title: Quantum curves from refined topological recursion: the genus 0 case

abstract:
  We formulate geometrically (without reference to physical models) a refined
topological recursion applicable to genus zero curves of degree two, inspired
by Chekhov-Eynard and Marchal, introducing new degrees of freedom in the
process. For such curves, we prove the fundamental properties of the recursion
analogous to the unrefined case. We show the quantization of spectral curves
due to Iwaki-Koike-Takei can be generalized to this setting and give the
explicit formula, which turns out to be related to the unrefined case by a
simple transformation. For an important collection of examples, we write down
the quantum curves and find that in the Nekrasov-Shatashvili limit, they take
an especially simple form.


title:
Encoding Cardiopulmonary Exercise Testing Time Series as Images for
  Classification using Convolutional Neural Network

abstract:
  Exercise testing has been available for more than a half-century and is a
remarkably versatile tool for diagnostic and prognostic information of patients
for a range of diseases, especially cardiovascular and pulmonary. With rapid
advancements in technology, wearables, and learning algorithm in the last
decade, its scope has evolved. Specifically, Cardiopulmonary exercise testing
(CPX) is one of the most commonly used laboratory tests for objective
evaluation of exercise capacity and performance levels in patients. CPX
provides a non-invasive, integrative assessment of the pulmonary,
cardiovascular, and skeletal muscle systems involving the measurement of gas
exchanges. However, its assessment is challenging, requiring the individual to
process multiple time series data points, leading to simplification to peak
values and slopes. But this simplification can discard the valuable trend
information present in these time series. In this work, we encode the time
series as images using the Gramian Angular Field and Markov Transition Field
and use it with a convolutional neural network and attention pooling approach
for the classification of heart failure and metabolic syndrome patients. Using
GradCAMs, we highlight the discriminative features identified by the model.


title:
Equivalence and Duality of Polycyclic Codes Associated with Trinomials
  over Finite Fields

abstract:
  In this paper, several conjectures proposed in [2] are studied, involving the
equivalence and duality of polycyclic codes associated with trinomials.
According to the results, we give methods to construct isodual and self-dual
polycyclic codes, and study the self-orthogonal and dual-containing polycyclic
codes over F2.


title:
Hot-lines topology and the fate of the spin resonance mode in
  three-dimensional unconventional superconductors

abstract:
  In the quasi-two-dimensional (quasi-2D) copper- and iron-based
superconductors, the onset of superconductivity is accompanied by a prominent
peak in the magnetic spectrum at momenta close to the wave-vector of the nearby
antiferromagnetic state. Such a peak is well described in terms of a spin
resonance mode, i.e., a spin-1 exciton theoretically predicted for quasi-2D
superconductors with a sign-changing gap. The same theories, however, indicate
that such a resonance mode should be absent in a three-dimensional (3D) system
with a spherical Fermi surface. This raises the question of the fate of the
spin resonance mode in layered unconventional superconductors that are not
strongly anisotropic, such as certain heavy-fermion compounds and potentially
the newly discovered nickelate superconductor NdNiO$_2$. Here, we use the
random-phase-approximation to calculate the dynamical spin susceptibility of 3D
superconductors with a $d_{x^2-y^2}$-wave gap symmetry and corrugated
cylindrical-like Fermi surfaces. By varying the out-of-plane hopping anisotropy
$t_z/t$, we demonstrate that the appearance of a spin resonance mode is
determined by the topology of the hot lines -- i.e. lines on the Fermi surface
that are connected by the magnetic wave-vector. For an in-plane
antiferromagnetic wave-vector, the hot lines undergo a topological transition
from open lines to closed loops at a critical $t_z/t$ value. The closed hot
lines cross the nodal superconducting lines, making the spin resonance mode
overdamped and incoherent. In contrast, for an out-of-plane antiferromagnetic
wave-vector, the hot lines remain open and the spin resonance mode remains
sharp. We discuss the experimental implications of our results for the
out-of-plane dispersion of the spin resonance mode and, more generally, for
inelastic neutron scattering experiments on unconventional superconductors.


title:
Comet 21P/Giacobini-Zinner: Narrowband Photometry of the Prototype of
  Carbon-Chain Depleted Comets at Multiple Apparitions

abstract:
  We obtained extensive narrowband photoelectric photometry of Comet
21P/Giacobini-Zinner with observations spanning 33 years. The original data
from 1985 (Schleicher et al. 1987) were re-reduced and are presented along with
data from three additional apparitions including 2018/19. The original
conclusion regarding Giacobini-Zinner's chemical composition remains unchanged,
with it having a 4-6x depletion in the carbon-chain molecules C2 and C3, and in
NH, as compared with both OH and CN. The comet continues to exhibit a large
asymmetry in production rates as a function of time and heliocentric distance,
with production reaching a peak 3-5 weeks prior to perihelion. All species,
including dust, follow the same general production rate curve each apparition,
and the carbon-bearing species are always very similar to one another. However,
OH and NH each differ in detail from the carbon-bearing species, implying
somewhat varied composition between source regions. Longer term, there are only
small secular changes among the apparitions before and near perihelion, but
larger changes are evident as the comet recedes from the Sun, suggestive of a
progressive precession of the rotation axis.


title:
Incentives in Social Decision Schemes with Pairwise Comparison
  Preferences

abstract:
  Social decision schemes (SDSs) map the preferences of individual voters over
multiple alternatives to a probability distribution over the alternatives. In
order to study properties such as efficiency, strategyproofness, and
participation for SDSs, preferences over alternatives are typically lifted to
preferences over lotteries using the notion of stochastic dominance (SD).
However, requiring strategyproofness or participation with respect to this
preference extension only leaves room for rather undesirable SDSs such as
random dictatorships. Hence, we focus on the natural but little understood
pairwise comparison (PC) preference extension, which postulates that one
lottery is preferred to another if the former is more likely to return a
preferred outcome. In particular, we settle three open questions raised by
Brandt (2017): (i) there is no Condorcet-consistent SDS that satisfies
PC-strategyproofness; (ii) there is no anonymous and neutral SDS that satisfies
PC-efficiency and PC-strategyproofness; and (iii) there is no anonymous and
neutral SDS that satisfies PC-efficiency and strict PC-participation. All three
impossibilities require m >= 4 alternatives and turn into possibilities when m
<= 3.


title: On the Equilibria and Bifurcations of a Rotating Double Pendulum

abstract:
  The double pendulum, a simple system of classical mechanics, is widely
studied as an example of, and testbed for, chaotic dynamics. In 2016, Maiti et
al. studied a generalization of the simple double pendulum with equal
point-masses at equal lengths, to a rotating double pendulum, fixed to a
coordinate system uniformly rotating about the vertical. In this paper, we
study a considerable generalization of the double pendulum, constructed from
physical pendula, and ask what equilibrium configurations exist for the system
across a comparatively large parameter space, as well as what bifurcations
occur in those equilibria. Elimination algorithms are employed to reduce
systems of polynomial equations, which allows for equilibria to be visualized,
and also to demonstrate which models within the parameter space exhibit
bifurcation. We find the DixonEDF algorithm for the Dixon resultant, written in
the computer algebra system (CAS) Fermat, to be capable to complete the
computation for the challenging system of equations that represents
bifurcation, while attempts with other algorithms were terminated after several
hours.


title: Brillouin Klein Bottle From Artificial Gauge Fields

abstract:
  A Brillouin zone is the unit for the momentum space of a crystal. It is
topologically a torus, and distinguishing whether a set of wave functions over
the Brillouin torus can be smoothly deformed to another leads to the
classification of various topological states of matter. Here, we show that
under $\mathbb{Z}_2$ gauge fields, i.e., hopping amplitudes with phases $\pm
1$, the fundamental domain of momentum space can assume the topology of a Klein
bottle. This drastic change of the Brillouin zone theory is due to the
projective symmetry algebra enforced by the gauge field. Remarkably, the
non-orientability of the Brillouin Klein bottle corresponds to the topological
classification by a $\mathbb{Z}_2$ invariant, in contrast to the Chern number
valued in $\mathbb{Z}$ for the usual Brillouin torus. The result is a novel
Klein-bottle insulator featuring topological modes at two edges related by a
nonlocal twist, radically distinct from all previous topological insulators.
Our prediction can be readily achieved in various artificial crystals, and the
discovery opens a new direction to explore topological physics by
gauge-field-modified fundamental structures of physics.


title: Some characterizations of compact Einstein-type manifolds

abstract:
  In this work, we investigate the geometry and topology of compact
Einstein-type manifolds with nonempty boundary. First, we prove a sharp
boundary estimate, as consequence we obtain under certain hypotheses that the
Hawking mass is bounded from bellow in terms of area. Then we give a
topological classification for its boundary. Finally, we prove a gap result for
a compact Einstein-type manifold with boundary.


title:
neuro2vec: Masked Fourier Spectrum Prediction for Neurophysiological
  Representation Learning

abstract:
  Extensive data labeling on neurophysiological signals is often prohibitively
expensive or impractical, as it may require particular infrastructure or domain
expertise. To address the appetite for data of deep learning methods, we
present for the first time a Fourier-based modeling framework for
self-supervised pre-training of neurophysiology signals. The intuition behind
our approach is simple: frequency and phase distribution of neurophysiology
signals reveal the underlying neurophysiological activities of the brain and
muscle. Our approach first randomly masks out a portion of the input signal and
then predicts the missing information from either spatiotemporal or the Fourier
domain. Pre-trained models can be potentially used for downstream tasks such as
sleep stage classification using electroencephalogram (EEG) signals and gesture
recognition using electromyography (EMG) signals. Unlike contrastive-based
methods, which strongly rely on carefully hand-crafted augmentations and
siamese structure, our approach works reasonably well with a simple transformer
encoder with no augmentation requirements. By evaluating our method on several
benchmark datasets, including both EEG and EMG, we show that our modeling
approach improves downstream neurophysiological related tasks by a large
margin.


title: Investigating the Optimal Neural Network Parameters for Decoding

abstract:
  Neural Networks have been proved to work as decoders in telecommunications,
so the ways of making it efficient will be investigated in this thesis. The
different parameters to maximize the Neural Network Decoder's efficiency will
be investigated. The parameters will be tested for inversion errors only.


