title:
Semiconductor-ferromagnet-superconductor planar heterostructures for 1D
  topological superconductivity

abstract:
  Hybrid structures of semiconducting (SM) nanowires, epitaxially grown
superconductors (SC), and ferromagnetic-insulator (FI) layers have been
explored experimentally and theoretically as alternative platforms for
topological superconductivity at zero magnetic field. Here, we analyze a
tripartite SM/FI/SC heterostructure but realized in a planar stacking geometry,
where the thin FI layer acts as a spin-polarized tunneling barrier between the
SM and the SC. We optimize the system's geometrical parameters using
microscopic simulations, finding the range of FI thicknesses for which the
hybrid system can be tuned into the topological regime. Within this range, and
thanks to the vertical confinement provided by the stacking geometry, trivial
and topological phases alternate regularly as the external gate is varied,
displaying a hard topological gap that can reach half of the SC one. This is a
significant improvement compared to setups using hexagonal nanowires, which
show erratic topological regions with typically smaller and softer gaps. Our
proposal provides a magnetic field-free planar design for quasi-one-dimensional
topological superconductivity with attractive properties for experimental
control and scalability.


title: Low rank orthogonal bundles and quadric fibrations

abstract:
  Let $C$ be a curve and $V \to C$ an orthogonal vector bundle of rank $r$. For
$r \le 6$, the structure of $V$ can be described using tensor, symmetric and
exterior products of bundles of lower rank, essentially due to the existence of
exceptional isomorphisms between $\mathrm{Spin} (r , \mathbb{C})$ and other
groups for these $r$. We analyze these structures in detail, and in particular
use them to describe moduli spaces of orthogonal bundles. Furthermore, the
locus of isotropic vectors in $V$ defines a quadric subfibration $Q_V \subset
\mathbb{P} V$. Using familiar results on quadrics of low dimension, we exhibit
isomorphisms between isotropic Quot schemes of $V$ and certain ordinary Quot
schemes of line subbundles. In particular, for $r \le 6$ this gives a method
for enumerating the isotropic subbundles of maximal rank and degree of a
general $V$, when there are finitely many.


title:
Understanding mass hierarchy in collisional energy loss through heavy
  flavor data

abstract:
  While experimental observations, such as the mass hierarchy effect, are
attributed and analyzed within radiative models, their interpretation crucially
depends on collisional energy loss contribution, which is often neglected in
such analyses. To our knowledge, neither a (direct) simple relation between
collisional energy loss and heavy quark mass is established, nor an observable
that quantifies this effect. On the other hand, the upcoming high-luminosity
measurements at RHIC and LHC will generate heavy flavor data with unprecedented
precision, providing an opportunity to utilize high-pT heavy flavor data to
analyze the interaction mechanisms in the quark-gluon plasma. To this end, we
employ a recently developed DREENA framework based on our dynamical energy loss
formalism to study the mass hierarchy in heavy flavor suppression. We present
i) Analytical derivation of a direct relation between collisional
suppression/energy loss and heavy quark mass. ii) A novel observable sensitive
only to the collisional energy loss mechanism to be tested by future
high-precision experiments. iii) Analytical and numerical extraction of the
mass hierarchy in collisional energy losses through this observable.


title:
QUAD: A Quality Aware Multi-Unit Double Auction Framework for IoT-Based
  Mobile Crowdsensing in Strategic Setting

abstract:
  Crowdsourcing with the intelligent agents carrying smart devices is becoming
increasingly popular in recent years. It has opened up meeting an extensive
list of real life applications such as measuring air pollution level, road
traffic information, and so on. In literature this is known as mobile
crowdsourcing or mobile crowdsensing. In this paper, the discussed set-up
consists of multiple task requesters (or task providers) and multiple IoT
devices (as task executors), where each of the task providers is having
multiple homogeneous sensing tasks. Each of the task requesters report bid
along with the number of homogeneous sensing tasks to the platform. On the
other side, we have multiple IoT devices that reports the ask (charge for
imparting its services) and the number of sensing tasks that it can execute.
The valuations of task requesters and IoT devices are private information, and
both might act strategically. One assumption that is made in this paper is that
the bids and asks of the agents (task providers and IoT devices) follow
decreasing marginal returns criteria. In this paper, a truthful mechanism is
proposed for allocating the IoT devices to the sensing tasks carried by task
requesters, that also keeps into account the quality of IoT devices. The
mechanism is truthful, budget balance, individual rational, computationally
efficient, and prior-free. The simulations are carried out to measure the
performance of the proposed mechanism against the benchmark mechanisms. The
code and the synthetic data are available at
\textbf{https://github.com/Samhitha-Jasti/QUAD-Implementation}.


title:
The Yield Curve as a Recession Leading Indicator. An Application for
  Gradient Boosting and Random Forest

abstract:
  Most representative decision tree ensemble methods have been used to examine
the variable importance of Treasury term spreads to predict US economic
recessions with a balance of generating rules for US economic recession
detection. A strategy is proposed for training the classifiers with Treasury
term spreads data and the results are compared in order to select the best
model for interpretability. We also discuss the use of SHapley Additive
exPlanations (SHAP) framework to understand US recession forecasts by analyzing
feature importance. Consistently with the existing literature we find the most
relevant Treasury term spreads for predicting US economic recession and a
methodology for detecting relevant rules for economic recession detection. In
this case, the most relevant term spread found is 3 month to 6 month, which is
proposed to be monitored by economic authorities. Finally, the methodology
detected rules with high lift on predicting economic recession that can be used
by these entities for this propose. This latter result stands in contrast to a
growing body of literature demonstrating that machine learning methods are
useful for interpretation comparing many alternative algorithms and we discuss
the interpretation for our result and propose further research lines aligned
with this work.


title:
Joint rotational invariance and adversarial training of a dual-stream
  Transformer yields state of the art Brain-Score for Area V4

abstract:
  Modern high-scoring models of vision in the brain score competition do not
stem from Vision Transformers. However, in this short paper, we provide
evidence against the unexpected trend of Vision Transformers (ViT) being not
perceptually aligned with human visual representations by showing how a
dual-stream Transformer, a CrossViT$~\textit{a la}$ Chen et al. (2021), under a
joint rotationally-invariant and adversarial optimization procedure yields 2nd
place in the aggregate Brain-Score 2022 competition averaged across all visual
categories, and currently (March 1st, 2022) holds the 1st place for the highest
explainable variance of area V4. In addition, our current Transformer-based
model also achieves greater explainable variance for areas V4, IT and Behaviour
than a biologically-inspired CNN (ResNet50) that integrates a frontal V1-like
computation module(Dapello et al.,2020). Our team was also the only entry in
the top-5 that shows a positive rank correlation between explained variance per
area and depth in the visual hierarchy.
  Against our initial expectations, these results provide tentative support for
an $\textit{"All roads lead to Rome"}$ argument enforced via a joint
optimization rule even for non biologically-motivated models of vision such as
Vision Transformers.


title: Strong Magneto-Elastic Coupling in Mn$_3$X (X = Ge, Sn)

abstract:
  We measure the full elastic tensors of Mn$_3$Ge and Mn$_3$Sn as a function of
temperature through their respective antiferromagnetic phase transitions. Large
discontinuities in the bulk moduli at the N\'eel transitions indicate strong
magnetoelastic coupling in both compounds. Strikingly, the discontinuities are
nearly a factor of 10 larger in Mn$_3$Ge than in Mn$_3$Sn. We use the
magnitudes of the discontinuities to calculate the pressure derivatives of the
N\'eel temperature, which are 39 K/GPa 14.3 K/GPa for Mn$_3$Ge and Mn$_3$Sn,
respectively. We measured in-plane shear modulus $c_{66}$, which couples
strongly to the magnetic order, in magnetic fields up to 18 T and found
quantitatively similar behavior in both compounds. Recent measurements have
demonstrated strong piezomagnetism in Mn$_3$Sn: our results suggest that
Mn$_3$Ge may be an even better candidate for this effect.


title:
Pilot Reuse for mMTC with Spatially Correlated MIMO Channels: A Channel
  Charting Approach

abstract:
  Massive multiple-input multiple-output (mMIMO) technology is a way to
increase the spectral efficiency of machine-type communications (MTC). To
exploit the benefits from large antenna arrays, accurate channel estimation
through pilot signals is needed. Massive MTC systems cannot avoid pilot reuse
due to the enormous numbers of connected devices. We propose a pilot reuse
algorithm based on channel charting (CC) to mitigate pilot contamination in a
multi-sector single-cell massive MTC system having spatially correlated
channels. We show that after creating an interference map via CC, a simple
strategy to allocate the pilot sequences can be implemented. The simulation
results show that the CC-based pilot reuse strategy improves channel estimation
accuracy, which subsequently improves the symbol detection performance and
increases the spectral efficiency compared to other existing schemes. Moreover,
the performance of the CC pilot assignment method approaches that of exhaustive
search pilot assignment for small network setups.


title: On the neural network flow of spin configurations

abstract:
  We study the so-called neural network flow of spin configurations in the 2-d
Ising ferromagnet. This flow is generated by successive reconstructions of spin
configurations, obtained by an artificial neural network like a restricted
Boltzmann machine or an autoencoder. It was reported recently that this flow
may have a fixed point at the critical temperature of the system, and even
allow the computation of critical exponents. Here we focus on the flow produced
by a fully-connected autoencoder, and we refute the claim that this flow
converges to the critical point of the system by directly measuring physical
observables, and showing that the flow strongly depends on the network
hyperparameters. We explore the network metric, the reconstruction error, and
we relate it to the so called intrinsic dimension of data, to shed light on the
origin and properties of the flow.


title: Triggering spin reversal in nanomolecules and nanoclusters on demand

abstract:
  Spin reversal in magnetic nanomolecules and nanoclusters is considered. A
method is suggested allowing, from one side, to keep for long time magnetic
polarization in a metastable state and, from the other side, for starting the
reversal process at any required time. This method can find applications for
the operation of storage memory devices and for the regulation of processes in
spintronics.


title: Continual Prompt Tuning for Dialog State Tracking

abstract:
  A desirable dialog system should be able to continually learn new skills
without forgetting old ones, and thereby adapt to new domains or tasks in its
life cycle. However, continually training a model often leads to a well-known
catastrophic forgetting issue. In this paper, we present Continual Prompt
Tuning, a parameter-efficient framework that not only avoids forgetting but
also enables knowledge transfer between tasks. To avoid forgetting, we only
learn and store a few prompt tokens' embeddings for each task while freezing
the backbone pre-trained model. To achieve bi-directional knowledge transfer
among tasks, we propose several techniques (continual prompt initialization,
query fusion, and memory replay) to transfer knowledge from preceding tasks and
a memory-guided technique to transfer knowledge from subsequent tasks.
Extensive experiments demonstrate the effectiveness and efficiency of our
proposed method on continual learning for dialog state tracking, compared with
state-of-the-art baselines.


