title: High-harmonic generation enhancement with graphene heterostructures

abstract:
  We investigate high-harmonic generation in graphene heterostructures
consisting of metallic nanoribbons separated from a graphene sheet by either a
few-nanometer layer of aluminum oxide or an atomic monolayer of hexagonal boron
nitride. The nanoribbons amplify the near-field at the graphene layer relative
to the externally applied pumping, thus allowing us to observe third- and
fifth-harmonic generation in the carbon monolayer at modest pump powers in the
mid-infrared. We study the dependence of the nonlinear signals on the ribbon
width and spacer thickness, as well as pump power and polarization, and
demonstrate enhancement factors relative to bare graphene reaching 1600 and
4100 for third- and fifth-harmonic generation, respectively. Our work supports
the use of graphene heterostructures to selectively enhance specific nonlinear
processes of interest, an essential capability for the design of nanoscale
nonlinear devices.


title: REx: Data-Free Residual Quantization Error Expansion

abstract:
  Deep neural networks (DNNs) are nowadays ubiquitous in the computer vision
landscape. However, they suffer from high computational costs in inference,
particularly when evaluated on edge devices. This problem is generally
addressed via post-hoc quantization, i.e. converting the DNN values (weights
and inputs) from floating point into e.g. int8, int4 or ternary quantization.
In this paper, we propose REx, a data-free quantization algorithm for
pre-trained models that is compliant with data protection regulations,
convenient and fast to execute. First, we improve upon the naive linear
quantization operator by decomposing the weights as an expansion of residual
quantization errors. Second, we propose a budgeted group-sparsity formulation
to achieve better accuracy vs. number of bit-wise operation trade-offs with
sparse, higher expansion orders. Third, we show that this sparse expansion can
be approximated by an ensemble of quantized neural networks to dramatically
improve the evaluation speed through more efficient parallelization. We provide
theoretical guarantees of the efficiency of REx as well as a thorough empirical
validation on several popular DNN architectures applied to multiple computer
vision problems, e.g. ImageNet classification, object detection as well as
semantic segmentation. In particular, we show that REx significantly
outperforms existing state-of-the-art data-free quantization techniques.


title:
To Fold or Not to Fold: a Necessary and Sufficient Condition on
  Batch-Normalization Layers Folding

abstract:
  Batch-Normalization (BN) layers have become fundamental components in the
evermore complex deep neural network architectures. Such models require
acceleration processes for deployment on edge devices. However, BN layers add
computation bottlenecks due to the sequential operation processing: thus, a
key, yet often overlooked component of the acceleration process is BN layers
folding. In this paper, we demonstrate that the current BN folding approaches
are suboptimal in terms of how many layers can be removed. We therefore provide
a necessary and sufficient condition for BN folding and a corresponding optimal
algorithm. The proposed approach systematically outperforms existing baselines
and allows to dramatically reduce the inference time of deep neural networks.


title:
Automatic Debate Evaluation with Argumentation Semantics and Natural
  Language Argument Graph Networks

abstract:
  The lack of annotated data on professional argumentation and complete
argumentative debates has led to the oversimplification and the inability of
approaching more complex natural language processing tasks. Such is the case of
the automatic debate evaluation. In this paper, we propose an original hybrid
method to automatically evaluate argumentative debates. For that purpose, we
combine concepts from argumentation theory such as argumentation frameworks and
semantics, with Transformer-based architectures and neural graph networks.
Furthermore, we obtain promising results that lay the basis on an unexplored
new instance of the automatic analysis of natural language arguments.


title:
Renormalization and a-priori bounds for Leray self-similar solutions to
  the generalized mild Navier-Stokes equations

abstract:
  We demonstrate that the problem of existence of Leray self-similar blow up
solutions in a generalized mild Navier-Stokes system with the fractional
Laplacian $(-\Delta)^{\gamma/2}$ can be stated as a fixed point problem for a
"renormalization" operator. We proceed to construct {\it a-priori} bounds, that
is a renormalization invariant precompact set in an appropriate weighted
$L^p$-space.
  As a consequence of a-priori bounds, we prove existence of renormalization
fixed points for $d \ge 2$ and $d<\gamma <2 d+2$, and existence of non-trivial
Leray self-similar mild solutions in $C^\infty([0,T),(H^k)^d \cap (L^p)^d)$,
$k>0, p \ge 2$, whose $(L^p)^d$-norm becomes unbounded in finite time $T$.


