title:
Resonance, symmetry, and bifurcation of periodic orbits in perturbed
  Rayleigh-B\'enard convection

abstract:
  This paper investigates the global structures of periodic orbits that appear
in Rayleigh-B\'enard convection, which is modeled by a two-dimensional
perturbed Hamiltonian model, by focusing upon resonance, symmetry and
bifurcation of the periodic orbits. First, we show the global structures of
periodic orbits in the extended phase space by numerically detecting the
associated periodic points on the Poincar\'e section. Then, we illustrate how
resonant periodic orbits appear and specifically clarify that there exist some
symmetric properties of such resonant periodic orbits which are projected on
the phase space; namely, the period $m$ and the winding number $n$ become odd
when an $m$-periodic orbit is symmetric with respect to the horizontal and
vertical center lines of a cell. Furthermore, the global structures of
bifurcations of periodic orbits are depicted when the amplitude $\varepsilon$
of the perturbation is varied, since in experiments the amplitude of the
oscillation of the convection gradually increases when the Rayleigh number is
raised.


title: Learning Where to Learn in Cross-View Self-Supervised Learning

abstract:
  Self-supervised learning (SSL) has made enormous progress and largely
narrowed the gap with the supervised ones, where the representation learning is
mainly guided by a projection into an embedding space. During the projection,
current methods simply adopt uniform aggregation of pixels for embedding;
however, this risks involving object-irrelevant nuisances and spatial
misalignment for different augmentations. In this paper, we present a new
approach, Learning Where to Learn (LEWEL), to adaptively aggregate spatial
information of features, so that the projected embeddings could be exactly
aligned and thus guide the feature learning better. Concretely, we reinterpret
the projection head in SSL as a per-pixel projection and predict a set of
spatial alignment maps from the original features by this weight-sharing
projection head. A spectrum of aligned embeddings is thus obtained by
aggregating the features with spatial weighting according to these alignment
maps. As a result of this adaptive alignment, we observe substantial
improvements on both image-level prediction and dense prediction at the same
time: LEWEL improves MoCov2 by 1.6%/1.3%/0.5%/0.4% points, improves BYOL by
1.3%/1.3%/0.7%/0.6% points, on ImageNet linear/semi-supervised classification,
Pascal VOC semantic segmentation, and object detection, respectively.


title:
Properties and Performance of the ABCDe Random Graph Model with
  Community Structure

abstract:
  In this paper, we investigate properties and performance of synthetic random
graph models with a built-in community structure. Such models are important for
evaluating and tuning community detection algorithms that are unsupervised by
nature. We propose a new implementation of the ABCD graph generator, ABCDe,
that uses multiple-threading. We discuss the implementation details of the
algorithm as well as compare it with both the previously available sequential
version of the ABCD model and with the parallel implementation of the standard
and extensively used LFR generator. We show that ABCDe is more than ten times
faster and scales better than the parallel implementation of LFR provided in
NetworKit. Moreover, the algorithm is not only faster but random graphs
generated by ABCD have similar properties to the ones generated by the original
LFR algorithm, while the parallelized NetworKit implementation of LFR produces
graphs that have noticeably different characteristics.


title: Turning rotating D-branes and BHs inside out their photon-halo

abstract:
  We extend our investigation on Couch-Torrence conformal inversions of BHs and
D-branes in various directions. We analyse asymptotically flat rotating charged
BHs in $D=4$, in particular extremal rotating BHs in STU supergavity, and find
invariance for special choices of the charges. Due to the dependence of the
critical radii on the impact parameter(s), the relation between the scattering
angle for geodesics outside the photon-halo and the in-spiralling angle for
geodesics inside the photon-halo is modified by the inclusion of a boundary
term. We also consider rotating BHs in $D=5$ and rotating D3-branes and find
invariance under generalised Couch-Torrence inversions for special choices of
the angular momenta. Alas we don't find any similar symmetry for smooth
horizonless geometries. Moreover, relying on the surprising connection between
classical BH perturbation theory and quantum Seiberg-Witten curves for ${\cal
N}=2$ SYM theories, we study scalar wave equations in these backgrounds and
identify the near super-radiant modes produced in near-extremal BH mergers.
Finally, we study scalar fluctuations around Kerr-Newman BHs in AdS$_4$ and
find stringent conditions for generalised Couch-Torrence symmetry that are
relaxed in the extremal case or by allowing a rescaling of the wave function.


title: Optimizing Elimination Templates by Greedy Parameter Search

abstract:
  We propose a new method for constructing elimination templates for efficient
polynomial system solving of minimal problems in structure from motion, image
matching, and camera tracking. We first construct a particular affine
parameterization of the elimination templates for systems with a finite number
of distinct solutions. Then, we use a heuristic greedy optimization strategy
over the space of parameters to get a template with a small size. We test our
method on 34 minimal problems in computer vision. For all of them, we found the
templates either of the same or smaller size compared to the state-of-the-art.
For some difficult examples, our templates are, e.g., 2.1, 2.5, 3.8, 6.6 times
smaller. For the problem of refractive absolute pose estimation with unknown
focal length, we have found a template that is 20 times smaller. Our
experiments on synthetic data also show that the new solvers are fast and
numerically accurate. We also present a fast and numerically accurate solver
for the problem of relative pose estimation with unknown common focal length
and radial distortion.


title:
A semiclassical treatment of spinor topological effects in driven,
  inhomogeneous insulators under external electromagnetic fields

abstract:
  Introducing internal degrees of freedom in the description of topological
insulators has led to a myriad of theoretical and experimental advances. Of
particular interest are the effects of periodic perturbations, either in time
or space, as they considerably enrich the variety of electronic responses, with
examples such as Thouless's charge pump and its higher dimensional cousins, or,
higher-order topological insulators. Here, we develop a semiclassical approach
to transport and accumulation of general spinor degrees of freedom, such as
physical spin, valley, or atomic orbits, in adiabatically driven, weakly
inhomogeneous insulators of dimensions one, two and three under external
electromagnetic fields. Specifically, we focus on physical spins and derive the
spin current and density up to third order in the spatio-temporal modulations
of the system. We, then, relate these contributions to geometrical and
topological objects -- the spin-Chern fluxes and numbers -- defined over the
higher-dimensional phase-space of the system, i.e., its combined
momentum-position-time coordinates. Furthermore, we provide a connection
between our semiclassical analysis and the modern theory of multipole moments
by introducing spin analogues of the electric dipole, quadrupole and octapole
moments. The results are showcased in concrete tight-binding models where the
induced responses are calculated analytically.

categories: cond-mat.mes-hall cond-mat.mtrl-sci math-ph math.MP


title:
Anisotropic Kelvin transform and the Sobolev anisotropic critical
  equation

abstract:
  We introduce and study a Kelvin-type transform in the anisotropic setting.
Then we exploit it to study symmetry properties of solution to the anisotropic
Sobolev critical equation in the entire space.


title:
Why Do Banks Find Business Process Compliance So Challenging? An
  Australian Case Study

abstract:
  Banks play an intrinsic role in any modern economy, recycling capital from
savers to borrowers. They are heavily regulated and there have been a
significant number of well publicized compliance failings in recent years. This
is despite Business Process Compliance (BPC) being both a well researched
domain in academia and one where significant progress has been made. This study
seeks to determine why Australian banks find BPC so challenging. We interviewed
22 senior managers from a range of functions within the four major Australian
banks to identify the key challenges. Not every process in every bank is facing
the same issues, but in processes where a bank is particularly challenged to
meet its compliance requirements, the same themes emerge. The compliance
requirement load they bear is excessive, dynamic and complex. Fulfilling these
requirements relies on impenetrable spaghetti processes, and the case for
sustainable change remains elusive, locking banks into a fail-fix cycle that
increases the underlying complexity. This paper proposes a conceptual framework
that identifies and aggregates the challenges, and a circuit-breaker approach
as an "off ramp" to the fail-fix cycle.


title:
Non-Parametric Stochastic Policy Gradient with Strategic Retreat for
  Non-Stationary Environment

abstract:
  In modern robotics, effectively computing optimal control policies under
dynamically varying environments poses substantial challenges to the
off-the-shelf parametric policy gradient methods, such as the Deep
Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic policy
gradient (TD3). In this paper, we propose a systematic methodology to
dynamically learn a sequence of optimal control policies non-parametrically,
while autonomously adapting with the constantly changing environment dynamics.
Specifically, our non-parametric kernel-based methodology embeds a policy
distribution as the features in a non-decreasing Euclidean space, therefore
allowing its search space to be defined as a very high (possible infinite)
dimensional RKHS (Reproducing Kernel Hilbert Space). Moreover, by leveraging
the similarity metric computed in RKHS, we augmented our non-parametric
learning with the technique of AdaptiveH- adaptively selecting a time-frame
window of finishing the optimal part of whole action-sequence sampled on some
preceding observed state. To validate our proposed approach, we conducted
extensive experiments with multiple classic benchmarks and one simulated
robotics benchmark equipped with dynamically changing environments. Overall,
our methodology has outperformed the well-established DDPG and TD3 methodology
by a sizeable margin in terms of learning performance.


title:
Higher-order correlation based real-time beamforming in photoacoustic
  imaging

abstract:
  Linear-array based photoacoustic images are reconstructed using the
conventional delay-and-sum (DAS) beamforming method. Although the DAS
beamformer is well suited for PA image formation, reconstructed images are
often afflicted by noises, sidelobes, and other intense artifacts due to
inaccurate assumptions of PA signal correlation. The work aims to develop an
inversion method that reduces the occurrence of sidelobes and artifacts and
improves image quality performance. We present a novel beamformer based on
higher-order signal correlation, where more number of delayed PA signals are
combined and summed up than the conventional delay-multiply-and-sum (DMAS). The
proposed technique provides efficient improvements in resolution, contrast, and
SNR compared to the traditional beamformers. Computational complexity in this
method is shrunk to the same order of DAS $O(N)$. Therefore, this beamformer
can be implemented in real-time PA image reconstruction. A GPU based study was
performed on computation time. Proposed method almost executes in the same time
frame of DAS and real-time DMAS. A validation study of the algorithm was
accomplished both numerically and experimentally. Higher-order DMAS beamformers
demonstrate superior reconstruction in all cases. The quantitative evaluation
of the ex-vivo phantom shows that the proposed method leads to 51% and 6%
improvement in FWHM, 81% and 39% improvement in SNR compared to DAS and DMAS,
respectively. Conclusively, the proposed algorithm is very much potential and
promising in real-time photoacoustic imaging and its applications.


