 years, would
enable such a cross-correlation detection with an overall signal-to-noise ratio
of $50$, although the current pathfinder generation of CO/[C II] surveys are
more likely to achieve a marginal $2\sigma$ detection against an earlier-stage
LSST WL dataset. The signal has modest astrophysical constraining power
yielding competitive constraints on cosmic molecular gas density at
$z\lesssim1$, and degeneracies between astrophysical parameters and the
intrinsic alignment amplitude mean that external information on either one
could allow the cross-correlation analysis to significantly improve its
constraints on the other.


title: A probe of interacting dark energy with redshift space distortions

abstract:
  The imprint of interacting dark energy (IDE) needs to be correctly identified
in order to avoid bias in constraints on IDE, on the largest scales. This paper
investigates the large-scale imprint of IDE in redshift space distortions. The
results suggest that, for a constant dark energy equation of state parameter,
an IDE model where the dark energy transfer rate is proportional to the dark
energy density, exhibits an alternating positive-negative effect in the
redshift space distortions angular power spectrum -- which varies with respect
to redshift z. The results also suggest that the redshift space distortions can
be used to distinguish IDE models at low z ~ 0.1. Moreover, the angular power
spectrum coss-correlations appear to hold the potential to be a viable tool for
detecting the imprint of IDE, at z < 0.7.


title:
Constraining the galaxy-halo connection of infrared-selected unWISE
  galaxies with galaxy clustering and galaxy-CMB lensing power spectra

abstract:
  We present the first detailed analysis of the connection between galaxies and
their dark matter halos for the unWISE galaxy catalog -- a full-sky,
infrared-selected sample built from WISE data, containing over 500 million
galaxies. Using unWISE galaxy-galaxy auto-correlation and Planck CMB
lensing-galaxy cross-correlation measurements down to 10 arcmin angular scales,
we constrain the halo occupation distribution (HOD), a model describing how
central and satellite galaxies are distributed within dark matter halos, for
three unWISE} galaxy samples at mean redshifts $\bar{z} \approx 0.6$, $1.1$,
and $1.5$. We constrain the characteristic minimum halo mass to host a central
galaxy, $M_\mathrm{min}^\mathrm{HOD} = 1.83^{+0.41}_{-1.63} \times 10^{12}
M_\odot/h$, $5.22^{+0.34}_{-4.80} \times 10^{12} M_\odot/h$, $6.60
^{+0.30}_{-1.11} \times 10^{13} M_\odot/h$ for the unWISE samples at
$\bar{z}\approx 0.6$, $1.1$, and $1.5$, respectively. We find that all three
samples are dominated by central galaxies, rather than satellites. Using our
constrained HOD models, we infer the effective linear galaxy bias for each
unWISE sample, and find that it does not evolve as steeply with redshift as
found in previous perturbation-theory-based analyses of these galaxies. We
discuss possible sources of systematic uncertainty in our results, the most
significant of which is the uncertainty on the galaxy redshift distribution.
Our HOD constraints provide a detailed, quantitative understanding of how the
unWISE galaxies populate the underlying dark matter halo distribution. These
constraints will have a direct impact on future studies employing the unWISE
galaxies as a cosmological and astrophysical probe, including measurements of
ionized gas thermodynamics and dark matter profiles via Sunyaev-Zel'dovich and
lensing cross-correlations.


title:
A Robust and Accurate Adaptive Approximation Method for a
  Diffuse-Interface Model of Binary-Fluid Flows

abstract:
  We present an adaptive simulation framework for binary-fluid flows, based on
the Abels-Garcke-Gr\"un Navier-Stokes-Cahn-Hilliard (AGG NSCH)
diffuse-interface model. The adaptive-refinement procedure is guided by a
two-level hierarchical a-posteriori error estimate, and it effectively resolves
the spatial multiscale behavior of the diffuse-interface model. To improve the
robustness of the solution procedure and avoid severe time-step restrictions
for small-interface thicknesses, we introduce an $\varepsilon$-continuation
procedure, in which the diffuse interface thickness ($\varepsilon$) are
enlarged on coarse meshes, and the mobility is scaled accordingly. To further
accelerate the computations and improve robustness, we apply a modified
Backward Euler scheme in the initial stages of the adaptive-refinement
procedure in each time step, and a Crank--Nicolson scheme in the final stages
of the refinement procedure. To enhance the robustness of the nonlinear
solution procedure, we introduce a partitioned solution procedure for the
linear tangent problems in Newton's method, based on a decomposition of the
NSCH system into its NS and CH subsystems. We conduct a systematic
investigation of the conditioning of the monolithic NSCH tangent matrix and of
its NS and CH subsystems for a representative 2D model problem. To illustrate
the properties of the presented adaptive simulation framework, we present
numerical results for a 2D oscillating water droplet suspended in air, and we
validate the obtained results versus those of a corresponding sharp-interface
model.


title: Between Whitehead groups and uniformization

abstract:
  For a given stationary set $S$ of countable ordinals we prove (in
$\mathbf{ZFC}$) that the assertion "every $S$-ladder system has
$\aleph_0$-uniformization" is equivalent to "every strongly $\aleph_1$-free
abelian group of cardinality $\aleph_1$ with non-freeness invariant $\subseteq
S$ is $\aleph_1$-coseparable, i.e. Ext$(G, \oplus_{i=0}^{\infty} \mathbb Z)=0$
(in particular Whitehead, i.e.\ Ext$(G, \mathbb Z)=0$)". This solves problems
B3 and B4 from Eklof and Mekler's book.