title: Knowledge Distillation: Bad Models Can Be Good Role Models

abstract:
  Large neural networks trained in the overparameterized regime are able to fit
noise to zero train error. Recent work \citep{nakkiran2020distributional} has
empirically observed that such networks behave as "conditional samplers" from
the noisy distribution. That is, they replicate the noise in the train data to
unseen examples. We give a theoretical framework for studying this conditional
sampling behavior in the context of learning theory. We relate the notion of
such samplers to knowledge distillation, where a student network imitates the
outputs of a teacher on unlabeled data. We show that samplers, while being bad
classifiers, can be good teachers. Concretely, we prove that distillation from
samplers is guaranteed to produce a student which approximates the Bayes
optimal classifier. Finally, we show that some common learning algorithms
(e.g., Nearest-Neighbours and Kernel Machines) can generate samplers when
applied in the overparameterized regime.


title:
Large $|k|$ behavior for the reflection coefficient for Davey-Stewartson
  II equations

abstract:
  The study of complex geometric optics solutions to a system of d-bar
equations appearing in the context of electrical impedance tomography and the
scattering theory of the integrable Davey-Stewartson II equations for large
values of the spectral parameter $k$ in \cite{KlSjSt20} is extended to the
reflection coefficient. For the case of potentials $q$ with compact support on
some domain $\Omega$ with smooth strictly convex boundary, improved asymptotic
relations are provided.


title:
Renormalization and existence of the finite-time blow up solutions for a
  one-dimensional analogue of the Navier-Stokes equations

abstract:
  The one-dimensional quasi-geostrophic equation is the one-dimensional
Fourier-space analogue of the famous Navier-Stokes equations. In their work Li
and Sinai have proposed a renormalization approach to the problem of existence
of finite-time blow up solutions of this equation. In this setting, existence
of finite time blow ups is a consequence of existence of a fixed point for a
certain renormalization operator on an appropriate functional space. They have
provided a proof of existence of complex-valued finite time blow up solutions
of the quasi-geostrophic equation.
  In this paper we revisit the renormalization problem for the
quasi-geostrophic blow ups, prove existence of a family of renormalization
fixed points, and deduce existence of real $C^\infty([0,T),C^\infty(\mathbb{R})
\cap L^2(\mathbb{R}))$ solutions to the quasi-geostrophic equation whose energy
and enstrophy become unbounded in finite time, different from those found in
the previous work of Li and Sinai.


title: Penguin Effects in $B_d^0\to J/\psi K^0_S$ and $B_s^0\to J/\psi\phi$

abstract:
  Controlling the contributions from doubly Cabibbo-suppressed penguin
topologies in the decays $B_d^0\to J/\psi K^0_S$ and $B_s^0\to J/\psi\phi$ is
mandatory to reach the highest possible precision in the measurement of the
$B^0_q$--$\bar B^0_q$ ($q=d,s$) mixing phases $\phi_d$ and $\phi_s$. The
penguin contributions can be determined using a strategy based on the $SU(3)$
flavour symmetry of QCD. Using the latest experimental data, we update our
combined analysis of the decays $B_d^0\to J/\psi K^0_S$, $B_s^0\to J/\psi\phi$
and their control channels $B_s^0\to J/\psi K^0_S$, $B_d^0\to J/\psi \pi^0$ and
$B_d^0\to J/\psi \rho^0$. This allows us to simultaneously determine the
penguin parameters and both mixing phases. We discuss how the branching
fractions of these decays can be used to probe the size of non-factorisable
$SU(3)$-breaking effects, which form the main theoretical uncertainty
associated with our $SU(3)$-based strategy, and provide new insights into the
factorisation approach.


title:
Efficient method to simulate non-perturbative dynamics of an open
  quantum system using a quantum computer

abstract:
  Classical non-perturbative simulations of open quantum systems dynamics face
several scalability problems, namely, exponential scaling as a function of
either the time length of the simulation or the size of the open system. In
this work, we propose a quantum computational method which we term as Quantum
Time Evolving Density operator with Orthogonal Polynomials Algorithm
(Q-TEDOPA), based on the known TEDOPA technique, avoiding any exponential
scaling in simulations of non-perturbative dynamics of open quantum systems on
a quantum computer. By performing an exact transformation of the Hamiltonian,
we obtain only local nearest-neighbour interactions, making this algorithm
suitable to be implemented in the current Noisy-Intermediate Scale Quantum
(NISQ) devices. We show how to implement the Q-TEDOPA using an IBM quantum
processor by simulating the exciton transport between two light-harvesting
molecules in the regime of moderate coupling strength to a non-Markovian
harmonic oscillator environment. Applications of the Q-TEDOPA span problems
which can not be solved by perturbation techniques belonging to different
areas, such as dynamics of quantum biological systems and strongly correlated
condensed matter systems.


