title: Scale-Invariance at the Core of Quantum Black Holes

abstract:
  We study spherically-symmetric solutions to a modified Einstein-Hilbert
action with Renormalization Group scale-dependent couplings, inspired by
Weinberg's Asymptotic Safety scenario for Quantum Gravity. The Renormalization
Group scale is identified with the Tolman temperature for an isolated
gravitational system in thermal equilibrium with Hawking radiation. As a
result, the point of infinite local temperature is shifted from the classical
black-hole horizon to the origin and coincides with a timelike curvature
singularity. Close to the origin, the spacetime is determined by the
scale-dependence of the cosmological constant in the vicinity of the Reuter
fixed point: the free components of the metric can be derived analytically and
are characterized by a radial power law with exponent $\alpha = \sqrt{3}-1$.
Away from the fixed point, solutions for different masses are studied
numerically and smoothly interpolate between the Schwarzschild exterior and the
scale-invariant interior. Whereas the exterior of objects with astrophysical
mass is described well by vacuum General Relativity, deviations become
significant at a Planck distance away from the classical horizon and could lead
to observational signatures. We further highlight potential caveats in this
intriguing result with regard to our choice of scale-identification and
identify future avenues to better understand quantum black holes in relation to
the key feature of scale-invariance.


title:
Improving sandwich variance estimation for marginal Cox analysis of
  cluster randomized trials

abstract:
  Cluster randomized trials (CRTs) frequently recruit a small number of
clusters, therefore necessitating the application of small-sample corrections
for valid inference. A recent systematic review indicated that CRTs reporting
right-censored, time-to-event outcomes are not uncommon, and that the marginal
Cox proportional hazards model is one of the common approaches used for primary
analysis. While small-sample corrections have been studied under marginal
models with continuous, binary and count outcomes, no prior research has been
devoted to the development and evaluation of bias-corrected sandwich variance
estimators when clustered time-to-event outcomes are analyzed by the marginal
Cox model. To improve current practice, we propose 9 bias-corrected sandwich
variance estimators for the analysis of CRTs using the marginal Cox model, and
report on a simulation study to evaluate their small-sample properties in terms
of relative bias and type I error rate. Our results indicate that the optimal
choice of bias-corrected sandwich variance estimator for CRTs with survival
outcomes can depend on the variability of cluster sizes, and can also differ
whether it is evaluated according to relative bias or type I error rate.
Finally, we illustrate the new variance estimators in a real-world CRT where
the conclusion about intervention effectiveness differs depending on the use of
small-sample bias corrections. The proposed sandwich variance estimators are
implemented in an R package CoxBcv.


title:
Triplet-Pair Spin Signatures from Macroscopically Aligned Heteroacenes
  in an Oriented Single Crystal

abstract:
  The photo-driven process of singlet fission generates coupled triplet pairs
(TT) with fundamentally intriguing and potentially useful properties. The
quintet 5TT0 sublevel is particularly interesting for quantum information
because it is highly entangled, addressable with microwave pulses, and could be
detected using optical techniques. Previous theoretical work on a model
Hamiltonian and nonadiabatic transition theory, called the JDE model, has
determined that this sublevel can be selectively populated if certain
conditions are met. Among the most challenging, the molecules within the dimer
undergoing singlet fission must have their principal magnetic axes parallel to
one another and to an applied Zeeman field B0. Here, we present time-resolved
paramagnetic resonance (TR-EPR) spectroscopy of a single crystal sample of a
novel tetracenethiophene compound featuring arrays of dimers aligned in this
manner, mounted so that the orientation of the field relative to the molecular
axes could be controlled. The observed spin sublevel populations in the paired
TT and unpaired (T+T) triplets are consistent with predictions from the JDE
model, including preferential 5TT0 formation at z||B0, with one caveat - two
5TT spin sublevels have little to no population. This may be due to crossings
between the 5TT and 3TT manifolds in the field range investigated by TR-EPR,
consistent with the inter-triplet exchange energy determined by monitoring
photoluminescence at varying magnetic fields.


title:
On Beltrami equations with the inverse conditions and hydrodynamic
  normalization

abstract:
  We consider problems concerning the existence of solutions of the Beltrami
equations and their convergence in the entire complex plane. We are mainly
interested in the case when these solutions satisfy the so-called hydrodynamic
normalization condition in the neighborhood of infinity. Under conditions
related to dilations of inverse mappings, we have established the existence of
such solutions in the class of continuous Sobolev mappings. We have also
obtained results on the locally uniform limit of a sequence of such solutions.


title: Heterogeneous Multi-Resource Allocation with Subset Demand Requests

abstract:
  We consider the problem of allocating multiple heterogeneous resources
geographically and over time to meet demands that require some subset of the
available resource types simultaneously at a specified time, location, and
duration. The objective is to maximize the total reward accrued from meeting (a
subset of) demands. We model this problem as an integer program, show that it
is NP-hard, and analyze the complexity of various special cases. We introduce
approximation algorithms and an extension to our problem that considers travel
costs. Finally, we test the performance of the integer programming model in an
extensive computational study.


title:
The efficiency of Quantum Mechanical Carnot Engine using the Woods Saxon
  model

abstract:
  The quantum engine cycle serves as an analogous representation of the
macroscopic nature of heat engines and the quantum regime of thermal devices
composed of a single element. In this work, the quantum mechanical properties
of the strongly non-linear quantum oscillator described by the Woods-Saxon SW
model (which has a relation with free particle FP in a box) is examined. Using
this model, a quantum-mechanical analogue of the Carnot cycle (i.e., Quantum
heat engine) has been constructed through the changes of both, the width L of
the well and its quantum state. This quantum heat engine has a cycle consisting
of adiabatic and isothermal quantum processes. The efficiency of the quantum
engine based on the Woods Saxon WS potential is derived, and it is proven to be
analogous to classical thermodynamic engines.


title:
SDSS IV MaNGA: Visual Morphological and Statistical Characterization of
  the DR15 sample

abstract:
  We present a detailed visual morphological classification for the 4614 MaNGA
galaxies in SDSS Data Release 15, using image mosaics generated from a
combination of r-band (SDSS and deeper DESI Legacy Surveys) images and their
digital post-processing. We distinguish 13 Hubble types and identify the
presence of bars and bright tidal debris. After correcting the MaNGA sample for
volume completeness, we calculate the morphological fractions, the bi-variate
distribution of type and stellar mass M*-where we recognise a morphological
transition "valley" around S0a-Sa types- and the variations of the g-i colour
and luminosity-weighted age over this distribution. We identified bars in 46.8%
of galaxies, present in all Hubble types later than S0. This fraction amounts
to a factor ~2 larger when compared with other works for samples in common. We
detected 14% of galaxies with tidal features, with the fraction changing with
M* and morphology. For 355 galaxies, the classification was uncertain; they are
visually faint, mostly of low/intermediate masses, low concentrations, and
disky in nature. Our morphological classification agrees well with other works
for samples in common, though some particular differences emerge, showing that
our image procedures allow us to identify a wealth of added value information
as compared to SDSS-based previous estimates. Based on our classification, we
also propose an alternative criteria for the E-S0 separation, in the structural
semi-major to semi-minor axis versus bulge to total light ratio (b/a-B/T) and
concentration versus semi-major to semi-minor axis (C-b/a) space.


title: Torus bundles over lens spaces

abstract:
  Let $\rho:\mathbb{Z}/p\rightarrow\operatorname{GL}_n(\mathbb{Z})$ be an
action of $\mathbb{Z}/p$ on a lattice and let
$\Gamma:=\mathbb{Z}^n\rtimes\mathbb{Z}/p$ be the corresponding semidirect
product. The torus bundle $M:=T^n\times_{\mathbb{Z}/p}S^{\ell}$ over the lens
space $S^{\ell}/\mathbb{Z}/p$ has fundamental group $\Gamma$. When
$\mathbb{Z}/p$ fixes only the origin of $\mathbb{Z}^n$, Davis and L\"uck
compute the $L$-groups $L^{\langle j\rangle}_m(\mathbb{Z}[\Gamma])$ and the
structure set $\mathcal{S}^{geo,s}(M)$. In this paper, we extend these
computations to all actions of $\mathbb{Z}/p$ on $\mathbb{Z}^n$. In particular,
we compute $L^{\langle j\rangle}_m(\mathbb{Z}[\Gamma])$ and
$\mathcal{S}^{geo,s}(M)$ in a case where $\underline{E}\Gamma$ has a
non-discrete singular set.


title:
Exchange interactions and spin dynamics in the layered honeycomb
  ferromagnet CrI$_3$

abstract:
  We derive the microscopic spin Hamiltonian for rhombohedral CrI$_3$ using
extensive first-principles density functional theory (DFT) calculations which
incorporate spin-orbit coupling and Hubbard U. Our calculations indicate a
dominant nearest-neighbor ferromagnetic Heisenberg exchange with weaker
further-neighbor Heisenberg terms. In addition, we find a Dzyaloshinskii-Moriya
interaction which primarily drives a topological gap in the spin-wave spectrum
at the Dirac point, and uncover a non-negligible antiferromagnetic Kitaev
coupling between the S=3/2 Cr moments. The out-of-plane magnetic moment is
stabilized by weak symmetric bond-dependent terms and a local single-ion
anisotropy. Using linear spin wave theory, we find that our exchange parameters
are in reasonably good agreement with inelastic neutron scattering (INS)
experiments. Employing classical Monte Carlo simulations, we study the magnetic
phase transition temperature $T_c$ and its evolution with an applied in-plane
magnetic field. We further demonstrate how future high-resolution INS
experiments on the magnon dispersion of single crystals in an in-plane magnetic
field may be used to quantitatively extract the strength of the
antiferromagnetic Kitaev exchange coupling.