title:
Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable
  Devices

abstract:
  Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices
using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts
(MAs) caused by movements of the subject's arm affect the performance of
PPG-based HR tracking. This is typically addressed coupling the PPG signal with
acceleration measurements from an inertial sensor. Unfortunately, most standard
approaches of this kind rely on hand-tuned parameters, which impair their
generalization capabilities and their applicability to real data in the field.
In contrast, methods based on deep learning, despite their better
generalization, are considered to be too complex to deploy on wearable devices.
  In this work, we tackle these limitations, proposing a design space
exploration methodology to automatically generate a rich family of deep
Temporal Convolutional Networks (TCNs) for HR monitoring, all derived from a
single "seed" model. Our flow involves a cascade of two Neural Architecture
Search (NAS) tools and a hardware-friendly quantizer, whose combination yields
both highly accurate and extremely lightweight models. When tested on the
PPG-Dalia dataset, our most accurate model sets a new state-of-the-art in Mean
Absolute Error. Furthermore, we deploy our TCNs on an embedded platform
featuring a STM32WB55 microcontroller, demonstrating their suitability for
real-time execution. Our most accurate quantized network achieves 4.41 Beats
Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of
47.65 mJ and a memory footprint of 412 kB. At the same time, the smallest
network that obtains a MAE < 8 BPM, among those generated by our flow, has a
memory footprint of 1.9 kB and consumes just 1.79 mJ per inference.


title:
Heterogeneous Random Laser with Switching Activity Visualized by Replica
  Symmetry Breaking Maps

abstract:
  In the past decade, complex networks of light emitters are proposed as novel
platforms for photonic circuits and lab-on-chip active devices. Lasing networks
made by connected multiple gain components and graphs of nanoscale random
lasers (RLs) obtained from complex meshes of polymeric nanofibers are
successful prototypes. However, in the reported research, mainly collective
emission from a whole network of resonators is investigated, and only in a few
cases, the emission from single points showing, although homogeneous and broad,
spatial emission. In all cases, simultaneous activation of the miniaturized
lasers is observed. Here, differently, we realize heterogeneous random lasers
made of ribbon-like and highly porous fibers with evident RL action from
separated micrometric domains that alternatively switch on and off by tuning
the pumping light intensity. We visualize this novel effect by building for the
first time replica symmetry breaking (RSB) maps of the emitting fibers with 2
{\mu}m spatial resolution. In addition, we calculate the spatial correlations
of the laser regions showing clearly an average extension of 50 {\mu}m. The
observed blinking effect is due to mode interaction along light guiding fibers
and opens new avenues in the fabrication of flexible photonic networks with
specific and adaptable activity.


title: Wind speed forecast using random forest learning method

abstract:
  Wind speed forecasting models and their application to wind farm operations
are attaining remarkable attention in the literature because of its benefits as
a clean energy source. In this paper, we suggested the time series machine
learning approach called random forest regression for predicting wind speed
variations. The computed values of mutual information and auto-correlation
shows that wind speed values depend on the past data up to 12 hours. The random
forest model was trained using ensemble from two weeks data with previous 12
hours values as input for every value. The computed root mean square error
shows that model trained with two weeks data can be employed to make reliable
short-term predictions up to three years ahead.


title:
Day-ahead prediction using time series partitioning with Auto-Regressive
  model

abstract:
  Wind speed forecasting has received a lot of attention in the recent past
from researchers due to its enormous benefits in the generation of wind power
and distribution. The biggest challenge still remains to be accurate prediction
of wind speeds for efficient operation of a wind farm. Wind speed forecasts can
be greatly improved by understanding its underlying dynamics. In this paper, we
propose a method of time series partitioning where the original 10 minutes wind
speed data is converted into a two-dimensional array of order (N x 144) where N
denotes the number of days with 144 the daily 10-min observations. Upon
successful time series partitioning, a point forecast is computed for each of
the 144 datasets extracted from the 10 minutes wind speed observations using an
Auto-Regressive (AR) process which is then combined together to give the (N+1)
st day forecast. The results of the computations show significant improvement
in the prediction accuracy when AR model is coupled with time series
partitioning.


title: Expanding Low-Density Latent Regions for Open-Set Object Detection

abstract:
  Modern object detectors have achieved impressive progress under the close-set
setup. However, open-set object detection (OSOD) remains challenging since
objects of unknown categories are often misclassified to existing known
classes. In this work, we propose to identify unknown objects by separating
high/low-density regions in the latent space, based on the consensus that
unknown objects are usually distributed in low-density latent regions. As
traditional threshold-based methods only maintain limited low-density regions,
which cannot cover all unknown objects, we present a novel Open-set Detector
(OpenDet) with expanded low-density regions. To this aim, we equip OpenDet with
two learners, Contrastive Feature Learner (CFL) and Unknown Probability Learner
(UPL). CFL performs instance-level contrastive learning to encourage compact
features of known classes, leaving more low-density regions for unknown
classes; UPL optimizes unknown probability based on the uncertainty of
predictions, which further divides more low-density regions around the cluster
of known classes. Thus, unknown objects in low-density regions can be easily
identified with the learned unknown probability. Extensive experiments
demonstrate that our method can significantly improve the OSOD performance,
e.g., OpenDet reduces the Absolute Open-Set Errors by 25%-35% on six OSOD
benchmarks. Code is available at: https://github.com/csuhan/opendet2.


title:
Advanced Skills through Multiple Adversarial Motion Priors in
  Reinforcement Learning

abstract:
  In recent years, reinforcement learning (RL) has shown outstanding
performance for locomotion control of highly articulated robotic systems. Such
approaches typically involve tedious reward function tuning to achieve the
desired motion style. Imitation learning approaches such as adversarial motion
priors aim to reduce this problem by encouraging a pre-defined motion style. In
this work, we present an approach to augment the concept of adversarial motion
prior-based RL to allow for multiple, discretely switchable styles. We show
that multiple styles and skills can be learned simultaneously without notable
performance differences, even in combination with motion data-free skills. Our
approach is validated in several real-world experiments with a wheeled-legged
quadruped robot showing skills learned from existing RL controllers and
trajectory optimization, such as ducking and walking, and novel skills such as
switching between a quadrupedal and humanoid configuration. For the latter
skill, the robot is required to stand up, navigate on two wheels, and sit down.
Instead of tuning the sit-down motion, we verify that a reverse playback of the
stand-up movement helps the robot discover feasible sit-down behaviors and
avoids tedious reward function tuning.


title: Moving Obstacle Avoidance: a Data-Driven Risk-Aware Approach

abstract:
  This paper proposes a new structured method for a moving agent to predict the
paths of dynamically moving obstacles and avoid them using a risk-aware model
predictive control (MPC) scheme. Given noisy measurements of the a priori
unknown obstacle trajectory, a bootstrapping technique predicts a set of
obstacle trajectories. The bootstrapped predictions are incorporated in the MPC
optimization using a risk-aware methodology so as to provide probabilistic
guarantees on obstacle avoidance. We validate our methods using simulations of
a 3-dimensional multi-rotor drone that avoids various moving obstacles, such as
a thrown ball and a frisbee with air drag.


title:
Micro-Randomized Trial with Flexible Design and Sample Size
  Considerations

abstract:
  Technological advancements have made it possible to deliver mobile health
interventions to individuals. A novel framework that has emerged from such
advancements is the just-in-time adaptive intervention (JITAI), which aims to
suggest the right support to the individuals when their needs arise. The
micro-randomized trial (MRT) design has been proposed recently to test the
proximal effects of these JITAIs. In an MRT, participants are repeatedly
randomized to one of the intervention categories of various components, at a
scale of hundreds or thousands of decision-points over the study. However, the
extant MRT framework only considers the components with a fixed number of
categories. We propose a novel extension of the MRT design, by allowing
flexible addition of more categories to the components during the study, in a
spirit analogous to adaptive platform trials. Accordingly, we develop a
methodology to allow for simultaneous comparisons of varying numbers of
categories over a given study period. The proposed methodology is motivated by
collaboration on the DIAMANTE study, which learns to adaptively deliver
multiple complex kinds of text messages to encourage physical activity among
the patients with Diabetes and Depression. We apply a methodology similar to
the generalized estimating equation approach on the longitudinal data arising
from the proposed MRT, to develop novel test statistics for assessing the
proximal effects and deriving the associated sample size calculators. We
conduct simulation studies to evaluate the sample size calculators, based on
both power and precision. We have developed an R shiny application of the
sample size calculators.


title: New Horizons: Scalar and Vector Ultralight Dark Matter

abstract:
  The last decade has seen unprecedented effort in dark matter model building
at all mass scales coupled with the design of numerous new detection
strategies. Transformative advances in quantum technologies have led to a
plethora of new high-precision quantum sensors and dark matter detection
strategies for ultralight ($<10\,$eV) bosonic dark matter that can be described
by an oscillating classical, largely coherent field. This white paper focuses
on searches for wavelike scalar and vector dark matter candidates.

categories: hep-ex astro-ph.CO hep-ph physics.ins-det quant-ph


title:
On Euler characteristic and Hitchin-Thorpe inequality for
  four-dimensional compact Ricci solitons

abstract:
  In this article, we investigate the geometry of $4$-dimensional compact
gradient Ricci solitons. We prove that, under an upper bound condition on the
range of the potential function, a $4$-dimensional compact gradient Ricci
soliton must satisfy the classical Hitchin-Thorpe inequality. In addition, some
volume estimates are also obtained.