title:
Mean-Field Type FBSDEs under Domination-Monotonicity Conditions and
  Application to LQ Problems

abstract:
  This paper is concerned with a class of mean-field type coupled
forward-backward stochastic differential equations (MF-FBSDEs, for short), in
which the coupling appears in integral terms, terminal terms, and initial
terms. Inspired by various mean-field type linear-quadratic (MF-LQ,for short)
optimal control problems, we proposed a type of randomized
domination-monotonicity conditions, under which and the usual Lipschitz
condition, we obtain a well-posedness result on MF-FBSDEs in the sense of
square integrability including the unique solvability, an estimate of the
solution, and the related continuous dependence property of the solution on the
coefficients.The result of MF-FBSDEs in turn extends MF-LQ problems in the
literature to a general situation where the initial states or the terminal
states are also controlled at the same time, and gives explicit expressions of
the related unique optimal controls.


title: Few-Shot Learning with Siamese Networks and Label Tuning

abstract:
  We study the problem of building text classifiers with little or no training
data, commonly known as zero and few-shot text classification. In recent years,
an approach based on neural textual entailment models has been found to give
strong results on a diverse range of tasks. In this work, we show that with
proper pre-training, Siamese Networks that embed texts and labels offer a
competitive alternative. These models allow for a large reduction in inference
cost: constant in the number of labels rather than linear. Furthermore, we
introduce label tuning, a simple and computationally efficient approach that
allows to adapt the models in a few-shot setup by only changing the label
embeddings. While giving lower performance than model fine-tuning, this
approach has the architectural advantage that a single encoder can be shared by
many different tasks.


title: Stability of Yang Mills Vacuum State

abstract:
  We examine the phenomena of the chromomagnetic gluon condensation in
Yang-Mills theory and the problem of stability of the vacuum state. The
stability of the vacuum state is analysed in the nonlinear regime. It is shown
that an apparent instability of the Yang Mills vacuum is a result of quadratic
approximation. In the case of (anti)self-dual fields the interaction of
chromomagnetic modes of the quantised field in the direction of zero modes is
calculated by using a new method of infrared regularisation as well as by the
integration over the collective variables of self-interacting zero modes. The
deformation of (anti)self-dual fields is also considered in the nonlinear
regime by the integration, in this case, over the collective variables of
self-interacting unstable modes. All these vacuum field configurations are
stable and indicate that the vacuum is stable and is a superposition of many
states.


title:
Continuous-variable measurement device independent quantum conferencing
  with post-selection

abstract:
  A continuous variable (CV), measurement device independent (MDI) quantum key
distribution (QKD) protocol is analyzed, enabling three parties to connect for
quantum conferencing. We utilise a generalised Bell detection at an untrusted
relay and a postselection procedure, in which distant parties reconcile on the
signs of the displacements of the quadratures of their prepared coherent
states. We derive the rate of the protocol under a collective pure-loss attack,
demonstrating improved rate-distance performance compared to the equivalent
non-post-selected protocol. In the symmetric configuration in which all the
parties lie the same distance from the relay, we find a positive key rate over
6 km. Such postselection techniques can be used to improve the rate of
multi-party quantum conferencing protocols at longer distances at the cost of
reduced performance at shorter distances.


title:
Absolute efficiency of a two-stage microchannel plate for electrons in
  the 30 - 900 eV energy range

abstract:
  We report on an apparatus able to measure the absolute detection efficiency
of a detector for electrons in the 30 - 900 eV range. In particular, we discuss
the characterisation of a two-stage chevron microchannel plate (MCP). The
measurements have been performed in the LASEC laboratory at Roma Tre
University, whit a custom-made electron gun. The very good stability of the
beam current in the fA range, together with the picoammeter nominal resolution
of 0.01 fA, allowed the measurement of the MCP absolute efficiency $\epsilon$.
We found an $\epsilon$ = (0.489 $\pm$ 0.003) with no evident energy dependence.
We fully characterised the MCP pulse shape distribution, which is
quasi-Gaussian with a well visible peak above the noise level. We measured a
68% variation of the average pulse height between 30 and 500 eV. Furthermore,
with a deeper analysis of the pulse shape, and in particular of the correlation
between pulse height, area and width, we found a method to discriminate single-
and multi- electron events occurring within a 10 ns time window.