title:
NEMAR: An open access data, tools, and compute resource operating on
  NeuroElectroMagnetic data

abstract:
  To take advantage of recent and ongoing advances in large-scale computational
methods, and to preserve the scientific data created by publicly funded
research projects, data archives must be created as well as standards for
specifying, identifying, and annotating deposited data. The OpenNeuro.org
archive, begun as a repository for magnetic resonance imaging (MRI) data, is
such an archive. We present a gateway to OpenNeuro for human electrophysiology
data (BIDS-formatted EEG and MEG, as well as intracranial data). The NEMAR
gateway allows users to visualize electrophysiological data, including
time-domain and frequency-domain dynamics time locked to sets of experimental
events recorded using BIDS- and HED-formatted data annotation. In addition,
NEMAR allows users to process archived EEG data on the XSEDE high-performance
resources at SDSC in conjunction with the Neuroscience Gateway (nsgportal.org),
a freely available and easy to use portal to leverage high-performance
computing resources for neuroscience research.


title: Coverage Properties of Empirical Bayes Intervals

abstract:
  This note is an invited discussion of the article "Confidence Intervals for
Nonparametric Empirical Bayes Analysis" by Ignatiadis and Wager. In this
discussion, I review some goals of empirical Bayes data analysis and the
contribution of Ignatiadis and Wager. Differences between across-group
inference and group-specific inference are discussed. Standard empirical Bayes
interval procedures focus on controlling the across-group average coverage
rate. However, if group-specific inferences are of primary interest, confidence
intervals with group-specific coverage control may be preferable.


title:
Bayesian Optimization Meets Hybrid Zero Dynamics: Safe Parameter
  Learning for Bipedal Locomotion Control

abstract:
  In this paper, we propose a multi-domain control parameter learning framework
that combines Bayesian Optimization (BO) and Hybrid Zero Dynamics (HZD) for
locomotion control of bipedal robots. We leverage BO to learn the control
parameters used in the HZD-based controller. The learning process is firstly
deployed in simulation to optimize different control parameters for a large
repertoire of gaits. Next, to tackle the discrepancy between the simulation and
the real world, the learning process is applied on the physical robot to learn
for corrections to the control parameters learned in simulation while also
respecting a safety constraint for gait stability. This method empowers an
efficient sim-to-real transition with a small number of samples in the real
world, and does not require a valid controller to initialize the training in
simulation. Our proposed learning framework is experimentally deployed and
validated on a bipedal robot Cassie to perform versatile locomotion skills with
improved performance on smoothness of walking gaits and reduction of
steady-state tracking errors.


title:
Improving the Energy Efficiency and Robustness of tinyML Computer Vision
  using Log-Gradient Input Images

abstract:
  This paper studies the merits of applying log-gradient input images to
convolutional neural networks (CNNs) for tinyML computer vision (CV). We show
that log gradients enable: (i) aggressive 1.5-bit quantization of first-layer
inputs, (ii) potential CNN resource reductions, and (iii) inherent robustness
to illumination changes (1.7% accuracy loss across 1/32...8 brightness
variation vs. up to 10% for JPEG). We establish these results using the PASCAL
RAW image data set and through a combination of experiments using neural
architecture search and a fixed three-layer network. The latter reveal that
training on log-gradient images leads to higher filter similarity, making the
CNN more prunable. The combined benefits of aggressive first-layer
quantization, CNN resource reductions, and operation without tight exposure
control and image signal processing (ISP) are helpful for pushing tinyML CV
toward its ultimate efficiency limits.


title: Inverse design of plasma metamaterial devices with realistic elements

abstract:
  In an expansion of a previous study [1], we apply inverse design methods to
produce two-dimensional plasma metamaterial devices with realistic plasma
elements which incorporate quartz envelopes, collisionality (loss), non-uniform
density profiles, and resistance to experimental error/perturbation.
Backpropagated finite difference frequency domain simulations are used to
design waveguides and demultiplexers operating under the transverse magnetic
polarization. Optimal devices with realistic elements are compared to previous
devices with idealized elements, and several parameter initialization schemes
for the optimization algorithm are explored. Demultiplexing and waveguiding are
demonstrated for microwave-regime devices composed of plasma elements with
reasonable space-averaged plasma frequencies ~10 GHz and a collision frequency
~1 GHz, allowing for future in-situ training and experimental realization of
these designs.


title:
Show Me What and Tell Me How: Video Synthesis via Multimodal
  Conditioning

abstract:
  Most methods for conditional video synthesis use a single modality as the
condition. This comes with major limitations. For example, it is problematic
for a model conditioned on an image to generate a specific motion trajectory
desired by the user since there is no means to provide motion information.
Conversely, language information can describe the desired motion, while not
precisely defining the content of the video. This work presents a multimodal
video generation framework that benefits from text and images provided jointly
or separately. We leverage the recent progress in quantized representations for
videos and apply a bidirectional transformer with multiple modalities as inputs
to predict a discrete video representation. To improve video quality and
consistency, we propose a new video token trained with self-learning and an
improved mask-prediction algorithm for sampling video tokens. We introduce text
augmentation to improve the robustness of the textual representation and
diversity of generated videos. Our framework can incorporate various visual
modalities, such as segmentation masks, drawings, and partially occluded
images. It can generate much longer sequences than the one used for training.
In addition, our model can extract visual information as suggested by the text
prompt, e.g., "an object in image one is moving northeast", and generate
corresponding videos. We run evaluations on three public datasets and a newly
collected dataset labeled with facial attributes, achieving state-of-the-art
generation results on all four.


title: Style-ERD: Responsive and Coherent Online Motion Style Transfer

abstract:
  Motion style transfer is a common method for enriching character animation.
Motion style transfer algorithms are often designed for offline settings where
motions are processed in segments. However, for online animation applications,
such as realtime avatar animation from motion capture, motions need to be
processed as a stream with minimal latency. In this work, we realize a
flexible, high-quality motion style transfer method for this setting. We
propose a novel style transfer model, Style-ERD, to stylize motions in an
online manner with an Encoder-Recurrent-Decoder structure, along with a novel
discriminator that combines feature attention and temporal attention. Our
method stylizes motions into multiple target styles with a unified model.
Although our method targets online settings, it outperforms previous offline
methods in motion realism and style expressiveness and provides significant
gains in runtime efficiency


title:
Electron acceleration from the interaction of three crossed parallel
  Alfv\'en waves

abstract:
  We study the non-linear interaction of three parallel Alfv\'en wave packets
(AWP) in an initially uniform plasma using 2.5D particle-in-a-cell (PIC)
numerical simulations. We aim to help to explain the observation of
suprathermal electrons by multiple Alfv\'en waves collision in regions where
these waves are trapped like IAR (Ionospheric Alfv\'en Resonator), Earth
radiation belts or coronal magnetic loops. In the context of APAWI process that
have been described by Mottez (2012, 2015), the interaction of two parallel
Alfv\'en waves (AW) generates longitudinal density modulations and parallel
electric field at the APAWI crossing region that can accelerate particles
effectively in the direction of the background magnetic field. Our simulations
show that when a third parallel AWP of different initial position arrives at
the APAWI crossing region, it gives rise to a strong parallel electron beams
($V \sim 5--7VTe$) at longitudinal cavity density gradients. We suggest that
velocity drift from outgoing AW generates interface waves in the transverse
direction, which allows propagating waves to develop parallel electric fields
by phase mixing process when the transverse scale of the wavy density gradient
(oblique gradient) is in the range of the electron inertial length.

categories: astro-ph.SR astro-ph.EP physics.plasm-ph physics.space-ph


title: Machine Learning Simulates Agent-Based Model Towards Policy

abstract:
  Public Policies are not intrinsically positive or negative. Rather, policies
provide varying levels of effects across different recipients.
Methodologically, computational modeling enables the application of a
combination of multiple influences on empirical data, thus allowing for
heterogeneous response to policies. We use a random forest machine learning
algorithm to emulate an agent-based model (ABM) and evaluate competing policies
across 46 Metropolitan Regions (MRs) in Brazil. In doing so, we use input
parameters and output indicators of 11,076 actual simulation runs and one
million emulated runs. As a result, we obtain the optimal (and non-optimal)
performance of each region over the policies. Optimum is defined as a
combination of production and inequality indicators for the full ensemble of
MRs. Results suggest that MRs already have embedded structures that favor
optimal or non-optimal results, but they also illustrate which policy is more
beneficial to each place. In addition to providing MR-specific policies'
results, the use of machine learning to simulate an ABM reduces the
computational burden, whereas allowing for a much larger variation among model
parameters. The coherence of results within the context of larger uncertainty
-- vis-\`a-vis those of the original ABM -- suggests an additional test of
robustness of the model. At the same time the exercise indicates which
parameters should policymakers intervene, in order to work towards optimum of
MRs.


title:
Confirming Brennan's conjecture numerically on a counterexample to
  Thurston's $K=2$ conjecture

abstract:
  It was shown by Bishop that if Thurston's $K = 2$ conjecture holds for some
planar domain, then Brennan's conjecture holds for the Riemann map of that
domain as well. In this paper we show numerically that the original
counterexample to Brennan's conjecture given by Epstein, Marden and Markovic is
not a counterexample to Brennan's conjecture.


title: Harmonic projections in negative curvature

abstract:
  In this paper we construct harmonic maps that are at a bounded distance from
nearest-point retractions to convex sets, in negatively curved manifolds.
Specifically, given a quasidisk $Q$ in hyperbolic space, we construct a
harmonic map to the hyperbolic plane that corresponds to the nearest-point
retraction to the convex hull of $Q$. If $M$ is a pinched Hadamard manifold so
that its isometry group acts with cobounded orbits, and if $S$ is a set in the
boundary at infinity of $M$, with the property that all elements of its orbit
under the isometry group of $M$ have dimension less than $\frac{n-1}{2}$, we
show that the nearest-point retraction to the convex hull of $S$ is a bounded
distance away from some harmonic map.


title: The Status and Future of Color Transparency and Nuclear Filtering

abstract:
  40 years after its introduction, the phenomenon of color transparency remains
a domain of controversial interpretations of experimental data. We review
present evidence for or against its manifestation in various exclusive hard
scattering reactions. The nuclear transparency experiments reveal whether short
distance processes dominate a scattering amplitude at some given kinematical
point. We plead for a new round of nuclear transparency measurements in a
variety of experimental set-ups, including near-forward exclusive reactions
related to generalized parton distribution (GPD) physics and near-backward
exclusive reactions related to transition distribution amplitudes (TDA)
physics.