title: Multi-task Deep Neural Networks for Massive MIMO CSI Feedback

abstract:
  Deep learning has been widely applied for the channel state information (CSI)
feedback in frequency division duplexing (FDD) massive multiple-input
multiple-output (MIMO) system. For the typical supervised training of the
feedback model, the requirements of large amounts of task-specific labeled data
can hardly be satisfied, and the huge training costs and storage usage of the
model in multiple scenarios are hindrance for model application. In this
letter, a multi-task learning-based approach is proposed to improve the
feasibility of the feedback network. An encoder-shared feedback architecture
and the corresponding training scheme are further proposed to facilitate the
implementation of the multi-task learning approach. The experimental results
indicate that the proposed multi-task learning approach can achieve
comprehensive feedback performance with considerable reduction of training cost
and storage usage of the feedback model.


title: A review of Federated Learning in Intrusion Detection Systems for IoT

abstract:
  Intrusion detection systems are evolving into intelligent systems that
perform data analysis searching for anomalies in their environment. The
development of deep learning technologies opened the door to build more complex
and effective threat detection models. However, training those models may be
computationally infeasible in most Internet of Things devices. Current
approaches rely on powerful centralized servers that receive data from all
their parties -- violating basic privacy constraints and substantially
affecting response times and operational costs due to the huge communication
overheads. To mitigate these issues, Federated Learning emerged as a promising
approach where different agents collaboratively train a shared model, neither
exposing training data to others nor requiring a compute-intensive centralized
infrastructure. This paper focuses on the application of Federated Learning
approaches in the field of Intrusion Detection. Both technologies are described
in detail and current scientific progress is reviewed and categorized. Finally,
the paper highlights the limitations present in recent works and presents some
future directions for this technology.


title:
K-invariant Hilbert Modules and Singular Vector Bundles on Bounded
  Symmetric Domains

abstract:
  We show that the "eigenbundle" (localization bundle) of certain Hilbert
modules over bounded symmetric domains of rank r is a "singular" vector bundle
(linearly fibrered complex analytic space) which decomposes as a stratified sum
of homogeneous vector bundles along a canonical stratification of length r+1.
The fibres are realized in terms of representation theory on the normal space
of the strata.


title:
Assimilation of magnetic resonance elastography data in an in silico
  brain model

abstract:
  This paper investigates a data assimilation approach for non-invasive
quantification of intracranial pressure from partial displacement data,
acquired through magnetic resonance elastography. Data assimilation is based on
a parametrized-background data weak methodology, in which the state of the
physical system -- tissue displacements and pressure fields -- is reconstructed
from partially available data assuming an underlying poroelastic biomechanics
model. For this purpose, a physics-informed manifold is built by sampling the
space of parameters describing the tissue model close to their physiological
ranges, to simulate the corresponding poroelastic problem, and compute a
reduced basis. Displacements and pressure reconstruction is sought in a reduced
space after solving a minimization problem that encompasses both the structure
of the reduced-order model and the available measurements. The proposed
pipeline is validated using synthetic data obtained after simulating the
poroelastic mechanics on a physiological brain. The numerical experiments
demonstrate that the framework can exhibit accurate joint reconstructions of
both displacement and pressure fields. The methodology can be formulated for an
arbitrary resolution of available displacement data from pertinent images. It
can also inherently handle uncertainty on the physical parameters of the
mechanical model by enlarging the physics-informed manifold accordingly.
Moreover, the framework can be used to characterize, in silico, biomarkers for
pathological conditions, by appropriately training the reduced-order model. A
first application for the estimation of ventricular pressure as an indicator of
abnormal intracranial pressure is shown in this contribution.


title:
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for
  Full-Batch GD

abstract:
  We provide sharp path-dependent generalization and excess error guarantees
for the full-batch Gradient Decent (GD) algorithm for smooth losses (possibly
non-Lipschitz, possibly nonconvex). At the heart of our analysis is a novel
generalization error technique for deterministic symmetric algorithms, that
implies average output stability and a bounded expected gradient of the loss at
termination leads to generalization. This key result shows that small
generalization error occurs at stationary points, and allows us to bypass
Lipschitz assumptions on the loss prevalent in previous work. For nonconvex,
convex and strongly convex losses, we show the explicit dependence of the
generalization error in terms of the accumulated path-dependent optimization
error, terminal optimization error, number of samples, and number of
iterations. For nonconvex smooth losses, we prove that full-batch GD
efficiently generalizes close to any stationary point at termination, under the
proper choice of a decreasing step size. Further, if the loss is nonconvex but
the objective is PL, we derive vanishing bounds on the corresponding excess
risk. For convex and strongly-convex smooth losses, we prove that full-batch GD
generalizes even for large constant step sizes, and achieves a small excess
risk while training fast. Our full-batch GD generalization error and excess
risk bounds are significantly tighter than the existing bounds for (stochastic)
GD, when the loss is smooth (but possibly non-Lipschitz).


title: E-values as unnormalized weights in multiple testing

abstract:
  Most standard weighted multiple testing methods require the weights on the
hypotheses to sum to one. We show that this normalization is not required when
the weights are not constants, but are themselves e-values obtained from
independent data. This could result in a massive increase in power, especially
if the non-null hypotheses have e-values much larger than one. More broadly, we
study how to combine an e-value and a p-value, and design multiple testing
procedures where both e-values and p-values are available for every hypothesis
(or one of them is available for an implied hypothesis). For false discovery
rate (FDR) control, analogous to the Benjamini-Hochberg procedure with p-values
(p-BH) and the recent e-BH procedure for e-values, we propose the ep-BH and the
pe-BH procedures, which have valid FDR guarantee under different dependence
assumptions. The procedures are designed based on several admissible combining
functions for p/e-values. The method can be directly applied to family-wise
error rate control problems. We also collect several miscellaneous results,
such as a tiny but uniform improvement of e-BH, a soft-rank permutation
e-value, and the use of e-values as masks in interactive multiple testing.


title: Density anomaly in a minimum model of water-alcohol mixtures

abstract:
  We study the change in the temperature of maximum density (TMD) of a
water-like solvent when small amounts of solute are added to the mixture. The
water is modeled as a two length scales potential, which is known to exhibit
water-like characteristic anomalies, while the solute is chosen to have an
attractive interaction with the solvent. If the solute is a structure maker the
TMD increases with the addition of solute, while if it is a structure breaker
the TMD decreases. Both in an exact one dimensional model and using molecular
dynamics in three dimensions we show that the necessary ingredient to represent
structure makers is a strong solute-solvent attraction.


title:
A thinning-based representation of the compound-Poisson INGARCH model,
  with an interpretation as a stochastic epidemic process

abstract:
  A thinning-based representation of the Poisson and certain compound Poisson
(CP) INGARCH models is proposed. This approaches them to the INAR model class,
which is equally thinning-based, and allows for the application of branching
process theory to derive stochastic properties. Notably, it is straightforward
to show that under mild conditions the CP-INGARCH(1, 1) model is geometrically
ergodic while all moments of the limiting-stationary distribution are finite.
Moreover, the new representation provides an interpretation of INGARCH models
as a stochastic epidemic process, specifically a simplified discrete-time SEIR
(susceptible-exposed-infectious-removed) model.


title:
On fractional differential operators defined by Khalil, Katugampola, and
  others

abstract:
  We show that current results in the spectral theory of fractional
Sturm-Liouville problems are simple consequences of ordinary Sturm-Liouville
theory and a new notion of generalized derivatives.


title: Understanding The Robustness in Vision Transformers

abstract:
  Recent studies show that Vision Transformers(ViTs) exhibit strong robustness
against various corruptions. Although this property is partly attributed to the
self-attention mechanism, there is still a lack of systematic understanding. In
this paper, we examine the role of self-attention in learning robust
representations. Our study is motivated by the intriguing properties of the
emerging visual grouping in Vision Transformers, which indicates that
self-attention may promote robustness through improved mid-level
representations. We further propose a family of fully attentional networks
(FANs) that strengthen this capability by incorporating an attentional channel
processing design. We validate the design comprehensively on various
hierarchical backbones. Our model achieves a state of-the-art 87.1% accuracy
and 35.8% mCE on ImageNet-1k and ImageNet-C with 76.8M parameters. We also
demonstrate state-of-the-art accuracy and robustness in two downstream tasks:
semantic segmentation and object detection. Code will be available at
https://github.com/NVlabs/FAN.


title: The finite range simple effective interaction including tensor terms

abstract:
  The prediction of single particle level crossing phenomenon between
$2p_{3/2}$ and $1f_{5/2}$ orbitals in $Ni$- and $Cu$-isotopic chains by the
finite range simple effective interaction without requiring the tensor part is
discussed. In this case the experimentally observed crossing could be studied
as a function of nuclear matter incompressibility, $K(\rho_0)$. The estimated
crossing for the neutron number $N$=46 could be reproduced by the equation of
state corresponding to $K(\rho_0)$=240 MeV. However, the observed proton gaps
between the $1h_{11/2}$ and $1g_{7/2}$ shells in $Sn$ and $Sb$ isotopic chain,
and the neutron gaps between the $1i_{13/2}$ and $1h_{9/2}$ shells in $N$=82
isotones, as well as the shell closure properties at $N$=28 require explicit
consideration of a tensor part as the central contribution is not enough to
initiate the required level splittings.


title: W-boson mass and electric dipole moments from colour-octet scalars

abstract:
  New coloured scalars in the Manohar-Wise model give sizeable contributions to
the Electric Dipole Moment (EDM) of the neutron through one-loop and two-loop
diagrams, computed in Reference [1]. Contributions from neutral scalars cancel
out for degenerate values of the masses, whose difference is related to the
oblique parameters $S$ and $T$. The Manohar-Wise model is able to explain the
recent value of the $W$-boson mass published by the CDF collaboration. We show,
however, that the case of total degeneracy of masses is strongly disfavoured in
this model, whose parameter space is substantially reduced when considering
also unitarity bounds.


title:
Differentiable Zooming for Multiple Instance Learning on Whole-Slide
  Images

abstract:
  Multiple Instance Learning (MIL) methods have become increasingly popular for
classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.
Most MIL methods operate at a single WSI magnification, by processing all the
tissue patches. Such a formulation induces high computational requirements, and
constrains the contextualization of the WSI-level representation to a single
scale. A few MIL methods extend to multiple scales, but are computationally
more demanding. In this paper, inspired by the pathological diagnostic process,
we propose ZoomMIL, a method that learns to perform multi-level zooming in an
end-to-end manner. ZoomMIL builds WSI representations by aggregating
tissue-context information from multiple magnifications. The proposed method
outperforms the state-of-the-art MIL methods in WSI classification on two large
datasets, while significantly reducing the computational demands with regard to
Floating-Point Operations (FLOPs) and processing time by up to 40x.