title: Effective Field Theories for the Exactly Solvable Stabilizer Spin Models

abstract:
  A series of exactly solvable spin models has enriched our understanding of
spin liquids and topological matter in the past few decades. The toric code in
two and three dimensions and the X-cube model are among the prototypes of such
models. Recently, two of the present authors added a variant of the X-cube
model called the F3 model to the list of stabilizer Hamiltonians. In this
paper, we provide a coherent prescription for obtaining all these stabilizer
Hamiltonians from their respective parent lattice gauge theories, which are
rank-1 U(1) lattice gauge theories (LGT) for the toric codes, rank-2 U(1) LGT
for the X-cube, and a hybrid of rank-1 and rank-2 U(1) LGTs for the F3 model.
We then develop the effective field theory (EFT) description of the
quasiparticle excitations and gauge field fluctuations through the consistent
application of the gauge principle. Well-known BF actions in (2+1) and (3+1)
dimensions as well as the EFT of the X-cube can be derived in this way. The
effective action for the F3 model is also obtained. Non-trivial effective
Lagrangians of the matter fields such as the $e$ and $m$ quasiparticles in the
toric codes, and fractons and lineons in the X-cube model are derived. They are
usually of quartic order in the field implying strong interaction (and even
confinement) among the quasiparticles and result in conservation laws
consistent with the restricted mobilities of fractons and lineons. The
effective field theory for the F3 model is also developed by following the
gauge principle. A tight-binding description of the quasiparticle dynamics is
proposed for all these models, which in some cases resembles the quadrupole
model studied in the context of the higher-order topological insulator.


title:
Optimization-Based Resiliency Verification in Microgrids via Maximal
  Adversarial Set Characterization

abstract:
  Critical energy infrastructures are increasingly relying on advanced sensing
and control technologies for efficient and optimal utilization of flexible
energy resources. Algorithmic procedures are needed to ensure that such systems
are designed to be resilient to a wide range of cyber-physical adversarial
events. This paper provides a robust optimization framework to quantify the
range of adversarial perturbations that a system can accommodate without
violating pre-specified resiliency metrics. An inner-approximation of the set
of adversarial events which can be mitigated by the available flexibility is
constructed using an optimization based approach. The proposed algorithm is
illustrated on an islanded microgrid example: a modified IEEE 123-node feeder
with distributed energy resources. Simulations are carried out to validate that
the resiliency metrics are met for any event sampled from the constructed
adversarial set for varying levels of available flexibility (energy reserves).


title:
Snowmass 2021 White Paper: Charged lepton flavor violation in the tau
  sector

abstract:
  Charged lepton flavor violation has long been recognized as unambiguous
signature of New Physics. Here we describe the physics capabilities and
discovery potential of New Physics models with charged lepton flavor violation
in the tau sector as its experimental signature. Current experimental status
from the B-Factory experiments BaBar, Belle and Belle II, and future prospects
at Super Tau Charm Factory, LHC, EIC and FCC-ee experiments to discover New
Physics via charged lepton flavor violation in the tau sector are discussed in
detail.
  Submitted to the Proceedings of the US Community Study on the Future of
Particle Physics (Snowmass 2021)


title:
UTSA NLP at SemEval-2022 Task 4: An Exploration of Simple Ensembles of
  Transformers, Convolutional, and Recurrent Neural Networks

abstract:
  The act of appearing kind or helpful via the use of but having a feeling of
superiority condescending and patronizing language can have have serious mental
health implications to those that experience it. Thus, detecting this
condescending and patronizing language online can be useful for online
moderation systems. Thus, in this manuscript, we describe the system developed
by Team UTSA SemEval-2022 Task 4, Detecting Patronizing and Condescending
Language. Our approach explores the use of several deep learning architectures
including RoBERTa, convolutions neural networks, and Bidirectional Long
Short-Term Memory Networks. Furthermore, we explore simple and effective
methods to create ensembles of neural network models. Overall, we experimented
with several ensemble models and found that the a simple combination of five
RoBERTa models achieved an F-score of .6441 on the development dataset and
.5745 on the final test dataset. Finally, we also performed a comprehensive
error analysis to better understand the limitations of the model and provide
ideas for further research.


title:
Learning What You Need from What You Did: Product Taxonomy Expansion
  with User Behaviors Supervision

abstract:
  Taxonomies have been widely used in various domains to underpin numerous
applications. Specially, product taxonomies serve an essential role in the
e-commerce domain for the recommendation, browsing, and query understanding.
However, taxonomies need to constantly capture the newly emerged terms or
concepts in e-commerce platforms to keep up-to-date, which is expensive and
labor-intensive if it relies on manual maintenance and updates. Therefore, we
target the taxonomy expansion task to attach new concepts to existing
taxonomies automatically. In this paper, we present a self-supervised and user
behavior-oriented product taxonomy expansion framework to append new concepts
into existing taxonomies. Our framework extracts hyponymy relations that
conform to users' intentions and cognition. Specifically, i) to fully exploit
user behavioral information, we extract candidate hyponymy relations that match
user interests from query-click concepts; ii) to enhance the semantic
information of new concepts and better detect hyponymy relations, we model
concepts and relations through both user-generated content and structural
information in existing taxonomies and user click logs, by leveraging
Pre-trained Language Models and Graph Neural Network combined with Contrastive
Learning; iii) to reduce the cost of dataset construction and overcome data
skews, we construct a high-quality and balanced training dataset from existing
taxonomy with no supervision. Extensive experiments on real-world product
taxonomies in Meituan Platform, a leading Chinese vertical e-commerce platform
to order take-out with more than 70 million daily active users, demonstrate the
superiority of our proposed framework over state-of-the-art methods. Notably,
our method enlarges the size of real-world product taxonomies from 39,263 to
94,698 relations with 88% precision.


title:
Casimir boundaries, monopoles, and deconfinement transition in 3+1
  dimensional compact electrodynamics

abstract:
  Compact U(1) gauge theory in 3+1 dimensions possesses the confining phase,
characterized by a linear raise of the potential between particles with
opposite electric charges at sufficiently large inter-particle separation. The
confinement is generated by condensation of Abelian monopoles at strong gauge
coupling. We study the properties of monopoles and the deconfining order
parameter in zero-temperature theory in the presence of ideally conducting
parallel metallic boundaries (plates) usually associated with the Casimir
effect. Using first-principle numerical simulations in compact U(1) lattice
gauge theory, we show that as the distance between the plates diminishes, the
vacuum in between the plates experiences a deconfining transition. The phase
diagram in the space of the gauge coupling and the inter-plane distance is
obtained.


title: Axion Dark Matter

abstract:
  Axions are well-motivated dark matter candidates with simple cosmological
production mechanisms. They were originally introduced to solve the strong CP
problem, but also arise in a wide range of extensions to the Standard Model.
This Snowmass white paper summarizes axion phenomenology and outlines
next-generation laboratory experiments proposed to detect axion dark matter.
There are vibrant synergies with astrophysical searches and advances in
instrumentation including quantum-enabled readout, high-Q resonators and
cavities and large high-field magnets. This white paper outlines a clear
roadmap to discovery, and shows that the US is well-positioned to be at the
forefront of the search for axion dark matter in the coming decade.

categories: hep-ex astro-ph.CO hep-ph physics.ins-det


title:
Sandboxing (AI-based) Unverified Controllers in Stochastic Games: An
  Abstraction-based Approach with Safe-visor Architecture

abstract:
  In this paper, we propose a construction scheme for a Safe-visor architecture
for sandboxing unverified controllers, e.g., artificial intelligence-based
(a.k.a. AI-based) controllers, in two-players non-cooperative stochastic games.
Concretely, we leverage abstraction-based approaches to construct a supervisor
that checks and decides whether or not to accept the inputs provided by the
unverified controller, and a safety advisor that provides fallback control
inputs to ensure safety whenever the unverified controller is rejected.
Moreover, by leveraging an ($\epsilon,\delta$)-approximate probabilistic
relation between the original game and its finite abstraction, we provide a
formal safety guarantee with respect to safety specifications modeled by
deterministic finite automata (DFA), while the functionality of the unverified
controllers is still exploited. To show the effectiveness of the proposed
results, we apply them to a control problem of a quadrotor tracking a moving
ground vehicle, in which an AI-based unverified controller is employed to
control the quadrotor.


title:
Temporal Cascade Model for Analyzing Spread in Evolving Networks with
  Disease Monitoring Applications

abstract:
  Current approaches for modeling propagation in networks (e.g., spread of
disease) are unable to adequately capture temporal properties of the data such
as order and duration of evolving connections or dynamic likelihoods of
propagation along these connections. Temporal models in evolving networks are
crucial in many applications that need to analyze dynamic spread. For example,
a disease-spreading virus has varying transmissibility based on interactions
between individuals occurring over time with different frequency, proximity,
and venue population density. To capture such behaviors, we first develop the
Temporal Independent Cascade (T-IC) model and propose a novel spread function,
that we prove to be submodular, with a hypergraph-based sampling strategy that
efficiently utilizes dynamic propagation probabilities. We then introduce the
notion of 'reverse spread' using the proposed T-IC processes, and develop
solutions to identify both sentinel/detector nodes and highly susceptible
nodes. The proven guarantees of approximation quality enable scalable analysis
of highly granular temporal networks. Extensive experimental results on a
variety of real-world datasets show that the proposed approach significantly
outperforms the alternatives in modeling both if and how spread occurs, by
considering evolving network topology as well as granular contact/interaction
information. Our approach has numerous applications, including its utility for
the vital challenge of monitoring disease spread. Utilizing the proposed
methods and T-IC, we analyze the impact of various intervention strategies over
real spatio-temporal contact networks. Our approach is shown also to be highly
effective in quantifying the importance of superspreaders, designing targeted
restrictions for controlling spread, and backward contact tracing.


title:
Quantitative hydrodynamic limits of the Langevin dynamics for gradient
  interface models

abstract:
  We study the Langevin dynamics corresponding to the $\nabla\phi$ (or
Ginzburg-Landau) interface model with a uniformly convex interaction potential.
We interpret these Langevin dynamics as a nonlinear parabolic equation forced
by white noise, which turns the problem into a nonlinear homogenization
problem. Using quantitative homogenization methods, we prove a quantitative
hydrodynamic limit, obtain the $C^2$ regularity of the surface tension, prove a
large-scale Lipschitz-type estimate for the trajectories of the dynamics, and
show that the fluctuation-dissipation relation can be seen as a commutativity
of homogenization and linearization. Finally, we explain why we believe our
techniques can be adapted to the setting of degenerate (non-uniformly) convex
interaction potentials.