title: Character of the "normal state" of the nickelate superconductors

abstract:
  The occurrence of superconductivity in proximity to various strongly
correlated phases of matter has drawn extensive focus on their normal state
properties, to develop an understanding of the state from which
superconductivity emerges. The recent finding of superconductivity in layered
nickelates raises similar interests. However, transport measurements of doped
infinite-layer nickelate thin films have been hampered by materials limitations
of these metastable compounds - in particular, a relatively high density of
extended defects. Here, by moving to a substrate
(LaAlO$_{3}$)$_{0.3}$(Sr$_{2}$TaAlO$_{6}$)$_{0.7}$ which better stabilizes the
growth and reduction conditions, we can synthesize the doping series of
Nd$_{1-x}$Sr$_{x}$NiO$_{2}$ essentially free from extended defects. This
enables the first examination of the 'intrinsic' temperature and doping
dependent evolution of the transport properties. The normal state resistivity
exhibits a low-temperature upturn in the underdoped regime, linear behavior
near optimal doping, and quadratic temperature dependence for overdoping. This
is strikingly similar to the copper oxides, despite key distinctions - namely
the absence of an insulating parent compound, multiband electronic structure,
and a Mott-Hubbard orbital alignment rather than the charge-transfer insulator
of the copper oxides. These results suggest an underlying universality in the
emergent electronic properties of both superconducting families.


title: Games on base matrices

abstract:
  Using a game characterization of distributivity, we show that base matrices
for $\mathcal{P}(\omega)/\text{fin}$ of regular height larger than
$\mathfrak{h}$ necessarily have maximal branches which are not cofinal.


title: Pulsating B stars in the Scorpius-Centaurus Association with TESS

abstract:
  We study 119 B stars located in the Scorpius-Centaurus Association using data
from NASA's TESS Mission. We see pulsations in 81 stars (68%) across the full
range of effective temperatures. In particular, we confirm previous reports of
low-frequency pulsations in stars whose temperatures fall between the
instability strips of SPB stars (slowly pulsating B stars) and $\delta$ Scuti
stars. By taking the stellar densities into account, we conclude that these
cannot be p modes and confirm previous suggestions that these are probably
rapidly-rotating SPB stars. We also confirm that they follow two
period-luminosity relations that are consistent with prograde sectoral g modes
that are dipole ($l=m=1$) and quadrupole ($l=m=2$), respectively. One of the
stars ($\xi^2$ Cen) is a hybrid pulsator that shows regular spacings in both g
and p modes. We confirm that $\alpha$ Cru has low-amplitude p-mode pulsations,
making it one of the brightest $\beta$ Cephei stars in the sky. We also find
several interesting binaries, including a very short-period heartbeat star (HD
132094), a previously unknown eclipsing binary ($\pi$ Lup), and an eclipsing
binary with high-amplitude tidally driven pulsations (HR 5846). The results
clearly demonstrate the power of TESS for studying variability in stellar
associations.


title: Online Learning of Reusable Abstract Models for Object Goal Navigation

abstract:
  In this paper, we present a novel approach to incrementally learn an Abstract
Model of an unknown environment, and show how an agent can reuse the learned
model for tackling the Object Goal Navigation task. The Abstract Model is a
finite state machine in which each state is an abstraction of a state of the
environment, as perceived by the agent in a certain position and orientation.
The perceptions are high-dimensional sensory data (e.g., RGB-D images), and the
abstraction is reached by exploiting image segmentation and the Taskonomy model
bank. The learning of the Abstract Model is accomplished by executing actions,
observing the reached state, and updating the Abstract Model with the acquired
information. The learned models are memorized by the agent, and they are reused
whenever it recognizes to be in an environment that corresponds to the stored
model. We investigate the effectiveness of the proposed approach for the Object
Goal Navigation task, relying on public benchmarks. Our results show that the
reuse of learned Abstract Models can boost performance on Object Goal
Navigation.


title:
Virtual Histological Staining of Label-Free Total Absorption
  Photoacoustic Remote Sensing (TA-PARS)

abstract:
  Histopathological visualizations are a pillar of modern medicine and
biological research. Surgical oncology relies exclusively on post-operative
histology to determine definitive surgical success and guide adjuvant
treatments. The current histology workflow is based on bright-field microscopic
assessment of histochemical stained tissues and has some major limitations. For
example, the preparation of stained specimens for brightfield assessment
requires lengthy sample processing, delaying interventions for days or even
weeks. Hence, there is a pressing need for improved histopathology methods. In
this paper, we present a deep-learning-based approach for virtual label-free
histochemical staining of total-absorption photoacoustic remote sensing
(TA-PARS) images of unstained tissue. TA-PARS provides an array of directly
measured label-free contrasts such as scattering and total absorption
(radiative and non-radiative), ideal for developing H&E colorizations without
the need to infer arbitrary tissue structures. We use a Pix2Pix generative
adversarial network (GAN) to develop visualizations analogous to H&E staining
from label-free TA-PARS images. Thin sections of human skin tissue were first
virtually stained with the TA-PARS, then were chemically stained with H&E
producing a one-to-one comparison between the virtual and chemical staining.
The one-to-one matched virtually- and chemically- stained images exhibit high
concordance validating the digital colorization of the TA-PARS images against
the gold standard H&E. TA-PARS images were reviewed by four dermatologic
pathologists who confirmed they are of diagnostic quality, and that resolution,
contrast, and color permitted interpretation as if they were H&E. The presented
approach paves the way for the development of TA-PARS slide-free histology,
which promises to dramatically reduce the time from specimen resection to
histological imaging.


title: NFSlicer: Data Movement Optimization for Shallow Network Functions

abstract:
  Network Function (NF) deployments on commodity servers have become ubiquitous
in datacenters and enterprise settings. Many commonly used NFs such as
firewalls, load balancers and NATs are shallow - i.e., they only examine the
packet's header, despite the entire packet being transferred on and off the
server. As a result, the gap between moved and inspected data when handling
large packets exceeds 20x. At modern network rates, such excess data movement
is detrimental to performance, hurting both the average and 90% tail latency of
large packets by up to 1.7x. Our thorough performance analysis identifies high
contention on the NIC-server PCIe interface and in the server's memory
hierarchy as the main bottlenecks.
  We introduce NFSlicer, a data movement optimization implemented as a NIC
extension to mitigate the bottlenecks stemming from data movement deluge in
deployments of shallow NFs on commodity servers. NFSlicer only transfers the
small portion of each packet that the deployed NFs actually inspect, by slicing
the packet's payload and temporarily storing it in on-NIC memory. When the
server later transmits the processed packet, NFSlicer splices it to its
previously sliced payload. We develop a software-based emulation platform and
demonstrate that NFSlicer effectively minimizes data movement between the NIC
and the server, bridging the latency gap between small and large packet NF
processing. On a range of shallow NFs handling 1518B packets, NFSlicer reduces
average and 90% tail latency by up to 17% / 29%, respectively.


title: Concept-based Explanations for Out-Of-Distribution Detectors

abstract:
  Out-of-distribution (OOD) detection plays a crucial role in ensuring the safe
deployment of deep neural network (DNN) classifiers. While a myriad of methods
have focused on improving the performance of OOD detectors, a critical gap
remains in interpreting their decisions. We help bridge this gap by providing
explanations for OOD detectors based on learned high-level concepts. We first
propose two new metrics for assessing the effectiveness of a particular set of
concepts for explaining OOD detectors: 1) detection completeness, which
quantifies the sufficiency of concepts for explaining an OOD-detector's
decisions, and 2) concept separability, which captures the distributional
separation between in-distribution and OOD data in the concept space. Based on
these metrics, we propose a framework for learning a set of concepts that
satisfy the desired properties of detection completeness and concept
separability and demonstrate the framework's effectiveness in providing
concept-based explanations for diverse OOD techniques. We also show how to
identify prominent concepts that contribute to the detection results via a
modified Shapley value-based importance score.


title:
A DSL for Defining Feature-Level Quality Constraints and the Aggregation
  of Evaluation Results in DevOps

abstract:
  Quality requirements typically differ among software features, e.g., due to
different usage contexts of the features, different impacts of related quality
deficiencies onto overall user satisfaction, or long-term plans of the
developing organization. For instance, maintainability requirements might be
particularly high for software features which are frequently used or bear
strategic value for the developing organization. Also, software features where
even the smallest delays are perceived as negative by the user will be
subjected to specially tight performance requirements.
  We defined an operational DSL to define software quality requirements as
individual feature-level constraints based on quantitative measures. The DSL
provides language elements to define the operationalization of measures from
external systems, time series operations, time filters, and the automatic
evaluation of these feature-level constraints in DevOps based on comparison
operators and threshold values. In addition, quality ratings summarize
evaluation results of features on an ordinal grading scheme. Likewise, quality
gates use these quality ratings to reflect the fitness of software features or
the overall software product using different states. Finally, we show an
example based on a widely-adopted secure mobile messaging app that illustrates
the interplay of the different DSL elements.


title:
A Quality Index Metric and Method for Online Self-Assessment of
  Autonomous Vehicles Sensory Perception

abstract:
  Perception is critical to autonomous driving safety. Camera-based object
detection is one of the most important methods for autonomous vehicle
perception. Current camera-based object detection solutions for autonomous
driving cannot provide feedback on the detection performance for each frame. We
propose an evaluation metric, namely the perception quality index (PQI), to
assess the camera-based object detection algorithm performance and provide the
perception quality feedback frame by frame. The method of the PQI generation is
by combining the fine-grained saliency map intensity with the object detection
algorithm's output results. Furthermore, we developed a superpixel-based
attention network (SPA-NET) to predict the proposed PQI evaluation metric by
using raw image pixels and superpixels as input. The proposed evaluation metric
and prediction network are tested on three open-source datasets. The proposed
evaluation metric can correctly assess the camera-based perception quality
under the autonomous driving environment according to the experiment results.
The network regression R-square values determine the comparison among models.
It is shown that a Perception Quality Index is useful in self-evaluating a
cameras visual scene perception.