title: Resolution of the Erd\H{o}s-Sauer problem on regular subgraphs

abstract:
  In this paper we completely resolve the well-known problem of Erd\H{o}s and
Sauer from 1975 which asks for the maximum number of edges an $n$-vertex graph
can have without containing a $k$-regular subgraph, for some fixed integer
$k\geq 3$. We prove that any $n$-vertex graph with average degree at least
$C_k\log \log n$ contains a $k$-regular subgraph. This matches the lower bound
of Pyber, R\"odl and Szemer\'edi and substantially improves an old result of
Pyber, who showed that average degree at least $C_k\log n$ is enough.
  Our method can also be used to settle asymptotically a problem raised by
Erd\H{o}s and Simonovits in 1970 on almost regular subgraphs of sparse graphs
and to make progress on the well-known question of Thomassen from 1983 on
finding subgraphs with large girth and large average degree.


title:
Event Detection Explorer: An Interactive Tool for Event Detection
  Exploration

abstract:
  Event Detection (ED) is an important task in natural language processing. In
the past few years, many datasets have been introduced for advancing ED machine
learning models. However, most of these datasets are under-explored because not
many tools are available for people to study events, trigger words, and event
mention instances systematically and efficiently. In this paper, we present an
interactive and easy-to-use tool, namely ED Explorer, for ED dataset and model
exploration. ED Explorer consists of an interactive web application, an API,
and an NLP toolkit, which can help both domain experts and non-experts to
better understand the ED task. We use ED Explorer to analyze a recent proposed
large-scale ED datasets (referred to as MAVEN), and discover several underlying
problems, including sparsity, label bias, label imbalance, and debatable
annotations, which provide us with directions to improve the MAVEN dataset. The
ED Explorer can be publicly accessed through http://edx.leafnlp.org/. The
demonstration video is available here
https://www.youtube.com/watch?v=6QPnxPwxg50.


title: On a converse of Sturm's comparison theorem

abstract:
  We show that Sturm's classical comparison theorem (SCT) on the interlacing of
zeros of solutions of pairs of real second order two-term ordinary differential
equations necessarily fails if the usual Sturmian-type conditions on the
coefficients are violated. We also show that the conditions on the coefficients
are, in some sense, best possible. We give a necessary and sufficient condition
for the failure of SCT and we note that its converse is false, and that the
comparison theorem may still hold with very mild hypotheses on the
coefficients.


title: Learning Value Functions from Undirected State-only Experience

abstract:
  This paper tackles the problem of learning value functions from undirected
state-only experience (state transitions without action labels i.e. (s,s',r)
tuples). We first theoretically characterize the applicability of Q-learning in
this setting. We show that tabular Q-learning in discrete Markov decision
processes (MDPs) learns the same value function under any arbitrary refinement
of the action space. This theoretical result motivates the design of Latent
Action Q-learning or LAQ, an offline RL method that can learn effective value
functions from state-only experience. Latent Action Q-learning (LAQ) learns
value functions using Q-learning on discrete latent actions obtained through a
latent-variable future prediction model. We show that LAQ can recover value
functions that have high correlation with value functions learned using ground
truth actions. Value functions learned using LAQ lead to sample efficient
acquisition of goal-directed behavior, can be used with domain-specific
low-level controllers, and facilitate transfer across embodiments. Our
experiments in 5 environments ranging from 2D grid world to 3D visual
navigation in realistic environments demonstrate the benefits of LAQ over
simpler alternatives, imitation learning oracles, and competing methods.


title:
One-loop central-emission vertex for two gluons in $\mathcal{N}=4$ super
  Yang-Mills theory

abstract:
  A necessary ingredient for extending the BFKL equation to
next-to-next-to-leading logarithmic (NNLL) accuracy is the one-loop central
emission vertex (CEV) for two gluons which are not strongly ordered in
rapidity. Here we consider the one-loop six-gluon amplitude in $\mathcal{N}=4$
super Yang-Mills (SYM) theory in a central next-to-multi-Regge kinematic (NMRK)
limit, we show that its dispersive part factorises in terms of the two-gluon
CEV, and we use it to extract the one-loop two-gluon CEV for any helicity
configuration within this theory. This is a component of the two-gluon CEV in
QCD. Although computed in the NMRK limit, both the colour structure and the
kinematic dependence of the two-gluon CEV capture much of the complexity of the
six-gluon amplitudes in general kinematics. In fact, the transcendental
functions of the latter can be conveniently written in terms of impact factors,
trajectories, single-emission CEVs and a remainder, which is a function of the
conformally invariant cross ratios which characterise the six-gluon amplitudes
in planar $\mathcal{N}=4$ SYM. Finally, as expected, in the MRK limit the
two-gluon CEV neatly factorises in terms of two single-emission CEVs.


title: Unobstructed embeddings in Hirzebruch surfaces

abstract:
  This paper continues the study of the ellipsoid embedding function of
symplectic Hirzebruch surfaces parametrized by $b \in (0,1)$, the size of the
symplectic blow-up. Cristofaro-Gardiner, et al. (arxiv: 2004.13062) found that
if a toric manifold has an infinite staircase, then the embedding function is
equal to the volume curve at the accumulation point of the staircase. Here, we
use almost toric fibrations to construct full-fillings at the accumulation
point for a family of recursively defined irrational $b$-values to show the
embedding function for those $b$ are unobstructed at the accumulation point,
and thus, potential staircase values. The family we consider is defined via a
family of obstructive classes defined in Magill-McDuff-Weiler
(arxiv:2203.06453). We see a correspondence between the recursive, interwoven
structure of the centers of the obstructive classes and the sequence of nodal
rays to mutate in the almost toric fibrations. This result is used in
Magill-McDuff-Weiler (arxiv:2203.06453) to show that these classes are
exceptional and that these $b$-values do have infinite staircases.


title: Spin waves and magnetic exchange Hamiltonian in CrSBr

abstract:
  CrSBr is an air-stable 2D van der Waals semiconducting magnet with great
technological promise, but its atomic-scale magnetic interactions -- crucial
information for high-frequency switching -- are poorly understood. We present
an experimental study to determine the CrSBr magnetic exchange Hamiltonian and
bulk magnon spectrum. We confirm the $A$-type antiferromagnetic order using
single crystal neutron diffraction. We also measure the magnon dispersions
using inelastic neutron scattering and rigorously fit the excitation modes to a
spin wave model. The magnon spectrum is well described by an intra-plane
ferromagnetic Heisenberg exchange model with seven nearest in-plane exchanges.
This fitted exchange Hamiltonian enables theoretical predictions of CrSBr
behavior: as one example, we use the fitted Hamiltonian to predict the presence
of chiral magnon edge modes with a spin-orbit enhanced CrSBr heterostructure.


title: GMM is Inadmissible Under Weak Identification

abstract:
  We consider estimation in moment condition models and show that under squared
error loss and bounds on identification strength, asymptotically admissible
(i.e. undominated) estimators must be Lipschitz functions of the sample
moments. GMM estimators are in general discontinuous in the sample moment
function, and are thus inadmissible under weak identification. We show, by
contrast, that quasi-Bayes posterior means and bagged, or bootstrap aggregated,
GMM estimators have superior continuity properties, while results in the
literaure imply that they are equivalent to GMM when identification is strong.
In simulations calibrated to published instrumental variables specifications,
we find that these alternatives often outperform GMM. Hence, quasi-Bayes and
bagged GMM present attractive alternatives to GMM.


title: Focal Sparse Convolutional Networks for 3D Object Detection

abstract:
  Non-uniformed 3D sparse data, e.g., point clouds or voxels in different
spatial positions, make contribution to the task of 3D object detection in
different ways. Existing basic components in sparse convolutional networks
(Sparse CNNs) process all sparse data, regardless of regular or submanifold
sparse convolution. In this paper, we introduce two new modules to enhance the
capability of Sparse CNNs, both are based on making feature sparsity learnable
with position-wise importance prediction. They are focal sparse convolution
(Focals Conv) and its multi-modal variant of focal sparse convolution with
fusion, or Focals Conv-F for short. The new modules can readily substitute
their plain counterparts in existing Sparse CNNs and be jointly trained in an
end-to-end fashion. For the first time, we show that spatially learnable
sparsity in sparse convolution is essential for sophisticated 3D object
detection. Extensive experiments on the KITTI, nuScenes and Waymo benchmarks
validate the effectiveness of our approach. Without bells and whistles, our
results outperform all existing single-model entries on the nuScenes test
benchmark at the paper submission time. Code and models are at
https://github.com/dvlab-research/FocalsConv.


title: Monochromatic paths in $2$-edge coloured graphs and hypergraphs

abstract:
  We answer a question of Gy\'arf\'as and S\'ark\"ozy from 2013 by showing that
every $2$-edge-coloured complete $3$-uniform hypergraph can be partitioned into
two monochromatic tight paths of different colours. Further, we give a lower
bound for the number of tight paths needed to partition any $2$-edge-coloured
complete $k$-partite $k$-uniform hypergraph, and show that any $2$-edge
coloured bipartite graph has a partition into a monochromatic cycle and a
monochromatic path, of different colours.


title: Disjoint isomorphic balanced clique subdivisions

abstract:
  A thoroughly studied problem in Extremal Graph Theory is to find the best
possible density condition in a host graph $G$ for guaranteeing the presence of
a particular subgraph $H$ in $G$. One such classical result, due to
Bollob\'{a}s and Thomason, and independently Koml\'{o}s and Szemer\'{e}di,
states that average degree $O(k^2)$ guarantees the existence of a
$K_k$-subdivision. We study two directions extending this result. On the one
hand, Verstra\"ete conjectured that the quadratic bound $O(k^2)$ would
guarantee already two vertex-disjoint isomorphic copies of a $K_k$-subdivision.
On the other hand, Thomassen conjectured that for each $k \in \mathbb{N}$ there
is some $d = d(k)$ such that every graph with average degree at least $d$
contains a balanced subdivision of $K_k$, that is, a copy of $K_k$ where the
edges are replaced by paths of equal length. Recently, Liu and Montgomery
confirmed Thomassen's conjecture, but the optimal bound on $d(k)$ remains open.
In this paper, we show that the quadratic bound $O(k^2)$ suffices to force a
balanced $K_k$-subdivision. This gives the optimal bound on $d(k)$ needed in
Thomassen's conjecture and implies the existence of $O(1)$ many vertex-disjoint
isomorphic $K_k$-subdivisions, confirming Verstra\"ete's conjecture in a strong
sense.