title:
GraphZeppelin: Storage-Friendly Sketching for Connected Components on
  Dynamic Graph Streams

abstract:
  Finding the connected components of a graph is a fundamental problem with
uses throughout computer science and engineering. The task of computing
connected components becomes more difficult when graphs are very large, or when
they are dynamic, meaning the edge set changes over time subject to a stream of
edge insertions and deletions. A natural approach to computing the connected
components on a large, dynamic graph stream is to buy enough RAM to store the
entire graph. However, the requirement that the graph fit in RAM is prohibitive
for very large graphs. Thus, there is an unmet need for systems that can
process dense dynamic graphs, especially when those graphs are larger than
available RAM.
  We present a new high-performance streaming graph-processing system for
computing the connected components of a graph. This system, which we call
GraphZeppelin, uses new linear sketching data structures (CubeSketches) to
solve the streaming connected components problem and as a result requires space
asymptotically smaller than the space required for a lossless representation of
the graph. GraphZeppelin is optimized for massive dense graphs: GraphZeppelin
can process millions of edge updates (both insertions and deletions) per
second, even when the underlying graph is far too large to fit in available
RAM. As a result GraphZeppelin vastly increases the scale of graphs that can be
processed.


title:
RAVIR: A Dataset and Methodology for the Semantic Segmentation and
  Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance
  Imaging

abstract:
  The retinal vasculature provides important clues in the diagnosis and
monitoring of systemic diseases including hypertension and diabetes. The
microvascular system is of primary involvement in such conditions, and the
retina is the only anatomical site where the microvasculature can be directly
observed. The objective assessment of retinal vessels has long been considered
a surrogate biomarker for systemic vascular diseases, and with recent
advancements in retinal imaging and computer vision technologies, this topic
has become the subject of renewed attention. In this paper, we present a novel
dataset, dubbed RAVIR, for the semantic segmentation of Retinal Arteries and
Veins in Infrared Reflectance (IR) imaging. It enables the creation of deep
learning-based models that distinguish extracted vessel type without extensive
post-processing. We propose a novel deep learning-based methodology, denoted as
SegRAVIR, for the semantic segmentation of retinal arteries and veins and the
quantitative measurement of the widths of segmented vessels. Our extensive
experiments validate the effectiveness of SegRAVIR and demonstrate its superior
performance in comparison to state-of-the-art models. Additionally, we propose
a knowledge distillation framework for the domain adaptation of RAVIR
pretrained networks on color images. We demonstrate that our pretraining
procedure yields new state-of-the-art benchmarks on the DRIVE, STARE, and
CHASE_DB1 datasets. Dataset link: https://ravirdataset.github.io/data/


title: Meta-modelling the climate of dry tide locked rocky planets

abstract:
  Rocky planets hosted by close-in extrasolar systems are likely to be tidally
locked in 1:1 spin-orbit resonance, a configuration where they exhibit
permanent dayside and nightside. Because of the resulting day-night temperature
gradient, the climate and large-scale circulation of these planets are strongly
determined by their atmospheric stability against collapse, which designates
the runaway condensation of greenhouse gases. To better constrain the surface
conditions of rocky planets located in the habitable zone of their host star,
it is therefore crucial to elucidate the mechanisms that govern the day-night
heat redistribution. As a first attempt to bridge the gap between multiple
modelling approaches ranging from idealised models to 3-D General Circulation
Models (GCM), we developed a General Circulation Meta-Model (GCMM) able to
reproduce both the closed-form solutions provided by analytical models and the
numerical solutions obtained from GCM simulations. We used this approach to
characterise the atmospheric stability of Earth-sized rocky planets with dry
atmospheres containing CO2, and we benchmarked it against 3-D GCM simulations
using THOR GCM. We observe that the collapse pressure below which collapse
occurs can vary by ~40% around the value predicted by analytical scaling laws
depending on the mechanisms taken into account among radiative transfer,
atmospheric dynamics, and turbulent diffusion. Particularly, we find (i) that
the turbulent diffusion taking place in the dayside planetary boundary layer
(PBL) globally tends to warm up the nightside surface hemisphere except in the
transition zone between optically thin and optically thick regimes, (ii) that
the PBL also significantly affects the day-night advection timescale, and (iii)
that the slow rotator approximation holds from the moment that the normalised
equatorial Rossby deformation radius is greater than 2.


title: Equal masses Eulerian relative equilibria on a rotating meridian of S^2

abstract:
  Relative equilibria on a rotating meridian on $\mathbb{S}^2$ in equal-mass
three-body problem under the cotangent potential are determined. We show the
existence of scalene and isosceles relative equilibria. Almost all isosceles
triangles, including equilateral, can form a relative equilibrium, except for
the two equal arc angles $\theta = \pi/2$. For $\theta\in (0,2\pi/3)\setminus
\{\pi/2\}$, the mid mass must be on the rotation axis, in our case, at the
north or south pole of $\mathbb{S}^2$. For $\theta\in (2\pi/3,\pi)$, the mid
mass must be on the equator. For $\theta=2\pi/3$, we obtain the equilateral
triangle, where the position of the masses is arbitrary. When the largest arc
angle $a_\ell$ is in $a_\ell\in (\pi/2,a_c)$, with $a_c=1.8124...$, two scalene
configurations exist for given $a_\ell$.


title: Circular tractrices and generalized Dini surfaces

abstract:
  We introduce a particular family of two-dimensional surfaces in $\mathbb R^4$
which generalize the classical Dini surfaces in $\mathbb R^3$.


title: Attributable Visual Similarity Learning

abstract:
  This paper proposes an attributable visual similarity learning (AVSL)
framework for a more accurate and explainable similarity measure between
images. Most existing similarity learning methods exacerbate the
unexplainability by mapping each sample to a single point in the embedding
space with a distance metric (e.g., Mahalanobis distance, Euclidean distance).
Motivated by the human semantic similarity cognition, we propose a generalized
similarity learning paradigm to represent the similarity between two images
with a graph and then infer the overall similarity accordingly. Furthermore, we
establish a bottom-up similarity construction and top-down similarity inference
framework to infer the similarity based on semantic hierarchy consistency. We
first identify unreliable higher-level similarity nodes and then correct them
using the most coherent adjacent lower-level similarity nodes, which
simultaneously preserve traces for similarity attribution. Extensive
experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets
demonstrate significant improvements over existing deep similarity learning
methods and verify the interpretability of our framework. Code is available at
https://github.com/zbr17/AVSL.


title:
Quantum optics of ultracold quantum gases: open systems beyond
  dissipation (habilitation thesis)

abstract:
  Quantum optics and ultracold gases are established fields, but they almost do
not overlap: the quantum nature of light is typically neglected in works on
ultracold atoms. In our work the quantumness of both light and ultracold matter
plays a key role. First, we show that light is a quantum nondemolition (QND)
probe of many-body phases: they can be distinguished by correlations and full
distribution functions (we consider bosons, fermions, and dipolar molecules).
Light is not only sensitive to densities, but also to the matter-field
interference. Second, we prove that the measurement backaction constitutes a
novel source of competitions in many-body systems, especially, for non-QND
cases. This leads to a plethora of new phenomena: oscillations of multipartite
entangled modes, protection and break-up of fermion pairs, antiferromagnetic
orders, long-range pair tunnelling and entanglement beyond Hubbard models. We
prove that feedback control induces phase transitions and tunes their
universality class. Third, the quantization of trapping potential (quantum
optical lattices) leads to novel phases, including both density orders
(supersolids, density waves) and bond orders of matter fields (superfluid and
supersolid dimers, trimers). Results beyond ultracold atoms include: We extend
the paradigm of feedback control from the state control to control of phase
transitions. We present the measurement backaction as a novel source of
competitions in many-body physics. We merge quantum Zeno dynamics and
non-Hermitian physics and show a novel type of Zeno phenomena with Raman
transitions beyond Zeno dynamics. We propose quantum simulators based on
collective light-matter interaction. Our models can be applied to arrays of
other systems (qubits). In general, quantum measurements and feedback produce
new phenomena untypical to both closed unitary systems and open dissipative
ones in many-body physics.

categories: quant-ph cond-mat.quant-gas physics.atom-ph physics.optics


title: Stealth metastable state of scalar-tensor thermodynamics

abstract:
  We investigate a puzzle in the recent thermodynamics of scalar-tensor
gravity, in which general relativity is a zero-temperature state of equilibrium
and scalar-tensor gravity is the non-equilibrium configuration of an effective
dissipative fluid. A stealth solution of Brans-Dicke gravity with constant
positive temperature is shown to be analogous to a metastable state for the
effective fluid and to suffer from an instability. The stability analysis
employs a version of the Bardeen-Ellis-Bruni-Hwang gauge-invariant formalism
for cosmological perturbations adapted to modified gravity. The metastable
state is destroyed by tensor perturbations.


title: On Chevalley's Extension Theorem

abstract:
  Professor Daniel Anderson informed me, recently, that there is an error in
the proof of Theorem 56 of Kaplansky's book on Commutative Rings. His (Dan's)
reason was "He (Kaplansky) orders by reverse inclusion but in the last line
uses inclusion, so we don't contradict maximality (which is minimality)". The
aim of this short note is to indicate that while Dan Anderson appears to be
correct in pointing out an error in the proof of Theorem 56 of the above
mentioned book, the statement of the theorem is a correct consequence of a
Theorem of Chevalley.


title: FedVLN: Privacy-preserving Federated Vision-and-Language Navigation

abstract:
  Data privacy is a central problem for embodied agents that can perceive the
environment, communicate with humans, and act in the real world. While helping
humans complete tasks, the agent may observe and process sensitive information
of users, such as house environments, human activities, etc. In this work, we
introduce privacy-preserving embodied agent learning for the task of
Vision-and-Language Navigation (VLN), where an embodied agent navigates house
environments by following natural language instructions. We view each house
environment as a local client, which shares nothing other than local updates
with the cloud server and other clients, and propose a novel federated
vision-and-language navigation (FedVLN) framework to protect data privacy
during both training and pre-exploration. Particularly, we propose a
decentralized training strategy to limit the data of each client to its local
model training and a federated pre-exploration method to do partial model
aggregation to improve model generalizability to unseen environments. Extensive
results on R2R and RxR datasets show that under our FedVLN framework,
decentralized VLN models achieve comparable results with centralized training
while protecting seen environment privacy, and federated pre-exploration
significantly outperforms centralized pre-exploration while preserving unseen
environment privacy.


title: Lifting of vector-valued automorphic forms

abstract:
  Recently, the first author [1] showed that the admissible vector-valued
automorphic forms lift to the admissible ones. In this article, we study the
lifts for the logarithmic vector-valued automorphic forms and explicitly
compute the Fourier coefficients of the lifted vector-valued automorphic forms.