title:
Detectability and parameter estimation of stellar origin black hole
  binaries with next generation gravitational wave detectors

abstract:
  We consider stellar-origin black hole binaries, which are among the main
astrophysical sources for next generation gravitational wave (GW) detectors
such as the Einstein Telescope (ET) and Cosmic Explorer (CE). Using population
models calibrated with the most recent LIGO/Virgo results from O3b run, we show
that ET and CE will be capable of detecting tens of thousands of such sources
(and virtually all of those present in our past light cone up to $z\lesssim
0.7$ for ET and $z\lesssim 1$ for CE) with a signal-to-noise ratio up to
several hundreds, irrespective of the detector design. When it comes to
parameter estimation, we use a Fisher-matrix analysis to assess the impact of
the design on the estimation of the intrinsic and extrinsic parameters. We find
that the CE detector, consisting of two distinct $L-$shape interferometers, has
better sky localization performance compared to ET in its triangular
configuration. We also find that the network is typically capable of measuring
the chirp mass, mass ratio and effective spin of the binary at order of
$10^{-4}$, $10^{-6}$ and $10^{-3}$ fractional error respectively. While the
fractional errors for the extrinsic parameters are of order $10^{-2}$ for the
sky localization, luminosity distance and inclination.


title:
Predicting the Bubble of Non-Fungible Tokens (NFTs): An Empirical
  Investigation

abstract:
  Our study empirically predicts the bubble of Non-Fungible Tokens (NFTs):
transferrable and unique digital assets on public blockchains. This subject is
important because, despite their strong market growth in 2021, NFTs have not
been studied in terms of bubble prediction. To achieve the purpose, we applied
Logarithmic Periodic Power Law (LPPL) model to the time-series price data of
major NFT projects, retrieved from nonfungible.com. Results implied that, as of
December 20, 2021, (i) NFTs in general are in a small bubble (predicting price
decline), (ii) Decentraland project is in a medium bubble (predicting price
decline), and (iii) Ethereum Name Service and ArtBlocks projects are in a small
negative bubble (predicting price increase). Future works are to refine the
prediction by considering heterogeneity of NFTs, comparing other methods, and
using more enriched data.


title:
Controlling dynamics of a COVID--19 mathematical model using a parameter
  switching algorithm

abstract:
  In this paper the dynamics of an autonomous mathematical models of COVID-19
depending on a real parameter bifurcation, is controlled by switching
periodically the parameter value. For this purpose the Parameter Switching (PS)
algorithm is used. With this technique, it is proved that every attractor of
the considered system can be numerically approximated and, therefore, the
system can be determined to evolve along, e.g., a stable periodic motion or a
chaotic attractor. In this way, the algorithm can be considered as a chaos
control or anticontrol (chaoticization)-like algorithm. Contrarily to existing
chaos control techniques which generate modified attractors, the obtained
attractors with the PS algorithm belong to the set system attractors. It is
analytically shown that using the PS algorithm, every system attractor can be
expressed as a convex combination of some existing attractors. Moreover, is
proved that the PS algorithm can be viewed as a generalization of
Parrondo\textsc{\char13}s paradox.


title: On thermalization of radiation in hydrostatic atmospheres

abstract:
  The problem of thermalization of radiation within a self-emitting hydrogen
isothermal atmosphere is considered for the case of a hydrostatic profile of
the plasma density. The probabilistic approach to define the thermalization
depth for the photon of a given frequency is used. Quantitative conclusions are
made on the value of the optical depth at which the radiation can be viewed as
thermalized up to a given frequency. The solutions of the radiative transfer
equations confirm these results. The frequency dependencies of the
single-interaction free-free absorption probability in a cold nonmagnetized and
magnetized plasma are calculated.


title:
TransSleep: Transitioning-aware Attention-based Deep Neural Network for
  Sleep Staging

abstract:
  Sleep staging is essential for sleep assessment and plays a vital role as a
health indicator. Many recent studies have devised various machine learning as
well as deep learning architectures for sleep staging. However, two key
challenges hinder the practical use of these architectures: effectively
capturing salient waveforms in sleep signals and correctly classifying
confusing stages in transitioning epochs. In this study, we propose a novel
deep neural network structure, TransSleep, that captures distinctive local
temporal patterns and distinguishes confusing stages using two auxiliary tasks.
In particular, TransSleep adopts an attention-based multi-scale feature
extractor module to capture salient waveforms; a stage-confusion estimator
module with a novel auxiliary task, epoch-level stage classification, to
estimate confidence scores for identifying confusing stages; and a context
encoder module with the other novel auxiliary task, stage-transition detection,
to represent contextual relationships across neighboring epochs. Results show
that TransSleep achieves promising performance in automatic sleep staging. The
validity of TransSleep is demonstrated by its state-of-the-art performance on
two publicly available datasets, Sleep-EDF and MASS. Furthermore, we performed
ablations to analyze our results from different perspectives. Based on our
overall results, we believe that TransSleep has immense potential to provide
new insights into deep learning-based sleep staging.


title: Web Page Content Extraction Based on Multi-feature Fusion

abstract:
  With the rapid development of Internet technology, people have more and more
access to a variety of web page resources. At the same time, the current rapid
development of deep learning technology is often inseparable from the huge
amount of Web data resources. On the other hand, NLP is also an important part
of data processing technology, such as web page data extraction. At present,
the extraction technology of web page text mainly uses a single heuristic
function or strategy, and most of them need to determine the threshold
manually. With the rapid growth of the number and types of web resources, there
are still problems to be solved when using a single strategy to extract the
text information of different pages. This paper proposes a web page text
extraction algorithm based on multi-feature fusion. According to the text
information characteristics of web resources, DOM nodes are used as the
extraction unit to design multiple statistical features, and high-order
features are designed according to heuristic strategies. This method
establishes a small neural network, takes multiple features of DOM nodes as
input, predicts whether the nodes contain text information, makes full use of
different statistical information and extraction strategies, and adapts to more
types of pages. Experimental results show that this method has a good ability
of web page text extraction and avoids the problem of manually determining the
threshold.