title:
Meta-free few-shot learning via representation learning with weight
  averaging

abstract:
  Recent studies on few-shot classification using transfer learning pose
challenges to the effectiveness and efficiency of episodic meta-learning
algorithms. Transfer learning approaches are a natural alternative, but they
are restricted to few-shot classification. Moreover, little attention has been
on the development of probabilistic models with well-calibrated uncertainty
from few-shot samples, except for some Bayesian episodic learning algorithms.
To tackle the aforementioned issues, we propose a new transfer learning method
to obtain accurate and reliable models for few-shot regression and
classification. The resulting method does not require episodic meta-learning
and is called meta-free representation learning (MFRL). MFRL first finds
low-rank representation generalizing well on meta-test tasks. Given the learned
representation, probabilistic linear models are fine-tuned with few-shot
samples to obtain models with well-calibrated uncertainty. The proposed method
not only achieves the highest accuracy on a wide range of few-shot learning
benchmark datasets but also correctly quantifies the prediction uncertainty. In
addition, weight averaging and temperature scaling are effective in improving
the accuracy and reliability of few-shot learning in existing meta-learning
algorithms with a wide range of learning paradigms and model architectures.


title:
A Model-Adaptive Clustering Method for Low-Carbon Energy System
  Optimization

abstract:
  Intermittent renewable energy resources like wind and solar pose great
uncertainty of multiple time scales, from minutes to years, on the design and
operation of power systems. Energy system optimization models have been
developed to find the least-cost solution to matching the uncertainty with
flexibility resources. However, input data that capture such multi-time-scale
uncertainty are characterized with a long time horizon and bring great
difficulty to solving the optimization model. Here we propose an adaptive
clustering method based on the decision variables of optimization model to
alleviate the computational complexity, in which the energy system is optimized
over selected representative time periods instead of the full time horizon. The
proposed clustering method is adaptive to various energy system optimization
models or settings, because it extracts features from the optimization models.
Results show that the proposed clustering method can significantly lower the
error in approximating the solution with the full time horizon, compared to
traditional clustering methods.


title:
A Review of In-Memory Space-Efficient Data Structures for Temporal
  Graphs

abstract:
  Temporal graphs model relationships among entities over time. Recent studies
applied temporal graphs to abstract complex systems such as continuous
communication among participants of social networks. Often, the amount of data
is larger than main memory, therefore, we need specialized structures that
balance space usage and query efficiency. In this paper, we review
space-efficient data structures that bring large temporal graphs from external
memory to primary memory and speed up specialized queries. We found a great
variety of studies using data compression techniques and self-indexed
compressed data structures. We point further research directions to improve the
current state-of-the-art.


title:
The Image of the Gassner Representation of the Pure Braid Subgroup has
  Pairwise Free Generators

abstract:
  While much is known about the faithfulness of the Burau representation, the
problem remains open for the Gassner representation for every $B_n$ with $n\geq
4$. We first find the definition of the Colored-Burau representation of
Ainshel, Ainshel, Goldfeld, and Lemieux and we show that this is equivalent,
when restricted to the pure braid subgroup, to the Gassner representation. The
methods of Abdulrahim and Knudson require analysis within the lower central
series of a free subgroup of the pure braid group. However, Lipschutz's work
gives a method for analyzing the Gassner representation and the Colored-Burau
structure reduces this analysis to basic linear algebra.


title: Structured Unitary Matrices and Quantum Entanglement

abstract:
  We explore the set of unitary matrices characterized by a given structure in
the context of their applications in the field of Quantum Information. In the
first part of the Thesis we focus on classification of special classes of
unitary matrices and possibility of introducing certain internal
parameterizations. Several new results and conjectures are discussed. Second
part of the Thesis is devoted to the concept of multipartite quantum
entanglement. We present a solution to the problem of absolutely maximally
entangled states of four subsystems with six levels each. Finally, we analyze
the excess of a matrix and the corresponding Bell inequalities. This
combination allows us to draw new conclusions related to quantum nonlocality.


title: Coarse-to-fine Q-attention with Tree Expansion

abstract:
  Coarse-to-fine Q-attention enables sample-efficient robot manipulation by
discretizing the translation space in a coarse-to-fine manner, where the
resolution gradually increases at each layer in the hierarchy. Although
effective, Q-attention suffers from "coarse ambiguity" - when voxelization is
significantly coarse, it is not feasible to distinguish similar-looking objects
without first inspecting at a finer resolution. To combat this, we propose to
envision Q-attention as a tree that can be expanded and used to accumulate
value estimates across the top-k voxels at each Q-attention depth. When our
extension, Q-attention with Tree Expansion (QTE), replaces standard Q-attention
in the Attention-driven Robot Manipulation (ARM) system, we are able to
accomplish a larger set of tasks; especially on those that suffer from "coarse
ambiguity". In addition to evaluating our approach across 12 RLBench tasks, we
also show that the improved performance is visible in a real-world task
involving small objects.


title: A Multivariate Spatial and Spatiotemporal ARCH Model

abstract:
  This paper introduces a multivariate spatiotemporal autoregressive
conditional heteroscedasticity (ARCH) model based on a vec-representation. The
model includes instantaneous spatial autoregressive spill-over effects in the
conditional variance, as they are usually present in spatial econometric
applications. Furthermore, spatial and temporal cross-variable effects are
explicitly modelled. We transform the model to a multivariate spatiotemporal
autoregressive model using a log-squared transformation and derive a consistent
quasi-maximum-likelihood estimator (QMLE). For finite samples and different
error distributions, the performance of the QMLE is analysed in a series of
Monte-Carlo simulations. In addition, we illustrate the practical usage of the
new model with a real-world example. We analyse the monthly real-estate price
returns for three different property types in Berlin from 2002 to 2014. We find
weak (instantaneous) spatial interactions, while the temporal autoregressive
structure in the market risks is of higher importance. Interactions between the
different property types only occur in the temporally lagged variables. Thus,
we see mainly temporal volatility clusters and weak spatial volatility
spill-overs.


title: Enhancement of Quantum Excitation Transport by Photonic Nonreciprocity

abstract:
  Enhanced interaction between two two-level emitters (e.g., atoms) by
nonreciprocal photonic media can be of benefit to broad areas, from quantum
information science to biological detection. Here we provide a detailed
analysis on why nonreciprocal photon-mediated interaction enhances inter-atomic
excitation transport efficiency. We investigate a system consisting of two
two-level emitters embedded in a generic photonic environment. By comparing
symmetric and asymmetric photon-exchange, we analytically show that breaking
electromagnetic reciprocity makes it possible for the cooperative decay rate to
exceed the spontaneous decay rate even in a translation-invariant homogeneous
system. This means that the excitation of an emitter must decay mostly into the
other emitter rather than leaking and dissipating into the reservoir photonic
modes. We also provide an example where a chain of two-level emitters
dominantly interact via the reciprocal modes of a plasmonic waveguide. We then
show that breaking reciprocity in such a system via driving a DC current
through the plasmonic material can drastically increase the probability of
photon emission from one emitter to another, leading to an order-of-magnitude
enhancement in quantum energy-transport efficiency.


title:
Imaging of PbWO4 Crystals for G Experiment Test Masses Using a Laser
  Interferometer

abstract:
  It is highly desirable for future measurements of Newton's gravitational
constant $G$ to use test/source masses that allow nondestructive, quantitative
internal density gradient measurements. High density optically transparent
materials are ideally suited for this purpose since their density gradient can
be measured with laser interferometry, and they allow in-situ optical metrology
methods for the critical distance measurements often needed in a $G$ apparatus.
We present an upper bound on possible internal density gradients in lead
tungstate (PbWO$_4$) crystals determined using a laser interferometer. We
placed an upper bound on the fractional atomic density gradient in two PbWO$_4$
test crystals of ${1 \over \rho}{d\rho \over dx}<2.1 \times 10^{-8}$ cm$^{-1}$.
This value is more than two orders of magnitude smaller than what is required
for $G$ measurements. They are also consistent with but more sensitive than a
recently reported measurements of the same samples, using neutron
interferometry. These results indicate that PbWO$_4$ crystals are well suited
to be used as test masses in $G$ experiments. Future measurements of internal
density gradients of test masses used for measurements of $G$ can now be
conducted non-destructively for a wide range of possible test masses.


title: Large-scale Volcanism and the Heat Death of Terrestrial Worlds

abstract:
  Large-scale volcanism has played a critical role in the long-term
habitability of Earth. Contrary to widely held belief, volcanism, rather than
impactors, has had the greatest influence on and bears most of the
responsibility for large-scale mass extinction events throughout Earth's
history. We examine the timing of large igneous provinces (LIPs) throughout
Earth's history to estimate the likelihood of nearly simultaneous events that
could drive a planet into an extreme moist or runaway greenhouse, leading to
the end of volatile cycling and causing the heat death of formerly temperate
terrestrial worlds. In one approach, we make a conservative estimate of the
rate at which sets of near-simultaneous LIPs (pairs, triplets, and quartets)
occur in a random history statistically the same as Earth's. We find that LIPs
closer in time than 0.1-1 million yr are likely; significantly, this is less
than the time over which terrestrial LIP environmental effects are known to
persist. In another approach, we assess the cumulative effects with simulated
time series consisting of randomly occurring LIP events with realistic time
profiles. Both approaches support the conjecture that environmental impacts of
LIPs, while narrowly avoiding grave effects on the climate history of Earth,
could have been responsible for the heat death of our sister world Venus.


title:
The Fission Barrier of Heaviest Nuclei From a Macroscopic-Microscopic
  Perspective

abstract:
  The concept of fission barrier - a parameter which enters in quantitative
estimates of various observables related to nuclear fission - is presented from
the point of view of theory based on the picture of nuclear deformation and
energy dependent on it. We describe the macroscopic-microscopic method of
calculating energy landscapes which is simpler than the selfconsistent mean
field approach, and, due to its two-component nature, seems to be easier to
adjust to experimental data. We present some models and methods used to find
the fission saddles. For the purpose of illustration, we present results on
fission barriers in actinides and superheavy nuclei, obtained within one
macroscopic-microscopic model. We discuss comparisons with results of other
models, including some of the mean-field type.