title:
Electronic and topological properties of the van der Waals layered
  superconductor PtTe

abstract:
  We report the crystal growth and structural and electronic properties of
superconducting, van der Waals layered PtTe. Easily cleavable crystals with a
plate-like morphology consistent with the layered structure were grown from a
platinum rich flux. A consistent determination of $T_c = 0.57$ K is made from
the onset of diamagnetism, the zero of resistivity, and the midpoint of the
heat capacity jump. The observed behavior is consistent with type-II
superconductivity, with upper critical field at $T=0$ estimated using the
Werthamer-Helfand-Hohenberg theory to be 143 and 65 Oe for fields out of and in
the plane, respectively. The heat capacity discontinuity is close to the weak
coupling BCS value. Density functional theory calculations and analysis of the
electronic structure finds that PtTe is a topological semimetal with numerous
surface states, but suggests the superconducting state itself may be
topologically trivial. Angle resolved photoemission spectroscopy reveals a
normal-state Fermi surface in remarkable agreement with theory, and confirms
the overall topological nature of the material by experimental identification
of the surface bands. Together, these findings identify PtTe as an interesting
example of a cleavable, topological, and superconducting material.


title:
Estimating a regression function in exponential families by model
  selection

abstract:
  Let $X_{1}=(W_{1},Y_{1}),\ldots,X_{n}=(W_{n},Y_{n})$ be $n$ pairs of
independent random variables. We assume that, for each $i\in\{1,\ldots,n\}$,
the conditional distribution of $Y_{i}$ given $W_{i}$ belongs to a
one-parameter exponential family with parameter
${\boldsymbol{\gamma}}^{\star}(W_{i})\in{\mathbb{R}}$, or at least, is close
enough to a distribution of this form. The objective of the present paper is to
estimate these conditional distributions on the basis of the observation
${\boldsymbol{X}}=(X_{1},\ldots,X_{n})$ and to do so, we propose a model
selection procedure together with a non-asymptotic risk bound for the resulted
estimator with respect to a Hellinger-type distance. When
${\boldsymbol{\gamma}}^{\star}$ does exist, the procedure allows to obtain an
estimator $\widehat{\boldsymbol{\gamma}}$ of ${\boldsymbol{\gamma}}^{\star}$
adapted to a wide range of the anisotropic Besov spaces. When
${\boldsymbol{\gamma}}^{\star}$ has a general additive or multiple index
structure, we construct suitable models and show the resulted estimators by our
procedure based on such models can circumvent the curse of dimensionality.
Moreover, we consider model selection problems for ReLU neural networks and
provide an example where estimation based on neural networks enjoys a much
faster converge rate than the classical models. Finally, we apply this
procedure to solve variable selection problem in exponential families. The
proofs in the paper rely on bounding the VC dimensions of several collections
of functions, which can be of independent interest.


title: Light transformed gluon correlators in CCFT

abstract:
  In the present work, we study celestial correlators of light transformed
gluon operators at tree level. We also discuss the transformation of light
transformed operators under the action of 4D translations. The two, three and
four-point functions arising from MHV amplitudes in terms of light transformed
operators satisfy translation invariance constraints, are non-distributional
and exactly match with the forms of ordinary CFT versions.


title:
Global classical solution for three-dimensional compressible isentropic
  magneto-micropolar fluid equations with Coulomb force and slip boundary
  condition in bounded domains

abstract:
  We study an initial boundary value problem of three-dimensional (3D)
compressible isentropic magneto-micropolar fluid equations with Coulomb force
and slip boundary condition in a bounded simply connected domain, whose
boundary has a finite number of two-dimensional connected components. Under the
assumption that the initial total energy is suitably small, we obtain the
global existence and uniqueness of classical solutions. In particular, the
density contains initial vacuum states and the solution allows large
oscillations. The basic framework is based on the method developed by Cai and
Li [6] for the Navier-Stokes system. The main ingredient of this paper is to
overcome the strong nonlinearity caused by the Coulomb force and the rotation
effect of micro-particles by applying piecewise-estimate method and delicate
analysis based on the effective viscous flux.


title: Short Topological Decompositions of Non-Orientable Surfaces

abstract:
  In this article, we investigate short topological decompositions of
non-orientable surfaces and provide algorithms to compute them. Our main result
is a polynomial-time algorithm that for any graph embedded in a non-orientable
surface computes a canonical non-orientable system of loops so that any loop
from the canonical system intersects any edge of the graph in at most 30
points. The existence of such short canonical systems of loops was well known
in the orientable case and an open problem in the non-orientable case. Our
proof techniques combine recent work of Schaefer-\v{S}tefankovi\v{c} with ideas
coming from computational biology, specifically from the signed reversal
distance algorithm of Hannenhalli-Pevzner. The existence of short canonical
non-orientable systems of loops confirms a special case of a conjecture of
Negami on the joint crossing number of two embeddable graphs. We also provide a
correction for an argument of Negami bounding the joint crossing number of two
non-orientable graph embeddings.


title:
Maximally Satisfying Lower Quotas in the Hospitals/Residents Problem
  with Ties and Incomplete Lists

abstract:
  To mitigate imbalance in the numbers of assignees in the Hospitals/Residents
problem, Goko et al. [Goko et al., Maximally Satisfying Lower Quotas in the
Hospitals/Residents Problem with Ties, Proc. STACS 2022, pp. 31:1--31:20.]
studied the Hospitals/Residents problem with lower quotas whose goal is to find
a stable matching that satisfies lower quotas as much as possible. In their
paper, preference lists are assumed to be complete. In this paper, we study
more general model where preference lists may be incomplete. For four natural
scenarios, we obtained maximum gaps of the best and worst solutions,
approximability results, and inapproximability results.


title:
Jacobian Calabi-Yau 3-fold and charge completeness in six-dimensional
  theory

abstract:
  We study aspects of an equivalent relation of the charge completeness in
six-dimensional (6D) $\mathcal{N}=(1,0)$ supergravity theory and a standard
assumption on the global structure of the gauge group involving F-theory
geometry, recently proved by Morrison and Taylor. We constructed and analyzed a
novel 6D supergravity theory, realized as F-theory, on an elliptically fibered
Calabi-Yau 3-fold. Our construction yields a novel 6D theory with Mordell-Weil
torsion $\mathbb{Z}_4\oplus\mathbb{Z}_4$. Furthermore, we deduce the gauge
group and matter fields arising in the 6D F-theory model on the constructed
elliptically fibered Calabi-Yau 3-fold. We also discuss the relations of the 6D
F-theory model constructed in this study to stable degeneration and the dual
heterotic string.


title:
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement
  Learning

abstract:
  Offline reinforcement learning algorithms promise to be applicable in
settings where a fixed dataset is available and no new experience can be
acquired. However, such formulation is inevitably offline-data-hungry and, in
practice, collecting a large offline dataset for one specific task over one
specific environment is also costly and laborious. In this paper, we thus 1)
formulate the offline dynamics adaptation by using (source) offline data
collected from another dynamics to relax the requirement for the extensive
(target) offline data, 2) characterize the dynamics shift problem in which
prior offline methods do not scale well, and 3) derive a simple dynamics-aware
reward augmentation (DARA) framework from both model-free and model-based
offline settings. Specifically, DARA emphasizes learning from those source
transition pairs that are adaptive for the target environment and mitigates the
offline dynamics shift by characterizing state-action-next-state pairs instead
of the typical state-action distribution sketched by prior offline RL methods.
The experimental evaluation demonstrates that DARA, by augmenting rewards in
the source offline dataset, can acquire an adaptive policy for the target
environment and yet significantly reduce the requirement of target offline
data. With only modest amounts of target offline data, our performance
consistently outperforms the prior offline RL methods in both simulated and
real-world tasks.


title: Global2Local: A Joint-Hierarchical Attention for Video Captioning

abstract:
  Recently, automatic video captioning has attracted increasing attention,
where the core challenge lies in capturing the key semantic items, like objects
and actions as well as their spatial-temporal correlations from the redundant
frames and semantic content. To this end, existing works select either the key
video clips in a global level~(across multi frames), or key regions within each
frame, which, however, neglect the hierarchical order, i.e., key frames first
and key regions latter. In this paper, we propose a novel joint-hierarchical
attention model for video captioning, which embeds the key clips, the key
frames and the key regions jointly into the captioning model in a hierarchical
manner. Such a joint-hierarchical attention model first conducts a global
selection to identify key frames, followed by a Gumbel sampling operation to
identify further key regions based on the key frames, achieving an accurate
global-to-local feature representation to guide the captioning. Extensive
quantitative evaluations on two public benchmark datasets MSVD and MSR-VTT
demonstrates the superiority of the proposed method over the state-of-the-art
methods.


title: Detection of Inelastic Dark Matter via Electron Recoils in SENSEI

abstract:
  The low-threshold experiment SENSEI, which uses the ultralow-noise silicon
Skipper-CCD to explore light dark matter from the halo, has achieved the most
rigorous limitations on light DM-electron scattering cross section. In this
work, we investigate the inelastic DM-electron scattering process with the
SENSEI data and derive the constraints on the inelastic dark matter model with
a $U(1)$ gauge boson as the mediator. Comparing with elastic scattering
process, we find that the down-scattering process with the mass splitting
$\delta \equiv m_{\chi_2}-m_{\chi_1}<0$ is more strongly constrained while the
up-scattering process $\delta>0$ gets the weaker limits. For the
down-scattering process with mass splitting $\delta \sim -5$ eV, the DM mass
$m_{\chi}$ can be excluded down to as low as 0.1 MeV.


title: Statistical Analysis on Brain Surfaces

abstract:
  In this paper, we review widely used statistical analysis frameworks for data
defined along cortical and subcortical surfaces that have been developed in
last two decades. The cerebral cortex has the topology of a 2D highly
convoluted sheet. For data obtained along curved non-Euclidean surfaces,
traditional statistical analysis and smoothing techniques based on the
Euclidean metric structure are inefficient. To increase the signal-to-noise
ratio (SNR) and to boost the sensitivity of the analysis, it is necessary to
smooth out noisy surface data. However, this requires smoothing data on curved
cortical manifolds and assigning smoothing weights based on the geodesic
distance along the surface. Thus, many cortical surface data analysis
frameworks are differential geometric in nature. The smoothed surface data is
then treated as smooth random fields and statistical inferences can be
performed within Keith Worsley's random field theory. The methods described in
this paper are illustrated with the hippocampus surface data set. Using this
case study, we will determine if there is an effect of family income on the
growth of hippocampus in children in detail. There are a total of 124 children
and 82 of them have repeat magnetic resonance images (MRI) two years later.