title: Your Policy Regularizer is Secretly an Adversary

abstract:
  Policy regularization methods such as maximum entropy regularization are
widely used in reinforcement learning to improve the robustness of a learned
policy. In this paper, we show how this robustness arises from hedging against
worst-case perturbations of the reward function, which are chosen from a
limited set by an imagined adversary. Using convex duality, we characterize
this robust set of adversarial reward perturbations under KL and
alpha-divergence regularization, which includes Shannon and Tsallis entropy
regularization as special cases. Importantly, generalization guarantees can be
given within this robust set. We provide detailed discussion of the worst-case
reward perturbations, and present intuitive empirical examples to illustrate
this robustness and its relationship with generalization. Finally, we discuss
how our analysis complements and extends previous results on adversarial reward
robustness and path consistency optimality conditions.


title:
Semantic Similarity Computing for Scientific Academic Conferences fused
  with domain features

abstract:
  Aiming at the problem that the current general-purpose semantic text
similarity calculation methods are difficult to use the semantic information of
scientific academic conference data, a semantic similarity calculation
algorithm for scientific academic conferences by fusion with domain features is
proposed. First, the domain feature information of the conference is obtained
through entity recognition and keyword extraction, and it is input into the
BERT network as a feature and the conference information. The structure of the
Siamese network is used to solve the anisotropy problem of BERT. The output of
the network is pooled and normalized, and finally the cosine similarity is used
to calculate the similarity between the two sessions. Experimental results show
that the SBFD algorithm has achieved good results on different data sets, and
the Spearman correlation coefficient has a certain improvement compared with
the comparison algorithm.


title: Deep Learning based Intelligent Coin-tap Test for Defect Recognition

abstract:
  The coin-tap test is a convenient and primary method for non-destructive
testing, while its manual on-site operation is tough and costly. With the help
of the latest intelligent signal processing method, convolutional neural
networks (CNN), we achieve an intelligent coin-tap test which exhibited
superior performance in recognizing the defects. However, this success of CNNs
relies on plenty of well-labeled data from the identical scenario, which could
be difficult to get for many real industrial practices. This paper further
develops transfer learning strategies for this issue, that is, to transfer the
model trained on data of one scenario to another. In experiments, the result
presents a notable improvement by using domain adaptation and pseudo label
learning strategies. Hence, it becomes possible to apply the model into
scenarios with none or little (less than 10\%) labeled data adopting the
transfer learning strategies proposed herein. In addition, we used a benchmark
dataset constructed ourselves throughout this study. This benchmark dataset for
the coin-tap test containing around 100,000 sound signals is published at
https://github.com/PPhub-hy/torch-tapnet.


title:
PhysioMTL: Personalizing Physiological Patterns using Optimal Transport
  Multi-Task Regression

abstract:
  Heart rate variability (HRV) is a practical and noninvasive measure of
autonomic nervous system activity, which plays an essential role in
cardiovascular health. However, using HRV to assess physiology status is
challenging. Even in clinical settings, HRV is sensitive to acute stressors
such as physical activity, mental stress, hydration, alcohol, and sleep.
Wearable devices provide convenient HRV measurements, but the irregularity of
measurements and uncaptured stressors can bias conventional analytical methods.
To better interpret HRV measurements for downstream healthcare applications, we
learn a personalized diurnal rhythm as an accurate physiological indicator for
each individual. We develop Physiological Multitask-Learning (PhysioMTL) by
harnessing Optimal Transport theory within a Multitask-learning (MTL)
framework. The proposed method learns an individual-specific predictive model
from heterogeneous observations, and enables estimation of an optimal transport
map that yields a push forward operation onto the demographic features for each
task. Our model outperforms competing MTL methodologies on unobserved
predictive tasks for synthetic and two real-world datasets. Specifically, our
method provides remarkable prediction results on unseen held-out subjects given
only $20\%$ of the subjects in real-world observational studies. Furthermore,
our model enables a counterfactual engine that generates the effect of acute
stressors and chronic conditions on HRV rhythms.


title: ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest

abstract:
  We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce
platform in China, for training and serving GNN-based recommendations over
web-scale graphs. ZOOMER is designed for tackling two challenges presented by
the massive user data at Taobao: low training/serving efficiency due to the
huge scale of the graphs, and low recommendation quality due to the information
overload which distracts the recommendation model from specific user
intentions. ZOOMER achieves this by introducing a key concept, Region of
Interests (ROI) in GNNs for recommendations, i.e., a neighborhood region in the
graph with significant relevance to a strong user intention. ZOOMER narrows the
focus from the whole graph and "zooms in" on the more relevant ROIs, thereby
reducing the training/serving cost and mitigating the information overload at
the same time. With carefully designed mechanisms, ZOOMER identifies the
interest expressed by each recommendation request, constructs an ROI subgraph
by sampling with respect to the interest, and guides the GNN to reweigh
different parts of the ROI towards the interest by a multi-level attention
module. Deployed as a large-scale distributed system, ZOOMER supports graphs
with billions of nodes for training and thousands of requests per second for
serving. ZOOMER achieves up to 14x speedup when downsizing sampling scales with
comparable (even better) AUC performance than baseline methods. Besides, both
the offline evaluation and online A/B test demonstrate the effectiveness of
ZOOMER.