title: Digital Twins for Dynamic Management of Blockchain Systems

abstract:
  Blockchain systems are challenged by the so-called Trilemma tradeoff:
decentralization, scalability and security. Infrastructure and node
configuration, choice of the Consensus Protocol and complexity of the
application transactions are cited amongst the factors that affect the
tradeoffs balance. Given that Blockchains are complex, dynamic dynamic systems,
a dynamic approach to their management and reconfiguration at runtime is deemed
necessary to reflect the changes in the state of the infrastructure and
application. This paper introduces the utilisation of Digital Twins for this
purpose. The novel contribution of the paper is design of a framework and
conceptual architecture of a Digital Twin that can assist in maintaining the
Trilemma tradeoffs of time critical systems. The proposed Digital Twin is
illustrated via an innovative approach to dynamic selection of Consensus
Protocols. Simulations results show that the proposed framework can effectively
support the dynamic adaptation and management of the Blockchain


title:
The upgraded low-background germanium counting facility Gator for
  high-sensitivity $\gamma$-ray spectrometry

abstract:
  We describe the upgrade and performance of the high-purity germanium counting
facility Gator, which is dedicated to low-background $\gamma$-ray spectrometry.
Gator is operated at the Gran Sasso Underground Laboratory in Italy, at an
average depth of 3600 meter water equivalent, and employed for material
screening and selection in ultra-low background, rare-event search experiments
in astroparticle physics. The detector is equipped with a passive shield made
of layers of copper, lead and polyethylene, and the sample cavity is purged
with gaseous nitrogen maintained at positive pressure for radon suppression.
After upgrading its enclosure, the background rate is (82.0$\pm$0.7)
counts/(kg$\cdot$day) in the energy region 100 keV to 2700 keV, a 20% reduction
compared to the previously reported rate. We show the stability of various
operation parameters as a function of time. We also summarize the sample
analysis procedure, and demonstrate Gator's sensitivity by examining one
material sample, a candidate photosensor for the DARWIN experiment.


title:
Studying First Passage Problems using Neural Networks: A Case Study in
  the Slit-Well Microfluidic Device

abstract:
  This study presents deep neural network solutions to a time-integrated
Smoluchowski equation modeling the mean first passage time of nanoparticles
traversing the slit-well microfluidic device. This physical scenario is
representative of a broader class of parameterized first passage problems in
which key output metrics are dictated by a complicated interplay of problem
parameters and system geometry. Specifically, whereas these types of problems
are commonly studied using particle simulations of stochastic differential
equation models, here the corresponding partial differential equation model is
solved using a method based on deep neural networks. The results illustrate
that the neural network method is synergistic with the time-integrated
Smoluchowski model: together, these are used to construct continuous mappings
from key physical inputs (applied voltage and particle diameter) to key output
metrics (mean first passage time and effective mobility). In particular, this
capability is a unique advantage of the time-integrated Smoluchowski model over
the corresponding stochastic differential equation models. Furthermore, the
neural network method is demonstrated to easily and reliably handle
geometry-modifying parameters, which is generally difficult to accomplish using
other methods.


title:
Smoothing and Global Attractors for the Hirota-Satsuma System on the
  Torus

abstract:
  We consider the Hirota-Satsuma system, a coupled KdV-type system, with
periodic boundary conditions. The first part of the paper concerns with the
smoothing estimates for the system. More precisely, it is shown that, for
initial data in a Sobolev space, the difference of the nonlinear and linear
evolutions lies in a smoother space. The smoothing gain we obtain depends very
much on the arithmetic nature of the coupling parameter $a$ which determines
the structure of the resonant sets in the estimates. In the second part, we
address the forced and damped Hirota-Satsuma system and obtain counterpart
smoothing estimates. As a consequence of these estimates, we prove the
existence and smoothness of a global attractor in the energy space.


title: From Hyperbolic Geometry Back to Word Embeddings

abstract:
  We choose random points in the hyperbolic disc and claim that these points
are already word representations. However, it is yet to be uncovered which
point corresponds to which word of the human language of interest. This
correspondence can be approximately established using a pointwise mutual
information between words and recent alignment techniques.


title:
Fractional Vortices, $\mathbb{Z}_2$ Gauge Theory, and the
  Confinement-Deconfinement Transition

abstract:
  In this paper we discuss the classical 3D XY model whose nearest-neighbor
interaction is a mixture of $\cos(\theta_i-\theta_j)$ (ferromagnetic) and
$\cos2(\theta_i-\theta_j)$ (nematic). This model is dual to a theory with
integer and half-integer vortices. While both types of vortices interact with a
non-compact $U(1)$ gauge field (the "EM" interaction), the half-vortices
interact with an extra interaction mediated by a $\mathbb{Z}_2$ gauge field. We
shall discuss the confinement-deconfinement transition of the half-integer
vortices, the Wilson, 't Hooft loops and their mutual statistics in path
integral language. In addition, we shall present a quantum version of the
classical model which exhibits these physics.


title:
Topological Fukaya category and mirror symmetry for toric Calabi-Yau
  3-orbifolds

abstract:
  We prove a version of homological mirror symmetry statement for toric
Calabi-Yau $3$-orbifolds, thus extending arXiv:1604.06448 to the case of
orbifolds under the mirror symmetry setting considered in arXiv:1604.07123. The
B-model is the matrix factorization category for the toric Calabi-Yau
$3$-orbifold with a superpotential; while the A-model is a topologically
defined Fukaya-type category on its mirror curve.


title: ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation

abstract:
  Recently, customized vision transformers have been adapted for human pose
estimation and have achieved superior performance with elaborate structures.
However, it is still unclear whether plain vision transformers can facilitate
pose estimation. In this paper, we take the first step toward answering the
question by employing a plain and non-hierarchical vision transformer together
with simple deconvolution decoders termed ViTPose for human pose estimation. We
demonstrate that a plain vision transformer with MAE pretraining can obtain
superior performance after finetuning on human pose estimation datasets.
ViTPose has good scalability with respect to model size and flexibility
regarding input resolution and token number. Moreover, it can be easily
pretrained using the unlabeled pose data without the need for large-scale
upstream ImageNet data. Our biggest ViTPose model based on the ViTAE-G backbone
with 1 billion parameters obtains the best 80.9 mAP on the MS COCO test-dev
set, while the ensemble models further set a new state-of-the-art for human
pose estimation, i.e., 81.1 mAP. The source code and models will be released at
https://github.com/ViTAE-Transformer/ViTPose.


title: Competing orders, the Wess-Zumino-Witten term, and spin liquids

abstract:
  In this paper, we demonstrate that in frustrated magnets when several
conventional (i.e., symmetry-breaking) orders compete, and are "intertwined" by
a Wess-Zumino-Witten (WZW) term, the possibility of spin liquid arises. The
resulting spin liquid could have excitations which carry fractional spins and
obey non-trivial self/mutual statistics. As a concrete example, we consider the
case where the competing orders are the N\'{e}el and valence-bond solid (VBS)
order on square lattice. Examining different scenarios of vortex condensation
from the VBS side, we show that the intermediate phases, including spin
liquids, between the N\'{e}el and VBS order always break certain symmetry.
Remarkably, our starting theory, without fractionalized particles (partons) and
guage field, predicts results agreeing with those derived from a parton theory.
This suggests that the missing link between the Ginzberg-Landau-Wilson action
of competing order and the physics of spin liquid is the WZW term.


title:
Measurement uncertainty and unicity of single number quantities
  describing the spatial decay of speech level in open-plan offices

abstract:
  The ISO 3382-3 standard (2012) defines single number quantities (SNQs) which
evaluate the acoustic quality of open-plan offices, but does not address the
issue of measurement uncertainties. This study focusses on the SNQs present in
this standard related to spatial decay of speech, i.e. D 2S , L pAS4m and r c.
The aim is to provide additional information to the limited literature on the
measurement uncertainties of these SNQs by use of both analytical developments
and a stochastic approach based on simulations. The accuracy of the analytical
developments was studied thanks to simulations of the sound propagation within
a series of offices (1 layout, 16 acoustic configurations with different screen
heights and different acoustic qualities of screens and ceiling). The SNQs
obtained in the simulations cover a wide range: D 2S between 3.4 and 7.5 dB(A),
L pAS4m between 40.6 and 51.9 dB(A) and r c between 2.5 and 14.7 m. Therefore,
the simulations are representative of a broad set of acoustic qualities.
Estimated uncertainties have a magnitude of 0.4 dB(A) for D 2S and vary between
0.4 and 0.7 dB(A) for L pAS4m and between 0.2 and 1.5 m for r c over a
measurement path comprising 7 measurement positions. The simulations also raise
the question of describing the acoustic quality of an office using a single
value for the indicators. The results of the simulations show that in some
cases, D 2S values significantly depend on the measurement path, leading to a
strong increase of its measurement uncertainty if a unique value is to be
considered.


title:
First constraints on axion-like particles from Galactic sub-PeV gamma
  rays

abstract:
  Experimental refinements and technical innovations in the field of extensive
air shower telescopes have enabled measurements of Galactic cosmic-ray
interactions in the sub-PeV (100 TeV to 1 PeV) range, providing new avenues for
the search for new physics and dark matter. For the first time, we exploit
sub-PeV (from 10 TeV to 1 PeV) observations of Galactic diffuse gamma rays by
Tibet AS$\gamma$ and HAWC to search for an axion-like-particle (ALP) induced
gamma-ray signal directly linked to the origin of the IceCube extragalactic
high-energy neutrino flux. Indeed, the production of high-energy neutrinos in
extragalatic sources implies the concomitant production of gamma rays at
comparable energies. Within the magnetic field of the neutrino emitting
sources, gamma rays may efficiently convert into ALPs and escape their host
galaxy unattenuated, propagate through intergalactic space, and reconvert into
gamma rays in the magnetic field of the Milky Way. Such a scenario creates an
all-sky diffuse high-energy gamma-ray signal in the sub-PeV range. Accounting
for the guaranteed Galactic astrophysical gamma-ray contributions from
cosmic-ray interactions with gas and radiation and from sub-threshold sources,
we set competitive upper limits on the photon-ALP coupling constant
$g_{a\gamma\gamma}$. We find $g_{a\gamma\gamma} < 2.1\times10^{-11}$ GeV$^{-1}$
for ALP masses $m_a \leq 2\times10^{-7}$ eV at a 95$\%$ confidence level,
progressively closing the mass gap towards ADMX limits.