title:
Nanofluidic trapping and enhanced Raman detection of single biomolecules
  in plasmonic bowl-shaped nanopore

abstract:
  Solid-state nanopores are emerging platforms for single-molecule protein
sequencing due to their tolerance to hash physiology environment and
compatibility with different electrical and optical detection methods. However,
they suffer from poor molecular manipulations that were twisted with and thus
limited by the detection methods. Here, we report a bowl-shaped plasmonic gold
nanopore on silicon nitride with hydrogel to demonstrate near-field nanofluidic
manipulation of DNA translocation for plasmon-enhanced Raman spectroscopic
detection. The hydrogel linearized the DNA, and the linear DNA was trapped in
the nanopore for tens of seconds due assumably to bipolar effect of the
nanopore that generate electroosmotic sheath flow and bipolar surface charge
distribution. Their combination led to a near-field confinement of the DNA in
the nanopore hot spot to allow stable Raman detection. We envision that a
combination of Raman spectroscopy with the bowl-shaped nanopores can succeed in
single-molecule protein sequencing in a label-free way


title:
Towards Visual-Prompt Temporal Answering Grounding in Medical
  Instructional Video

abstract:
  The temporal answering grounding in the video (TAGV) is a new task naturally
derived from temporal sentence grounding in the video (TSGV). Given an
untrimmed video and a text question, this task aims at locating the matching
span from the video that can semantically answer the question. Existing methods
tend to formulate the TAGV task with a visual span-based question answering
(QA) approach by matching the visual frame span queried by the text question.
However, due to the weak correlations and huge gaps of the semantic features
between the textual question and visual answer, existing methods adopting
visual span predictor perform poorly in the TAGV task. To bridge these gaps, we
propose a visual-prompt text span localizing (VPTSL) method, which introduces
the timestamped subtitles as a passage to perform the text span localization
for the input text question, and prompts the visual highlight features into the
pre-trained language model (PLM) for enhancing the joint semantic
representations. Specifically, the context query attention is utilized to
perform cross-modal interaction between the extracted textual and visual
features. Then, the highlight features are obtained through the video-text
highlighting for the visual prompt. To alleviate semantic differences between
textual and visual features, we design the text span predictor by encoding the
question, the subtitles, and the prompted visual highlight features with the
PLM. As a result, the TAGV task is formulated to predict the span of subtitles
matching the visual answer. Extensive experiments on the medical instructional
dataset, namely MedVidQA, show that the proposed VPTSL outperforms the
state-of-the-art (SOTA) method by 28.36% in terms of mIOU with a large margin,
which demonstrates the effectiveness of the proposed visual prompt and the text
span predictor.


title: Towards Personalized Intelligence at Scale

abstract:
  Personalized Intelligence (PI) is the problem of providing customized AI
experiences tailored to each individual user. In many applications, PI is
preferred or even required. Existing personalization approaches involve
fine-tuning pre-trained models to create new customized models. However, these
approaches require a significant amount of computation to train, scaling with
model size and the number of users, inhibiting PI to be realized widely. In
this work, we introduce a novel model architecture and training/inference
framework to enable Personalized Intelligence at scale. We achieve this by
attaching a Personalization Head (PH) to pre-trained language models (LM).
During training, the base LMs are frozen and only the parameters in PH are
updated and are unique per user. This results in significantly smaller overall
model sizes and training cost than traditional fine-tuning approaches when
scaled across many users. We evaluate PHs on academia and industry-focused
datasets and show that the PHs outperform zeroshot baseline in F1 score and are
significantly more scalable than traditional fine-tuning approaches. We
identify key factors required for effective PH design and training.


title:
Monte Carlo Simulation of Sugar Synthesis on Icy Dust Particles
  Intermittently Irradiated by UV in a Protoplanetary Disk

abstract:
  Context. While synthesis of organic molecules in molecular clouds or
protoplanetary disks is complex, observations of interstellar grains, analyses
of carbonaceous chondrites, and UV photochemistry experiments are rapidly
developing and providing constraints on and clues to the complex organic
molecule synthesis in space. It motivates us to construct a theoretical
synthesis model.
  Aims. We develop a new code to simulate global reaction sequences of organic
molecules to apply it for sugar synthesis by intermittent UV irradiation on the
surface of icy particles in a protoplanetary disk. Here we show the first
results of our new simulation.
  Methods. We apply a Monte Carlo method to select reaction sequences from all
possible reactions, using the graph-theoretic matrix model for chemical
reactions and modeling reactions on the icy particles during UV irradiation.
  Results. We here obtain the results consistent with the organic molecules in
carbonaceous chondrites and obtained by the experiments, however, through a
different pathway from the conventional formose reactions previously suggested.
During UV irradiation, loosely-bonded O-rich large molecules are continuously
created and destroyed. After UV irradiation is turned off, the ribose abundance
rapidly increases, through the decomposition of the large molecules with
break-ups of O-O bonds and replacements of C-OH by C-H to reach O/C = 1 for
sugars. The sugar abundance is regulated mostly by the total atomic ratio H/O
of starting materials, but not by their specific molecule forms. Deoxyribose is
simultaneously synthesized, and most of the molecules end up with complex
C-rich molecules.


title:
Direct Numerical Simulation of high Prandtl number fluids and
  supercritical carbon dioxide canonical flows using the spectral element
  method

abstract:
  The design of advanced nuclear reactors (Gen IV) involves an array of
challenging fluid-flow issues that affect safety and performance. Currently,
these problems are addressed in an ad-hoc manner at varying scales which are
time-consuming and expensive. The creation of a high-resolution heat transfer
numerical database has the potential to help develop to accurate and
inexpensively reduced resolution heat transfer models. Such models can help
address industrial-driven issues associated with the heat transfer behavior of
advanced reactors. The models can be developed using the multiscale hierarchy
developed as part of the recently DOE-funded center of excellence for
thermal-fluids applications in nuclear energy. Ultimately this can lead to
fast-running reliable models, thus accelerating the deployment of advanced
reactors. In this paper, we performed a series of Direct Numerical Simulation
using the spectral element codes Nek5000 and NekRS to investigate heat transfer
in mixed convection conditions. First, we investigate the heat transfer of the
flow in heated parallel plates for high Prandtl number fluids. The calculated
database will eventually be used to evaluate existing heat transfer
correlations and some modifications will be proposed for cases where no
satisfactory choice is available. We have also investigated the heated transfer
alteration phenomena in a straight heated tube for supercritical carbon dioxide
(sCO2). The low-Mach-number approximation is used to decouple thermal and
dynamic pressure, as pressure drop is negligible in this problem. The
properties of sCO2 are calculated using multi-region polynomials. We observed
that the heat transfer deterioration occurred in combination with the property
changes of sCO2 and the depreciation of turbulence kinetic energy (TKE) for
upward flow. Whereas, in downward flow, the heat transfer is enhanced thanks to
the increase of TKE.


title: Summarizing a virtual robot's past actions in natural language

abstract:
  We propose and demonstrate the task of giving natural language summaries of
the actions of a robotic agent in a virtual environment. We explain why such a
task is important, what makes it difficult, and discuss how it might be
addressed. To encourage others to work on this, we show how a popular existing
dataset that matches robot actions with natural language descriptions designed
for an instruction following task can be repurposed to serve as a training
ground for robot action summarization work. We propose and test several methods
of learning to generate such summaries, starting from either egocentric video
frames of the robot taking actions or intermediate text representations of the
actions used by an automatic planner. We provide quantitative and qualitative
evaluations of our results, which can serve as a baseline for future work.


title: Dissipative time crystals originated from parity-time symmetry

abstract:
  We give a number of evidences that a class of dissipative time crystals
emerge when $\mathcal{PT}$ symmetry of the systems are restored in collective
spin systems with Lindblad dynamics. First, we show that a standard model of
boundary time crystals (BTCs) satisfies a Liouvillian $\mathcal{PT}$ symmetry
and prove that BTC exists only when the steady state is $\mathcal{PT}$
symmetric in the large spin limit. Similar statement is also confirmed
numerically for another BTC model. Conversely we also show, for a model
commonly studied in the context of Liouvillian $\mathcal{PT}$ symmety, that its
$\mathcal{PT}$ symmetric phase is a boundary time crystal. In addition we
discuss a mechanism of appearance of BTC by developing a perturbation theory
for a class of the 1 spin models under weak dissipation. As a result, we show
that the BTCs appear in the first-order correction when the total gain and loss
are exactly balanced. These results strongly suggest that the BTCs are time
crystals originated from $\mathcal{PT}$ symmetry.


title:
FlexBlock: A Flexible DNN Training Accelerator with Multi-Mode Block
  Floating Point Support

abstract:
  Training deep neural networks (DNNs) is a computationally expensive job,
which can take weeks or months even with high performance GPUs. As a remedy for
this challenge, community has started exploring the use of more efficient data
representations in the training process, e.g., block floating point (BFP).
However, prior work on BFP-based DNN accelerators rely on a specific BFP
representation making them less versatile. This paper builds upon an
algorithmic observation that we can accelerate the training by leveraging
multiple BFP precisions without compromising the finally achieved accuracy.
Backed up by this algorithmic opportunity, we develop a flexible DNN training
accelerator, dubbed FlexBlock, which supports three different BFP precision
modes, possibly different among activation, weight, and gradient tensors. While
several prior works proposed such multi-precision support for DNN accelerators,
not only do they focus only on the inference, but also their core utilization
is suboptimal at a fixed precision and specific layer types when the training
is considered. Instead, FlexBlock is designed in such a way that high core
utilization is achievable for i) various layer types, and ii) three BFP
precisions by mapping data in a hierarchical manner to its compute units. We
evaluate the effectiveness of FlexBlock architecture using well-known DNNs on
CIFAR, ImageNet and WMT14 datasets. As a result, training in FlexBlock
significantly improves the training speed by 1.5~5.3x and the energy efficiency
by 2.4~7.0x on average compared to other training accelerators and incurs
marginal accuracy loss compared to full-precision training.