title:
Network Services Anomalies in NFV: Survey, Taxonomy, and Verification
  Methods

abstract:
  Network Function Virtualization (NFV) has emerged as a disruptive networking
architecture whose galloping evolution is prompting enterprises to outsource
network functions to the cloud and ultimately harvest the fruits of cloud
computing, including elasticity, pay-as-you-go billing model, and on-demand
services provisioning. However, many reluctant enterprises oppose the benefits
of this outsourcing to their critical and pressing concerns about security,
trust, and compliance. The latter anticipate possible security and QoS policy
violations stemming from dishonest behaviors by cloud providers, attacks by
co-resident competitors, misconfiguration by cloud administrators, or
implementations flaws by NFV developers. As a result, migrating sensitive
workloads to the cloud requires enterprises to first assess risks by gaining
knowledge of possible network services' anomalies and second, to build trust in
the cloud by designing effective mechanisms to detect such anomalies. This
survey provides scrutiny of network services anomalies that may occur in the
NFV environments. We first present a taxonomy of network service anomalies and
analyze their negative impacts on critical service attributes, including
security and performance. Second, we compare and classify the existing
anomalies' verification mechanisms from the literature. Finally, we point out
the literature gap and identify future research directions for anomalies
verification in NFV.


title:
Hamiltonian approach to modelling interfacial internal waves over
  variable bottom

abstract:
  We study the effects of an uneven bottom on the internal wave propagation in
the presence of stratification and underlying non-uniform currents. Thus, the
presented models incorporate vorticity (wave-current interactions), geophysical
effects (Coriolis force) and a variable bathymetry. An example of the physical
situation described above is well illustrated by the equatorial internal waves
in the presence of the Equatorial Undercurrent (EUC). We find that the
interface (physically coinciding with the thermocline and the pycnocline)
satisfies in the long wave approximation a KdV-mKdV type equation with variable
coefficients. The soliton propagation over variable depth leads to effects such
as soliton fission, which is analysed and studied numerically as well.


title: A Small Gain Analysis of Single Timescale Actor Critic

abstract:
  We consider a version of actor-critic which uses proportional step-sizes and
only one critic update with a single sample from the stationary distribution
per actor step. We provide an analysis of this method using the small-gain
theorem. Specifically, we prove that this method can be used to find a
stationary point, and that the resulting sample complexity improves the state
of the art for actor-critic methods to $O \left(\mu^{-2} \epsilon^{-2} \right)$
to find an $\epsilon$-approximate stationary point where $\mu$ is the condition
number associated with the critic.


title:
Sparsity-Inducing Categorical Prior Improves Robustness of the
  Information Bottleneck

abstract:
  The information bottleneck framework provides a systematic approach to learn
representations that compress nuisance information in inputs and extract
semantically meaningful information about the predictions. However, the choice
of the prior distribution that fix the dimensionality across all the data can
restrict the flexibility of this approach to learn robust representations. We
present a novel sparsity-inducing spike-slab prior that uses sparsity as a
mechanism to provide flexibility that allows each data point to learn its own
dimension distribution. In addition, it provides a mechanism to learn a joint
distribution of the latent variable and the sparsity. Thus, unlike other
approaches, it can account for the full uncertainty in the latent space.
Through a series of experiments using in-distribution and out-of-distribution
learning scenarios on the MNIST and Fashion-MNIST data we show that the
proposed approach improves the accuracy and robustness compared with the
traditional fixed -imensional priors as well as other sparsity-induction
mechanisms proposed in the literature.


title: All quantum measurements are asymptotically equivalent

abstract:
  We consider the problem of reproducing one quantum measurement given the
ability to perform another. In particular, given the availability of a --
possibly imperfect -- quantum measurement which can be performed multiple
times, we study to what extent it can reproduce the measurement statistics and
post-measurement state of a second target measurement, in the most general
setting where both the available and target measurements are arbitrary
generalised quantum measurements. We show that this general problem in fact
reduces to the ability to reproduce the statistics of von Neumann measurements,
and that in the asymptotic limit of infinitely many uses of the available
measurement, a simple protocol based upon `classical cloning' is able to
perfectly achieve this task. This shows that asymptotically all (non-trivial)
quantum measurements are equivalent. We also study optimal protocols for a
fixed number of uses of the available measurement, and show that better
protocols than classical cloning can be found in general. Our protocols show
that the average error in reproducing a target measurement drops off
exponentially fast, and that one can, for example, rapidly improve imperfect
measurements by performing them a small number of times. This includes, but is
not limited to, improving both noisy and lossy quantum measurements. Finally,
we show that in a setting where we perform multiple measurements in parallel,
we can moreover achieve finite-rate measurement reproduction, by using
block-coding techniques from classical information theory.


title:
A Search for Low-mass Dark Matter via Bremsstrahlung Radiation and the
  Migdal Effect in SuperCDMS

abstract:
  In this paper, we present a re-analysis of SuperCDMS data using a profile
likelihood approach to search for sub-GeV dark matter particles (DM) through
two inelastic scattering channels: bremsstrahlung radiation and the Migdal
effect. By considering possible inelastic scattering channels, experimental
sensitivity can be extended to DM masses that would otherwise be undetectable
through the DM-nucleon elastic scattering channel, given the energy threshold
of current experiments. We exclude DM masses down to $220~\textrm{MeV}/c^2$ at
$2.7 \times 10^{-30}~\textrm{cm}^2$ via the bremsstrahlung channel. The Migdal
channel search excludes DM masses down to $30~\textrm{MeV}/c^2$ at $5.0 \times
10^{-30}~\textrm{cm}^2$.


title: A Scenario Approach to Risk-Aware Safety-Critical System Verification

abstract:
  With the growing interest in deploying robots in unstructured and uncertain
environments, there has been increasing interest in factoring risk into
safety-critical control development. Similarly, the authors believe risk should
also be accounted in the verification of these controllers. In pursuit of
sample-efficient methods for uncertain black-box verification then, we first
detail a method to estimate the Value-at-Risk of arbitrary scalar random
variables without requiring \textit{apriori} knowledge of its distribution.
Then, we reformulate the uncertain verification problem as a Value-at-Risk
estimation problem making use of our prior results. In doing so, we provide
fundamental sampling requirements to bound with high confidence the volume of
states and parameters for a black-box system that could potentially yield
unsafe phenomena. We also show that this procedure works independent of system
complexity through simulated examples of the Robotarium.


title: Improved Pathwise Coordinate Descent for Power Penalties

abstract:
  Pathwise coordinate descent algorithms have been used to compute entire
solution paths for lasso and other penalized regression problems quickly with
great success. They improve upon cold start algorithms by solving the problems
that make up the solution path sequentially for an ordered set of tuning
parameter values, instead of solving each problem separately. However,
extending pathwise coordinate descent algorithms to more the general bridge or
power family of $\ell_q$ penalties is challenging. Faster algorithms for
computing solution paths for these penalties are needed because $\ell_q$
penalized regression problems can be nonconvex and especially burdensome to
solve. In this paper, we show that a reparameterization of $\ell_q$ penalized
regression problems is more amenable to pathwise coordinate descent algorithms.
This allows us to improve computation of the mode-thresholding function for
$\ell_q$ penalized regression problems in practice and introduce two separate
pathwise algorithms. We show that either pathwise algorithm is faster than the
corresponding cold-start alternative, and demonstrate that different pathwise
algorithms may be more likely to reach better solutions.


title: False clustering rate control in mixture models

abstract:
  The clustering task consists in delivering labels to the members of a sample.
For most data sets, some individuals are ambiguous and intrinsically difficult
to attribute to one or another cluster. However, in practical applications,
misclassifying individuals is potentially disastrous. To overcome this
difficulty, the idea followed here is to classify only a part of the sample in
order to obtain a small misclassification rate. This approach is well known in
the supervised setting, and referred to as classification with an abstention
option. The purpose of this paper is to revisit this approach in an
unsupervised mixture-model framework. The problem is formalized in terms of
controlling the false clustering rate (FCR) below a prescribed level {\alpha},
while maximizing the number of classified items. New procedures are introduced
and their behavior is shown to be close to the optimal one by establishing
theoretical results and conducting numerical experiments. An application to
breast cancer data illustrates the benefits of the new approach from a
practical viewpoint.


title: Attraction Between Electron Pairs in High Temperature Superconductors

abstract:
  It is proposed that in high temperature superconductors Cooper pairs form and
condense due to the monotonic-oscillatory transition in the pair potential of
mean force, which occurs quite generally at high coupling in charge systems. It
is shown that the predicted transition temperatures are broadly in line with
measured superconducting transition temperatures for reasonable values of the
total electron density and the residual dielectric permittivity arising from
the immobile electrons. The predicted transition is independent of the isotopic
masses of the solid. Consequent design principles for high temperature
superconductors are discussed.


title: A reverse Expected Shortfall optimization formula

abstract:
  The celebrated Expected Shortfall (ES) optimization formula implies that ES
at a fixed probability level is the minimum of a linear real function plus a
scaled mean excess function. We establish a reverse ES optimization formula,
which says that a mean excess function at any fixed threshold is the maximum of
an ES curve minus a linear function. Despite being a simple result, this
formula reveals elegant symmetries between the mean excess function and the ES
curve, as well as their optimizers. The reverse ES optimization formula is
closely related to the Fenchel-Legendre transforms, and our formulas are
generalized from ES to optimized certainty equivalents, a popular class of
convex risk measures. We analyze worst-case values of the mean excess function
under two popular settings of model uncertainty to illustrate the usefulness of
the reverse ES optimization formula, and this is further demonstrated with an
application using insurance datasets.


title:
Geodesic Gramian Denoising Applied to the Images Contaminated With Noise
  Sampled From Diverse Probability Distributions

abstract:
  As quotidian use of sophisticated cameras surges, people in modern society
are more interested in capturing fine-quality images. However, the quality of
the images might be inferior to people's expectations due to the noise
contamination in the images. Thus, filtering out the noise while preserving
vital image features is an essential requirement. Current existing denoising
methods have their own assumptions on the probability distribution in which the
contaminated noise is sampled for the method to attain its expected denoising
performance. In this paper, we utilize our recent Gramian-based filtering
scheme to remove noise sampled from five prominent probability distributions
from selected images. This method preserves image smoothness by adopting
patches partitioned from the image, rather than pixels, and retains vital image
features by performing denoising on the manifold underlying the patch space
rather than in the image domain. We validate its denoising performance, using
three benchmark computer vision test images applied to two state-of-the-art
denoising methods, namely BM3D and K-SVD.