title: On Circular Tractrices in $\mathbb R^3$

abstract:
  We explore geometric properties of circular analogues of tractrices and
pseudospheres in $\mathbb R^3$.


title: Pupil-aware Holography

abstract:
  Holographic displays promise to deliver unprecedented display capabilities in
augmented reality applications, featuring wide field of view, wide color gamut,
spatial resolution and depth cues all in a compact form factor. While emerging
holographic display approaches have been successful in achieving large
{\'e}tendue and high image quality as seen by a camera, the large etendue also
reveals a problem that makes existing displays impractical: the sampling of the
holographic field by the eye pupil. Existing methods have not investigated this
issue due to the lack of displays with large enough etendue, and, as such, they
suffer from severe artifacts with varying eye pupil size and location.
  We show that the holographic field as sampled by the eye pupil is highly
varying for existing display setups, and we propose the first pupil-aware
holography approach that maximizes the perceptual image quality irrespective of
the size, location and orientation of the eye pupil in a near-eye holographic
display. We validate the proposed approach both in simulations and a prototype
holographic display configuration, and show that our method eliminates severe
artifacts and significantly outperforms existing approaches.


title:
Learning to Prompt for Open-Vocabulary Object Detection with
  Vision-Language Model

abstract:
  Recently, vision-language pre-training shows great potential in
open-vocabulary object detection, where detectors trained on base classes are
devised for detecting new classes. The class text embedding is firstly
generated by feeding prompts to the text encoder of a pre-trained
vision-language model. It is then used as the region classifier to supervise
the training of a detector. The key element that leads to the success of this
model is the proper prompt, which requires careful words tuning and ingenious
design. To avoid laborious prompt engineering, there are some prompt
representation learning methods being proposed for the image classification
task, which however can only be sub-optimal solutions when applied to the
detection task. In this paper, we introduce a novel method, detection prompt
(DetPro), to learn continuous prompt representations for open-vocabulary object
detection based on the pre-trained vision-language model. Different from the
previous classification-oriented methods, DetPro has two highlights: 1) a
background interpretation scheme to include the proposals in image background
into the prompt training; 2) a context grading scheme to separate proposals in
image foreground for tailored prompt training. We assemble DetPro with ViLD, a
recent state-of-the-art open-world object detector, and conduct experiments on
the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365
datasets. Experimental results show that our DetPro outperforms the baseline
ViLD in all settings, e.g., +3.4 APbox and +3.0 APmask improvements on the
novel classes of LVIS. Code and models are available at
https://github.com/dyabel/detpro.


title: Neural Vocoder is All You Need for Speech Super-resolution

abstract:
  Speech super-resolution (SR) is a task to increase speech sampling rate by
generating high-frequency components. Existing speech SR methods are trained in
constrained experimental settings, such as a fixed upsampling ratio. These
strong constraints can potentially lead to poor generalization ability in
mismatched real-world cases. In this paper, we propose a neural vocoder based
speech super-resolution method (NVSR) that can handle a variety of input
resolution and upsampling ratios. NVSR consists of a mel-bandwidth extension
module, a neural vocoder module, and a post-processing module. Our proposed
system achieves state-of-the-art results on the VCTK multi-speaker benchmark.
On 44.1 kHz target resolution, NVSR outperforms WSRGlow and Nu-wave by 8% and
37% respectively on log spectral distance and achieves a significantly better
perceptual quality. We also demonstrate that prior knowledge in the pre-trained
vocoder is crucial for speech SR by performing mel-bandwidth extension with a
simple replication-padding method. Samples can be found in
https://haoheliu.github.io/nvsr.


title: Topology optimization under thermo-elastic buckling

abstract:
  The focus of this paper is on topology optimization of continuum structures
subject to thermally induced buckling. Popular strategies for solving such
problems include Solid Isotropic Material with Penalization (SIMP) and Rational
Approximation of Material Properties (RAMP). Both methods rely on material
parameterization, and can sometimes exhibit pseudo buckling modes in regions
with low pseudo-densities. Here we consider a level-set approach that relies on
the concept of topological sensitivity. Topological sensitivity analysis for
thermo-elastic buckling is carried out via direct and adjoint formulations.
Then, an augmented Lagrangian formulation is presented that exploits these
sensitivities to solve a buckling constrained problem. Numerical experiments in
3D illustrate the robustness and efficiency of the proposed method.


title: The Environment and Constraints on the Mass of FRB 190520B

abstract:
  Recent observations of FRB 20190520B have revealed rapid fluctuation of its
Dispersion Measure within apparently fixed bounds, as well as a reversal of its
Rotation Measure. The fluctuations of Dispersion Measure are uncorrelated with
the intervals between bursts, setting upper bounds $\sim 10\,$s on any
characteristic time scale of the dispersing region; it must be very compact.
Measurements of the full dependence of the dispersive time delay on frequency
may determine the actual electron density and the size of this region. It is
possible to set a lower bound on the mass of the FRB source from constraints on
the size of the dispersing region and its time scale of variation. Comparison
of the variations of DM and RM leads to an estimate of the magnetic field $\sim
500\,\mu$G.


title:
Differentiable Microscopy Designs an All Optical Quantitative Phase
  Microscope

abstract:
  Ever since the first microscope by Zacharias Janssen in the late 16th
century, scientists have been inventing new types of microscopes for various
tasks. Inventing a novel architecture demands years, if not decades, worth of
scientific experience and creativity. In this work, we introduce Differentiable
Microscopy ($\partial\mu$), a deep learning-based design paradigm, to aid
scientists design new interpretable microscope architectures. Differentiable
microscopy first models a common physics-based optical system however with
trainable optical elements at key locations on the optical path. Using
pre-acquired data, we then train the model end-to-end for a task of interest.
The learnt design proposal can then be simplified by interpreting the learnt
optical elements. As a first demonstration, based on the optical 4-$f$ system,
we present an all-optical quantitative phase microscope (QPM) design that
requires no computational post-reconstruction. A follow-up literature survey
suggested that the learnt architecture is similar to the generalized phase
concept developed two decades ago. We then incorporate the generalized phase
contrast concept to simplify the learning procedure. Furthermore, this physical
optical setup is miniaturized using a diffractive deep neural network (D2NN).
We outperform the existing benchmark for all-optical phase-to-intensity
conversion on multiple datasets, and ours is the first demonstration of its
kind on D2NNs. The proposed differentiable microscopy framework supplements the
creative process of designing new optical systems and would perhaps lead to
unconventional but better optical designs.


title:
Differentiable Microscopy for Content and Task Aware Compressive
  Fluorescence Imaging

abstract:
  The trade-off between throughput and image quality is an inherent challenge
in microscopy. To improve throughput, compressive imaging under-samples image
signals; the images are then computationally reconstructed by solving a
regularized inverse problem. Compared to traditional regularizers, Deep
Learning based methods have achieved greater success in compression and image
quality. However, the information loss in the acquisition process sets the
compression bounds. Further improvement in compression, without compromising
the reconstruction quality is thus a challenge. In this work, we propose
differentiable compressive fluorescence microscopy ($\partial \mu$) which
includes a realistic generalizable forward model with learnable-physical
parameters (e.g. illumination patterns), and a novel physics-inspired inverse
model. The cascaded model is end-to-end differentiable and can learn optimal
compressive sampling schemes through training data. With our model, we
performed thousands of numerical experiments on various compressive microscope
configurations. Our results suggest that learned sampling outperforms widely
used traditional compressive sampling schemes at higher compressions ($\times
100- 1000$) in terms of reconstruction quality. We further utilize our
framework for Task Aware Compression. The experimental results show superior
performance on segmentation tasks even at extremely high compression ($\times
4096$).


title: Stratifying integral representations via equivariant homotopy theory

abstract:
  We prove that the derived category of $R$-linear representations of a finite
group $G$ is stratified for any regular commutative ring $R$. As an
application, we obtain a classification of localizing tensor ideals of ordinary
$R$-linear $G$-representations whose underlying $R$-module is projective.


title:
New candidates for magnetar counterparts from a deep search with the
  Hubble Space Telescope

abstract:
  We report the discovery of six new magnetar counterpart candidates from deep
near-infrared Hubble Space Telescope imaging. The new candidates are among a
sample of nineteen magnetars for which we present HST data obtained between
2018-2020. We confirm the variability of previously established near-infrared
counterparts, and newly identify candidates for PSRJ1622-4950,
SwiftJ1822.3-1606, CXOUJ171405.7-381031, SwiftJ1833-0832, SwiftJ1834.9-0846 and
AXJ1818.8-1559 based on their proximity to X-ray localisations. The new
candidates are compared with the existing counterpart population in terms of
their colours, magnitudes, and near-infrared to X-ray spectral indices. We find
two candidates for AXJ1818.8-1559 which are both consistent with previously
established counterparts. The other new candidates are likely to be chance
alignments, or otherwise have a different origin for their near-infrared
emission not previously seen in magnetar counterparts. Further observations and
studies of these candidates are needed to firmly establish their nature.


title: A Cellular Automaton Model for the generation of Brainwaves

abstract:
  We describe a cellular-automaton based, two-dimensional (2D) lattice model
which generates global oscillations in the EEG spectrum as well as time series
of local field potentials resembling those observed during slow wave sleep.
This is made possible by the presence of pacemakers (local oscillators) which
can be spontaneously ignited and whose activity propagates through space to
synchronize all connected nodes.


title: Controllable Dynamic Multi-Task Architectures

abstract:
  Multi-task learning commonly encounters competition for resources among
tasks, specifically when model capacity is limited. This challenge motivates
models which allow control over the relative importance of tasks and total
compute cost during inference time. In this work, we propose such a
controllable multi-task network that dynamically adjusts its architecture and
weights to match the desired task preference as well as the resource
constraints. In contrast to the existing dynamic multi-task approaches that
adjust only the weights within a fixed architecture, our approach affords the
flexibility to dynamically control the total computational cost and match the
user-preferred task importance better. We propose a disentangled training of
two hypernetworks, by exploiting task affinity and a novel branching
regularized loss, to take input preferences and accordingly predict
tree-structured models with adapted weights. Experiments on three multi-task
benchmarks, namely PASCAL-Context, NYU-v2, and CIFAR-100, show the efficacy of
our approach. Project page is available at https://www.nec-labs.com/~mas/DYMU.