title: Quasi-semilattices on networks

abstract:
  This paper introduces the tensor representation of a network, here tensors
are the primitive structures of the network. In view of tensor chains, two
binary operations on tensor sets are defined: chain addition and reducing.
Based on the reducing operation, the tensor chain representation of subnetworks
of a network is given, and it is proved that all connected subnetworks of a
network (here refers to the tensor chain generated by primitive structures)
form a quasi-semilattice with respect to reducing, namely {\it network
quasi-semilattices}. Here, quasi-semilattices refer to algebraic systems that
are idempotent commutative and do not satisfy the association law. Then, we
discuss the subalgebra structures of the network quasi-semilattice in terms of
two equivalent relations $\sigma$ and $\delta$. $\delta$ is a congruence. Each
$\delta$-class forms a semilattice with respect to reducing, that is, an
idempotent commutative semigroup, and also each $\delta$-class has an order
structure with the maximum element and minimum elements. Here, the minimum
elements correspond to the spanning tree in graph theory. Finally, we discuss
how three path algebras: graph inverse semigroups, Leavitt path algebra and
Cuntz-Krieger graph $C^*$-algebra are constructed in terms of tensors with
respect to chain-addition.


title: Clumsy Packing of Polyominoes in Finite Space

abstract:
  Clumsy packing is considered an inefficient packing, meaning we find the
minimum number of objects we can pack into a space so that we can not pack any
more object. Thus we are effectively spacing out the objects as far apart as
possible so that we can not fit another object. In this paper we consider
clumsy packing of polyominoes in a finite spaces which must consider boundary
conditions. We examine rectangle, $L$, $T$, and plus polyominoes of various
sizes.


title:
Set-valued prediction in hierarchical classification with constrained
  representation complexity

abstract:
  Set-valued prediction is a well-known concept in multi-class classification.
When a classifier is uncertain about the class label for a test instance, it
can predict a set of classes instead of a single class. In this paper, we focus
on hierarchical multi-class classification problems, where valid sets
(typically) correspond to internal nodes of the hierarchy. We argue that this
is a very strong restriction, and we propose a relaxation by introducing the
notion of representation complexity for a predicted set. In combination with
probabilistic classifiers, this leads to a challenging inference problem for
which specific combinatorial optimization algorithms are needed. We propose
three methods and evaluate them on benchmark datasets: a na\"ive approach that
is based on matrix-vector multiplication, a reformulation as a knapsack problem
with conflict graph, and a recursive tree search method. Experimental results
demonstrate that the last method is computationally more efficient than the
other two approaches, due to a hierarchical factorization of the conditional
class distribution.


title: PNM: Pixel Null Model for General Image Segmentation

abstract:
  A major challenge in image segmentation is classifying object boundaries.
Recent efforts propose to refine the segmentation result with boundary masks.
However, models are still prone to misclassifying boundary pixels even when
they correctly capture the object contours. In such cases, even a perfect
boundary map is unhelpful for segmentation refinement. In this paper, we argue
that assigning proper prior weights to error-prone pixels such as object
boundaries can significantly improve the segmentation quality. Specifically, we
present the \textit{pixel null model} (PNM), a prior model that weights each
pixel according to its probability of being correctly classified by a random
segmenter. Empirical analysis shows that PNM captures the misclassification
distribution of different state-of-the-art (SOTA) segmenters. Extensive
experiments on semantic, instance, and panoptic segmentation tasks over three
datasets (Cityscapes, ADE20K, MS COCO) confirm that PNM consistently improves
the segmentation quality of most SOTA methods (including the vision
transformers) and outperforms boundary-based methods by a large margin. We also
observe that the widely-used mean IoU (mIoU) metric is insensitive to
boundaries of different sharpness. As a byproduct, we propose a new metric,
\textit{PNM IoU}, which perceives the boundary sharpness and better reflects
the model segmentation performance in error-prone regions.


title: Identifying, and constructing, complex magnon band topology

abstract:
  Magnetically ordered materials tend to support bands of coherent propagating
spin wave, or magnon, excitations. Topologically protected surface states of
magnons offer a new path towards coherent spin transport for spintronics
applications. In this work we explore the variety of topological magnon band
structures and provide insight into how to efficiently identify topological
magnon bands in materials. We do this by adapting the topological quantum
chemistry approach that has used constraints imposed by time reversal and
crystalline symmetries to enumerate a large class of topological electronic
bands. We show how to identify physically relevant models of gapped magnon band
topology by using so-called decomposable elementary band representations, and
in turn discuss how to use symmetry data to infer the presence of exotic
symmetry enforced nodal topology.


title: A smart electric bike for smart cities

abstract:
  This is a Masters Thesis completed at University College Dublin, Ireland in
2017 which involved augmenting an off-the-shelf electric bike with sensors to
enable new services to be delivered to cyclists in cities. The application of
primary interest was to control the cyclist's ventilation rate based on the
concentration of local air pollutants. Detailed modelling and system design is
presented for our Cyberphysical system which consisted of a modified BTwin
e-bike, Cycle Analyst sensors, the cyclist themselves, a Bluetooth connected
smartphone and our algorithms. Control algorithms to regulate the proportion of
power the cyclist provided as a proxy for their ventilation rate were proposed
and validated in a basic way, which were later proven significantly further in
Further Work (see IEEE Transactions on Intelligent Transportation Systems
paper: https://ieeexplore.ieee.org/abstract/document/8357977). The basic idea
was to provide more electrical assistance to cyclists in areas of high air
pollution to reduce the cyclist ventilation rate and thereby the amount of air
pollutants inhaled. This presents an interesting control challenge due to the
human-in-the-loop characteristics and the potential for impactful real life
applications. A background literature review is provided on energy as it
relates to cycling and some other applications are also discussed. A link to a
video which demonstrates the system is provided, and also to a blog published
by IBM Research about the system.


title: Early-Universe Model Building

abstract:
  Theoretical investigations into the evolution of the early universe are an
essential part of particle physics that allow us to identify viable extensions
to the Standard Model as well as motivated parameter space that can be probed
by various experiments and observations. In this white paper, we review
particle physics models of the early universe. First, we outline various models
that explain two essential ingredients of the early universe (dark matter and
baryon asymmetry) and those that seek to address current observational
anomalies. We then discuss dynamics of the early universe in models of neutrino
masses, axions, and several solutions to the electroweak hierarchy problem.
Finally, we review solutions to naturalness problems of the Standard Model that
employ cosmological dynamics.


title: Absolute Calibration of Cepheid Period-Luminosity Relations in NGC 4258

abstract:
  NGC 4258 is one of the most important anchors for calibrating the Cepheid
period--luminosity relations (PLRs) owing to its accurate distance measured
from water maser motions. We expand on previous efforts and carry out a new
Cepheid search in this system using the Hubble Space Telescope (HST). We
discover and measure a sample of 669 Cepheids in four new and two archival NGC
4258 fields, doubling the number of known Cepheids in this galaxy and obtaining
an absolute calibration of their optical PLRs. We determine a Wesenheit PLR of
$-2.574(\pm0.034) -3.294(\pm0.042) \log P$, consistent with an independent
Large Magellanic Cloud (LMC) calibration at the level of $0.032\pm0.044$~mag in
its zeropoint, after accounting for a metallicity dependence of
$-0.20\pm0.05$~mag\,dex$^{-1}$ (Riess et al. 2006). Our determination of the
PLR slope also agrees with the LMC-based value within their uncertainties. We
attempt to characterize the metallicity effect of Cepheid PLRs using only the
NGC 4258 sample, but a relatively narrow span of abundances limits our
sensitivity and yields a Wesenheit zero-point dependence of $-0.07 \pm 0.21$
mag\,dex$^{-1}$. The Cepheid measurements presented in this study have been
used as part of the data to derive the Hubble constant in a companion paper by
the SH0ES team.


title:
A second-generation range-separated hybrid van der Waals density
  functional

abstract:
  We define, test, and illustrate use of a range-separated (screened) hybrid,
termed vdW-DF2-ahbr and abbreviated AHBR, that uses the second-generation
nonlocal-correlation formulation [PRB {\bf 82}, 081101(R) (2010)] within the
van der Waals density functional (vdW-DF) method [ROPP {\bf 78}, 066501
(2015)]. It supplements the vdW-DF-ahcx [JPCM {\bf 34}, 025902 (2022)]
(abbreviated AHCX) which relies on the first general-geometry formulation of
the nonlocal correlation energy [PRL 92, 246401 (2004)]. We build the AHBR off
a new analytical-hole analysis of the exchange in vdW-DF2-b86r [PRB {\bf 89},
121103 (2014)]. Like AHCX, the AHBR uses an exchange enhancement with a
large-gradient behavior that prevents spurious exchange binding and with a
small-gradient form that is set from many-body perturbation analysis. Unlike
AHCX, however, the AHBR relies on a slightly different interpretation of this
input, moving the resulting exchange description closer to PBEsol exchange [PRL
{\bf 100}, 136406 (2008)]. We validate that the range-separated hybrid (RSH)
vdW-DF AHBR retains and exceeds the strong AHCX performance both on bulk and in
a complete planewave benchmarking across the full GMTKN55 suite on broad
molecular properties [PCCP {\bf 19}, 32184 (2017)]. Interestingly, the AHBR
accuracy gains are most pronounced for the class of barrier-heights benchmarks,
suggesting that the AHBR is evenly robust for transition-state and binding
properties. Finally, we show that AHBR correctly predicts DNA base-pair
stacking energies, the binding-site preference for CO/Pt(111), and CO$_2$
adsorption in Mg-MOF74.


title:
The mechanism that drives electrostatic solitary waves to propagate in
  the Earth's magnetosphere and solar wind

abstract:
  The origin of the solitary waves in the Earth's magnetosphere and the solar
wind, particularly close to the magnetic reconnection regions, is still an open
question. For that purpose, we used the fluid model to obtain the mechanism
behind the formation of solitary waves in space plasmas with a variety of
components. Our findings reveal that at the separatrix of the magnetic
reconnection in the Earth's magnetotail, the counter-ejected electron beam
leads to a broadening of the pulse amplitude. While increasing the electron
beam positive velocity causes a bit increase in the soliton amplitude.
Moreover, we observed that the soliton profile is very sensitive to the plasma
parameters like density and temperature. The calculated electric field using
the current model and the measured electric field in the Earth's magnetosphere
and solar wind is highlighted.