title: Injection testing backed refactoring

abstract:
  Injection-based testing while refactoring is a pattern that minimizes the
need for manual editing when altering the behaviour of a code base. Neither
does it rely on a compilation or a linking process nor does it make assumptions
on the structure of the code. Thus, it can be particularly useful for
refactoring code that has been written in scripting languages, and specifically
targets the research and engineering context. We describe the pattern and
propose a set of functions for its application. The applicability of code
injection for refactoring is highlighted via specific examples for deriving
unit and integration tests. Finally, we comment on the customizing of the
pattern and give practical advice for its implementation.


title: Revisiting Model-based Value Expansion

abstract:
  Model-based value expansion methods promise to improve the quality of value
function targets and, thereby, the effectiveness of value function learning.
However, to date, these methods are being outperformed by Dyna-style algorithms
with conceptually simpler 1-step value function targets. This shows that in
practice, the theoretical justification of value expansion does not seem to
hold. We provide a thorough empirical study to shed light on the causes of
failure of value expansion methods in practice which is believed to be the
compounding model error. By leveraging GPU based physics simulators, we are
able to efficiently use the true dynamics for analysis inside the model-based
reinforcement learning loop. Performing extensive comparisons between true and
learned dynamics sheds light into this black box. This paper provides a better
understanding of the actual problems in value expansion. We provide future
directions of research by empirically testing the maximum theoretical
performance of current approaches.


title: Random matrix analysis of deep neural network weight matrices

abstract:
  Neural networks have been used successfully in a variety of fields, which has
led to a great deal of interest in developing a theoretical understanding of
how they store the information needed to perform a particular task. We study
the weight matrices of trained deep neural networks using methods from random
matrix theory (RMT) and show that the statistics of most of the singular values
follow universal RMT predictions. This suggests that they are random and do not
contain system specific information, which we investigate further by comparing
the statistics of eigenvector entries to the universal Porter-Thomas
distribution. We find that for most eigenvectors the hypothesis of randomness
cannot be rejected, and that only eigenvectors belonging to the largest
singular values deviate from the RMT prediction, indicating that they may
encode learned information. We analyze the spectral distribution of such large
singular values using the Hill estimator and find that the distribution cannot
be characterized by a tail index, i.e. is not of power law type.


title:
MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance
  Segmentation

abstract:
  This paper studies the 3D instance segmentation problem, which has a variety
of real-world applications such as robotics and augmented reality. Since the
surroundings of 3D objects are of high complexity, the separating of different
objects is very difficult. To address this challenging problem, we propose a
novel framework to group and refine the 3D instances. In practice, we first
learn an offset vector for each point and shift it to its predicted instance
center. To better group these points, we propose a Hierarchical Point Grouping
algorithm to merge the centrally aggregated points progressively. All points
are grouped into small clusters, which further gradually undergo another
clustering procedure to merge into larger groups. These multi-scale groups are
exploited for instance prediction, which is beneficial for predicting instances
with different scales. In addition, a novel MaskScoreNet is developed to
produce binary point masks of these groups for further refining the
segmentation results. Extensive experiments conducted on the ScanNetV2 and
S3DIS benchmarks demonstrate the effectiveness of the proposed method. For
instance, our approach achieves a 66.4\% mAP with the 0.5 IoU threshold on the
ScanNetV2 test set, which is 1.9\% higher than the state-of-the-art method.


title: Clusters in an epidemic model with long-range dispersal

abstract:
  In presence of long range dispersal, epidemics spread in spatially
disconnected regions known as clusters. Here, we characterize exactly their
statistical properties in a solvable model, in both the supercritical
(outbreak) and critical regimes. We identify two diverging length scales,
corresponding to the bulk and the outskirt of the epidemic. We reveal a
nontrivial critical exponent that governs the cluster number, the distribution
of their sizes and of the distances between them. We also discuss applications
to depinning avalanches with long range elasticity.

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


title:
Four-Vector Optical Dirac Equation and Spin-Orbit Interaction of
  Structured Light

abstract:
  The spin-orbit interaction of light is a crucial concept for understanding
electromagnetic properties of material and realizing the spin-controlled
manipulation of optical fields. Achieving these goals requires a complete
description of spin-dependent optical phenomena in the context of vector-wave
mechanics. We develop an extended Dirac theory for optical fields in generic
media, which was found to be akin to a non-Hermitian chiral-extension of
massive fermions with anomalous magnetic momentums moving in an external
pseudo-magnetic field. This similarity allows us to investigate optical
behaviors of material by effective field theory methods and can find wide
applications in metamaterial, photonic topological insulators, etc. We
demonstrate this method by studying the spin-orbit interaction of structured
lights in the spin-degenerate medium and inhomogeneous isotropic medium, which
leads to both the spin-orbital-Hall effects and spin-to-orbital angular
momentum conversion. Of importance, our approach provides a simple and clear
physical insight into the spin-orbit interaction of lights in generic media,
and could potentially bridge our understanding of topological insulator between
electronic and photonic systems.