title:
On the Montgomery--Vaughan weighted generalization of Hilbert's
  inequality

abstract:
  This paper concerns the problem of determining the optimal constant in the
Montgomery--Vaughan weighted generalization of Hilbert's inequality. We
consider an approach pursued by previous authors via a parametric family of
inequalities. We obtain upper and lower bounds for the constants in
inequalities in this family. A lower bound indicates that the method in its
current form cannot achieve any value below $3.19497$, so cannot achieve the
conjectured constant $\pi$. The problem of determining the optimal constant
remains open.


title: Existence results for a super Toda system

abstract:
  We solve a super Toda system on a closed Riemann surface of genus~$\gamma>1$
and with some particular spin structures. This generalizes the min-max methods
and results for super Liouville equations and gives new existence results for
super Toda systems.


abstract:
  Deep learning models tend to forget their earlier knowledge while
incrementally learning new tasks. This behavior emerges because the parameter
updates optimized for the new tasks may not align well with the updates
suitable for older tasks. The resulting latent representation mismatch causes
forgetting. In this work, we propose ELI: Energy-based Latent Aligner for
Incremental Learning, which first learns an energy manifold for the latent
representations such that previous task latents will have low energy and the
current task latents have high energy values. This learned manifold is used to
counter the representational shift that happens during incremental learning.
The implicit regularization that is offered by our proposed methodology can be
used as a plug-and-play module in existing incremental learning methodologies.
We validate this through extensive evaluation on CIFAR-100, ImageNet subset,
ImageNet 1k and Pascal VOC datasets. We observe consistent improvement when ELI
is added to three prominent methodologies in class-incremental learning, across
multiple incremental settings. Further, when added to the state-of-the-art
incremental object detector, ELI provides over 5% improvement in detection
accuracy, corroborating its effectiveness and complementary advantage to
existing art.


title:
Matroids satisfying the matroidal Cayley--Bacharach property and ranks
  of covering flats

abstract:
  Let $M$ be a matroid satisfying a matroidal analogue of the Cayley-Bacharach
condition. Given a number $k \ge 2$, we show that there is no nontrivial bound
on ranks of a $k$-tuple of flats covering the underlying set of $M$. This
addresses a question of Levinson-Ullery motivated by earlier results which show
that bounding the number of points satisfying the Cayley-Bacharach condition
forces them to lie on low-dimensional linear subspaces. We also explore the
general question what matroids satisfy the matroidal Cayley-Bacharach condition
of a given degree and its relation to the geometry of generalized permutohedra
and graphic matroids.


title: GIRAFFE HD: A High-Resolution 3D-aware Generative Model

abstract:
  3D-aware generative models have shown that the introduction of 3D information
can lead to more controllable image generation. In particular, the current
state-of-the-art model GIRAFFE can control each object's rotation, translation,
scale, and scene camera pose without corresponding supervision. However,
GIRAFFE only operates well when the image resolution is low. We propose GIRAFFE
HD, a high-resolution 3D-aware generative model that inherits all of GIRAFFE's
controllable features while generating high-quality, high-resolution images
($512^2$ resolution and above). The key idea is to leverage a style-based
neural renderer, and to independently generate the foreground and background to
force their disentanglement while imposing consistency constraints to stitch
them together to composite a coherent final image. We demonstrate
state-of-the-art 3D controllable high-resolution image generation on multiple
natural image datasets.


title:
Laser-assisted scattering of a muon neutrino by an electron within the
  Electroweak theory

abstract:
  In accordance with the electroweak theory, we perform, in the first-Born
approximation, a detailed analytical treatment of the differential
cross-section (DCS) for the elastic scattering process
$e^{-}+\nu_{\mu}\longrightarrow e^{-}+\nu_{\mu}$ in both absence and presence
of a circularly polarized laser field. This scattering process is examined by
using Dirac-Volkov wave functions for charged particles within an external
laser field. The theoretical results obtained for the differential
cross-section without laser field are compared with other theoretical results
within the framework of Fermi theory. These results revealed to us the most
important points that contribute to the understanding of the electroweak theory
role in solving the problem of unitarity violation of the differential
cross-section in Fermi theory. The differential cross section is modified
significantly by inserting the electrons into an electromagnetic field. In
addition, the influence of the laser strength and its frequency on the photons
exchange between the colliding system and the laser field are also included and
discussed. These effects are heavily related to the order and argument of the
ordinary Bessel functions introduced in the theoretical calculation.


title:
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object
  Detection

abstract:
  In this paper, we propose the LiDAR Distillation to bridge the domain gap
induced by different LiDAR beams for 3D object detection. In many real-world
applications, the LiDAR points used by mass-produced robots and vehicles
usually have fewer beams than that in large-scale public datasets. Moreover, as
the LiDARs are upgraded to other product models with different beam amount, it
becomes challenging to utilize the labeled data captured by previous versions'
high-resolution sensors. Despite the recent progress on domain adaptive 3D
detection, most methods struggle to eliminate the beam-induced domain gap. We
find that it is essential to align the point cloud density of the source domain
with that of the target domain during the training process. Inspired by this
discovery, we propose a progressive framework to mitigate the beam-induced
domain shift. In each iteration, we first generate low-beam pseudo LiDAR by
downsampling the high-beam point clouds. Then the teacher-student framework is
employed to distill rich information from the data with more beams. Extensive
experiments on Waymo, nuScenes and KITTI datasets with three different
LiDAR-based detectors demonstrate the effectiveness of our LiDAR Distillation.
Notably, our approach does not increase any additional computation cost for
inference.


title:
Frame-wise Action Representations for Long Videos via Sequence
  Contrastive Learning

abstract:
  Prior works on action representation learning mainly focus on designing
various architectures to extract the global representations for short video
clips. In contrast, many practical applications such as video alignment have
strong demand for learning dense representations for long videos. In this
paper, we introduce a novel contrastive action representation learning (CARL)
framework to learn frame-wise action representations, especially for long
videos, in a self-supervised manner. Concretely, we introduce a simple yet
efficient video encoder that considers spatio-temporal context to extract
frame-wise representations. Inspired by the recent progress of self-supervised
learning, we present a novel sequence contrastive loss (SCL) applied on two
correlated views obtained through a series of spatio-temporal data
augmentations. SCL optimizes the embedding space by minimizing the
KL-divergence between the sequence similarity of two augmented views and a
prior Gaussian distribution of timestamp distance. Experiments on FineGym,
PennAction and Pouring datasets show that our method outperforms previous
state-of-the-art by a large margin for downstream fine-grained action
classification. Surprisingly, although without training on paired videos, our
approach also shows outstanding performance on video alignment and fine-grained
frame retrieval tasks. Code and models are available at
https://github.com/minghchen/CARL_code.


title: Requirements Elicitation in Cognitive Service for Recommendation

abstract:
  Nowadays, cognitive service provides more interactive way to understand
users' requirements via human-machine conversation. In other words, it has to
capture users' requirements from their utterance and respond them with the
relevant and suitable service resources. To this end, two phases must be
applied: I.Sequence planning and Real-time detection of user requirement,
II.Service resource selection and Response generation. The existing works
ignore the potential connection between these two phases. To model their
connection, Two-Phase Requirement Elicitation Method is proposed. For the phase
I, this paper proposes a user requirement elicitation framework (URef) to plan
a potential requirement sequence grounded on user profile and personal
knowledge base before the conversation. In addition, it can also predict user's
true requirement and judge whether the requirement is completed based on the
user's utterance during the conversation. For the phase II, this paper proposes
a response generation model based on attention, SaRSNet. It can select the
appropriate resource (i.e. knowledge triple) in line with the requirement
predicted by URef, and then generates a suitable response for recommendation.
The experimental results on the open dataset \emph{DuRecDial} have been
significantly improved compared to the baseline, which proves the effectiveness
of the proposed methods.


title: Benchmarks for Solar Radiation Time Series Forecasting

abstract:
  With an ever-increasing share of intermittent renewable energy in the world's
energy mix,there is an increasing need for advanced solar power forecasting
models to optimize the operation and control of solar power plants. In order to
justify the need for more elaborate forecast modeling, one must compare the
performance of advanced models with naive reference methods. On this point, a
rigorous formalism using statistical tools, variational calculation and
quantification of noise in the measurement is studied and five naive reference
forecasting methods are considered, among which there is a newly proposed
approach called ARTU (a particular autoregressive model of order two). These
methods do not require any training phase nor demand any (or almost no)
historical data. Additionally, motivated by the well-known benefits of ensemble
forecasting, a combination of these models is considered, and then validated
using data from multiple sites with diverse climatological characteristics,
based on various error metrics, among which some are rarely used in the field
of solar energy. The most appropriate benchmarking method depends on the
salient features of the variable being forecast (e.g., seasonality, cyclicity,
or conditional heteoroscedasity) as well as the forecast horizon. Hence, to
ensure a fair benchmarking, forecasters should endeavor to discover the most
appropriate naive reference method for their setup by testing all available
options. Among the methods proposed in this paper, the combination and ARTU
statistically offer the best results for the proposed study conditions.


title: Domino: Discovering Systematic Errors with Cross-Modal Embeddings

abstract:
  Machine learning models that achieve high overall accuracy often make
systematic errors on important subsets (or slices) of data. Identifying
underperforming slices is particularly challenging when working with
high-dimensional inputs (e.g. images, audio), where important slices are often
unlabeled. In order to address this issue, recent studies have proposed
automated slice discovery methods (SDMs), which leverage learned model
representations to mine input data for slices on which a model performs poorly.
To be useful to a practitioner, these methods must identify slices that are
both underperforming and coherent (i.e. united by a human-understandable
concept). However, no quantitative evaluation framework currently exists for
rigorously assessing SDMs with respect to these criteria. Additionally, prior
qualitative evaluations have shown that SDMs often identify slices that are
incoherent. In this work, we address these challenges by first designing a
principled evaluation framework that enables a quantitative comparison of SDMs
across 1,235 slice discovery settings in three input domains (natural images,
medical images, and time-series data). Then, motivated by the recent
development of powerful cross-modal representation learning approaches, we
present Domino, an SDM that leverages cross-modal embeddings and a novel
error-aware mixture model to discover and describe coherent slices. We find
that Domino accurately identifies 36% of the 1,235 slices in our framework - a
12 percentage point improvement over prior methods. Further, Domino is the
first SDM that can provide natural language descriptions of identified slices,
correctly generating the exact name of the slice in 35% of settings.