title:
High-dimensional Bayesian Optimization of Hyperparameters for an
  Attention-based Network to Predict Materials Property: a Case Study on
  CrabNet using Ax and SAASBO

abstract:
  Expensive-to-train deep learning models can benefit from an optimization of
the hyperparameters that determine the model architecture. We optimize 23
hyperparameters of a materials informatics model, Compositionally-Restricted
Attention-Based Network (CrabNet), over 100 adaptive design iterations using
two models within the Adaptive Experimentation (Ax) Platform. This includes a
recently developed Bayesian optimization (BO) algorithm, sparse axis-aligned
subspaces Bayesian optimization (SAASBO), which has shown exciting performance
on high-dimensional optimization tasks. Using SAASBO to optimize CrabNet
hyperparameters, we demonstrate a new state-of-the-art on the experimental band
gap regression task within the materials informatics benchmarking platform,
Matbench (~4.5% decrease in mean absolute error (MAE) relative to incumbent).
Characteristics of the adaptive design scheme as well as feature importances
are described for each of the Ax models. SAASBO has great potential to both
improve existing surrogate models, as shown in this work, and in future work,
to efficiently discover new, high-performing materials in high-dimensional
materials science search spaces.


title:
Learning Personalized Item-to-Item Recommendation Metric via Implicit
  Feedback

abstract:
  This paper studies the item-to-item recommendation problem in recommender
systems from a new perspective of metric learning via implicit feedback. We
develop and investigate a personalizable deep metric model that captures both
the internal contents of items and how they were interacted with by users.
There are two key challenges in learning such model. First, there is no
explicit similarity annotation, which deviates from the assumption of most
metric learning methods. Second, these approaches ignore the fact that items
are often represented by multiple sources of meta data and different users use
different combinations of these sources to form their own notion of similarity.
  To address these challenges, we develop a new metric representation embedded
as kernel parameters of a probabilistic model. This helps express the
correlation between items that a user has interacted with, which can be used to
predict user interaction with new items. Our approach hinges on the intuition
that similar items induce similar interactions from the same user, thus fitting
a metric-parameterized model to predict an implicit feedback signal could
indirectly guide it towards finding the most suitable metric for each user. To
this end, we also analyze how and when the proposed method is effective from a
theoretical lens. Its empirical effectiveness is also demonstrated on several
real-world datasets.


title:
Single Carrier Frequency Domain Detectors for Internet of Underwater
  Things

abstract:
  This paper proposes low complexity detection for internet of underwater
things (IoUT)s communication. The signal is transmitted from the source to the
destination using several sensors. To simplify the computational operations at
the transmitter and the sensory nodes, a single carrier frequency domain
equalizer (SC-FDE) is proposed and amplify-and-forward (AF) protocols are
employed. Fast Fourier transform (FFT) and use of cyclic prefix (CP) are also
proposed to simplify these algorithms when compared to time-domain
equalization. As precise channel data is difficult to capture in underwater
communications, the adaptive implementation of FDE is proposed as a solution
that can be employed when the channel experiences a fast doppler shift. The two
adaptive detectors are based on the least mean-square (LMS) and recursive least
square (RLS) principles. Numerical simulations show that the performance of the
bit error rate (BER) performance of the proposed detectors is close to that of
the ideal minimum mean square error (MMSE).


title: Standing Forest Coin (SFC)

abstract:
  This article describes a proposal to create a digital currency that allows
the decentralized collection of resources directed to initiatives and
activities that aim to protect the Brazilian Amazon ecosystem by using
blockchain and digital contracts. In addition to the digital currency, the goal
is to design a smart contract based in oracles to ensure credibility and
security for investors and donors of financial resources invested in projects
within the Standing Forest Coin (SFC - standingforest.org).


title: R3M: A Universal Visual Representation for Robot Manipulation

abstract:
  We study how visual representations pre-trained on diverse human video data
can enable data-efficient learning of downstream robotic manipulation tasks.
Concretely, we pre-train a visual representation using the Ego4D human video
dataset using a combination of time-contrastive learning, video-language
alignment, and an L1 penalty to encourage sparse and compact representations.
The resulting representation, R3M, can be used as a frozen perception module
for downstream policy learning. Across a suite of 12 simulated robot
manipulation tasks, we find that R3M improves task success by over 20% compared
to training from scratch and by over 10% compared to state-of-the-art visual
representations like CLIP and MoCo. Furthermore, R3M enables a Franka Emika
Panda arm to learn a range of manipulation tasks in a real, cluttered apartment
given just 20 demonstrations. Code and pre-trained models are available at
https://tinyurl.com/robotr3m.


title:
VideoMAE: Masked Autoencoders are Data-Efficient Learners for
  Self-Supervised Video Pre-Training

abstract:
  Pre-training video transformers on extra large-scale datasets is generally
required to achieve premier performance on relatively small datasets. In this
paper, we show that video masked autoencoders (VideoMAE) are data-efficient
learners for self-supervised video pre-training (SSVP). We are inspired by the
recent ImageMAE and propose customized video tube masking and reconstruction.
These simple designs turn out to be effective for overcoming information
leakage caused by the temporal correlation during video reconstruction. We
obtain three important findings on SSVP: (1) An extremely high proportion of
masking ratio (i.e., 90% to 95%) still yields favorable performance of
VideoMAE. The temporally redundant video content enables higher masking ratio
than that of images. (2) VideoMAE achieves impressive results on very small
datasets (i.e., around 3k-4k videos) without using any extra data. This is
partially ascribed to the challenging task of video reconstruction to enforce
high-level structure learning. (3) VideoMAE shows that data quality is more
important than data quantity for SSVP. Domain shift between pre-training and
target datasets are important issues in SSVP. Notably, our VideoMAE with the
vanilla ViT backbone can achieve 83.9% on Kinects-400, 75.3% on
Something-Something V2, 90.8% on UCF101, and 61.1% on HMDB51 without using any
extra data. Code will be released at https://github.com/MCG-NJU/VideoMAE.