title: Quantized function algebras at $q=0$: type $A_{n}$ case

abstract:
  We describe the quantised function algebras at $q=0$ for the $q$ deformations
of the type $A_{n}$ compact Lie groups. The associated $C^{*}$-algebra
$A_{n}(0)$ is given by a set of generators and relations. We obtain these
relations by looking at the irreducible representations of the quantised
function algebras for $q>0$ and taking limit as $q\to 0+$. We then determine
all the irreducible representations of this $C^{*}$-algebra in the case $n=2$
and exhibit a faithful representation that is an analogue of the representation
of $A_{1}(0)$ used by Chakraborty & Pal in their approximate equivalence
theorem involving the GNS representation of $C(SU_{q}(2))$. We also prove that
$A_{2}(0)$ is not a compact quantum group, though it has a natural bialgebra
structure.


title: Federated Learning with Position-Aware Neurons

abstract:
  Federated Learning (FL) fuses collaborative models from local nodes without
centralizing users' data. The permutation invariance property of neural
networks and the non-i.i.d. data across clients make the locally updated
parameters imprecisely aligned, disabling the coordinate-based parameter
averaging. Traditional neurons do not explicitly consider position information.
Hence, we propose Position-Aware Neurons (PANs) as an alternative, fusing
position-related values (i.e., position encodings) into neuron outputs. PANs
couple themselves to their positions and minimize the possibility of
dislocation, even updating on heterogeneous data. We turn on/off PANs to
disable/enable the permutation invariance property of neural networks. PANs are
tightly coupled with positions when applied to FL, making parameters across
clients pre-aligned and facilitating coordinate-based parameter averaging. PANs
are algorithm-agnostic and could universally improve existing FL algorithms.
Furthermore, "FL with PANs" is simple to implement and computationally
friendly.


title: Modular symmetry of soft supersymmetry breaking terms

abstract:
  We study the modular symmetry of soft supersymmetry breaking terms. Soft
scalar masses and $A$-term coefficients are invariant under the modular
symmetry when we regard $F$-term as a spurion with the modular weight $-2$.
Their flavor structure is determined by the same symmetry as Yukawa couplings,
i.e., fermion masses. The modular symmetric behavior of $\mu$-term and $B$-term
depends on how the $\mu$-term is generated.


title:
Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG
  Background Creation

abstract:
  We address the problem of generating a 360-degree image from a single image
with a narrow field of view by estimating its surroundings. Previous methods
suffered from overfitting to the training resolution and deterministic
generation. This paper proposes a completion method using a transformer for
scene modeling and novel methods to improve the properties of a 360-degree
image on the output image. Specifically, we use CompletionNets with a
transformer to perform diverse completions and AdjustmentNet to match color,
stitching, and resolution with an input image, enabling inference at any
resolution. To improve the properties of a 360-degree image on an output image,
we also propose WS-perceptual loss and circular inference. Thorough experiments
show that our method outperforms state-of-the-art (SOTA) methods both
qualitatively and quantitatively. For example, compared to SOTA methods, our
method completes images 16 times larger in resolution and achieves 1.7 times
lower Frechet inception distance (FID). Furthermore, we propose a pipeline that
uses the completion results for lighting and background of 3DCG scenes. Our
plausible background completion enables perceptually natural results in the
application of inserting virtual objects with specular surfaces.


title: Dynamic Structure in Four-strategy Game: Theory and Experiment

abstract:
  Game dynamics theory, as a field of science, the consistency of theory and
experiment is essential. In the past 10 years, important progress has been made
in the merging of the theory and experiment in this field, in which dynamics
cycle is the presentation. However, the merging works have not got rid of the
constraints of Euclidean two-dimensional cycle so far. This paper uses a
classic four-strategy game to study the dynamic structure (non-Euclidean
superplane cycle). The consistency is in significant between the three ways:
(1) the analytical results from evolutionary dynamics equations, (2)
agent-based simulation results from learning models and (3) laboratory results
from human subjects game experiments. The consistency suggests that, game
dynamic structure could be quantitatively predictable, observable and
controllable in general.