title:
A Deep Learning Approach for Thermal Plume Prediction of Groundwater
  Heat Pumps

abstract:
  Climate control of buildings makes up a significant portion of global energy
consumption, with groundwater heat pumps providing a suitable alternative. To
prevent possibly negative interactions between heat pumps throughout a city,
city planners have to optimize their layouts in the future. We develop a novel
data-driven approach for building small-scale surrogates for modelling the
thermal plumes generated by groundwater heat pumps in the surrounding
subsurface water. Building on a data set generated from 2D numerical
simulations, we train a convolutional neural network for predicting
steady-state subsurface temperature fields from a given subsurface velocity
field. We show that compared to existing models ours can capture more complex
dynamics while still being quick to compute. The resulting surrogate is thus
well-suited for interactive design tools by city planners.


title: Learning to segment fetal brain tissue from noisy annotations

abstract:
  Automatic fetal brain tissue segmentation can enhance the quantitative
assessment of brain development at this critical stage. Deep learning methods
represent the state of the art in medical image segmentation and have also
achieved impressive results in brain segmentation. However, effective training
of a deep learning model to perform this task requires a large number of
training images to represent the rapid development of the transient fetal brain
structures. On the other hand, manual multi-label segmentation of a large
number of 3D images is prohibitive. To address this challenge, we segmented 272
training images, covering 19-39 gestational weeks, using an automatic
multi-atlas segmentation strategy based on deformable registration and
probabilistic atlas fusion, and manually corrected large errors in those
segmentations. Since this process generated a large training dataset with noisy
segmentations, we developed a novel label smoothing procedure and a loss
function to train a deep learning model with smoothed noisy segmentations. Our
proposed methods properly account for the uncertainty in tissue boundaries. We
evaluated our method on 23 manually-segmented test images of a separate set of
fetuses. Results show that our method achieves an average Dice similarity
coefficient of 0.893 and 0.916 for the transient structures of younger and
older fetuses, respectively. Our method generated results that were
significantly more accurate than several state-of-the-art methods including
nnU-Net that achieved the closest results to our method. Our trained model can
serve as a valuable tool to enhance the accuracy and reproducibility of fetal
brain analysis in MRI.


title:
Deep Learning and Artificial General Intelligence: Still a Long Way to
  Go

abstract:
  In recent years, deep learning using neural network architecture, i.e. deep
neural networks, has been on the frontier of computer science research. It has
even lead to superhuman performance in some problems, e.g., in computer vision,
games and biology, and as a result the term deep learning revolution was
coined. The undisputed success and rapid growth of deep learning suggests that,
in future, it might become an enabler for Artificial General Intelligence
(AGI). In this article, we approach this statement critically showing five
major reasons of why deep neural networks, as of the current state, are not
ready to be the technique of choice for reaching AGI.


title:
Reconstructing equations of motion for cell phenotypic transitions:
  integration of data science and dynamical systems theory

abstract:
  Dynamical systems theory has long been fruitfully applied to describe
cellular processes, while a main challenge is lack of quantitative information
for constraining models. Advances of quantitative approaches, especially single
cell techniques, have accelerated the emergence of a new direction of
reconstructing the equations of motion of a cellular system from quantitative
single cell data, thus places studies under the framework of dynamical systems
theories, as compared to the currently dominant statistics-based approaches.
Here I review a selected number of recent studies using live- and fixed- cell
data, and provide my perspective on the future development.


title:
A Systematic Survey of Attack Detection and Prevention in Connected and
  Autonomous Vehicles

abstract:
  The number of Connected and Autonomous Vehicles (CAVs) is increasing rapidly
in various smart transportation services and applications due to many benefits
to society, people, and the environment. Several research surveys were
conducted in the domain of CAVs. Such surveys primarily focus on various
security threats and vulnerabilities in the domain of CAVs to classify
different types of attacks, impacts of attacks, attacks features, cyber-risk,
defense methodologies against attacks, and safety standards in CAVs. However,
the importance of attacks detection and prevention approaches for CAVs has not
been discussed extensively in the state-of-the-art surveys, and there is a
clear gap in the existing literature on such methodologies to detect new and
conventional threats and protect the CAV system from unexpected hazards on the
road. There are some surveys with a limited discussion on Attacks Detection and
Prevention Systems (ADPS), but such surveys provide only partial coverage of
different types of ADPS for CAVs. Furthermore, there is a scope for discussing
security, privacy, and efficiency challenges in ADPS that can give an overview
of important security and performance attributes.
  This survey paper presents the significance of CAVs, potential challenges in
CAVs, and an explanation of important security and privacy properties, attack
scenarios, possible attacks in CAV, and performance evaluation parameters for
ADPS. This survey paper extensively provides a discussion on the overview of
different ADPS categories and state-of-the-art research works based on each
ADPS category that gives the latest findings in this research domain. This
survey also discusses crucial and open security research problems that are
required to be focused on a secure deployment of CAVs in the market.


title: Error Correction Code Transformer

abstract:
  Error correction code is a major part of the communication physical layer,
ensuring the reliable transfer of data over noisy channels. Recently, neural
decoders were shown to outperform classical decoding techniques. However, the
existing neural approaches present strong overfitting due to the exponential
training complexity, or a restrictive inductive bias due to reliance on Belief
Propagation. Recently, Transformers have become methods of choice in many
applications thanks to their ability to represent complex interactions between
elements. In this work, we propose to extend for the first time the Transformer
architecture to the soft decoding of linear codes at arbitrary block lengths.
We encode each channel's output dimension to high dimension for better
representation of the bits information to be processed separately. The
element-wise processing allows the analysis of the channel output reliability,
while the algebraic code and the interaction between the bits are inserted into
the model via an adapted masked self-attention module. The proposed approach
demonstrates the extreme power and flexibility of Transformers and outperforms
existing state-of-the-art neural decoders by large margins at a fraction of
their time complexity.


title:
The vagus nerve regulates immunometabolic homeostasis in the ovine fetus
  near term: impact on terminal ileum

abstract:
  The contribution of the vagus nerve to inflammation and glucosensing in the
fetus is not understood. We hypothesized that vagotomy (Vx) will trigger a rise
in systemic glucose levels and this will be enhanced during systemic and
organ-specific inflammation. Efferent vagus nerve stimulation (VNS) should
reverse this phenotype. Near-term fetal sheep (n=57) were surgically prepared
with vascular catheters and ECG electrodes as control and treatment groups
(lipopolysaccharide (LPS), Vx+LPS, Vx+LPS+selective efferent VNS). Fetal
arterial blood samples were drawn for 7 days to profile inflammation (IL-6),
insulin, blood gas and metabolism (glucose). At 54 h, a necropsy was performed;
terminal ileum macrophages; CD11c (M1 phenotype) immunofluorescence was
quantified to detect inflammation. Across the treatment groups, blood gas and
cardiovascular changes indicated mild septicemia. At 3 h, in the LPS group IL-6
peaked; that peak was decreased in Vx+LPS400 and doubled in Vx+LPS800 group;
the efferent VNS sped up the reduction of the inflammatory response profile
over 54 h. M1 macrophage activity was increased in the LPS and Vx+LPS800 groups
only. Glucose and insulin levels in the Vx+LPS group were respectively 1.3-fold
and 2.3-fold higher vs. control at 3 h, and the efferent VNS normalized glucose
levels. Complete withdrawal of vagal innervation results in a 72h delayed onset
of sustained increase in glucose levels for at least 54h and intermittent
hyperinsulinemia. Under conditions of moderate fetal inflammation, this is
related to higher levels of gut inflammation; the efferent VNS reduces the
systemic inflammatory response as well as restores both the levels of glucose
and terminal ileum inflammation, but not the insulin levels. Our findings
reveal a novel regulatory, hormetic, role of the vagus nerve in the
immunometabolic response to endotoxin in near-term fetuses.


title: Nonextensive hydrodynamics of boost-invariant plasmas

abstract:
  We use quasiparticle anisotropic hydrodynamics to study the non-conformal and
non-extensive dynamics of a system undergoing boost-invariant Bjorken
expansion. To introduce nonextensivity, we use an underlying Tsallis
distribution with a time-dependent nonextensivity parameter $q$. By taking
moments of the quasiparticle Boltzmann equation in the relaxation-time
approximation, we obtain dynamical equations which allow us to determine the
time evolution of all microscopic parameters including $q$. We compare
numerical solutions for bulk observables obtained using the nonextensive
evolution with results obtained using quasiparticle anisotropic hydrodynamics
with a Boltzmann distribution function ($q \rightarrow 1$). We show that the
evolution of the temperature, pressure ratio, and scaled energy density, are
quite insensitive to which distribution function is assumed. However, we find
significant differences in the early-time evolution of the bulk pressure which
are observed for even small deviations from the Boltzmann distribution
function. Finally, we discuss the existence of non-conformal hydrodynamic
attractors for the longitudinal and transverse pressures, the bulk and shear
viscous corrections, and the nonextensivity parameter $q$.


title:
Equatorial and polar quasinormal modes and quasiperiodic oscillations of
  quantum deformed Kerr black hole

abstract:
  In this paper we focus on the relation between quasinormal modes (QNMs) and a
rotating black hole shadows. As a specific example, we consider the quantum
deformed Kerr black hole obtained via Newman--Janis--Azreg-A\"{\i}nou
algorithm. In particular, using the geometric-optics correspondence between the
parameters of a QNMs and the conserved quantities along geodesics, we show
that, in the eikonal limit, the real part of QNMs is related to the Keplerian
frequency for equatorial orbits. To this end, we explore the typical shadow
radius for the viewing angles, $\theta_0=\pi/2$, and obtained an interesting
relation in the case of viewing angle $\theta_0=0$ (or equivalently
$\theta_0=\pi$). Furthermore we have computed the corresponding equatorial and
polar modes and the thermodynamical stability of the quantum deformed Kerr
black hole. We also investigate other astrophysical applications such as the
quasiperiodic oscillations and the motion of S2 star to constrain the quantum
deforming parameter.