title: Distances Release with Differential Privacy in Tree and Grid Graph

abstract:
  Data about individuals may contain private and sensitive information. The
differential privacy (DP) was proposed to address the problem of protecting the
privacy of each individual while keeping useful information about a population.
Sealfon (2016) introduced a private graph model in which the graph topology is
assumed to be public while the weight information is assumed to be private.
That model can express hidden congestion patterns in a known transportation
system. In this paper, we revisit the problem of privately releasing
approximate distances between all pairs of vertices in (Sealfon 2016). Our goal
is to minimize the additive error, namely the difference between the released
distance and actual distance under private setting. We propose improved
solutions to that problem for several cases.
  For the problem of privately releasing all-pairs distances, we show that for
tree with depth $h$, we can release all-pairs distances with additive error
$O(\log^{1.5} h \cdot \log^{1.5} V)$ for fixed privacy parameter where $V$ the
number of vertices in the tree, which improves the previous error bound
$O(\log^{2.5} V)$, since the size of $h$ can be as small as $O(\log V)$. Our
result implies that a $\log V$ factor is saved, and the additive error in tree
can be smaller than the error on array/path. Additionally, for the grid graph
with arbitrary edge weights, we also propose a method to release all-pairs
distances with additive error $\tilde O(V^{3/4}) $ for fixed privacy
parameters. On the application side, many cities like Manhattan are composed of
horizontal streets and vertical avenues, which can be modeled as a grid graph.


title: Sound Localization by Self-Supervised Time Delay Estimation

abstract:
  Sounds reach one microphone in a stereo pair sooner than the other, resulting
in an interaural time delay that conveys their directions. Estimating a sound's
time delay requires finding correspondences between the signals recorded by
each microphone. We propose to learn these correspondences through
self-supervision, drawing on recent techniques from visual tracking. We adapt
the contrastive random walk of Jabri et al. to learn a cycle-consistent
representation from unlabeled stereo sounds, resulting in a model that performs
on par with supervised methods on "in the wild" internet recordings. We also
propose a multimodal contrastive learning model that solves a visually-guided
localization task: estimating the time delay for a particular person in a
multi-speaker mixture, given a visual representation of their face. Project
site: https://ificl.github.io/stereocrw/


title:
From One Hand to Multiple Hands: Imitation Learning for Dexterous
  Manipulation from Single-Camera Teleoperation

abstract:
  We propose to perform imitation learning for dexterous manipulation with
multi-finger robot hand from human demonstrations, and transfer the policy to
the real robot hand. We introduce a novel single-camera teleoperation system to
collect the 3D demonstrations efficiently with only an iPad and a computer. One
key contribution of our system is that we construct a customized robot hand for
each user in the physical simulator, which is a manipulator resembling the same
kinematics structure and shape of the operator's hand. This provides an
intuitive interface and avoid unstable human-robot hand retargeting for data
collection, leading to large-scale and high quality data. Once the data is
collected, the customized robot hand trajectories can be converted to different
specified robot hands (models that are manufactured) to generate training
demonstrations. With imitation learning using our data, we show large
improvement over baselines with multiple complex manipulation tasks.
Importantly, we show our learned policy is significantly more robust when
transferring to the real robot. More videos can be found in the
https://yzqin.github.io/dex-teleop-imitation .


title: Role of Hydrodynamic Drainage in Adhesion to Wet Surfaces

abstract:
  Tree frogs possess soft structured toe pads with channels that help squeeze
out fluid from the region between the toe pad and the contacting surfaces. This
structure enables them to walk on wet and rough surfaces. We present our
preliminary result aiming at understanding the role of surface structure on
hydrodynamic drainage forces. We have used the surface forces apparatus to
measure the drainage forces (approach and retraction) in silicone oil. We
compare the drainage force measured between two smooth PMMA surfaces with the
one measure when one of the two surfaces has a structure with channels. We have
observed that the presence of channels reduces the hydrodynamic drainage forces
when the surfaces come together and reduces the adhesion when the surfaces are
pulled apart. Our force measurements were compared to Reynolds theory and we
discuss the possible role of elastohydrodynamic deformation in tree frog
adhesion.


title:
AI-Assisted Authentication: State of the Art, Taxonomy and Future
  Roadmap

abstract:
  Artificial Intelligence (AI) has found its applications in a variety of
environments ranging from data science to cybersecurity. AI helps break through
the limitations of traditional algorithms and provides more efficient and
flexible methods for solving problems. In this paper, we focus on the
applications of artificial intelligence in authentication, which is used in a
wide range of scenarios including facial recognition to access buildings,
keystroke dynamics to unlock smartphones. With the emerging AI-assisted
authentication schemes, our comprehensive survey provides an overall
understanding on a high level, which paves the way for future research in this
area. In contrast to other relevant surveys, our research is the first of its
kind to focus on the roles of AI in authentication.


title: One-shot Federated Learning without Server-side Training

abstract:
  Federated Learning (FL) has recently made significant progress as a new
machine learning paradigm for privacy protection. Due to the high communication
cost of traditional FL, one-shot federated learning is gaining popularity as a
way to reduce communication cost between clients and the server. Most of the
existing one-shot FL methods are based on Knowledge Distillation; however,
distillation based approach requires an extra training phase and depends on
publicly available data sets. In this work, we consider a novel and challenging
setting: performing a single round of parameter aggregation on the local models
without server-side training on a public data set. In this new setting, we
propose an effective algorithm for Model Aggregation via Exploring Common
Harmonized Optima (MA-Echo), which iteratively updates the parameters of all
local models to bring them close to a common low-loss area on the loss surface,
without harming performance on their own data sets at the same time. Compared
to the existing methods, MA-Echo can work well even in extremely non-identical
data distribution settings where the support categories of each local model
have no overlapped labels with those of the others. We conduct extensive
experiments on two popular image classification data sets to compare the
proposed method with existing methods and demonstrate the effectiveness of
MA-Echo, which clearly outperforms the state-of-the-arts.


title:
Frequency Plan Design for Multibeam Satellite Constellations Using
  Linear Programming

abstract:
  Upcoming large satellite constellations and the advent of tighter steerable
beams will offer unprecedented flexibility. This new flexibility will require
resource management strategies to be operated in high-dimensional and dynamic
environments, as existing satellite operators are unaccustomed to operational
flexibility and automation. Frequency assignment policies have the potential to
drive constellations' performance in this new context, and are no exception to
real-time and scalability requirements. The majority of frequency assignment
methods proposed in the literature fail to fulfill these two requirements, or
are unable to meet them without falling short on bandwidth and/or power
efficiency. In this paper we propose a new frequency assignment method designed
to prioritize operational requirements. We present an algorithm based on
Integer Linear Programming (ILP) that is able to fully define a frequency plan
while respecting key system constraints such as handovers and interference. We
are able to encode operators' goals such as bandwidth maximization or power
reduction and produce optimal or quasi-optimal plans according to such
objectives. In our experiments, we find our method is able to allocate at least
50% more bandwidth and reduce power consumption by 40% compared to previous
operational benchmarks. The performance advantage of our method compared to
previous solutions increases with the dimensionality of the constellation; in
an experiment with a 5,000-beam MEO constellation we find that we can allocate
three times more bandwidth.


title:
Enhancing Privacy against Inversion Attacks in Federated Learning by
  using Mixing Gradients Strategies

abstract:
  Federated learning reduces the risk of information leakage, but remains
vulnerable to attacks. We investigate how several neural network design
decisions can defend against gradients inversion attacks. We show that
overlapping gradients provides numerical resistance to gradient inversion on
the highly vulnerable dense layer. Specifically, we propose to leverage
batching to maximise mixing of gradients by choosing an appropriate loss
function and drawing identical labels. We show that otherwise it is possible to
directly recover all vectors in a mini-batch without any numerical optimisation
due to the de-mixing nature of the cross entropy loss. To accurately assess
data recovery, we introduce an absolute variation distance (AVD) metric for
information leakage in images, derived from total variation. In contrast to
standard metrics, e.g. Mean Squared Error or Structural Similarity Index, AVD
offers a continuous metric for extracting information in noisy images. Finally,
our empirical results on information recovery from various inversion attacks
and training performance supports our defense strategies. These strategies are
also shown to be useful for deep convolutional neural networks such as LeNET
for image recognition. We hope that this study will help guide the development
of further strategies that achieve a trustful federation policy.


title:
Self-Supervised Information Bottleneck for Deep Multi-View Subspace
  Clustering

abstract:
  In this paper, we explore the problem of deep multi-view subspace clustering
framework from an information-theoretic point of view. We extend the
traditional information bottleneck principle to learn common information among
different views in a self-supervised manner, and accordingly establish a new
framework called Self-supervised Information Bottleneck based Multi-view
Subspace Clustering (SIB-MSC). Inheriting the advantages from information
bottleneck, SIB-MSC can learn a latent space for each view to capture common
information among the latent representations of different views by removing
superfluous information from the view itself while retaining sufficient
information for the latent representations of other views. Actually, the latent
representation of each view provides a kind of self-supervised signal for
training the latent representations of other views. Moreover, SIB-MSC attempts
to learn the other latent space for each view to capture the view-specific
information by introducing mutual information based regularization terms, so as
to further improve the performance of multi-view subspace clustering. To the
best of our knowledge, this is the first work to explore information bottleneck
for multi-view subspace clustering. Extensive experiments on real-world
multi-view data demonstrate that our method achieves superior performance over
the related state-of-the-art methods.


title: Tensor-simple spectrum of unitary flows

abstract:
  Unitary flows $T_t$ of dynamic origin are proposed such that for every
countable subset $Q\subset (0,+\infty)$ the tensor product $\bigotimes_{q\in Q}
T_q $ has simple spectrum. This property is generic for flows preserving the
sigma-finite measure.


title:
Detectability of Wandering Intermediate-Mass Black Holes in the Milky
  Way Galaxy from Radio to X-rays

abstract:
  Intermediate-mass black holes (IMBHs, $10^{3-6} \, \rm{M_\odot}$), are
typically found at the center of dwarf galaxies and might be wandering, thus
far undetected, in the Milky Way (MW). We use model spectra for
advection-dominated accretion flows to compute the typical fluxes, in a range
of frequencies spanning from radio to X-rays, emitted by a putative population
of $10^5 \, \rm{M_\odot}$ IMBHs wandering in five realistic, volume-weighted,
MW environments. We predict that $\sim 27\%$ of the wandering IMBHs can be
detected in the X-ray with Chandra, $\sim 37\%$ in the near-infrared with the
Roman Space Telescope, $\sim 49\%$ in the sub-mm with CMB-S4 and $\sim 57\%$ in
the radio with ngVLA. We find that the brightest fluxes are emitted by IMBHs
passing through molecular clouds or cold neutral medium, where they are always
detectable. We propose criteria to facilitate the selection of candidates in
multi-wavelength surveys. Specifically, we compute the X-ray to optical ratio
($\alpha_{\rm ox}$) and the optical to sub-mm ratio, as a function of the
accretion rate of the IMBH. We show that at low rates the sub-mm emission of
IMBHs is significantly higher than the optical, UV and X-ray emission. Finally,
we place upper limits on the number $N_\bullet$ of these objects in the MW:
$N_\bullet<2000$ and $N_\bullet<100$, based on our detectability expectations
and current lack of detections in molecular clouds and cold neutral medium,
respectively. These predictions will guide future searches of IMBHs in the MW,
which will be instrumental to understanding their demographics and evolution.