title:
Will Chinese Twenty-four Solar Terms Affect Stock Return: Evidence from
  Shanghai Index of China

abstract:
  In this article, readers will see the impact on Chinese stock index brought
by twenty-four solar terms, a unique division of annual season in Chinese
tradition. Based on the data in the past 26 years, the study focused on whether
the daily return (revenue) of Shanghai Index shows significant value and
special feature on and after each solar term.On several solar terms did the
index return result large mean value and high probability of extreme value
occurrence such as on solar term No.1 and No.3 while on solar term No.2 and
solar term No.4, the results were completely the opposite.The study also found
that the volatility of index return during those solar terms in the beginning
of the year were more active than the rest of them. Index return 10 days and
15days after solar term No.6 and solar term No.8 displayed high final return
and large volatility whereas in any cases, the index went very steady after
solar term No.18.The study also proposed that it is almost impossible to make
numeric prediction with the current technical analysis tools, the effective way
in stock analysis to collect more feature and characteristics based on
historical data, identifying if the similar situation is happening when similar
feature of stock shows up in the future.


title:
Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace
  Based on Denoising Convolutional Autoencoder and Bidirectional Long
  Short-Term Memory

abstract:
  Optical time-domain reflectometry (OTDR) has been widely used for
characterizing fiber optical links and for detecting and locating fiber faults.
OTDR traces are prone to be distorted by different kinds of noise, causing
blurring of the backscattered signals, and thereby leading to a misleading
interpretation and a more cumbersome event detection task. To address this
problem, a novel method combining a denoising convolutional autoencoder (DCAE)
and a bidirectional long short-term memory (BiLSTM) is proposed, whereby the
former is used for noise removal of OTDR signals and the latter for fault
detection, localization, and diagnosis with the denoised signal as input. The
proposed approach is applied to noisy OTDR signals of different levels of input
SNR ranging from -5 dB to 15 dB. The experimental results demonstrate that: (i)
the DCAE is efficient in denoising the OTDR traces and it outperforms other
deep learning techniques and the conventional denoising methods; and (ii) the
BiLSTM achieves a high detection and diagnostic accuracy of 96.7% with an
improvement of 13.74% compared to the performance of the same model trained
with noisy OTDR signals.


title:
Grandes fraudes y gobiernos corporativos en la Econom\'ia desde mediados
  del siglo XX

abstract:
  The international financial system is currently not yet prepared to face a
foreseeable crisis mainly motivated by the dichotomy between the real economy
and the virtual economy. Skepticism is widespread even when it comes to
investments in sustainable economy. The concentration of capital in a few
persons is one of the greatest risks for the possible reiteration of economic
crises. The benevolent sentences of the courts to some of the fraudsters do not
contribute to dispelling the ghost of fraud nor to the disappearance of tax
havens. From the diachronic perspective, it is observed that economic crises
are increasingly frequent and incidents always in the financial field; which
forces us to rethink an economic model on an international scale in which there
is a greater weight of the economic policy of governments over the power of
multinational companies in the context of globalization. In the context of
Corporate Social Responsibility, Corporate Governance is listed as one of the
fundamental levers to curb large business fraud, but its efficiency seems
insufficient due to the lack of international regulations and the ignorance of
hidden forces in what has been known as fiscal and financial engineering. The
application of liberal policies in an unorthodox way is causing real social
gaps in the distribution of income and is undermining the current capitalist
system. The need to implement corporate governments is recommended as one of
the essential formulas for sustaining the international economic system.


title: Journey of Cryptocurrency in India In View of Financial Budget 2022-23

abstract:
  Recently, Indian Finance minister Nirmala Sitharaman announced in Union
budget 2022-23 that Indian government will put 30% tax (the highest tax slab in
India) on income generated from cryptocurrencies. Big financial institutions,
experts and academicians have different opinions in this regard. They claim
that it would be the end of cryptocurrency market in India or it would be
possible that RBI (Reserve Bank of India) may launch its own crypto or digital
currency. So in this context, in this article, the journey and future aspects
of cryptocurrency in India are discussed and we hope that it will be a
reference for further research and discussion in this area.


title:
Transactional Interpretation for the Principle of Minimum Fisher
  Information

abstract:
  The principle of minimum Fisher information states that in the set of
acceptable probability distributions characterizing the given system, it is
best done by the one that minimizes the corresponding Fisher information. This
principle can be applied to transaction processes, the dynamics of which can be
interpreted as the market tendency to minimize the information revealed about
itself. More information involves higher costs (information is physical). The
starting point for our considerations is a description of the market derived
from the assumption of minimum Fisher information for a strategy with a fixed
financial risk. Strategies of this type that minimize Fisher information
overlap with the well-known eigenstates of a the quantum harmonic oscillator.
The analytical extension of this field of strategy to the complex vector space
(traditional for quantum mechanics) suggests the study of the interference of
the oscillator eigenstates in terms of their minimization of Fisher
information. It is revealed that the minimum value of Fisher information of the
superposition of the two strategies being the ground state and the second
excited state of the oscillator, has Fisher information less than the ground
state of the oscillator. Similarly, less information is obtained for the system
of strategies (the oscillator eigenstates) randomized by the Gibbs
distribution. We distinguish two different views on the description of Fisher
information. One of them, the classical, is based on the value of Fisher
information. The second, we call it transactional, expresses Fisher information
from the perspective of the constant risk of market strategies. The orders of
the market strategies derived from these two descriptions are different. From a
market standpoint, minimizing Fisher information is equivalent to minimizing
risk.