title:
On local conservation of information content in Schwarzschild black
  holes

abstract:
  The central equations in classical general relativity are the Einstein Field
Equations, which accurately describe not only the generation of
pseudo-Riemannian curvature by matter and radiation manifesting as
gravitational effects, but more importantly mass-energy dynamics, evolution and
distribution on the space-time manifold. Herein, we introduce a geometric phase
in general relativity corresponding to Schwarzschild black hole information
content. This quantity appropriately satisfies a local conservation law subject
to minimal coupling, with other desirable properties such as the quantization
of the black hole horizon in units of Planck area. The local conservation law
is imposed by field equations, which not only contain the trace of Einstein
Field Equations, but also a complex-valued function with properties analogous
to the quantum-mechanical wave function. Such success attests to the utility of
the proposed field equations in capturing key aspects of quantum gravity
theories.


title: M\"{o}bius random law and infinite rank-one maps

abstract:
  We prove that Sarnak's conjecture holds for any infinite measure symbolic
rank-one map. We further extended Bourgain-Sarnak's result, which says that the
M\"{o}bius function is a good weight for the ergodic theorem, to maps acting on
$\sigma$-finite measure spaces. We also discuss and extend Bourgain's theorem
by establishing that there is a class of maps for which the M\"{o}bius
disjointness property holds for any continuous bounded function. Our proof
allows us to obtain an extension of Bourgain's theorem on M\"{o}bius
disjointness for bounded rank one maps and a simple and self-contained proof of
this fact.


title:
Chaotic and Clumpy Galaxy Formation in an Extremely Massive
  Reionization-Era Halo

abstract:
  The SPT0311-58 system at z=6.900 is an extremely massive structure within the
reionization epoch, and offers a chance to understand the formation of galaxies
in an extreme peak in the primordial density field. We present 70mas Atacama
Large Millimeter/submillimeter Array observations of the dust continuum and CII
158um emission in the central pair of galaxies and reach physical resolution
~100-350pc, among the most detailed views of any reionization-era system to
date. The observations resolve the source into at least a dozen kiloparsec-size
clumps. The global kinematics and high turbulent velocity dispersion within the
galaxies present a striking contrast to recent claims of dynamically cold
thin-disk kinematics in some dusty galaxies just 800Myr later at z~4. We
speculate that both gravitational interactions and fragmentation from massive
parent disks have likely played a role in the overall dynamics and formation of
clumps in the system. Each clump individually is comparable in mass to other
6<z<8 galaxies identified in rest-UV/optical deep field surveys, but with star
formation rates elevated by ~3-5x. Internally, the clumps themselves bear close
resemblance to greatly scaled-up versions of virialized cloud-scale structures
identified in low-redshift galaxies. Our observations are qualitatively similar
to the chaotic and clumpy assembly within massive halos seen in simulations of
high-redshift galaxies.


title:
Searching Far and Long I: Pilot ALMA 2mm Follow-up of Bright Dusty
  Galaxies as a Redshift Filter

abstract:
  A complete census of dusty star-forming galaxies (DSFGs) at early epochs is
necessary to constrain the obscured contribution to the cosmic star formation
rate density (CSFRD), however DSFGs beyond $z \sim 4$ are both rare and hard to
identify from photometric data alone due to degeneracies in submillimeter
photometry with redshift. Here, we present a pilot study obtaining follow-up
Atacama Large Millimeter Array (ALMA) $2\,$mm observations of a complete sample
of 39 $850\,\rm\mu m$-bright dusty galaxies in the SSA22 field. Empirical
modeling suggests $2\,$mm imaging of existing samples of DSFGs selected at
$850\,\rm\mu m - 1\,$mm can quickly and easily isolate the "needle in a
haystack" DSFGs that sit at $z>4$ or beyond. Combining archival submillimeter
imaging with our measured ALMA $2\,$mm photometry ($1\sigma \sim
0.08\,$mJy$\,$beam$^{-1}$ rms), we characterize the galaxies' IR SEDs and use
them to constrain redshifts. With available redshift constraints fit via the
combination of six submillimeter bands, we identify 6/39 high-$z$ candidates
each with $>50\%$ likelihood to sit at $z > 4$, and find a positive correlation
between redshift and $2\,$mm flux density. Specifically, our models suggest the
addition of $2\,$mm to a moderately constrained IR SED will improve the
accuracy of a millimeter-derived redshift from $\Delta z/(1+z) = 0.3$ to
$\Delta z/(1+z) = 0.2$. Our IR SED characterizations provide evidence for
relatively high emissivity spectral indices ($\langle \beta \rangle =
2.4\pm0.3$) in the sample. We measure that especially bright ($S_{850\rm\mu
m}>5.55\,$mJy) DSFGs contribute $\sim10$% to the cosmic-averaged CSFRD from
$2<z<5$, confirming findings from previous work with similar samples.


title: On the Sound Speed in Neutron Stars

abstract:
  Determining the sound speed $c_s$ in compact stars is an important open
question with numerous implications on the behaviour of matter at large
densities and hence on gravitational-wave emission from neutron stars. To this
scope, we construct more than $10^7$ equations of state (EOSs) with continuous
sound speed and build more than $10^8$ nonrotating stellar models consistent
not only with nuclear theory and perturbative QCD, but also with astronomical
observations. In this way, we find that EOSs with sub-conformal sound speeds,
i.e. with $c^2_s < 1/3$ within the stars, are possible in principle but very
unlikely in practice, being only $0.03\%$ of our sample. Hence, it is natural
to expect that $c^2_s > 1/3$ somewhere in the stellar interior. Using our large
sample, we obtain estimates at $95\%$ credibility of neutron-star radii for
representative stars with $1.4$ and $2.0$ solar masses,
$R_{1.4}=12.42^{+0.52}_{-0.99}\,{\rm km}$, $R_{2.0}=12.12^{+1.11}_{-1.23}\,{\rm
km}$, and for the binary tidal deformability of the GW170817 event,
$\tilde\Lambda_{1.186}=485^{+225}_{-211}$. Interestingly, our lower-bounds on
the radii are in very good agreement with the prediction derived from very
different arguments, namely, the threshold mass. Finally, we provide simple
analytic expressions to determine the minimum and maximum values of
$\tilde\Lambda$ as a function of the chirp mass.


title: Extraction of the higher-twist parton distribution $e(x)$ from CLAS data

abstract:
  This manuscript presents the new analysis of recent CLAS and CLAS12 data
aimed at the extraction of $e(x)$. This publication benefits from a
proof-of-principle analysis of a smaller preliminary data set published in a
preprint form in \url{1405.7659}.
  We present the first point-by-point extraction of a twist-3 PDF. The scalar
PDF, $e(x)$, is accessed through the analysis of the data for the
$\sin\phi_R$-moment of the beam-spin asymmetry for dihadron production in
semi-inclusive DIS off proton target at CLAS and CLAS12. The dihadron formalism
allows for use of collinear framework, hence calling for a minimal set of
approximations and hypotheses. The extracted PDF $e(x)$ carries insights into
the physics of the largely-unexplored quark-gluon correlations, and its first
Mellin moment is related to the marginally-known scalar charge of the nucleon.
We show that the proton flavor combination of the scalar PDF is non-zero at
more than $74\%$ probability.


title: Visible Neutrino Decays and the Impact of the Daughter-Neutrino Mass

abstract:
  We compute the differential decay width of two- and three-body neutrino
decays, assuming neutrinos are Dirac fermions and allowing for the possibility
that the decay-daughters have nonzero masses. We examine different hypotheses
for the interaction that mediates neutrino decay and concentrate on identifying
circumstances where the decay-daughters can significantly impact the
neutrino-decay signature at different experiments. We are especially interested
in decay daughters produced by right-chiral neutrino fields, when the mass of
the daughter plays a decisive role. As a concrete example, we compare the
effects of visible and invisible antineutrino decays at the JUNO experimental
setup.


title:
Effects of critical correlations on quantum percolation in two
  dimensions

abstract:
  We analyze the out-of-equilibrium dynamics of a quantum particle coupled to
local magnetic degrees of freedom that undergo a classical phase transition.
Specifically, we consider a two-dimensional tight-binding model that interacts
with a background of classical spins in thermal equilibrium, which are subject
to Ising interactions and act as emergent, correlated disorder for the quantum
particle. Particular attention is devoted to temperatures close to the
ferromagnet-to-paramagnet transition. To capture the salient features of the
classical transition, namely the effects of long-range correlations, we focus
on the strong coupling limit, in which the model can be mapped onto a quantum
percolation problem on spin clusters generated by the Ising model. By
inspecting several dynamical probes such as energy level statistics, inverse
participation ratios, and wave-packet dynamics, we provide evidence that the
classical phase transition might induce a delocalization-localization
transition in the quantum system at certain energies. We also identify further
important features due to the presence of Ising correlations, such as the
suppression of compact localized eigenstates.


title:
z~2-9 Galaxies magnified by the Hubble Frontier Field Clusters I: Source
  Selection and Surface Density-Magnification Constraints from >2500 galaxies

abstract:
  We assemble a large comprehensive sample of 2534 z~2, 3, 4, 5, 6, 7, 8, and 9
galaxies lensed by the six clusters from the Hubble Frontier Fields (HFF)
program. Making use of the availability of multiple independent magnification
models for each of the HFF clusters and alternatively treating one of the
models as the "truth," we show that the median magnification factors from the
v4 parametric models are typically reliable to values of 30 to 50, and in one
case to 100. Using the median magnification factor from the latest v4 models,
we estimate the UV luminosities of the 2534 lensed z~2-9 galaxies, finding
sources as faint as -12.4 mag at z~3 and -12.9 mag at z~7. We explicitly
demonstrate the power of the surface density-magnification relations Sigma(z)
vs. mu in the HFF clusters to constrain both distant galaxy properties and
cluster lensing properties. Based on the Sigma(z) vs. mu relations, we show
that the median magnification estimates from existing public models must be
reliable predictors of the true magnification mu to mu<15 (95% confidence). We
also use the observed Sigma(z) vs. mu relations to derive constraints on the
evolution of the luminosity function faint-end slope from z~7 to z~2, showing
that faint-end slope results can be consistent with blank-field studies if, and
only if, the selection efficiency shows no strong dependence on the
magnification factor mu. This can only be the case if very low luminosity
galaxies are very small, being unresolved in deep lensing probes.