title: Significance of Fidelity Deviation in Continuous Variable Teleportation

abstract:
  Performance of quantum teleportation is typically measured by the average
fidelity, an overlap between the input and output states. Along with the first
moment, we introduce the second moment of fidelity in CV teleportation, i.e.,
the fidelity deviation as the figures of merit to assess the protocol's
efficiency. We show that CV states, both Gaussian and non Gaussian, can be
better characterized by considering both average fidelity and fidelity
deviation, which is not possible with only average fidelity. Moreover, we shed
light on the performance of the teleportation protocol in two different input
scenarios - one is when input states are sampled from constrained uniform
distribution while the other one is Gaussian suppression of the input states
which again lead to a different classification of CV states according to their
performance. The entire analysis is carried out in noiseless and noisy
scenarios with noise being incorporated in the measurement and the shared
channels. We also report that one type of noise can make the protocol robust
against the other one which leads to a `constructive effect' and identify the
noise models which are responsible for decreasing average fidelity and
increment in fidelity deviation.


title: Encompassing Tests for Nonparametric Regressions

abstract:
  We set up a formal framework to characterize encompassing of nonparametric
models through the L2 distance. We contrast it to previous literature on the
comparison of nonparametric regression models. We then develop testing
procedures for the encompassing hypothesis that are fully nonparametric. Our
test statistics depend on kernel regression, raising the issue of bandwidth's
choice. We investigate two alternative approaches to obtain a "small bias
property" for our test statistics. We show the validity of a wild bootstrap
method, and we illustrate the attractive features of our tests for small and
moderate samples.


title: First measurement of high-energy reactor antineutrinos at Daya Bay

abstract:
  This Letter reports the first measurement of high-energy reactor
antineutrinos at Daya Bay, with nearly 9000 inverse beta decay candidates in
the prompt energy region of 8-12 MeV observed over 1958 days of data
collection. A multivariate analysis is used to separate 2500 signal events from
background statistically. The hypothesis of no reactor antineutrinos with
energy above 10 MeV is rejected with a significance of 6.2 standard deviations.
A 29\% deficit of reactor antineutrinos in the prompt energy region of 8-12 MeV
is observed compared to a recent model prediction. We provide the unfolded
antineutrino spectrum above 7 MeV as a data-based reference for other
experiments. This result provides the first direct observation of the
production of antineutrinos from several high-$Q_{\beta}$ isotopes in
commercial reactors.


title: The center of the modular super Yangian $Y_{m|n}$

abstract:
  The present paper is devoted to studying the super Yangian $Y_{m|n}$
associated to the general linear Lie superalgebra $\mathfrak{gl}_{m|n}$ over a
field of positive characteristic. We extend Drinfeld-type presentations of
$Y_{m|n}$ and the special super Yangian $SY_{m|n}$ to positive characteristic.
Moreover, the center $Z(Y_{m|n})$ of $Y_{m|n}$ is described: it is generated by
its Harish-Chandra center together with a large $p$-center. We also study the
$p$-center of $SY_{m|n}$ and provide another description of the $p$-center of
$Y_{m|n}$ in terms of the RTT generators.


title:
Amplitude analysis and branching fraction measurement of $D_{s}^{+} \to
  K^-K^+\pi^+\pi^+\pi^-$

abstract:
  Using $e^{+}e^{-}$ annihilation data corresponding to a total integrated
luminosity of $6.32$ fb$^{-1}$ collected at the center-of-mass energies between
4.178 and 4.226 GeV with the BESIII detector, we perform an amplitude analysis
of the decay $D^+_s \to K^-K^+\pi^+\pi^+\pi^-$ and determine the relative
fractions and phases of different intermediate processes. Absolute branching
fraction of $D^+_s\to K^-K^+\pi^+\pi^+\pi^-$ decay is measured to be
($6.60\pm0.47_{\rm stat.}\pm0.35_{\rm syst.})\times 10^{-3}$. The dominant
intermediate process is $D_{s}^{+} \to a_1(1260)^+\phi, \phi\to K^-K^+,
a_1(1260)^+\to \rho\pi^+, \rho\to\pi^+\pi^-$, with a branching fraction of
$(5.16\pm0.41_{\rm stat.}\pm0.27_{\rm syst.})\times 10^{-3}$.


title: Comment on "Theory of growth of number entropy in disordered systems"

abstract:
  We comment on the recent preprint by Ghosh and \v{Z}nidari\v{c}
[arXiv:2112.12987] which studies particle fluctuations in the Heisenberg model
with random magnetic fields after a quantum quench. The authors present
arguments for an intermediate power-law growth in time of the particle
fluctuations and a system-size independent saturation at long times, claiming
consistency of their results with many-body localization (MBL) for strong
disorder. Here we show that the unusual initial states considered in this work,
which show only local initial dynamics, are not suitable to investigate MBL in
small systems. Furthermore, we show that the power-law scaling for the number
entropy is system-size dependent and does not hold for typical initial states
with extensive energy fluctuations. We also argue that for the cases where the
authors find that an effective resonance model works, the dynamics is of single
particle type and unrelated to MBL.


title: Algebraic Learning: Towards Interpretable Information Modeling

abstract:
  Along with the proliferation of digital data collected using sensor
technologies and a boost of computing power, Deep Learning (DL) based
approaches have drawn enormous attention in the past decade due to their
impressive performance in extracting complex relations from raw data and
representing valuable information. Meanwhile, though, rooted in its notorious
black-box nature, the appreciation of DL has been highly debated due to the
lack of interpretability. On the one hand, DL only utilizes statistical
features contained in raw data while ignoring human knowledge of the underlying
system, which results in both data inefficiency and trust issues; on the other
hand, a trained DL model does not provide to researchers any extra insight
about the underlying system beyond its output, which, however, is the essence
of most fields of science, e.g. physics and economics.
  This thesis addresses the issue of interpretability in general information
modeling and endeavors to ease the problem from two scopes. Firstly, a
problem-oriented perspective is applied to incorporate knowledge into modeling
practice, where interesting mathematical properties emerge naturally which cast
constraints on modeling. Secondly, given a trained model, various methods could
be applied to extract further insights about the underlying system. These two
pathways are termed as guided model design and secondary measurements.
Remarkably, a novel scheme emerges for the modeling practice in statistical
learning: Algebraic Learning (AgLr). Instead of being restricted to the
discussion of any specific model, AgLr starts from idiosyncrasies of a learning
task itself and studies the structure of a legitimate model class. This novel
scheme demonstrates the noteworthy value of abstract algebra for general AI,
which has been overlooked in recent progress, and could shed further light on
interpretable information modeling.


title:
Privacy-friendly Synthetic Data for the Development of Face Morphing
  Attack Detectors

abstract:
  The main question this work aims at answering is: "can morphing attack
detection (MAD) solutions be successfully developed based on synthetic data?".
Towards that, this work introduces the first synthetic-based MAD development
dataset, namely the Synthetic Morphing Attack Detection Development dataset
(SMDD). This dataset is utilized successfully to train three MAD backbones
where it proved to lead to high MAD performance, even on completely unknown
attack types. Additionally, an essential aspect of this work is the detailed
legal analyses of the challenges of using and sharing real biometric data,
rendering our proposed SMDD dataset extremely essential. The SMDD dataset,
consisting of 30,000 attack and 50,000 bona fide samples, is publicly available
for research purposes.


title: Towards Semantic Communications: A Paradigm Shift

abstract:
  The last seventy years have witnessed the transition of communication from
Shannon's theoretical concept to current high-efficient practical systems.
Classical communication systems address the capability-deficiency issue mainly
by module-stacking and technique-densification with ever-increasing complexity.
In such a traditional viewpoint, classical source coding only uses explicit
probabilistic models to compress data, regardless of the meaning of transmitted
source messages. Also, channel coded transmission does not identify the source
content. In this sense, state-of-the-art communication systems work merely at
the technical level as summarized by Weaver. Unlike the traditional system
design philosophy, this article proposes a new route to boost the system
capabilities towards intelligence-endogenous and primitive-concise
communications. The communication paradigm upgrades to the semantic level,
which is radically different since all the key techniques imply the use of
meanings of transmitted data, thus deeply changing the design of the
communication system. This paradigm shifting unveils a promising direction due
to its ability to offer an identical quality of service with much lower data
transmission requirement. Different from other similar works, this article
constitutes a brief tutorial on the framework of semantic communications, its
gain analyzed from the information theory perspective, a method to calculate
the semantic compression bound, and an exemplary use case of semantic
communications.


title:
Continuum damping of topologically-protected edge modes at the boundary
  of a magnetized plasma

abstract:
  The topological properties of the magnetized plasma have recently been
studied for Hermitian (undamped) edge states. Taking a step further, we present
non-Hermitian effects (collisionless damping) of topological edge states in an
inhomogeneous magnetized plasma. We show that at an inhomogeneous plasma-vacuum
interface, a resonant coupling between an edge state and a local plasma bulk
mode (upper-hybrid) leads to collisionless damping of the former. By analysis
of theoretical model and ab-initio particle-in-cell simulation, we found that
the damping rate increases with the increasing scale length of density
inhomogeneity. We also present that coupling with bulk modes does not break the
topological protection of damped edge states.


title: Generating Practical Adversarial Network Traffic Flows Using NIDSGAN

abstract:
  Network intrusion detection systems (NIDS) are an essential defense for
computer networks and the hosts within them. Machine learning (ML) nowadays
predominantly serves as the basis for NIDS decision making, where models are
tuned to reduce false alarms, increase detection rates, and detect known and
unknown attacks. At the same time, ML models have been found to be vulnerable
to adversarial examples that undermine the downstream task. In this work, we
ask the practical question of whether real-world ML-based NIDS can be
circumvented by crafted adversarial flows, and if so, how can they be created.
We develop the generative adversarial network (GAN)-based attack algorithm
NIDSGAN and evaluate its effectiveness against realistic ML-based NIDS. Two
main challenges arise for generating adversarial network traffic flows: (1) the
network features must obey the constraints of the domain (i.e., represent
realistic network behavior), and (2) the adversary must learn the decision
behavior of the target NIDS without knowing its model internals (e.g.,
architecture and meta-parameters) and training data. Despite these challenges,
the NIDSGAN algorithm generates highly realistic adversarial traffic flows that
evade ML-based NIDS. We evaluate our attack algorithm against two
state-of-the-art DNN-based NIDS in whitebox, blackbox, and restricted-blackbox
threat models and achieve success rates which are on average 99%, 85%, and 70%,
respectively. We also show that our attack algorithm can evade NIDS based on
classical ML models including logistic regression, SVM, decision trees and
KNNs, with a success rate of 70% on average. Our results demonstrate that
deploying ML-based NIDS without careful defensive strategies against
adversarial flows may (and arguably likely will) lead to future compromises.