title: Inverse Reynolds-dominance approach to transient fluid dynamics

abstract:
  We consider the evolution equations for the bulk viscous pressure, diffusion
current and shear tensor derived within the second order relativistic
dissipative hydrodynamics from kinetic theory. By matching the higher order
moments directly to the dissipative quantities, all terms which are of second
order in the Knudsen number Kn vanish, leaving only terms of order
$\mathcal{O}(\Re \, {\rm Kn})$ and $\mathcal{O}({\rm Re}^{-2})$ in the
relaxation equations, where $\Re$ is the inverse Reynolds number. We therefore
refer to this scheme as the Inverse Reynolds Dominance (IReD) approach. The
remaining (non-vanishing) transport coefficients can be obtained exclusively in
terms of the inverse of the collision matrix. This procedure fixes
unambiguously the relaxation times of the dissipative quantities, which are no
longer related to the eigenvalues of the inverse of the collision matrix. In
particular, we find that the relaxation times corresponding to higher order
moments grow as their order increases, thereby contradicting the
\textit{separation of scales} paradigm. The formal (up to second order)
equivalence with the standard DNMR approach is proven and the connection
between the IReD transport coefficients and the usual DNMR ones is established.


title: Improving the Fairness of Chest X-ray Classifiers

abstract:
  Deep learning models have reached or surpassed human-level performance in the
field of medical imaging, especially in disease diagnosis using chest x-rays.
However, prior work has found that such classifiers can exhibit biases in the
form of gaps in predictive performance across protected groups. In this paper,
we question whether striving to achieve zero disparities in predictive
performance (i.e. group fairness) is the appropriate fairness definition in the
clinical setting, over minimax fairness, which focuses on maximizing the
performance of the worst-case group. We benchmark the performance of nine
methods in improving classifier fairness across these two definitions. We find,
consistent with prior work on non-clinical data, that methods which strive to
achieve better worst-group performance do not outperform simple data balancing.
We also find that methods which achieve group fairness do so by worsening
performance for all groups. In light of these results, we discuss the utility
of fairness definitions in the clinical setting, advocating for an
investigation of the bias-inducing mechanisms in the underlying data generating
process whenever possible.


title:
AI Poincar\'{e} 2.0: Machine Learning Conservation Laws from
  Differential Equations

abstract:
  We present a machine learning algorithm that discovers conservation laws from
differential equations, both numerically (parametrized as neural networks) and
symbolically, ensuring their functional independence (a non-linear
generalization of linear independence). Our independence module can be viewed
as a nonlinear generalization of singular value decomposition. Our method can
readily handle inductive biases for conservation laws. We validate it with
examples including the 3-body problem, the KdV equation and nonlinear
Schr\"odinger equation.

categories: cs.LG astro-ph.EP nlin.SI physics.class-ph physics.flu-dyn


title:
Submission to the Commission on Taxation and Welfare on introducing a
  site value tax

abstract:
  This submission to the Irish Commission on Taxation and Welfare advocates the
introduction of a site value tax in Ireland. Ireland has high and volatile
property prices, constraining social and economic development. Site values are
the main driver of these phenomena. Taxing site values would reduce both the
level and volatility of property prices, and thus help to alleviate these
problems. Site value tax has many other beneficial features. For example, it
captures price gains due to the community and government rather than owners'
efforts and thus diminishes the incentive to buy land for speculative reasons.
Site value tax can be used to finance infrastructural investments, help
facilitate site assembly for development and as a support for the maintenance
of protected structures. Site value tax is also a tax on wealth.


title: StructToken : Rethinking Semantic Segmentation with Structural Prior

abstract:
  In this paper, we present structure token (StructToken), a new paradigm for
semantic segmentation. From a perspective on semantic segmentation as per-pixel
classification, the previous deep learning-based methods learn the per-pixel
representation first through an encoder and a decoder head and then classify
each pixel representation to a specific category to obtain the semantic masks.
Differently, we propose a structure-aware algorithm that takes structural
information as prior to predict semantic masks directly without per-pixel
classification. Specifically, given an input image, the learnable structure
token interacts with the image representations to reason the final semantic
masks. Three interaction approaches are explored and the results not only
outperform the state-of-the-art methods but also contain more structural
information. Experiments are conducted on three widely used datasets including
ADE20k, Cityscapes, and COCO-Stuff 10K. We hope that structure token could
serve as an alternative for semantic segmentation and inspire future research.


title:
A Hybrid Mesh-neural Representation for 3D Transparent Object
  Reconstruction

abstract:
  We propose a novel method to reconstruct the 3D shapes of transparent objects
using hand-held captured images under natural light conditions. It combines the
advantage of explicit mesh and multi-layer perceptron (MLP) network, a hybrid
representation, to simplify the capture setting used in recent contributions.
After obtaining an initial shape through the multi-view silhouettes, we
introduce surface-based local MLPs to encode the vertex displacement field
(VDF) for the reconstruction of surface details. The design of local MLPs
allows to represent the VDF in a piece-wise manner using two layer MLP
networks, which is beneficial to the optimization algorithm. Defining local
MLPs on the surface instead of the volume also reduces the searching space.
Such a hybrid representation enables us to relax the ray-pixel correspondences
that represent the light path constraint to our designed ray-cell
correspondences, which significantly simplifies the implementation of
single-image based environment matting algorithm. We evaluate our
representation and reconstruction algorithm on several transparent objects with
ground truth models. Our experiments show that our method can produce
high-quality reconstruction results superior to state-of-the-art methods using
a simplified data acquisition setup.