title: High-dimensional Censored Regression via the Penalized Tobit Likelihood

abstract:
  The Tobit model has long been the standard method for regression with a
left-censored response in economics. In spite of its enduring popularity, the
Tobit model has not been extended for high-dimensional regression. To fill this
gap, we propose several penalized Tobit models for high-dimensional censored
regression. We use Olsen's (1978) convex reparameterization of the Tobit
negative log-likelihood as the basis for our models, leveraging the fact that
the negative log-likelihood satisfies the quadratic majorization condition to
develop a generalized coordinate descent algorithm for computing the solution
path. Theoretically, we analyze the Tobit lasso and Tobit with a folded concave
penalty, deriving a bound for the $\ell_2$ estimation loss for the former and
proving that a local linear approximation estimator for the latter possesses
the strong oracle property. Through an extensive simulation study, we find that
our penalized Tobit models provide more accurate predictions and parameter
estimates than their least-squares counterparts on high-dimensional data with a
censored response. We demonstrate the superior prediction and variable
selection performance of the penalized Tobit models on Mroz's 1975 women's
labor supply data.


title:
Efficient perturbative framework for coupling of radiative and guided
  modes in nearly periodic surfaces

abstract:
  We present a semi-analytical framework for computing the coupling of
radiative and guided waves in slowly varying (nearly uniform or nearly
periodic) surfaces, which is especially relevant to the exploitation of
nonlocal effects in large-area metasurfaces. Our framework bridges a gap in the
theory of slowly varying surfaces: aside from brute-force numerical
simulations, current approximate methods can model either guided or radiative
waves, but cannot easily model their coupling. We solve this problem by
combining two methods: the locally periodic approximation (LPA), which
approximates radiative scattering by composing a set of periodic scattering
problems, and spatial coupled-wave theory (CWT), which allows the perturbative
modeling of guided waves using an eigenmode expansion. We derive our framework
both for nearly uniform and nearly periodic surfaces, and we validate each case
against brute-force FDTD simulations, which show increasing agreement as the
surface varies more slowly.


title: Constructing Nitsche's method for variational problems

abstract:
  Nitsche's method is a well-established approach for weak enforcement of
boundary conditions for partial differential equations (PDEs). It has many
desirable properties, including the preservation of variational consistency and
the fact that it yields symmetric, positive-definite discrete linear systems
that are not overly ill-conditioned. In recent years, the method has gained in
popularity in a number of areas, including isogeometric analysis, immersed
methods, and contact mechanics. However, arriving at a formulation based on
Nitsche's method can be a mathematically arduous process, especially for
high-order PDEs. Fortunately, the derivation is conceptually straightforward in
the context of variational problems. To facilitate the process, we devised an
abstract framework for constructing Nitsche's method for these types of
problems in [J. Benzaken, J. A. Evans, S. McCormick, and R. Tamstorf, Nitsche's
method for linear Kirchhoff-Love shells: Formulation, error analysis, and
verification, Comput. Methods Appl. Mech. Eng., 374 (2021), p. 113544]. The
goal of this paper is to elucidate the process through a sequence of didactic
examples. First, we show the derivation of Nitsche's method for Poisson's
equation to gain an intuition for the various steps. Next, we present the
abstract framework and then revisit the derivation for Poisson's equation to
use the framework and add mathematical rigor. In the process, we extend our
derivation to cover the vector-valued setting. Armed with a basic recipe, we
then show how to handle a higher-order problem by considering the vector-valued
biharmonic equation and the linearized Kirchhoff-Love plate. In the end, the
hope is that the reader will be able to apply Nitsche's method to any problem
that arises from variational principles.


title: Galois module structure of some elementary $p$-abelian extensions

abstract:
  We determine the Galois module structure of the parameterizing space of
elementary $p$-abelian extensions of a field $K$ when $\text{Gal}(K/F)$ is any
finite $p$-group, under the assumption that the maximal pro-$p$ quotient of the
absolute Galois group of $F$ is a free, finitely generated pro-$p$ group, and
that $F$ contains a primitive $p$th root of unity if $\text{char}(F) \neq p$.


title:
Reinforcement Learning in Modern Biostatistics: Constructing Optimal
  Adaptive Interventions

abstract:
  Reinforcement learning (RL) is acquiring a key role in the space of adaptive
interventions (AIs), attracting a substantial interest within methodological
and theoretical literature and becoming increasingly popular within health
sciences. Despite potential benefits, its application in real life is still
limited due to several operational and statistical challenges--in addition to
ethical and cost issues among others--that remain open in part due to poor
communication and synergy between methodological and applied scientists. In
this work, we aim to bridge the different domains that contribute to and may
benefit from RL, under a unique framework that intersects the areas of RL,
causal inference, and AIs, among others. We provide the first unified
instructive survey on RL methods for building AIs, encompassing both dynamic
treatment regimes (DTRs) and just-in-time adaptive interventions in mobile
health (mHealth). We outline similarities and differences between the two
areas, and discuss their implications for using RL. We combine our relevant
methodological knowledge with motivating studies in both DTRs and mHealth to
illustrate the tremendous collaboration opportunities between statistical, RL,
and healthcare researchers in the space of AIs.


title:
Sustainable Verbal and Non-verbal Human-Robot Interaction Through Cloud
  Services

abstract:
  This article presents the design and the implementation of CAIR: a cloud
system for knowledge-based autonomous interaction devised for Social Robots and
other conversational agents. The system is particularly convenient for low-cost
robots and devices. Developers are provided with a sustainable solution to
manage verbal and non-verbal interaction through a network connection, with
about 3,000 topics of conversation ready for "chit-chatting" and a library of
pre-cooked plans that only needs to be grounded into the robot's physical
capabilities. The system is structured as a set of REST API endpoints so that
it can be easily expanded by adding new APIs to improve the capabilities of the
clients connected to the cloud. Another key feature of the system is that it
has been designed to make the development of its clients straightforward: in
this way, multiple devices can be easily endowed with the capability of
autonomously interacting with the user, understanding when to perform specific
actions, and exploiting all the information provided by cloud services. The
article outlines and discusses the results of the experiments performed to
assess the system's performance in terms of response time, paving the way for
its use both for research and market solutions. Links to repositories with
clients for ROS and popular robots such as Pepper and NAO are given.


title: An SDP Relaxation for the Sparse Integer Least Square Problem

abstract:
  In this paper, we study the sparse integer least square problem (SILS), which
is the NP-hard variant of the least square problem, where we only consider
sparse {0, +1, -1}-vectors. We propose an l1-based SDP relaxation to SILS, and
provide sufficient conditions for our SDP relaxation to solve SILS. The class
of data input which guarantee that SDP solves SILS is broad enough to cover
many cases in real-world applications, such as privacy preserving
identification, and multiuser detection. To show this, we specialize our
sufficient conditions to two special cases of SILS with relevant applications:
the feature extraction problem and the integer sparse recovery problem. We show
that our SDP relaxation can solve the feature extraction problem with
sub-Gaussian data, under some weak conditions on the second moment of the
covariance matrix. Next, we show that our SDP relaxation can solve the integer
sparse recovery problem under some conditions that can be satisfied both in
high and low coherence settings. We also show that in the high coherence
setting, our SDP relaxation performs better than other l1-based methods, such
as Lasso and Dantzig Selector.


title:
Progress in understanding short-range structure in nuclei: an
  experimental perspective

abstract:
  High-energy electron scattering is a clean and precise probe for measurements
of hadronic and nuclear structure, with a key role in understanding the role of
high-momentum nucleons (and quarks) in nuclei. Jefferson Lab has dramatically
expanded our understanding of the high-momentum nucleons generated by
short-range correlations, providing sufficient insight to model much of their
impact on nuclear structure in neutron stars, and in low- to medium-energy
scattering observables including neutrino oscillation measurements. These
short-range correlations also appear to be related to the modification of the
quark distributions in nuclei, and efforts to improve our understanding of the
internal structure of these short-distance and high-momentum configurations in
nuclei will provide important input on a wide range of high-energy observables.


title:
DEC-LOS-RRT: Decentralized Path Planning for Multi-robot Systems with
  Line-of-sight Constrained Communication

abstract:
  Decentralized planning for multi-agent systems, such as fleets of robots in a
search-and-rescue operation, is often constrained by limitations on how agents
can communicate with each other. One such limitation is the case when agents
can communicate with each other only when they are in line-of-sight (LOS).
Developing decentralized planning methods that guarantee safety is difficult in
this case, as agents that are occluded from each other might not be able to
communicate until it's too late to avoid a safety violation. In this paper, we
develop a decentralized planning method that explicitly avoids situations where
lack of visibility of other agents would lead to an unsafe situation. Building
on top of an existing Rapidly-exploring Random Tree (RRT)-based approach, our
method guarantees safety at each iteration. Simulation studies show the
effectiveness of our method and compare the degradation in performance with
respect to a clairvoyant decentralized planning algorithm where agents can
communicate despite not being in LOS of each other.


title:
Unveiling the Signature of Adsorbed Organic Solvents for Molecular
  Electronics through STM Approaches

abstract:
  After evaporation of the organic solvents, benzene, toluene, and cyclohexane
on gold substrates, Scanning Tunneling Microscope (STM) shows the presence of a
remaining adsorbed layer. The different solvent molecules were individually
observed at ambient conditions, and their electronic transport properties
characterized through the STM in the Break Junction approach. The combination
of both techniques reveals, on one hand, that solvents are not fully evaporated
over the gold electrode and, secondly, determines the role of the electronic
transport of the solvents in molecular electronics.