title: Relative State Quantum Logic

abstract:
  A projective quantum logic in terms of relative states is developed,
emphasizing the importance of information transfer between a system under study
and its environment. The need for accounting for the historical evolution of
system is highlighted and it is found that the conjunction of observations
involving conjugate variables can be consistently defined but is found to be
non-commutative. It is shown that the Birkhoff and von Neumann approach to
quantum logic is unable to deal with such conjunctions. It is found that whilst
the proposed scheme is still not distributive in general, the discrepancy is
directly related to interference effects that may disappear when information is
transferred from the system to its environment. It is argued that the
probabilities associated with projections be mapped to an orthocomplemented
ternary logic, in which it is shown that the law of the excluded middle still
holds.


title:
Training Protocol Matters: Towards Accurate Scene Text Recognition via
  Training Protocol Searching

abstract:
  The development of scene text recognition (STR) in the era of deep learning
has been mainly focused on novel architectures of STR models. However, training
protocol (i.e., settings of the hyper-parameters involved in the training of
STR models), which plays an equally important role in successfully training a
good STR model, is under-explored for scene text recognition. In this work, we
attempt to improve the accuracy of existing STR models by searching for optimal
training protocol. Specifically, we develop a training protocol search
algorithm, based on a newly designed search space and an efficient search
algorithm using evolutionary optimization and proxy tasks. Experimental results
show that our searched training protocol can improve the recognition accuracy
of mainstream STR models by 2.7%~3.9%. In particular, with the searched
training protocol, TRBA-Net achieves 2.1% higher accuracy than the
state-of-the-art STR model (i.e., EFIFSTR), while the inference speed is 2.3x
and 3.7x faster on CPU and GPU respectively. Extensive experiments are
conducted to demonstrate the effectiveness of the proposed method and the
generalization ability of the training protocol found by our search method.
Code is available at https://github.com/VDIGPKU/STR_TPSearch.


title: Efficient Long-Range Attention Network for Image Super-resolution

abstract:
  Recently, transformer-based methods have demonstrated impressive results in
various vision tasks, including image super-resolution (SR), by exploiting the
self-attention (SA) for feature extraction. However, the computation of SA in
most existing transformer based models is very expensive, while some employed
operations may be redundant for the SR task. This limits the range of SA
computation and consequently the SR performance. In this work, we propose an
efficient long-range attention network (ELAN) for image SR. Specifically, we
first employ shift convolution (shift-conv) to effectively extract the image
local structural information while maintaining the same level of complexity as
1x1 convolution, then propose a group-wise multi-scale self-attention (GMSA)
module, which calculates SA on non-overlapped groups of features using
different window sizes to exploit the long-range image dependency. A highly
efficient long-range attention block (ELAB) is then built by simply cascading
two shift-conv with a GMSA module, which is further accelerated by using a
shared attention mechanism. Without bells and whistles, our ELAN follows a
fairly simple design by sequentially cascading the ELABs. Extensive experiments
demonstrate that ELAN obtains even better results against the transformer-based
SR models but with significantly less complexity. The source code can be found
at https://github.com/xindongzhang/ELAN.


title: Regularity and stable ranges of FI-modules

abstract:
  We give refined bounds for the regularity of FI-modules and the stable ranges
of FI-modules for various forms of their stabilization studied in the
representation stability literature. We show that our bounds are sharp in
several cases. We apply these to get explicit stable ranges for diagonal
coinvariant algebras, and improve those for ordered configuration spaces of
manifolds and congruence subgroups of general linear groups.


title: Entanglement entropy in a four-dimensional cosmological background

abstract:
  We compute the holographic entanglement entropy of a thermalized CFT on a
time-dependent background in four dimensions. We consider a slab configuration
extending beyond the cosmological horizon of a
Friedmann-Lemaitre-Robertson-Walker metric. We identify a volume term that
corresponds to the thermal entropy of the CFT, as well as terms proportional to
the proper area of the entangling surface which are associated with strongly
entangled degrees of freedom in the vicinity of this surface or with the
expansion.


title:
A flexible method for estimating luminosity functions via Kernel Density
  Estimation -- II. Generalization and Python implementation

abstract:
  We propose a generalization of our previous KDE (kernel density estimation)
method for estimating luminosity functions (LFs). This new upgrade further
extend the application scope of our KDE method, making it a very flexible
approach which is suitable to deal with most of bivariate LF calculation
problems. From the mathematical point of view, usually the LF calculation can
be abstracted as a density estimation problem in the bounded domain of
$\{Z_1<z<Z_2,~ L>f_{\mathrm{lim}}(z) \}$. We use the transformation-reflection
KDE method ($\hat{\phi}$) to solve the problem, and introduce an approximate
method ($\hat{\phi}_{\mathrm{1}}$) based on one-dimensional KDE to deal with
the small sample size case. In practical applications, the different versions
of LF estimators can be flexibly chosen according to the Kolmogorov-Smirnov
test criterion. Based on 200 simulated samples, we find that for both cases of
dividing or not dividing redshift bins, especially for the latter, our method
performs significantly better than the traditional binning method
$\hat{\phi}_{\mathrm{bin}}$. Moreover, with the increase of sample size $n$,
our LF estimator converges to the true LF remarkably faster than
$\hat{\phi}_{\mathrm{bin}}$. To implement our method, we have developed a
public, open-source Python Toolkit, called \texttt{kdeLF}. With the support of
\texttt{kdeLF}, our KDE method is expected to be a competitive alternative to
existing nonparametric estimators, due to its high accuracy and excellent
stability. \texttt{kdeLF} is available at
\url{http://github.com/yuanzunli/kdeLF} with extensive documentation available
at \url{http://kdelf.readthedocs.org/en/latest~}.


title:
Complex-Valued Time Series Based Prediction: Case of Solar Irradiance
  Probabilistic Prediction

abstract:
  This paper describes a new way to predict real time series using
complex-valued elements. An example is given in the case of the short-term
probabilistic global solar irradiance forecasts with measurement as real part
and an estimate of the volatility as imaginary part. A simple complex
autoregressive model is tested with data collected in Corsica island (France).
Results show that, even if this approach is simple to set up and requires very
little resource and data, both deterministic and probabilistic forecasts
generated by this model are in agreement with experimental data (root mean
square error ranging from 0.196 to 0.325 considering all studied horizons). In
addition, it exhibits sometimes a better accuracy than classical models like
Gaussian process, bootstrap methodology or even more sophisticated model like
quantile regression. The number of models that it is possible to build by
generating complex-valued time series is substantial. Indeed, by using
exogenous or ordinal variables and computed quantities coupled with complex (or
multi-complex) numbers, many studies and many fields of physics could benefit
from this methodology and from the many models that result from it.


title:
Existence, structure, and robustness of ground states of a NLSE in 3D
  with a point defect

abstract:
  We study the ground states for the Schr\"odinger equation with a focusing
nonlinearity and a point interaction in dimension three. We establish that
ground states exist for every value of the mass; moreover they are positive,
radially symmetric, decreasing along the radial direction, and show a
Coulombian singularity at the location of the point interaction. Remarkably,
the existence of the ground states is independent of the attractive or
repulsive character of the point interaction.


title:
Valid and efficient imprecise-probabilistic inference across a spectrum
  of partial prior information

abstract:
  Bayesian inference quantifies uncertainty directly and formally using
classical probability theory, while frequentist inference does so indirectly
and informally through the use of procedures with error rate control. Both have
merits in the appropriate context, but the context isn't binary. There's an
entire spectrum of contexts depending on what, if any, partial prior
information is available to incorporate, with "Bayesian" and "frequentist"
sitting on opposite ends of the spectrum corresponding to a complete, fully
precise prior distribution and a vacuous, fully imprecise prior distribution,
respectively. Common examples between the two extremes include those
high-dimensional problems where, e.g., sparsity assumptions are relevant but
fall short of determining a complete prior distribution. This paper ties the
two frameworks together by treating those cases where only partial prior
information is available using the theory of imprecise probability. The end
result is a unified framework of (imprecise-probabilistic) statistical
inference with a new validity condition that implies both frequentist-style
error rate control for derived procedures and Bayesian-style no-sure-loss
properties, relative to the given partial prior information. This theory
contains both the classical "Bayesian" and "frequentist" frameworks as special
cases, since they're both valid in this new sense relative to their respective
partial priors. Different constructions of these valid inferential models are
considered, and compared based on their efficiency.


title:
Detecting intrinsic global geometry of an obstacle via layered
  scattering

abstract:
  Given a closed $k$-dimensional submanifold $K$, incapsulated in a compact
domain $M \subset \mathbb E^n$, $k \leq n-2$, we consider the problem of
determining the intrinsic geometry of the obstacle $K$ (like volume, integral
curvature) from the scattering data, produced by the reflections of geodesic
trajectories from the boundary of a tubular $\epsilon$-neighborhood $\mathsf
T(K, \epsilon)$ of $K$ in $M$. The geodesics that participate in this
scattering emanate from the boundary $\partial M$ and terminate there after a
few reflections from the boundary $\partial \mathsf T(K, \epsilon)$. However,
the major problem in this setting is that a ray (a billiard trajectory) may get
stuck in the vicinity of $K$ by entering some trap there so that this ray will
have infinitely many reflections from $\partial \mathsf T(K, \epsilon)$. To
rule out such a possibility, we modify the geometry of a tube $\mathsf T(K,
\epsilon)$ by building it from spherical bubbles. We need to use $\lceil
\dim(K)/2\rceil$ many bubbling tubes $\{\mathsf T(K, \epsilon_j)\}_j$ for
detecting certain global invariants of $K$, invariants which reflect its
intrinsic geometry. Thus the words "layered scattering" in the title. These
invariants were studied by Hermann Weyl in his classical theory of tubes
$\mathsf T(K, \epsilon)$ and their volumes.