title: Unsupervised Salient Object Detection with Spectral Cluster Voting

abstract:
  In this paper, we tackle the challenging task of unsupervised salient object
detection (SOD) by leveraging spectral clustering on self-supervised features.
We make the following contributions: (i) We revisit spectral clustering and
demonstrate its potential to group the pixels of salient objects; (ii) Given
mask proposals from multiple applications of spectral clustering on image
features computed from various self-supervised models, e.g., MoCov2, SwAV,
DINO, we propose a simple but effective winner-takes-all voting mechanism for
selecting the salient masks, leveraging object priors based on framing and
distinctiveness; (iii) Using the selected object segmentation as pseudo
groundtruth masks, we train a salient object detector, dubbed SelfMask, which
outperforms prior approaches on three unsupervised SOD benchmarks. Code is
publicly available at https://github.com/NoelShin/selfmask.


title:
A homogenized constrained mixture model of cardiac growth and
  remodeling: Analyzing mechanobiological stability and reversal

abstract:
  Cardiac growth and remodeling (G&R) patterns change ventricular size, shape,
and function both globally and locally. Biomechanical, neurohormonal, and
genetic stimuli drive these patterns through changes in myocyte dimension and
fibrosis. We propose a novel microstructure-motivated model that predicts
organ-scale G&R in the heart based on the homogenized constrained mixture
theory. Previous models, based on the kinematic growth theory, reproduced
consequences of G&R in bulk myocardial tissue by prescribing the direction and
extent of growth but neglected underlying cellular mechanisms. In our model,
the direction and extent of G&R emerge naturally from intra- and extra cellular
turnover processes in myocardial tissue constituents and their preferred
homeostatic stretch state. We additionally propose a method to obtain a
mechanobiologically equilibrated reference configuration. We test our model on
an idealized 3D left ventricular geometry and demonstrate that our model aims
to maintain tensional homeostasis in hypertension conditions. In a stability
map, we identify regions of stable and unstable G&R from an identical parameter
set with varying systolic pressures and growth factors. Furthermore, we show
the extent of G&R reversal after returning the systolic pressure to baseline
following stage 1 and 2 hypertension. A realistic model of organ-scale cardiac
G&R has the potential to identify patients at risk of heart failure, enable
personalized cardiac therapies, and facilitate the optimal design of medical
devices.


title:
Unsupervised Pre-Training on Patient Population Graphs for Patient-Level
  Predictions

abstract:
  Pre-training has shown success in different areas of machine learning, such
as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging.
However, it has not been fully explored for clinical data analysis. Even though
an immense amount of Electronic Health Record (EHR) data is recorded, data and
labels can be scarce if the data is collected in small hospitals or deals with
rare diseases. In such scenarios, pre-training on a larger set of EHR data
could improve the model performance. In this paper, we apply unsupervised
pre-training to heterogeneous, multi-modal EHR data for patient outcome
prediction. To model this data, we leverage graph deep learning over population
graphs. We first design a network architecture based on graph transformer
designed to handle various input feature types occurring in EHR data, like
continuous, discrete, and time-series features, allowing better multi-modal
data fusion. Further, we design pre-training methods based on masked imputation
to pre-train our network before fine-tuning on different end tasks.
Pre-training is done in a fully unsupervised fashion, which lays the groundwork
for pre-training on large public datasets with different tasks and similar
modalities in the future. We test our method on two medical datasets of patient
records, TADPOLE and MIMIC-III, including imaging and non-imaging features and
different prediction tasks. We find that our proposed graph based pre-training
method helps in modeling the data at a population level and further improves
performance on the fine tuning tasks in terms of AUC on average by 4.15% for
MIMIC and 7.64% for TADPOLE.


title:
Generalized multifractality at the spin quantum Hall transition:
  Percolation mapping and pure-scaling observables

abstract:
  This work extends the analysis of the generalized multifractality of critical
eigenstates at the spin quantum Hall transition in two-dimensional disordered
superconductors [J. F. Karcher et al, Annals of Physics, 435, 168584 (2021)]. A
mapping to classical percolation is developed for a certain set of
generalized-multifractality observables. In this way, exact analytical results
for the corresponding exponents are obtained. Furthermore, a general
construction of positive pure-scaling eigenfunction observables is presented,
which permits a very efficient numerical determination of scaling exponents. In
particular, all exponents corresponding to polynomial pure-scaling observables
up to the order $q=5$ are found numerically. For the observables for which the
percolation mapping is derived, analytical and numerical results are in perfect
agreement with each other. The analytical and numerical results unambiguously
demonstrate that the generalized parabolicity (i.e., proportionality to
eigenvalues of the quadratic Casimir operator) does not hold for the spectrum
of generalized-multifractality exponents. This excludes
Wess-Zumino-Novikov-Witten models, and, more generally, any theories with local
conformal invariance, as candidates for the fixed-point theory of the spin
quantum Hall transition. The observable construction developed in this work
paves a way to investigation of generalized multifractality at
Anderson-localization critical points of various symmetry classes.