title: On the third problem of Halmos on Banach spaces

abstract:
  Assume that $X$ is a complex separable infinite dimensional Banach space and
$\mathcal{B}(X)$ denotes the Banach algebra of all bounded linear operators
from $X$ to itself. In 1970, P.R. Halmos raised ten open problems in Hilbert
spaces. The third one is the following: If an intransitive operator $T$ has an
inverse, is its inverse also intransitive? This question is closely related to
the invariant subspace problem. Ever since Enflo's celebrated counterexample on
$\ell_1$ answered the invariant subspace problem in negative, the Banach space
setting of the third question of Halmos has become more interesting. In this
paper, we give an affirmative answer to this problem under certain spectral
conditions. As an application, we show that for an invertible operator $T$ with
Dunford's Property ($C$), if $T^{-1}$ is intransitive and there exists a
connected component $\Omega$ of $int\sigma(T^{-1})^\land$ which is off the
origin such that $\Omega\cap\rho_F(T^{-1})\neq \emptyset$, then $T$ is also
intransitive. In the end of the paper, we show that a sufficient and necessary
condition for that there exists a bounded linear operator without non-trivial
invariant subspaces on the infinite dimensional space $L_1(\Omega,\sum,\mu)$
(resp., $C(K)$, the space of bounded continuous functions on a complete metric
space $K$) is that $(\Omega,\sum,\mu)$ is $\sigma$-finite (resp., $K$ is
compact).


title:
Non-Markovian thermal operations boosting the performance of quantum
  heat engines

abstract:
  It is investigated whether non-Markovianity, i.e., the memory effects
resulting from the coupling of the system to its environment, can be beneficial
for the performance of quantum heat engines. Specifically, two physical models
are considered. The first one is a well known single-qubit Otto engine; the
non-Markovian behaviour is there implemented by replacing standard
thermalization strokes with so-called extremal thermal operations which cannot
be realized without the memory effects. The second one is a three-stroke engine
in which the cycle consists of two extremal thermal operations and a single
qubit rotation. It is shown that the non-Markovian Otto engine can generate
more work-per-cycle for a given efficiency than its Markovian counterpart,
whereas performance of both setups is superior to the three-stroke engine.
Furthermore, both the non-Markovian Otto engine and the three-stroke engine can
reduce the work fluctuations in comparison with the Markovian Otto engine, with
their relative advantage depending on the performance target. This demonstrates
the beneficial influence of non-Markovianity on both the average performance
and the stability of operation of quantum heat engines.


title: Are High-Resolution Event Cameras Really Needed?

abstract:
  Due to their outstanding properties in challenging conditions, event cameras
have become indispensable in a wide range of applications, ranging from
automotive, computational photography, and SLAM. However, as further
improvements are made to the sensor design, modern event cameras are trending
toward higher and higher sensor resolutions, which result in higher bandwidth
and computational requirements on downstream tasks. Despite this trend, the
benefits of using high-resolution event cameras to solve standard computer
vision tasks are still not clear. In this work, we report the surprising
discovery that, in low-illumination conditions and at high speeds,
low-resolution cameras can outperform high-resolution ones, while requiring a
significantly lower bandwidth. We provide both empirical and theoretical
evidence for this claim, which indicates that high-resolution event cameras
exhibit higher per-pixel event rates, leading to higher temporal noise in
low-illumination conditions and at high speeds. As a result, in most cases,
high-resolution event cameras show a lower task performance, compared to lower
resolution sensors in these conditions. We empirically validate our findings
across several tasks, namely image reconstruction, optical flow estimation, and
camera pose tracking, both on synthetic and real data. We believe that these
findings will provide important guidelines for future trends in event camera
development.


title:
Using Machine Learning to generate an open-access cropland map from
  satellite images time series in the Indian Himalayan Region

abstract:
  Crop maps are crucial for agricultural monitoring and food management and can
additionally support domain-specific applications, such as setting cold supply
chain infrastructure in developing countries. Machine learning (ML) models,
combined with freely-available satellite imagery, can be used to produce
cost-effective and high spatial-resolution crop maps. However, accessing ground
truth data for supervised learning is especially challenging in developing
countries due to factors such as smallholding and fragmented geography, which
often results in a lack of crop type maps or even reliable cropland maps. Our
area of interest for this study lies in Himachal Pradesh, India, where we aim
at producing an open-access binary cropland map at 10-meter resolution for the
Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline
that relies on Sentinel-2 satellite images time series. We investigated two
pixel-based supervised classifiers, support vector machines (SVM) and random
forest (RF), which are used to classify per-pixel time series for binary
cropland mapping. The ground truth data used for training, validation and
testing was manually annotated from a combination of field survey reference
points and visual interpretation of very high resolution (VHR) imagery. We
trained and validated the models via spatial cross-validation to account for
local spatial autocorrelation and selected the RF model due to overall
robustness and lower computational cost. We tested the generalization
capability of the chosen model at the pixel level by computing the accuracy,
recall, precision, and F1-score on hold-out test sets of each district,
achieving an average accuracy for the RF (our best model) of 87%. We used this
model to generate a cropland map for three districts of Himachal Pradesh,
spanning 14,600 km2, which improves the resolution and quality of existing
public maps.