title:
The Host Galaxy of the Recoiling Black Hole Candidate in 3C 186: An Old
  Major Merger Remnant at the Center of a z=1 Cluster

abstract:
  3C186, a radio-loud quasar at $z=1.0685$, was previously reported to have
both velocity and spatial offsets from its host galaxy, and has been considered
as a promising candidate for a gravitational wave recoiling black hole
triggered by a black hole merger. Another possible scenario is that 3C186 is in
an on-going galaxy merger, exhibiting a temporary displacement. In this study,
we present analyses of new deep HST/WFC3-IR and ACS images, aiming to
characterize the host galaxy and test this alternative scenario. We carefully
measure the light-weighted center of the host and reveal a significant spatial
offset from the quasar core ($11.1\pm0.1$kpc). The direction of the confirmed
offset aligns almost perpendicularly to the radio jet. We do not find evidence
of a recent merger, such as a young starburst in disturbed outskirts, but only
marginal light concentration in F160W at $\sim30$kpc. The host consists of
matured ($>200$Myr) stellar populations and one compact star-forming region. We
compare with hydro-dynamical simulations and find that those observed features
are consistently seen in late-stage merger remnants. Taken together, those
pieces of evidence indicate that the system is not an on-going/young merger
remnant, suggesting that the recoiling black hole scenario is still a plausible
explanation for the puzzling nature of 3C186.


title: Quantum Simulations of Dark Sector Showers

abstract:
  We consider dark sector scenarios where dark matter is accompanied by a dark
photon and multiple-flavor dark fermions charged under the dark gauge group. We
study quantum interference effects in dark sector jets, where multiple dark
photons are emitted from high-energy dark fermions. We perform fully quantum
simulations of dark sector showers and compare the results against those of the
classical Monte-Carlo simulations. We find important differences in probability
distributions of dark photon countings between quantum and classical
computations. When the number of dark-fermion flavors is large, we find
significant enhancements in large numbers of dark photon emissions. Such
enhancements can provide distinguishing signals for our scenarios at particle
colliders.


title:
Black hole to photosphere: 3D GRMHD simulations of collapsars reveal
  wobbling and hybrid composition jets

abstract:
  Long-duration $\gamma$-ray bursts (GRBs) accompany the collapse of massive
stars and carry important information about the central engine. However, no 3D
models have been able to follow these jets from their birth by a rotating
black-hole (BH) to the photosphere. We present the first such 3D
general-relativity magnetohydrodynamic simulations, which span over $6$ orders
of magnitude in space and time. The collapsing stellar envelope forms an
accretion disk, which drags inward the magnetic flux that accumulates around
the BH, becomes dynamically-important and launches bipolar jets. The jets reach
the photosphere at $\sim10^{12}$ cm with an opening angle,
$\theta_j\sim6^\circ$ and a Lorentz factor, $\Gamma_j\lesssim30$, unbind
$\gtrsim90\%$ of the star and leave the BH mass essentially unchanged after the
initial core-collapse. We find that: (i) The disk-jet system spontaneously
develops misalignment relative to the BH rotational axis. As a result, the jet
direction wobbles with an angle $\theta_t\sim12^\circ$, which can naturally
explain quiescent times in GRB lightcurves. The effective opening angle for
detection $\theta_j+\theta_t$ suggests that the intrinsic (beaming-corrected)
GRB rate is lower by an order of magnitude than standard estimates. This
implies that successful GRBs can be much rarer than currently thought, and
emerge in only $\sim0.1\%$ of supernovae Ib/c. A possible explanation is that
jets are either not launched or choked inside most supernova Ib/c progenitors.
(ii) The magnetic energy in the jet decreases due to dissipation and mixing
with the stellar material, resulting in jets with a hybrid composition of
magnetic and thermal components at the photosphere, where $\sim 20\%$ of the
gas maintains magnetization $\sigma\gtrsim0.1$. This indicates that both a
photospheric component and magnetic reconnection play a role in the GRB prompt
emission.


title:
Constraining SIDM with halo shapes: revisited predictions from realistic
  simulations of early-type galaxies

abstract:
  We study the effect of self-interacting dark matter (SIDM) and baryons on the
shapes of early-type galaxies (ETGs) and their dark matter haloes, comparing
them to the predictions of the standard cold dark matter (CDM) scenario. We use
a sample of five zoom-in simulations of haloes hosting ETGs ($M_{\text vir}\sim
10^{13}M_{\odot}$ and $M_{*}\sim10^{11}M_{\odot}$), simulated in CDM and a SIDM
model with constant cross-section of $\sigma_T/m_\chi = 1\ \mathrm{cm}^2
\mathrm{g}^{-1}$, with and without the inclusion of baryonic physics. We
measure the three-dimensional and projected shapes of the dark matter haloes
and their baryonic content by means of the inertia tensor and compare our
measurements to the results of gravitational lensing and X-ray observations. We
find that the inclusion of baryons greatly reduces the differences between CDM
and a SIDM and thus the ability to draw constraints on the basis of shapes. We
find that lensing measurements clearly reject the predictions from CDM
dark-matter-only simulations, whereas they show a different degree of
preference for the CDM and SIDM hydro scenarios, and cannot discard the SIDM
dark-matter-only case. The shapes of the X-ray emitting gas are also comparable
to observational results in both hydro runs, with CDM predicting higher
elongations only in the very center. Contrary to previous claims at the scale
of elliptical galaxies, we conclude that both CDM and our SIDM model with
$\sigma_T/m_\chi = 1\ \mathrm{cm}^2 \mathrm{g}^{-1}$ are able to explain
observed distributions of halo shapes, once baryons are included in the
simulations


title: Cosmology from the kinetic polarized Sunyaev Zel'dovich effect

abstract:
  The cosmic microwave background (CMB) photons that scatter off free electrons
in the large-scale structure induce a linear polarization pattern proportional
to the remote CMB temperature quadrupole observed in the electrons' rest frame.
The associated blackbody polarization anisotropies are known as the polarized
Sunyaev Zel'dovich (pSZ) effect. Relativistic corrections to the remote
quadrupole field give rise to a non-blackbody polarization anisotropy
proportional to the square of the transverse peculiar velocity field; this is
the kinetic polarized Sunyaev Zel'dovich (kpSZ) effect. In this paper, we
forecast the ability of future CMB and galaxy surveys to detect the kpSZ
effect, finding that a statistically significant detection is within the reach
of planned experiments. We further introduce a quadratic estimator for the
square of the peculiar velocity field based on a galaxy survey and CMB
polarization. Finally, we outline how the kpSZ effect is a probe of cosmic
birefringence and primordial non-Gaussianity, forecasting the reach of future
experiments.


title:
Prospects for multi-messenger detection of binary neutron star mergers
  in the fourth LIGO-Virgo-KAGRA observing run

abstract:
  The joint detection of GW170817 and GRB 170817A opened the era of
multi-messenger astronomy with gravitational waves (GWs) and provided the first
direct probe that at least some binary neutron star (BNS) mergers are
progenitors of short gamma-ray bursts (S-GRBs). In the next years, we expect to
have more multi-messenger detections of BNS mergers, thanks to the increasing
sensitivity of GW detectors. Here, we present a comprehensive study on the
prospects for joint GW and electromagnetic observations of merging BNSs in the
fourth LIGO--Virgo--KAGRA observing run with \emph{Fermi}, \emph{Swift},
INTEGRAL and SVOM. This work combines accurate population synthesis models with
simulations of the expected GW signals and the associated S-GRBs, considering
different assumptions about the GRB jet structure. We show that the expected
rate of joint GW and electromagnetic detections could be up to $\sim$ 6
yr$^{-1}$ when \emph{Fermi}/GBM is considered. Future joint observations will
help us to better constrain the association between BNS mergers and S-GRBs, as
well as the geometry of the GRB jets.


title:
A random matrix theory for the robustness, quantization, and end-to-end
  correlation of zero-bias conductance peaks in class D ensemble: Searching for
  Majorana zero modes in disordered quantum dots and wires

abstract:
  We develop a general theory to study strong random quenched disorder effects
in systems of experimental relevance in the search for Majorana zero modes
(MZMs) in topological superconductors. Using the random matrix theory in class
D ensemble, we simulate the transport properties of random quantum dots (QDs)
by attaching leads and calculating the differential conductance in the S matrix
formalism. To add the concept of the length to the random system so that
disordered Majorana nanowires can be simulated by the random matrix theory, we
generalize the model of a single QD to a chain of QDs by analogy with the SC-SM
nanowire Majorana platform. We first define a new concept, the robustness of
zero-bias conductance peaks (ZBCPs) in terms of an effective random Hamiltonian
considering the self-energy of leads. We then study the joint distribution for
the robustness and ZBCPs and find a strong correlation that the zero-bias
conductance peak with stronger robustness is also prone to carry a larger
conductance peak near $2e^2/h$. This trend is more prominent in shorter chains
than in longer chains. This is consistent with experimentally observed
zero-bias conductance associated with disorder-induced trivial Andreev bound
states (the so-called ugly ZBCP). Finally, we study the end-to-end correlation
of the disorder-induced zero-bias conductances from two leads by calculating
the normalized mutual information, which estimates the degrees of the
correlation arising from the trivial ZBCPs. Our work provides an estimate of
several important metrics used in SC-SM experiments to determine the nature of
ZBCPs, including the robustness, quantization, and end-to-end correlation of
the trivial ZBCPs. Therefore, in order to claim any evidence for the MZM, one
must establish the observed ZBCPs to have considerable statistical significance
well beyond what we find in this work to exist for the trivial peaks.