categories: cond-mat.dis-nn cond-mat.stat-mech math-ph math.MP


title:
Effects of electrostatic interactions on Kapitza resistance in hexagonal
  boron nitride-water interfaces

abstract:
  Electrostatic interactions in nanoscale systems can influence the heat
transfer mechanism and interfacial properties. This study uses molecular
dynamics simulations to investigate the impact of various electrostatic
interactions on the Kapitza resistance ($R_k$) on a hexagonal boron
nitride-water system. The Kapitza resistance at hexagonal boron nitride
nanotube (hBNNT)-water interface reduces with an increase in diameter of the
nanotube due to more aggregation of water molecules per unit surface area. An
increase in the partial charges on boron and nitride caused the reduction in
$R_k$. With the increase in partial charge, a better hydrogen bonding between
hBNNT and water was observed, whereas the structure and order of the water
molecules remain the same. Nevertheless, the addition of NaCl salt into water
does not have any influence on interfacial thermal transport. $R_k$ remains
unchanged with electrolyte concentration since the cumulative Coulombic
interaction between the ions, and the hBNNT is significantly less when compared
with water molecules. Furthermore, the effect of electric field strength on
interfacial heat transfer is also investigated by providing uniform positive
and negative surface charges on the outermost hBN layers. $R_k$ is nearly
independent of the practical range of applied electric fields and decreases
with an increasing electric field for extreme field strengths until the
electro-freezing phenomenon occurs. The ordering of water molecules towards the
charged surface leads to an increase in the layering effect, causing the
reduction in $R_k$ in the presence of an electric field.


title: Charged Accelerating BTZ black holes

abstract:
  In this paper, we first extract general uncharged accelerating BTZ black hole
solutions and study some of their properties. Our analysis shows that
spacetime's asymptotical behavior depends on four parameters (the cosmological
constant, mass, the acceleration parameter, and the topological constant).
Then, we study the temperature of these black holes and find a divergence
point, which this point reveals a phase transition between physical and
non-physical black holes. Next, we extend our study for extracting charged
accelerating BTZ black hole solutions in the presence of a nonlinear
electrodynamics field, known as conformally invariant Maxwell. Our findings
indicate that there is a coupling between the electrical charge and other
quantities of the accelerating BTZ black holes. We find that the asymptotical
behavior of charged accelerating BTZ black holes depends on six parameters (the
cosmological constant, the electrical charge, mass, the acceleration parameter,
topological constant, and angular coordinate). Finally, we investigate the
effects of various parameters on the temperature of these black holes. There
are two exciting behaviors for the temperature by considering different
cosmological constant values. A divergence point exists for the temperature
when the cosmological constant is negative. However, for the positive value of
the cosmological constant, there are three critical points in the temperature;
two roots and a divergence point. These points give us information about phase
transitions between physical and non-physical black holes.


title:
Evaluation of Non-Invasive Thermal Imaging for detection of Viability of
  Onchocerciasis worms

abstract:
  Onchocerciasis is causing blindness in over half a million people in the
world today. Drug development for the disease is crippled as there is no way of
measuring effectiveness of the drug without an invasive procedure. Drug
efficacy measurement through assessment of viability of onchocerca worms
requires the patients to undergo nodulectomy which is invasive, expensive,
time-consuming, skill-dependent, infrastructure dependent and lengthy process.
In this paper, we discuss the first-ever study that proposes use of machine
learning over thermal imaging to non-invasively and accurately predict the
viability of worms. The key contributions of the paper are (i) a unique thermal
imaging protocol along with pre-processing steps such as alignment,
registration and segmentation to extract interpretable features (ii) extraction
of relevant semantic features (iii) development of accurate classifiers for
detecting the existence of viable worms in a nodule. When tested on a
prospective test data of 30 participants with 48 palpable nodules, we achieved
an Area Under the Curve (AUC) of 0.85.


title:
MR Image Denoising and Super-Resolution Using Regularized Reverse
  Diffusion

abstract:
  Patient scans from MRI often suffer from noise, which hampers the diagnostic
capability of such images. As a method to mitigate such artifact, denoising is
largely studied both within the medical imaging community and beyond the
community as a general subject. However, recent deep neural network-based
approaches mostly rely on the minimum mean squared error (MMSE) estimates,
which tend to produce a blurred output. Moreover, such models suffer when
deployed in real-world sitautions: out-of-distribution data, and complex noise
distributions that deviate from the usual parametric noise models. In this
work, we propose a new denoising method based on score-based reverse diffusion
sampling, which overcomes all the aforementioned drawbacks. Our network,
trained only with coronal knee scans, excels even on out-of-distribution in
vivo liver MRI data, contaminated with complex mixture of noise. Even more, we
propose a method to enhance the resolution of the denoised image with the same
network. With extensive experiments, we show that our method establishes
state-of-the-art performance, while having desirable properties which prior
MMSE denoisers did not have: flexibly choosing the extent of denoising, and
quantifying uncertainty.


title: Are Evolutionary Algorithms Safe Optimizers?

abstract:
  We consider a type of constrained optimization problem, where the violation
of a constraint leads to an irrevocable loss, such as breakage of a valuable
experimental resource/platform or loss of human life. Such problems are
referred to as safe optimization problems (SafeOPs). While SafeOPs have
received attention in the machine learning community in recent years, there was
little interest in the evolutionary computation (EC) community despite some
early attempts between 2009 and 2011. Moreover, there is a lack of acceptable
guidelines on how to benchmark different algorithms for SafeOPs, an area where
the EC community has significant experience in. Driven by the need for more
efficient algorithms and benchmark guidelines for SafeOPs, the objective of
this paper is to reignite the interest of this problem class in the EC
community. To achieve this we (i) provide a formal definition of SafeOPs and
contrast it to other types of optimization problems that the EC community is
familiar with, (ii) investigate the impact of key SafeOP parameters on the
performance of selected safe optimization algorithms, (iii) benchmark EC
against state-of-the-art safe optimization algorithms from the machine learning
community, and (iv) provide an open-source Python framework to replicate and
extend our work.