title:
Heavy Neutral Lepton Searches at the Electron-Ion Collider: A Snowmass
  Whitepaper

abstract:
  In this whitepaper, we consider the model of heavy neutral leptons (HNLs) as
an example to explore the potential of new physics searches at the Electron-Ion
Collider (EIC). We propose two broad categories of search strategies depending
on the HNL lifetime: direct searches for the prompt decay of HNLs with a short
lifetime and displaced vertex searches for long-lived ones. After identifying
the most promising signals and the corresponding backgrounds, we perform a
detailed simulation to estimate the sensitivity of the EIC to HNLs, accounting
for detector thresholds, resolutions, and geometric acceptance. We derive
projections for the EIC reach to the HNL squared mixing angle as a function of
the HNL mass under the electron flavor mixing dominance hypothesis. Our
findings indicate that the EIC can provide comparable sensitivity to the
existing constraints for the prompt searches, while the displaced vertex
searches can cover substantial new ground for HNLs in the 1-10 GeV mass range.
Our proposed strategies are generally applicable to other new physics scenarios
as well and motivate additional phenomenological exploration and dedicated
future searches at the EIC.


title: Bredon and Farrell-Jones homology of Artin groups of dihedral type

abstract:
  For Artin groups of dihedral type, we compute the Bredon homology groups of
the classifying space for the family of virtually cyclic subgroups with
coefficients in the K-theory of a group ring, and the Farrell-Jones homology
groups of their classifying spaces with coefficients in regular rings.


title:
A Stochastic Binary Vertex-Triggering Resetting Algorithm for Global
  Synchronization of Pulse-Coupled Oscillators

abstract:
  In this paper, we propose a novel stochastic binary resetting algorithm for
networks of pulse-coupled oscillators (or, simply, agents) to reach global
synchronization. The algorithm is simple to state: Every agent in a network
oscillates at a common frequency. Upon completing an oscillation, an agent
generates a Bernoulli random variable to decide whether it sends pulses to all
of its out-neighbors or it stays quiet. Upon receiving a pulse, an agent resets
its state by following a binary phase update rule. We show that such an
algorithm can guarantee global synchronization of the agents almost surely as
long as the underlying information flow topology is a rooted directed graph.
The proof of the result relies on the use of a stochastic hybrid dynamical
system approach. Toward the end of the paper, we present numerical
demonstrations for the validity of the result and, also, numerical studies
about the times needed to reach synchronization for various information flow
topologies.


title:
Universal Thermal Corrections to Symmetry-Resolved Entanglement Entropy
  and Full Counting Statistics

abstract:
  The total entanglement entropy of a quantum system with an internal symmetry
can be decomposed into the contributions of individual charge sectors. The
contribution of each sector is same and scale as $\frac{1}{2}\ln(\ln l)$. These
results hold true for ground states. In this work, we consider the same problem
for the thermal states. To do so, we consider the symmetry-resolved R\'{e}nyi
and entanglement entropies for two-dimensional conformal field theories on a
circle at nonzero temperature. We assume a unique ground state with a nonzero
mass gap induced by the system's finite size and then calculate the leading
corrections to the contributions of individual charge sectors in a
low-temperature expansion. Besides the size of the mass gap and the degeneracy
of the first excited state, these universal corrections depend only on the
four-point correlation function of the primary fields. We also obtain thermal
corrections to the full counting statistics of the ground state and find that
the fluctuations of probabilities scale as $e^{-2 \pi \Delta_{\psi} \beta /L}$,
where $\Delta_{\psi}$ is the scaling dimension of the lowest weight states. As
an example, we explicitly evaluate the thermal corrections to the
symmetry-resolved entanglement entropy and FCS for the spinless fermions.


title: Packings and Steiner systems in polar spaces

abstract:
  A finite classical polar space of rank $n$ consists of the totally isotropic
subspaces of a finite vector space equipped with a nondegenerate form such that
$n$ is the maximal dimension of such a subspace. A $t$-Steiner system in a
finite classical polar space of rank $n$ is a collection $Y$ of totally
isotropic $n$-spaces such that each totally isotropic $t$-space is contained in
exactly one member of $Y$. Nontrivial examples are known only for $t=1$ and
$t=n-1$. We give an almost complete classification of such $t$-Steiner systems,
showing that such objects can only exist in some corner cases. This
classification result arises from a more general result on packings in polar
spaces.


title:
Directional ergodicity and weak mixing for actions of $\mathbb R^d$ and
  $\mathbb Z^d$

abstract:
  We define notions of direction $L$ ergodicity, weak mixing, and mixing for a
measure preserving $\mathbb Z^d$ action $T$ on a Lebesgue probability space
$(X,\mu)$, where $L\subseteq\mathbb R^d$ is a linear subspace. For $\mathbb
R^d$ actions these notions clearly correspond to the same properties for the
restriction of $T$ to $L$. For $\mathbb Z^d$ actions $T$ we define them by
using the restriction of the unit suspension $\widetilde T$ to the direction
$L$ and to the subspace of $L^2(\widetilde X,\widetilde \mu)$ perpendicular to
the suspension rotation factor. We show that for $\mathbb Z^d$ actions these
properties are spectral invariants, as they clearly are for $\mathbb R^d$
actions. We show that for weak mixing actions $T$ in both cases, directional
ergodicity implies directional weak mixing. For ergodic $\mathbb Z^d$ actions
$T$ we explore the relationship between directional properties defined via unit
suspensions and embeddings of $T$ in $\mathbb R^d$ actions. Genericity
questions and the structure of non-ergodic and non-weakly mixing directions are
also addressed.


title: Spin-chain-star systems: entangling multiple chains of spin qubits

abstract:
  We consider spin-chain-star systems characterized by N-wise many-body
interactions between the spins in each chain and the central one. We show that
such systems can be exactly mapped into standard spin-star systems through
unitary transformations. Such an approach allows the solution of the dynamic
problem of an XX spin-chain-star model and transparently shows the emergence of
quantum correlations in the system, based on the idea of entanglement between
chains.


title:
Memory effects on energy loss and diffusion of heavy quarks in the
  quark-gluon plasma

abstract:
  We study the dynamics of heavy quarks in a thermalized quark-gluon plasma
with a time-correlated thermal noise, $\eta$. In this case it is said that
$\eta$ has memory. We use an integro-differential Langevin equation in which
the memory enters via the thermal noise and the dissipative force. We assume
that the time correlations of the noise decay exponentially on a time scale,
$\tau$, that we treat as a free parameter. We compute the effects of $\tau\neq
0$ on the thermalization time of the heavy quarks, on their momentum broadening
and on the nuclear modification factor. We find that overall memory slows down
the momentum evolution of heavy quarks: in fact, transverse momentum broadening
and the formation of $R_{AA}$ are slowed down by memory and the thermalization
time of the heavy quarks become larger. The potential impact on other
observables is discussed briefly.


title:
Joint $q$-moments and shift invariance for the multi-species $q$-TAZRP
  on the infinite line

abstract:
  This paper presents a novel method for computing certain particle locations
in the multi-species $q$-TAZRP (totally asymmetric zero range process). The
method is based on a decomposition of the process into its discrete-time
embedded Markov chain, which is described more generally as a monotone process
on a graded partially ordered set; and an independent family of exponential
random variables. A further ingredient is explicit contour integral formulas
for the transition probabilities of the $q$-TAZRP. The main result of this
method is a shift invariance for the multi-species $q$-TAZRP on the infinite
line.
  By a previously known Markov duality result, these particle locations are the
same as joint $q$-moments. One particular special case is that for step initial
conditions, ordered multi-point joint $q$-moments of the $n$-species $q$-TAZRP
match the $n$-point joint $q$-moments of the single-species $q$-TAZRP. Thus, we
conjecture that the Airy$_2$ process describes the joint multi-point
fluctuations of multi-species $q$-TAZRP.


title: A Survey on Deep Graph Generation: Methods and Applications

abstract:
  Graphs are ubiquitous in encoding relational information of real-world
objects in many domains. Graph generation, whose purpose is to generate new
graphs from a distribution similar to the observed graphs, has received
increasing attention thanks to the recent advances of deep learning models. In
this paper, we conduct a comprehensive review on the existing literature of
graph generation from a variety of emerging methods to its wide application
areas. Specifically, we first formulate the problem of deep graph generation
and discuss its difference with several related graph learning tasks. Secondly,
we divide the state-of-the-art methods into three categories based on model
architectures and summarize their generation strategies. Thirdly, we introduce
three key application areas of deep graph generation. Lastly, we highlight
challenges and opportunities in the future study of deep graph generation.


title: Unbounded generalizations of the Fuglede-Putnam theorem

abstract:
  In this paper, we prove and disprove several generalizations of unbounded
versions of the Fuglede-Putnam theorem.


title:
Composite picosecond control of atomic state through a nanofiber
  interface

abstract:
  Accurate control of single emitters at nanophotonic interfaces may greatly
expand the accessible quantum states of coupled optical spins in the confined
geometry and to unveil exotic nonlinear quantum optical effects. However, the
optical control is challenged by spatially varying light-atom coupling strength
generic to nanophotonics. We demonstrate numerically that despite the
near-field inhomogenuity, nearly perfect atomic state control can be achieved
by exploiting geometric robustness of optical transitions with composite
picosecond excitations. Our proposal is followed by a proof-of-principle
demonstration where an $N=3$ composite sequence is applied to robustly invert
the D1 population of free-flying $^{85}$Rb atoms trespassing a nanofiber
interface. The precise control is confirmed by comparing the D2 fiber
transmission with full-level simulation of the mesoscopic light-atom
interaction across the composite parameter space. We project the scheme to
large $N$ for precise phase patterning and arbitrary optical dipole control at
the nanophotonic interface.


title: Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs

abstract:
  We revisit large kernel design in modern convolutional neural networks
(CNNs). Inspired by recent advances in vision transformers (ViTs), in this
paper, we demonstrate that using a few large convolutional kernels instead of a
stack of small kernels could be a more powerful paradigm. We suggested five
guidelines, e.g., applying re-parameterized large depth-wise convolutions, to
design efficient high-performance large-kernel CNNs. Following the guidelines,
we propose RepLKNet, a pure CNN architecture whose kernel size is as large as
31x31, in contrast to commonly used 3x3. RepLKNet greatly closes the
performance gap between CNNs and ViTs, e.g., achieving comparable or superior
results than Swin Transformer on ImageNet and a few typical downstream tasks,
with lower latency. RepLKNet also shows nice scalability to big data and large
models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K,
which is very competitive among the state-of-the-arts with similar model sizes.
Our study further reveals that, in contrast to small-kernel CNNs, large-kernel
CNNs have much larger effective receptive fields and higher shape bias rather
than texture bias. Code & models at
https://github.com/megvii-research/RepLKNet.