title:
Detailed properties of gravitational-wave mergers from flyby
  perturbations of wide binary black holes in the field

abstract:
  Wide black hole binaries (wide-BBHs; $\geqslant 10^3$ AU) in the field can be
perturbed by random stellar flybys that excite their eccentricities. Once a
wide binary is driven to a sufficiently small pericenter approach,
gravitational wave (GW) emission becomes significant, and the binary inspirals
and merges. In our previous study, using simplified models for wide-BBHs, we
found that successive flybys lead to significant merger fractions of wide-BBHs
in less than Hubble time, making the flyby perturbation mechanism a relevant
contributor to the production rate of GW-sources. However, the exact rates and
detailed properties of the resulting GW sources depend on the wide binary
progenitors. In this paper we use detailed population synthesis models for the
initial wide-BBH population, considering several populations corresponding to
different natal-kick models and metallicities, and then follow the wide-BBHs
evolution due to flyby perturbations and GW-emission. We show that the
cumulative effect of flybys is conductive for the production of GW sources in
non-negligible rates of $1-20$ Gpc$^{-3}$ yr$^{-1}$, which are sensitive to the
natal kicks model. Such rates are relevant to the observationally inferred
rate. Our models, now derived from detailed population of binaries, provide the
detailed properties of the produced GW-sources, including mass-functions and
delay times. The GW mergers are circularized when enter the aLIGO band; have a
preference for high velocity dispersion host galaxies (in particular
ellipticals); have a relatively uniform delay-time distribution; and likely
have mildly correlated (less than isolated evolution channels and more than
dynamical channels) prograde spin-spin and spin-orbits.


title: Refining Control Barrier Functions through Hamilton-Jacobi Reachability

abstract:
  Safety filters based on Control Barrier Functions (CBFs) have emerged as a
practical tool for the safety-critical control of autonomous systems. These
approaches encode safety through a value function and enforce safety by
imposing a constraint on the time derivative of this value function. However,
synthesizing a valid CBF that is not overly conservative in the presence of
input constraints is a notorious challenge. In this work, we propose refining
candidate CBFs using formal verification methods to obtain a valid CBF. In
particular, we update an expert-synthesized or backup CBF using dynamic
programming (DP) based reachability analysis. Our framework guarantees that
with every DP iteration the obtained CBF is provably at least as safe as the
prior iteration and converges to a valid CBF. Therefore, our proposed method
can be used in-the-loop for robotic systems. We demonstrate the practicality of
our method to enhance safety and/or reduce conservativeness on a range of
nonlinear control-affine systems using various CBF synthesis techniques in
simulation.


title:
The impact of relativistic corrections on the detectability of
  dark-matter spikes with gravitational waves

abstract:
  Black holes located within a dark matter cloud can create overdensity regions
known as dark matter spikes. The presence of spikes modifies the
gravitational-wave signals from binary systems through changes in the
gravitational potential or dynamical friction effects. We assess the importance
of including relativistic effects in both the dark matter distribution and the
dynamical friction. As a first step we numerically calculate the particle dark
matter spike distribution in full general relativity, using both Hernquist and
Navarro-Frenk-White profiles in a Schwarzschild background, and we produce
analytical fits to the spike profiles for a large range of scale parameters.
Then we use a post-Newtonian prescription for the gravitational-wave dephasing
to estimate the effect of relativistic corrections to the spike profile and to
the dynamical friction. Finally we include the torques generated by dynamical
friction in fast-to-generate relativistic models for circular extreme
mass-ratio inspirals around a nonspinning black hole. We find that both types
of relativistic corrections positively impact the detectability of dark matter
effects, leading to higher dephasings and mismatches between gravitational-wave
signals with and without dark matter spikes.


title:
Finite resource performance of small satellite-based quantum key
  distribution missions

abstract:
  In satellite-based quantum key distribution (QKD), the number of secret bits
that can be generated in a single satellite pass over the ground station is
severely restricted by the pass duration and the free-space optical channel
loss. High channel loss may decrease the signal-to-noise ratio due to
background noise, reduce the number of generated raw key bits, and increase the
quantum bit error rate (QBER), all of which have detrimental effects on the
output secret key length. Under finite-size security analysis, higher QBER
increases the minimum raw key length necessary for non-zero key length
extraction due to less efficient reconciliation and post-processing overheads.
We show that recent developments in finite key analysis allow three different
small-satellite-based QKD projects CQT-Sat, UK-QUARC-ROKS, and QEYSSat to
produce secret keys even under very high loss conditions, improving on
estimates based on older finite key bounds.


title:
Impact and detectability of spin-tidal couplings in neutron star
  inspirals

abstract:
  The gravitational wave signal from a binary neutron star merger carries the
imprint of the deformability properties of the coalescing bodies, and then of
the equation of state of neutron stars. In current models of the waveforms
emitted in these events, the contribution of tidal deformation is encoded in a
set of parameters, the tidal Love numbers. More refined models include
tidal-rotation couplings, described by an additional set of parameters, the
rotational tidal Love numbers, which appear in the waveform at $6.5$
post-Newtonian order. For neutron stars with spins as large as $\sim0.1$, we
show that neglecting tidal-rotation couplings may lead to a significant error
in the parameter estimation by third-generation gravitational wave detectors.
By performing a Fisher matrix analysis we assess the measurability of
rotational tidal Love numbers, showing that their contribution in the waveform
could be measured by third-generation detectors. Our results suggest that
current models of tidal deformation in late inspiral should be improved in
order to avoid waveform systematics and extract reliable information from
gravitational wave signals observed by next generation detectors.


title:
PolyLoss: A Polynomial Expansion Perspective of Classification Loss
  Functions

abstract:
  Cross-entropy loss and focal loss are the most common choices when training
deep neural networks for classification problems. Generally speaking, however,
a good loss function can take on much more flexible forms, and should be
tailored for different tasks and datasets. Motivated by how functions can be
approximated via Taylor expansion, we propose a simple framework, named
PolyLoss, to view and design loss functions as a linear combination of
polynomial functions. Our PolyLoss allows the importance of different
polynomial bases to be easily adjusted depending on the targeting tasks and
datasets, while naturally subsuming the aforementioned cross-entropy loss and
focal loss as special cases. Extensive experimental results show that the
optimal choice within the PolyLoss is indeed dependent on the task and dataset.
Simply by introducing one extra hyperparameter and adding one line of code, our
Poly-1 formulation outperforms the cross-entropy loss and focal loss on 2D
image classification, instance segmentation, object detection, and 3D object
detection tasks, sometimes by a large margin.


title:
Impact of Correlated Noise on the Mass Precision of Earth-analog Planets
  in Radial Velocity Surveys

abstract:
  Characterizing the masses and orbits of near-Earth-mass planets is crucial
for interpreting observations from future direct imaging missions (e.g., HabEx,
LUVOIR). Therefore, the Exoplanet Science Strategy report (National Academies
of Sciences, Engineering, and Medicine 2018) recommended further research so
future extremely precise radial velocity surveys could contribute to the
discovery and/or characterization of near-Earth-mass planets in the habitable
zones of nearby stars prior to the launch of these future imaging missions.
Newman et al. (2021) simulated such 10-year surveys under various telescope
architectures, demonstrating they can precisely measure the masses of
potentially habitable Earth-mass planets in the absence of stellar variability.
Here, we investigate the effect of stellar variability on the signal-to-noise
ratio (SNR) of the planet mass measurements in these simulations. We find that
correlated noise due to active regions has the largest effect on the observed
mass SNR, reducing the SNR by a factor of $\sim$5.5 relative to the
no-variability scenario -- granulation reduces by a factor of $\sim$3, while
p-mode oscillations has little impact on the proposed survey strategies. We
show that in the presence of correlated noise, 5-cm s$^{-1}$ instrumental
precision offers little improvement over 10-cm s$^{-1}$ precision, highlighting
the need to mitigate astrophysical variability. With our noise models,
extending the survey to 15 years doubles the number of Earth-analogs with mass
SNR $>$ 10, and reaching this threshold for any Earth-analog orbiting a star
$>$ 0.76 M$_{\odot}$ in a 10-year survey would require an increase in number of
observations per star from that in Newman et al. (2021).


title:
Supercritical growth pathway to overmassive black holes at cosmic dawn:
  coevolution with massive quasar hosts

abstract:
  Observations of the most luminous quasars at high redshifts ($z > 6$) have
revealed that the largest supermassive black holes (SMBHs) at those epochs tend
to be substantially overmassive relative to their host galaxies compared to the
local relations, suggesting they experienced rapid early growth phases. We
propose an assembly model for the SMBHs that end up in rare massive
$\sim10^{12}~M_{\odot}$ host halos at $z \sim 6-7$, applying a kinetic feedback
prescription for BHs accreting above the Eddington rate, provided by radiation
hydrodynamic simulations for the long-term evolution of the accretion-flow
structure. The large inflow rates into these halos during their assembly enable
the formation of $>10^9~M_{\odot}$ SMBHs by $z \sim 6$, even starting from
stellar-mass seeds at $z \sim 30$, and even in the presence of outflows that
reduce the BH feeding rate, especially at early times. This mechanism also
naturally yields a high BH-to-galaxy mass ratio of $> 0.01$ before the SMBH
mass reaches $M_{\rm BH} > 10^9~M_{\odot}$ by $z \sim 6$. These fast-growing
SMBH progenitors are bright enough to be detected by upcoming observations with
the James Webb Space Telescope over a wide range of redshift ($7 < z < 15$),
regardless of how they were seeded.


title:
AGN jets do not prevent the saturation of conduction by the heat
  buoyancy instability in simulated galaxy clusters

abstract:
  Centres of galaxy clusters must be efficiently reheated to avoid a cooling
catastrophe. One potential reheating mechanism is anisotropic thermal
conduction, which could transport thermal energy from intermediate radii to the
cluster center. However, if fields are not re-randomised, anisotropic thermal
conduction drives the heat buoyancy instability (HBI) which reorients magnetic
field lines and shuts off radial heat fluxes. We revisit the efficiency of
thermal conduction under the influence of spin-driven AGN jets in idealised
magneto-hydrodynamical simulations with anisotropic thermal conduction. Despite
the black hole spin's ability to regularly re-orientate the jet so that the
jet-induced turbulence is driven in a quasi-isotropic fashion, the HBI remains
efficient outside the central 50 kpc of the cluster, where the reservoir of
heat is the largest. As a result, conduction plays no significant role in
regulating the cooling of the intra-cluster medium if central active galactic
nuclei are the sole source of turbulence.