title:
Exploring the circular polarisation of low-frequency solar radio bursts
  with LOFAR

abstract:
  The Sun is an active star that often produces numerous bursts of
electromagnetic radiation at radio wavelengths. Low frequency radio bursts have
recently been brought back to light with the advancement of novel radio
interferometers. However, their polarisation properties have not yet been
explored in detail, especially with the Low Frequency Array (LOFAR), due to
difficulties in calibrating the data and accounting for instrumental leakage.
Here, using a unique method to correct the polarisation observations, we
explore the circular polarisation of different sub-types of solar type III
radio bursts and a type I noise storm observed with LOFAR, which occurred
during March-April 2019. We analysed six individual radio bursts from two
different dates. We present the first Stokes V low frequency images of the Sun
with LOFAR in tied-array mode observations. We find that the degree of circular
polarisation for each of the selected bursts increases with frequency for
fundamental emission, while this trend is either not clear or absent for
harmonic emission. The type III bursts studied, that are part of a
long--lasting type III storm, can have different senses of circular
polarisation, occur at different locations and have different propagation
directions. This indicates that the type III bursts forming a classical type
III storm do not necessarily have a common origin but instead they indicate the
existence of multiple, possibly unrelated, acceleration processes originating
from solar minimum active regions.


title:
Part-based Pseudo Label Refinement for Unsupervised Person
  Re-identification

abstract:
  Unsupervised person re-identification (re-ID) aims at learning discriminative
representations for person retrieval from unlabeled data. Recent techniques
accomplish this task by using pseudo-labels, but these labels are inherently
noisy and deteriorate the accuracy. To overcome this problem, several
pseudo-label refinement methods have been proposed, but they neglect the
fine-grained local context essential for person re-ID. In this paper, we
propose a novel Part-based Pseudo Label Refinement (PPLR) framework that
reduces the label noise by employing the complementary relationship between
global and part features. Specifically, we design a cross agreement score as
the similarity of k-nearest neighbors between feature spaces to exploit the
reliable complementary relationship. Based on the cross agreement, we refine
pseudo-labels of global features by ensembling the predictions of part
features, which collectively alleviate the noise in global feature clustering.
We further refine pseudo-labels of part features by applying label smoothing
according to the suitability of given labels for each part. Thanks to the
reliable complementary information provided by the cross agreement score, our
PPLR effectively reduces the influence of noisy labels and learns
discriminative representations with rich local contexts. Extensive experimental
results on Market-1501 and MSMT17 demonstrate the effectiveness of the proposed
method over the state-of-the-art performance. The code is available at
https://github.com/yoonkicho/PPLR.


title: Using Malliavin calculus to solve a chemical diffusion master equation

abstract:
  We propose a novel method to solve a chemical diffusion master equation of
birth and death type. This is an infinite system of Fokker-Planck equations
where the different components are coupled by reaction dynamics similar in form
to a chemical master equation. This system was proposed in [3] for modelling
the probabilistic evolution of chemical reaction kinetics associated with
spatial diffusion of individual particles. Using some basic tools and ideas
from infinite dimensional Gaussian analysis we are able to reformulate the
aforementioned infinite system of Fokker-Planck equations as a single evolution
equation solved by a generalized stochastic process and written in terms of
Malliavin derivatives and differential second quantization operators. Via this
alternative representation we link certain finite dimensional projections of
the solution of the original problem to the solution of a single partial
differential equations of Ornstein-Uhlenbeck type containing as many variables
as the dimension of the aforementioned projection space.


title:
Noncommutative numerable principal bundles from group actions on
  C*-algebras

abstract:
  We introduce a notion of a noncommutative (or quantum) numerable principal
bundle in the setting of actions of locally compact Hausdorff groups on
C*-algebras. More precisely, we give a definition of a locally trivial
$G$-C*-algebra, which is a noncommutative counterpart of the total space of a
locally compact Hausdorff numerable principal $G$-bundle. To obtain this
generalization we have to go beyond the Gelfand-Naimark duality and use the
multipliers of the Pedersen ideal. In the case of an action of a compact
Hausdorff group on a unital C*-algebra our definition is implied by the
finiteness of the local-triviality dimension of the action. Furthermore, we
prove that if $A$ is a locally trivial $G$-C*-algebra, then the $G$-action on
$A$ is free in a certain sense, which in many cases coincides with the known
notions of freeness due to Rieffel and Ellwood.