title: Local Hadwiger's Conjecture

abstract:
  We propose local versions of Hadwiger's Conjecture, where only balls of
radius $\Omega(\log v(G))$ around each vertex are required to be
$K_{t}$-minor-free. We ask: if a graph is locally-$K_{t}$-minor-free, is it
$t$-colourable? We show that the answer is yes when $t \leq 5$, even in the
stronger setting of list-colouring, and we complement this result with a
$O(\log v(G))$-round distributed colouring algorithm in the LOCAL model.
Further, we show that for large enough values of $t$, we can list-colour
locally-$K_{t}$-minor-free graphs with $13 \cdot \max\left\{h(t),\left\lceil
\frac{31}{2}(t-1) \right\rceil \right\}$ colours, where $h(t)$ is any value
such that all $K_{t}$-minor-free graphs are $h(t)$-list-colourable. We again
complement this with a $O(\log v(G))$-round distributed algorithm.


title:
Cross-grating phase microscopy (CGM): In-silico experiments, noise and
  accuracy

abstract:
  Cross-grating phase microscopy (CGM) is a quantitative phase microscopy
technique based on the association of a 2-dimensional diffraction grating
(cross-grating) and a regular camera sensor, separated by a millimetric
distance. This simple association enables the high-resolution imaging of the
complex electric field amplitude of a light beam (intensity and phase) from a
single image acquisition. While CGM has been used for metrology applications in
cell biology and nanophotonics applications this last decade, there has been
little study on its basics. In this article, we provide a numerical algorithm
that enables the in silico data acquisition, to easily vary and observe the
effects of all the CGM experimental parameters using computer means. In the
frame on this article, we illustrate the interest of this numerical algorithm
by using it to explain and quantify the effects of several important CGM
parameters (grating-camera distance, pixel size, light intensity, numerical
apertures, etc) on the noise, precision and trueness of CGM measurements. This
work is aimed to push the limits of CGM toward advanced applications in
biomicroscopy and nanophotonics.


title: Enhanced Photon Squeezing in Two-Photon Dicke Model

abstract:
  We explore the phenomena of quadrature squeezing of photons in the Two-Photon
Dicke Model under the mean-field approximation in the thermodynamic limit. The
strength of photon squeezing is maximized in the region where the coupling
strength is of the same order of magnitude as one of the fundamental frequency
of the system. This particular region is termed as the unbounded region. The
squeezing of the photonic quadratures can be observed only in the superradiant
phase where the squeezing is well beyond the standard quantum limit both near
the quantum critical point as well as the unbounded region. However, prolonged
squeezing is only obtained in the latter case. Furthermore, we explore the
critical behavior of photon squeezing near the unbounded region.


title:
Food Recipe Recommendation Based on Ingredients Detection Using Deep
  Learning

abstract:
  Food is essential for human survival, and people always try to taste
different types of delicious recipes. Frequently, people choose food
ingredients without even knowing their names or pick up some food ingredients
that are not obvious to them from a grocery store. Knowing which ingredients
can be mixed to make a delicious food recipe is essential. Selecting the right
recipe by choosing a list of ingredients is very difficult for a beginner cook.
However, it can be a problem even for experts. One such example is recognising
objects through image processing. Although this process is complex due to
different food ingredients, traditional approaches will lead to an inaccuracy
rate. These problems can be solved by machine learning and deep learning
approaches. In this paper, we implemented a model for food ingredients
recognition and designed an algorithm for recommending recipes based on
recognised ingredients. We made a custom dataset consisting of 9856 images
belonging to 32 different food ingredients classes. Convolution Neural Network
(CNN) model was used to identify food ingredients, and for recipe
recommendations, we have used machine learning. We achieved an accuracy of 94
percent, which is quite impressive.


title:
Cloud Continents: Terraforming Venus Efficiently by Means of a Floating
  Artificial Surface

abstract:
  The similarity of Venus and Earth in bulk properties make Venus an appealing
target for future colonization. Several proposals have been put forward for
colonizing and even terraforming Venus despite the extreme conditions on the
planet's surface. Such a terraforming project would face large challenges
centered around removing Venus's massive carbon dioxide atmosphere and
replacing it with a habitable environment. I review past proposals and propose
a new method for terraforming Venus by building an artificial surface in the
much more hospitable upper atmosphere where the temperature and pressure are
both Earth-like. Such a surface could be built with locally produced materials
and would float above the bulk of the atmosphere using nitrogen as a lifting
gas. This would allow the engineering of a breathable atmosphere above the
surface and would remove the need to import or export extreme amounts of mass,
except for comparatively modest quantities of water. The engineering,
logistical, and energy requirements of this method are surveyed. I find that
such a terraforming project could be completed in a minimum of 200 years in a
best-case scenario, comparable to other proposals, with significantly lower
resource costs.


title: MQXFA Final Design Report

abstract:
  The MQXFA Quadrupole magnets will be installed in High Luminosity LHC to form
the Q1 and Q3 inner triplet optical elements in front of the interaction points
1 (ATLAS) and 5 (CMS). A pair of MQXFA units is assembled in a stainless steel
helium vessel, including the end domes, to make the Q1 Cold Mass or the Q3 Cold
Mass. The US HL LHC Accelerator Upgrade Project* is responsible for the design,
manufacturing and test of the Q1/Q3 Cold Masses and the complete MQXFA magnets.
CERN provides the cryostat components and is responsible for integration and
installation in HL LHC. The MQXFA quadrupoles have 150 mm aperture, 4.2 m
magnetic length, nominal gradient of 132.2 T/m, and coil peak field of 11.3 T.
They use Nb_3Sn conductor and a support structure made of segmented aluminum
shells pre-loaded by using bladders and keys. This report presents the final
design of the MQXFA quadrupole magnets.
  *Supported by the U.S. Department of Energy, Office of Science, Office of
High Energy Physics


title:
Characterization of the polarized synchrotron emission from Planck and
  WMAP data

abstract:
  The purpose of this work is to characterize the diffuse Galactic polarized
synchrotron. We present EE, BB, and EB power spectra estimated
cross-correlating Planck and WMAP polarization frequency maps at 23 and 30 GHz,
for a set of six sky regions covering from 30% to 94% of the sky. The EE and BB
angular power spectra show a steep decay of the spectral amplitude as a
function of multipole, approximated by a power law with power indices around
-2.9 for both components. The B/E ratio is about 0.22. The EB cross-component
is compatible with zero at 1$\sigma$, with upper constraint on the EB/EE ratio
of 1.2% at the 2$\sigma$ level. The recovered SED, in the frequency range 23-30
GHz, shows E and B power-law spectral indices compatible between themselves
with a value of about -3.


title: Network Bandwidth Allocation Problem For Cloud Computing

abstract:
  Cloud computing enables ubiquitous, convenient, and on-demand network access
to a shared pool of computing resources. Cloud computing technologies create
tremendous commercial values in various areas, while many scientific challenges
have arisen accordingly. The process of transmitting data through networks is
characterized by some distinctive characteristics such as nonlinear, nonconvex
and even noncontinuous cost functions generated by pricing schemes,
periodically updated network topology, as well as replicable data within
network nodes. Because of these characteristics, data transfer scheduling is a
very challenging problem both engineeringly and scientifically. On the other
hand, the cost for bandwidth is a major component of the operating cost for
cloud providers, and thus how to save bandwidth cost is extremely important for
them to supply service with minimized cost. We propose the Network Bandwidth
Allocation (NBA) problem for cloud computing and formulate it as an integer
programming model on a high level, with which more comprehensive and rigorous
scientific studies become possible. We also show that the NBA problem captures
some of the major cloud computing scenarios including the content delivery
network (CDN), the live video delivery network (LVDN), the real-time
communication network (RTCN), and the cloud wide area network (Cloud-WAN).


title:
LOFAR detection of faint radio emission from the supernova remnant
  SRGeJ0023+3625=G116.6-26.1: probing the Milky Way synchrotron halo

abstract:
  A supernova remnant (SNR) candidate SRGe~J0023+3625 = G116.6-26.1 was
recently discovered in the \textit{SRG}/eROSITA all-sky X-ray survey. This
large ($\sim 4$ deg in diameter) SNR candidate lacks prominent counterparts in
other bands. Here we report detection of radio emission from G116.6-26.1 in the
LOFAR Two-metre Sky Survey (LoTTS-DR2). Radio images show a shell-like
structure coincident with the X-ray boundary of the SNR. The measured surface
brightness of radio emission from this SNR is very low. Extrapolation of the
observed surface brightness to 1~GHz places G116.6-26.1 well below other
objects in the $\Sigma-D$ diagram. We argue that the detected radio flux might
be consistent with the minimal level expected in the van der Laan adiabatic
compression model, provided that the volume emissivity of the halo gas in the
LOFAR band is $\sim 10^{-42}\,{\rm Wm^{-3}Hz^{-1} sr^{-1}}$. If true, this SNR
can be considered as a prototypical example of an evolved SNR in the Milky Way
halo. In the X-ray and radio bands, such SNRs can be used as probes of thermal
and non-thermal components constituting the Milky Way halo.


title: Dewetting dynamics of sheared thin polymer films: an experimental study

abstract:
  An experimental investigation is reported on the effect of shear on the
bursting of molten ultra-thin polymer films embedded in an immiscible matrix.
By using an optical microscope coupled with a shearing hotstage, the dewetting
dynamics, i.e. the growth of dewetting holes is monitored over time at various
shear rates. It is observed that their circularity is modified by shear and
that for all temperatures and thicknesses studied, the growth speed of the
formed holes rapidly increases with increasing shear rate. A model balancing
capillary forces and viscous dissipation while taking into account
shear-thinning is then proposed and captures the main features of the
experimental data, such as the ellipsoid shape of the holes and the faster
dynamics in the direction parallel to the shear. This research will help to
understand the instabilities occurring during processing of layered polymeric
structures, such as multilayer coextrusion.


title: SciNLI: A Corpus for Natural Language Inference on Scientific Text

abstract:
  Existing Natural Language Inference (NLI) datasets, while being instrumental
in the advancement of Natural Language Understanding (NLU) research, are not
related to scientific text. In this paper, we introduce SciNLI, a large dataset
for NLI that captures the formality in scientific text and contains 107,412
sentence pairs extracted from scholarly papers on NLP and computational
linguistics. Given that the text used in scientific literature differs vastly
from the text used in everyday language both in terms of vocabulary and
sentence structure, our dataset is well suited to serve as a benchmark for the
evaluation of scientific NLU models. Our experiments show that SciNLI is harder
to classify than the existing NLI datasets. Our best performing model with
XLNet achieves a Macro F1 score of only 78.18% and an accuracy of 78.23%
showing that there is substantial room for improvement.