title:
Plant Species Recognition with Optimized 3D Polynomial Neural Networks
  and Variably Overlapping Time-Coherent Sliding Window

abstract:
  Recently, the EAGL-I system was developed to rapidly create massive labeled
datasets of plants intended to be commonly used by farmers and researchers to
create AI-driven solutions in agriculture. As a result, a publicly available
plant species recognition dataset composed of 40,000 images with different
sizes consisting of 8 plant species was created with the system in order to
demonstrate its capabilities. This paper proposes a novel method, called
Variably Overlapping Time-Coherent Sliding Window (VOTCSW), that transforms a
dataset composed of images with variable size to a 3D representation with fixed
size that is suitable for convolutional neural networks, and demonstrates that
this representation is more informative than resizing the images of the dataset
to a given size. We theoretically formalized the use cases of the method as
well as its inherent properties and we proved that it has an oversampling and a
regularization effect on the data. By combining the VOTCSW method with the 3D
extension of a recently proposed machine learning model called 1-Dimensional
Polynomial Neural Networks, we were able to create a model that achieved a
state-of-the-art accuracy of 99.9% on the dataset created by the EAGL-I system,
surpassing well-known architectures such as ResNet and Inception. In addition,
we created a heuristic algorithm that enables the degree reduction of any
pre-trained N-Dimensional Polynomial Neural Network and which compresses it
without altering its performance, thus making the model faster and lighter.
Furthermore, we established that the currently available dataset could not be
used for machine learning in its present form, due to a substantial class
imbalance between the training set and the test set. Hence, we created a
specific preprocessing and a model development framework that enabled us to
improve the accuracy from 49.23% to 99.9%.


title: Thermal hadron resonances in chiral and $U(1)_A$ restoration

abstract:
  We review recent work on thermal resonances and their connection with chiral
symmetry and $U(1)_A$ restoration within the QCD phase diagram. In particular,
the $f_0(500)$ and $K_0^* (700)$ states generated from $\pi\pi$ and $\pi K$
scattering within Unitarized Chiral Perturbation Theory (ChPT) at finite
temperature allow one to describe scalar susceptibilities, which combined with
Ward Identities yield interesting conclusions regarding the interplay between
chiral and $U(1)_A$ restoration, key to understand the nature of the transition


title: On the square peg problem

abstract:
  We show that if $\gamma$ is a Jordan curve in $\mathbb{R}^2$ which is close
to a $C^2$ Jordan curve $\beta$ in $\mathbb{R}^2$, then $\gamma$ contains an
inscribed square. In particular, if $\kappa > 0$ is the maximum unsigned
curvature of $\beta$ and there is a map $f$ from the image of $\gamma$ to the
image of $\beta$ with $||f(x) - x|| < \frac{1}{10 \kappa}$ and $f \circ \gamma$
having winding number $1$, then $\gamma$ has an inscribed square of positive
sidelength.


title: On a random model of forgetting

abstract:
  Georgiou, Katkov and Tsodyks considered the following random process. Let
$x_1,x_2,\ldots $ be an infinite sequence of independent, identically
distributed, uniform random points in $[0,1]$. Starting with $S=\{0\}$, the
elements $x_k$ join $S$ one by one, in order. When an entering element is
larger than the current minimum element of $S$, this minimum leaves $S$. Let
$S(1,n)$ denote the content of $S$ after the first $n$ elements $x_k$ join.
Simulations suggest that the size $|S(1,n)|$ of $S$ at time $n$ is typically
close to $n/e$. Here we first give a rigorous proof that this is indeed the
case, and that in fact the symmetric difference of $S(1,n)$ and the set
$\{x_k\ge 1-1/e: 1 \leq k \leq n \}$ is of size at most $\tilde{O}(\sqrt n)$
with high probability. Our main result is a more accurate description of the
process implying, in particular, that as $n$ tends to infinity $ n^{-1/2}\big(
|S(1,n)|-n/e \big) $ converges to a normal random variable with variance
$3e^{-2}-e^{-1}$.


title: Dynamical Effects of Colliding Outflows in Binary Systems

abstract:
  The outflow of an object traveling in a fluid can shape the fluid morphology
by forming a forward bow shock which accelerates the object via gravitational
feedback. This dynamical effect, namely "dynamical anti-friction", has been
studied in idealized infinite uniform media, which suffers from the convergence
problem due to the long-range nature of gravitation. In this work, we conduct
global 3D hydrodynamic simulations to study this effect in the scenario of a
binary system, where the collision of outflows from both stars creates a
suitable configuration. We demonstrate with simulations that a dense and slow
outflow can give rise to a positive torque on the binary and lead to the
expansion of the orbit. As an application, we show that binaries consisting of
an AGB star and an outflowing pulsar can experience $\sim 10~\%$ orbit
expansion during the AGB stage, in addition to the contribution from mass-loss.
We also prove that the gravitational force drops as $O(r^{-3})$ from the center
of mass in the binary scenarios, which guarantees a quick converge of the
overall effect.


title:
Chance-Constrained Optimization in Contact-Rich Systems for Robust
  Manipulation

abstract:
  This paper presents a chance-constrained formulation for robust trajectory
optimization during manipulation. In particular, we present a
chance-constrained optimization for Stochastic Discrete-time Linear
Complementarity Systems (SDLCS). To solve the optimization problem, we
formulate Mixed-Integer Quadratic Programming with Chance Constraints (MIQPCC).
In our formulation, we explicitly consider joint chance constraints for
complementarity as well as states to capture the stochastic evolution of
dynamics. We evaluate robustness of our optimized trajectories in simulation on
several systems. The proposed approach outperforms some recent approaches for
robust trajectory optimization for SDLCS.


title: How to Train Unstable Looped Tensor Network

abstract:
  A rising problem in the compression of Deep Neural Networks is how to reduce
the number of parameters in convolutional kernels and the complexity of these
layers by low-rank tensor approximation. Canonical polyadic tensor
decomposition (CPD) and Tucker tensor decomposition (TKD) are two solutions to
this problem and provide promising results. However, CPD often fails due to
degeneracy, making the networks unstable and hard to fine-tune. TKD does not
provide much compression if the core tensor is big. This motivates using a
hybrid model of CPD and TKD, a decomposition with multiple Tucker models with
small core tensor, known as block term decomposition (BTD). This paper proposes
a more compact model that further compresses the BTD by enforcing core tensors
in BTD identical. We establish a link between the BTD with shared parameters
and a looped chain tensor network (TC). Unfortunately, such strongly
constrained tensor networks (with loop) encounter severe numerical instability,
as proved by y (Landsberg, 2012) and (Handschuh, 2015a). We study perturbation
of chain tensor networks, provide interpretation of instability in TC,
demonstrate the problem. We propose novel methods to gain the stability of the
decomposition results, keep the network robust and attain better approximation.
Experimental results will confirm the superiority of the proposed methods in
compression of well-known CNNs, and TC decomposition under challenging
scenarios


title: The Fragmentation Kernel in Multinary/Multicomponent Fragmentation

abstract:
  The fragmentation equation is commonly expressed in terms of two functions,
the rate of fragmentation and the mean number of fragments. In the case of
binary fragmentation an alternative description is possible based on the
fragmentation kernel, a function from which the rate of fragmentation and the
mean distribution of fragments can be obtained. We extend the fragmentation
kernel to multinary/multicomponent fragmentation and derive expressions for
certain special cases of random and non random fragmentation.


title:
Measurement of the $^1S_0$ neutron-neutron effective range in
  neutron-deuteron breakup

abstract:
  We report the most precise determination of the neutron-neutron effective
range parameter ($r_{nn}$) from neutron-neutron quasifree scattering in
neutron-deuteron breakup. The experiment setup utilized a collimated beam of
15.5~MeV neutrons and an array of eight neutron detectors positioned at angles
sensitive to several quasifree scattering kinematic configurations. The two
neutrons emitted from the breakup reaction were detected in coincidence and
time-of-flight techniques were used to determine their energies. The
beam-target luminosity was measured in-situ with the yields from
neutron-deuteron elastic scattering. The value of $r_{nn}$ was determined by
comparing our cross-section data to rigorous Faddeev-type calculations using
the CD Bonn nucleon-nucleon potential. We obtained a value of $r_{nn} = 2.86
\pm 0.01(stat) \pm 0.10 (sys)$ fm. This result was confirmed using a semilocal
momentum-space regularized N$^4$LO$^+$ chiral interaction potential.


title: Exceptional sets for spinor regular ternary quadratic forms

abstract:
  The goal of this note is to provide an analysis of the positive integers that
are represented everywhere locally, but not globally, by each of the 29 spinor
regular positive definite integral ternary quadratic forms that are not
regular.


title:
Low-cost prediction of molecular and transition state partition
  functions via machine learning

abstract:
  We have generated an open-source dataset of over 30000 organic chemistry gas
phase partition functions. With this data, a machine learning deep neural
network estimator was trained to predict partition functions of unknown organic
chemistry gas phase transition states. This estimator only relies on reactant
and product geometries and partition functions. A second machine learning deep
neural network was trained to predict partition functions of chemical species
from their geometry. Our models accurately predict the logarithm of test set
partition functions with a maximum mean absolute error of 2.7%. Thus, this
approach provides a means to reduce the cost of computing reaction rate
constants ab initio. The models were also used to compute transition state
theory reaction rate constants prefactors and the results were in quantitative
agreement with the corresponding ab initio calculations with an accuracy of
98.3% on the log scale.


title: Scaling R-GCN Training with Graph Summarization

abstract:
  Training of Relational Graph Convolutional Networks (R-GCN) is a memory
intense task. The amount of gradient information that needs to be stored during
training for real-world graphs is often too large for the amount of memory
available on most GPUs. In this work, we experiment with the use of graph
summarization techniques to compress the graph and hence reduce the amount of
memory needed. After training the R-GCN on the graph summary, we transfer the
weights back to the original graph and attempt to perform inference on it. We
obtain reasonable results on the AIFB, MUTAG and AM datasets. Our experiments
show that training on the graph summary can yield a comparable or higher
accuracy to training on the original graphs.Furthermore, if we take the time to
compute the summary out of the equation, we observe that the smaller graph
representations obtained with graph summarization methods reduces the
computational overhead. However, further experiments are needed to evaluate
additional graph summary models and whether our findings also holds true for
very large graphs.


title: Constraining the shape of Milky Way satellites with distance gradients

abstract:
  We combine the Dark Energy Camera Legacy Survey (DECaLS) DR8 photometry with
Gaia photometry to study the 3-D structure of Bootes I, Draco, Ursa Minor,
Sextans and Sculptor dwarf galaxies using blue horizontal branch (BHB) stars as
distance indicators. We construct a new colour-absolute magnitude of BHB stars
that we use to measure the distance gradients within the body of the dwarf
galaxies. We detect a statistically significant non-zero gradient only in
Sextans and Sculptor. Through modeling of the gradient and 2-D density of the
systems by triaxial Plummer models we find that the distance gradients in both
dwarf galaxies are inconsistent with prolate shape, but compatible with oblate
or triaxial shapes. In order to explain the observed gradients, oblate models
of Sextans and Sculptor need to have a significant intrinsic ellipticity larger
than $0.47$ for Sextans and $0.46$ for Sculptor. The flattened oblate shape may
imply a significant anisotropy in velocity distribution in order to be
consistent with the lack of significant velocity gradients in these systems.


title:
Bivariational Principle for an Antisymmetrized Product of Nonorthogonal
  Geminals Appropriate for Strong Electron Correlation

abstract:
  We develop a bivariational principle for an antisymmetric product of
nonorthogonal geminals. Special cases reduce to the antisymmetric product of
strongly-orthogonal geminals (APSG), the generalized valence bond-perfect
pairing (GVB-PP), and the antisymmetrized geminal power (AGP) wavefunctions.
The presented method employs wavefunctions of the same type as
Richardson-Gaudin (RG) states, but which are not eigenvectors of a model
Hamiltonian which would allow for more freedom in the mean-field. The general
idea is to work with the same state in a primal picture in terms of pairs, and
in a dual picture in terms of pair-holes. This leads to an asymmetric energy
expression which may be optimized bivariationally, and is strictly variational
when the two representations are consistent. The general approach may be useful
in other contexts, such as for computationally feasible variational
coupled-cluster methods.


title:
Using Donald Trump's COVID-19 Vaccine Endorsement to Give Public Health
  a Shot in the Arm: A Large-Scale Ad Experiment

abstract:
  We report a large-scale randomized controlled trial designed to assess
whether the partisan cue of a pro-vaccine message from Donald Trump would
induce Americans to get COVID-19 vaccines. Our study involved presenting a
27-second advertisement to millions of U.S. YouTube users in October 2021.
Results indicate that the campaign increased the number of vaccines in the
average treated county by 103. Spread across 1,014 treated counties, the total
effect of the campaign was an estimated increase of 104,036 vaccines. The
campaign was cost-effective: with an overall budget of about \$100,000, the
cost to obtain an additional vaccine was about \$1 or less.


title:
Percent Grade Scale Amplifies Racial/Ethnic Inequities in Introductory
  Physics

abstract:
  In previous work we analyzed databases for 95 classes to show that the
percent grade scale was correlated with a much higher student fail rate than
the 4.0 grade scale. This paper builds on this work and investigates equity
gaps occurring under both scales. By employing a "Course Deficit Model" we
attribute the responsibility for closing the gaps to those who are responsible
for the policies that guide the course. When comparing course grades in classes
graded using the percent scale with those in courses graded using the 4.0
scale, we find that students identifying as belonging to racial or ethnic
minorities underrepresented in physics suffer a grade penalty under both grade
scales but suffer an extra penalty under percent scale graded courses. We then
use the fraction of A grades each student earns on individual exam items as a
proxy for the instructor's perception of each student's understanding of the
course material to control for student understanding and find that the extra
grade penalty students from groups underrepresented in physics students suffer
under percent scale grading is independent of the student's understanding of
physics. When we control for more student level variables to determine the
source of the grade scale dependent penalty, we find that it is primarily the
low F grades (partial credit scores) on exam problems that are the source of
these inequities. We present an argument that switching from percent scale
grading to a 4.0 grade scale (or similar grades scale) could reduce equity gaps
by 20-25\% without making any other course changes or controlling for any
incoming differences between students.


title: Approximating projections by quantum operations

abstract:
  Using techniques from semidefinite programming, we study the problem of
finding a closest quantum channel to the projection onto a matricial subsystem.
We derive two invariants of matricial subsystems which are related to the
quantum Lov\'asz theta function of Duan, Severini, and Winter.


title: Target Network and Truncation Overcome The Deadly Triad in $Q$-Learning

abstract:
  $Q$-learning with function approximation is one of the most empirically
successful while theoretically mysterious reinforcement learning (RL)
algorithms, and was identified in Sutton (1999) as one of the most important
theoretical open problems in the RL community. Even in the basic linear
function approximation setting, there are well-known divergent examples. In
this work, we show that \textit{target network} and \textit{truncation}
together are enough to provably stabilize $Q$-learning with linear function
approximation, and we establish the finite-sample guarantees. The result
implies an $O(\epsilon^{-2})$ sample complexity up to a function approximation
error. Moreover, our results do not require strong assumptions or modifying the
problem parameters as in existing literature.


title: The integral cohomology rings of Peterson varieties in type A

abstract:
  In this paper, we study the ring structure of the integral cohomology of the
Peterson variety of type $\text{A}_{n-1}$. We give two kinds of descriptions:
(1) we show that it is isomorphic to the $\mathfrak{S}_n$-invariant subring of
the integral cohomology ring of the permutohedral variety, (2) we determine the
ring structure in terms of ring generators and their relations.


title:
Online Stabilization of Unknown Networked Systems with Communication
  Constraints

abstract:
  We investigate the problem of stabilizing an unknown networked linear system
under communication constraints and adversarial disturbances. We propose the
first provably stabilizing algorithm for the problem. The algorithm uses a
distributed version of nested convex body chasing to maintain a consistent
estimate of the network dynamics and applies system level synthesis to
determine a distributed controller based on this estimated model. Our approach
avoids the need for system identification and accommodates a broad class of
communication delay while being fully distributed and scaling favorably with
the number of subsystems in the network.


title: This Week's Finds in Mathematical Physics (51-100)

abstract:
  These are the second 50 issues of This Week's Finds of Mathematical Physics,
from April 23, 1995 to March 23, 1997. These issues discuss quantum gravity,
topological quantum field theory and other topics in mathematics and physics.
They also include two expository series: one on ADE classifications and one on
categories and higher categories. They were typeset in 2020 by Tim Hosgood. If
you see typos or other problems please report them. (I already know the cover
page looks weird).


title:
Extracting linguistic speech patterns of Japanese fictional characters
  using subword units

abstract:
  This study extracted and analyzed the linguistic speech patterns that
characterize Japanese anime or game characters. Conventional morphological
analyzers, such as MeCab, segment words with high performance, but they are
unable to segment broken expressions or utterance endings that are not listed
in the dictionary, which often appears in lines of anime or game characters. To
overcome this challenge, we propose segmenting lines of Japanese anime or game
characters using subword units that were proposed mainly for deep learning, and
extracting frequently occurring strings to obtain expressions that characterize
their utterances. We analyzed the subword units weighted by TF/IDF according to
gender, age, and each anime character and show that they are linguistic speech
patterns that are specific for each feature. Additionally, a classification
experiment shows that the model with subword units outperformed that with the
conventional method.


title:
Bistability in orbital trajectories of a chiral self-propelled particle
  interacting with an external field

abstract:
  In this work, the dynamics of a self-propelled stochastic particle under the
influence of an axisymmetric light field was experimentally studied. The
particle under consideration has the main characteristic of carrying a light
sensor in an eccentric location. For the chosen experimental conditions, the
emerging trajectories were orbital, and, more interestingly, they presented two
preferential radial distances. A mathematical model incorporating the key
experimental components was introduced. By means of numerical simulations and
theoretical analysis, it was found that, in addition to the orbiting behavior,
the sensor location could produce trapped or diffusive behaviors. Furthermore,
the study revealed that stochastic perturbation and the eccentric location of
the sensor are responsible for inducing bistability in the orbital
trajectories, in agreement with the experimental observations.


title:
Important Object Identification with Semi-Supervised Learning for
  Autonomous Driving

abstract:
  Accurate identification of important objects in the scene is a prerequisite
for safe and high-quality decision making and motion planning of intelligent
agents (e.g., autonomous vehicles) that navigate in complex and dynamic
environments. Most existing approaches attempt to employ attention mechanisms
to learn importance weights associated with each object indirectly via various
tasks (e.g., trajectory prediction), which do not enforce direct supervision on
the importance estimation. In contrast, we tackle this task in an explicit way
and formulate it as a binary classification ("important" or "unimportant")
problem. We propose a novel approach for important object identification in
egocentric driving scenarios with relational reasoning on the objects in the
scene. Besides, since human annotations are limited and expensive to obtain, we
present a semi-supervised learning pipeline to enable the model to learn from
unlimited unlabeled data. Moreover, we propose to leverage the auxiliary tasks
of ego vehicle behavior prediction to further improve the accuracy of
importance estimation. The proposed approach is evaluated on a public
egocentric driving dataset (H3D) collected in complex traffic scenarios. A
detailed ablative study is conducted to demonstrate the effectiveness of each
model component and the training strategy. Our approach also outperforms
rule-based baselines by a large margin.


title:
Training privacy-preserving video analytics pipelines by suppressing
  features that reveal information about private attributes

abstract:
  Deep neural networks are increasingly deployed for scene analytics, including
to evaluate the attention and reaction of people exposed to out-of-home
advertisements. However, the features extracted by a deep neural network that
was trained to predict a specific, consensual attribute (e.g. emotion) may also
encode and thus reveal information about private, protected attributes (e.g.
age or gender). In this work, we focus on such leakage of private information
at inference time. We consider an adversary with access to the features
extracted by the layers of a deployed neural network and use these features to
predict private attributes. To prevent the success of such an attack, we modify
the training of the network using a confusion loss that encourages the
extraction of features that make it difficult for the adversary to accurately
predict private attributes. We validate this training approach on image-based
tasks using a publicly available dataset. Results show that, compared to the
original network, the proposed PrivateNet can reduce the leakage of private
information of a state-of-the-art emotion recognition classifier by 2.88% for
gender and by 13.06% for age group, with a minimal effect on task accuracy.


title: Boosting Crowd Counting via Multifaceted Attention

abstract:
  This paper focuses on the challenging crowd counting task. As large-scale
variations often exist within crowd images, neither fixed-size convolution
kernel of CNN nor fixed-size attention of recent vision transformers can well
handle this kind of variation. To address this problem, we propose a
Multifaceted Attention Network (MAN) to improve transformer models in local
spatial relation encoding. MAN incorporates global attention from a vanilla
transformer, learnable local attention, and instance attention into a counting
model. Firstly, the local Learnable Region Attention (LRA) is proposed to
assign attention exclusively for each feature location dynamically. Secondly,
we design the Local Attention Regularization to supervise the training of LRA
by minimizing the deviation among the attention for different feature
locations. Finally, we provide an Instance Attention mechanism to focus on the
most important instances dynamically during training. Extensive experiments on
four challenging crowd counting datasets namely ShanghaiTech, UCF-QNRF, JHU++,
and NWPU have validated the proposed method. Codes:
https://github.com/LoraLinH/Boosting-Crowd-Counting-via-Multifaceted-Attention.


title: A Jordan-Chevalley decomposition beyond algebraic groups

abstract:
  A decomposition of definable groups G in o-minimal structures inspired by the
Jordan-Chevalley decomposition of linear algebraic groups is presented. As a
consequence, we give a description of the commutator subgroup and show that
when G has definable Levi subgroups, then [G, G] is definable. Along the way,
we study disconnected groups, definable p-groups and 0-groups, abstractly
compact subgroups, and give a few other structure decompositions.


title: Safe Reinforcement Learning for Legged Locomotion

abstract:
  Designing control policies for legged locomotion is complex due to the
under-actuated and non-continuous robot dynamics. Model-free reinforcement
learning provides promising tools to tackle this challenge. However, a major
bottleneck of applying model-free reinforcement learning in real world is
safety. In this paper, we propose a safe reinforcement learning framework that
switches between a safe recovery policy that prevents the robot from entering
unsafe states, and a learner policy that is optimized to complete the task. The
safe recovery policy takes over the control when the learner policy violates
safety constraints, and hands over the control back when there are no future
safety violations. We design the safe recovery policy so that it ensures safety
of legged locomotion while minimally intervening in the learning process.
Furthermore, we theoretically analyze the proposed framework and provide an
upper bound on the task performance. We verify the proposed framework in four
locomotion tasks on a simulated and real quadrupedal robot: efficient gait,
catwalk, two-leg balance, and pacing. On average, our method achieves 48.6%
fewer falls and comparable or better rewards than the baseline methods in
simulation. When deployed it on real-world quadruped robot, our training
pipeline enables 34% improvement in energy efficiency for the efficient gait,
40.9% narrower of the feet placement in the catwalk, and two times more jumping
duration in the two-leg balance. Our method achieves less than five falls over
the duration of 115 minutes of hardware time.


title:
Theoretical results and modeling under the discrete Birnbaum-Saunders
  distribution

abstract:
  In this paper, we discuss some theoretical results and properties of a
discrete version of the Birnbaum-Saunders distribution. We present a proof of
the unimodality of this model. Moreover, results on moments, quantile function,
reliability and order statistics are also presented. In addition, we propose a
regression model based on the discrete Birnbaum-Saunders distribution. The
model parameters are estimated by the maximum likelihood method and a Monte
Carlo study is performed to evaluate the performance of the estimators.
Finally, we illustrate the proposed methodology with the use of real data sets.


title: Integral domains and the IDF property

abstract:
  An integral domain $D$ is called an irreducible-divisor-finite domain
(IDF-domain) if every nonzero element of $D$ has finitely many irreducible
divisors up to associates. The study of IDF-domains dates back to the
seventies. In this paper, we investigate various aspects of the IDF property.
In 2009, P. Malcolmson and F. Okoh proved that the IDF property does not ascend
from integral domains to their corresponding polynomial rings, answering a
question posed by D. D. Anderson, D. F. Anderson, and the second author two
decades before. Here we prove that the IDF property ascends in the class of
PSP-domains, generalizing the known result (also by Malcolmson and Okoh) that
the IDF property ascends in the class of GCD-domains. We put special emphasis
on IDF-domains where every nonunit is divisible by an irreducible, which we
call TIDF-domains, and we also consider PIDF-domains, which form a special
class of IDF-domains introduced by Malcolmson and Okoh in 2006. We investigate
both the TIDF and the PIDF properties under taking polynomial rings and
localizations. We also delve into their behavior under monoid domain and $D+M$
constructions.


title: Why Constant-Composition Codes Reduce Nonlinear Interference Noise

abstract:
  A time-domain perturbation model of the nonlinear Schrodinger equation is
used to study wavelength-division multiplexed communication over a single
polarization. The model explains (a) why constant-composition codes offer an
improvement in signal to noise ratio compared with independent and uniform
selection of constellation points and (b) why similar gains are obtained using
carrier recovery algorithms even without using constant-composition codes.


title: Quasi-normal modes of dyonic black holes and magneto-hydrodynamics

abstract:
  We revisit the magneto-hydrodynamics in (2+1) dimensions and confirm that it
is consistent with the quasi-normal modes of the (3+1) dimensional dyonic black
holes in the most general set-up with finite density, magnetic field and wave
vector. We investigate all possible modes (sound, shear, diffusion, cyclotron
etc.) and their interplay. For the magneto-hydrodynamics we perform a complete
and detailed analysis correcting some prefactors in the literature, which is
important for the comparison with quasi-normal modes. For the quasi-normal mode
computations in holography we identify the independent fluctuation variables of
the dyonic black holes, which is nontrivial at finite density and magnetic
field. As an application of the quasi-normal modes of the dyonic black holes we
investigate a transport property, the diffusion constant. We find that the
diffusion constant at finite density and magnetic field saturates the lower
bound at low temperature. We show that this bound can be understood from the
pole-skipping point.


title:
SwarmUS: An open hardware and software on-board platform for swarm
  robotics development

abstract:
  Real life implementations of distributed swarm robotics are rare. The
standardization of a general purpose swarm robotics platform could greatly
accelerate swarm robotics towards real life implementations. The SwarmUS
platform is an open-source hardware and software on-board embedded system
designed to be added onto existing robots while providing them with swarm
features, thus proposing a new take on the platform standardization problem.
These features include a distributed relative localization system based on
Ultra-Wideband, a local communication system based on Wi-Fi and a distributed
coordination system based on the Buzz programming language between robots
connected within a SwarmUS platform. Additionally, a human-swarm interaction
mobile application and an emulation of the platform in the Robot Operating
System (ROS) is presented. Finally, an implementation of the system was
realized and tested on two types of robots : a TurtleBot3 Burger and two
Pioneer 2DX.


title: A Hele-Shaw Limit With A Variable Upper Bound and Drift

abstract:
  We investigate a generalized Hele-Shaw equation with a source and drift terms
where the density is constrained by an upper-bound density constraint that
varies in space and time. By using a generalized porous medium equation
approximation, we are able to construct a weak solution to the generalized
Hele-Shaw equations under mild assumptions. Then we establish uniqueness of
weak solutions to the generalized Hele-Shaw equations. Our next main result is
a pointwise characterization of the density variable in the generalized
Hele-Shaw equations when the system is in the congestion case. To obtain such a
characterization for the congestion case, we derive a new uniform lower bounds
on the time derivative pressure of the generalized porous medium equation via a
refined Aronson-Benilan estimate that implies monotonicity on the density and
pressure.


title:
Acceleration of Federated Learning with Alleviated Forgetting in Local
  Training

abstract:
  Federated learning (FL) enables distributed optimization of machine learning
models while protecting privacy by independently training local models on each
client and then aggregating parameters on a central server, thereby producing
an effective global model. Although a variety of FL algorithms have been
proposed, their training efficiency remains low when the data are not
independently and identically distributed (non-i.i.d.) across different
clients. We observe that the slow convergence rates of the existing methods are
(at least partially) caused by the catastrophic forgetting issue during the
local training stage on each individual client, which leads to a large increase
in the loss function concerning the previous training data at the other
clients. Here, we propose FedReg, an algorithm to accelerate FL with alleviated
knowledge forgetting in the local training stage by regularizing locally
trained parameters with the loss on generated pseudo data, which encode the
knowledge of previous training data learned by the global model. Our
comprehensive experiments demonstrate that FedReg not only significantly
improves the convergence rate of FL, especially when the neural network
architecture is deep and the clients' data are extremely non-i.i.d., but is
also able to protect privacy better in classification problems and more robust
against gradient inversion attacks. The code is available at:
https://github.com/Zoesgithub/FedReg.


title:
Liouville property and existence of entire solutions of Hessian
  equations

abstract:
  In this paper, we establish the existence and uniqueness theorem for entire
solutions of Hessian equations with prescribed asymptotic behavior at infinity.
This extends the previous results on Monge-Amp\`{e}re equations. Our approach
also makes the prescribed asymptotic order optimal within the range preset in
exterior Dirichlet problems. In addition, we show a Liouville type result for
$k$-convex solutions. This partly removes the $(k+1)$- or $n$-convexity
restriction imposed in existing work.


title: Rational solutions of Painlev\'{e}-II equation as Gram determinant

abstract:
  Under the Flaschka-Newell Lax pair, the Darboux transformation for the
Painlev\'{e}-II equation is constructed by the limiting technique. With the aid
of the Darboux transformation, the rational solutions are represented by the
Gram determinant, and then we give the large $y$ asymptotics of the determinant
and the rational solutions. Finally, the solution of the corresponding
Riemann-Hilbert problem is obtained from the Darboux matrices.


title:
Cluster-based Contrastive Disentangling for Generalized Zero-Shot
  Learning

abstract:
  Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen
classes by training only the seen classes, in which the instances of unseen
classes tend to be biased towards the seen class. In this paper, we propose a
Cluster-based Contrastive Disentangling (CCD) method to improve GZSL by
alleviating the semantic gap and domain shift problems. Specifically, we first
cluster the batch data to form several sets containing similar classes. Then,
we disentangle the visual features into semantic-unspecific and
semantic-matched variables, and further disentangle the semantic-matched
variables into class-shared and class-unique variables according to the
clustering results. The disentangled learning module with random swapping and
semantic-visual alignment bridges the semantic gap. Moreover, we introduce
contrastive learning on semantic-matched and class-unique variables to learn
high intra-set and intra-class similarity, as well as inter-set and inter-class
discriminability. Then, the generated visual features conform to the underlying
characteristics of general images and have strong discriminative information,
which alleviates the domain shift problem well. We evaluate our proposed method
on four datasets and achieve state-of-the-art results in both conventional and
generalized settings.