title: Universal sums of generalized heptagonal numbers

abstract:
  In this paper, we consider representations of integers as sums of generalized
heptagonal numbers with a prescribed number of repeats of each heptagonal
number appearing in the sum. In particular, we investigate the classification
of such sums which are universal, i.e., those that represent every positive
integer. We prove an explicit finite bound such that a given sum is universal
if and only if it represents positive integer up to the given bound.


title: Brain-inspired Multilayer Perceptron with Spiking Neurons

abstract:
  Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field of
computer vision tasks. Without inductive bias, MLPs perform well on feature
extraction and achieve amazing results. However, due to the simplicity of their
structures, the performance highly depends on the local features communication
machenism. To further improve the performance of MLP, we introduce information
communication mechanisms from brain-inspired neural networks. Spiking Neural
Network (SNN) is the most famous brain-inspired neural network, and achieve
great success on dealing with sparse data. Leaky Integrate and Fire (LIF)
neurons in SNNs are used to communicate between different time steps. In this
paper, we incorporate the machanism of LIF neurons into the MLP models, to
achieve better accuracy without extra FLOPs. We propose a full-precision LIF
operation to communicate between patches, including horizontal LIF and vertical
LIF in different directions. We also propose to use group LIF to extract better
local features. With LIF modules, our SNN-MLP model achieves 81.9%, 83.3% and
83.5% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs,
respectively, which are state-of-the-art results as far as we know.


title:
Transformer Feed-Forward Layers Build Predictions by Promoting Concepts
  in the Vocabulary Space

abstract:
  Transformer-based language models (LMs) are at the core of modern NLP, but
their internal prediction construction process is opaque and largely not
understood. In this work, we make a substantial step towards unveiling this
underlying prediction process, by reverse-engineering the operation of the
feed-forward network (FFN) layers, one of the building blocks of transformer
models. We view the token representation as a changing distribution over the
vocabulary, and the output from each FFN layer as an additive update to that
distribution. Then, we analyze the FFN updates in the vocabulary space, showing
that each update can be decomposed to sub-updates corresponding to single FFN
parameter vectors, each promoting concepts that are often human-interpretable.
We then leverage these findings for controlling LM predictions, where we reduce
the toxicity of GPT2 by almost 50%, and for improving computation efficiency
with a simple early exit rule, saving 20% of computation on average.


title: ObjectFormer for Image Manipulation Detection and Localization

abstract:
  Recent advances in image editing techniques have posed serious challenges to
the trustworthiness of multimedia data, which drives the research of image
tampering detection. In this paper, we propose ObjectFormer to detect and
localize image manipulations. To capture subtle manipulation traces that are no
longer visible in the RGB domain, we extract high-frequency features of the
images and combine them with RGB features as multimodal patch embeddings.
Additionally, we use a set of learnable object prototypes as mid-level
representations to model the object-level consistencies among different
regions, which are further used to refine patch embeddings to capture the
patch-level consistencies. We conduct extensive experiments on various datasets
and the results verify the effectiveness of the proposed method, outperforming
state-of-the-art tampering detection and localization methods.


title: Probing the anomalous tqgamma couplings in photon-proton collisions

abstract:
  The top quark flavor changing neutral current processes are extremely
suppressed within the Standard Model. Nevertheless, they could be enhanced in a
new physics model beyond the Standard Model. Investigating the top quark's
flavor changing neutral current interactions at colliders would be an important
test in terms of new physics. In this work, we examine the potentials of the
processes ${e^{-}} {p} {\rightarrow} {e^{-}} {\gamma}^{*} {p} {\rightarrow}
{e^{-}} W^{+} b {\gamma}^{*}{\rightarrow} {e^{-}} {j} {\bar{j}} b {\gamma}^{*}
$ and ${e^{-}} {p} {\rightarrow} {e^{-}} {\gamma}^{*} {p} {\rightarrow} {e^{-}}
W^{+} b {\gamma}^{*}{\rightarrow} {e^{-}} \textit{l} {\nu}_\textit{l} b
{\gamma}^{*} $ at the FCC-eh with $\sqrt{s} = 7.08 $ and $ 10.0 TeV $ to study
anomalous $tq\gamma$ couplings via effective Lagrangians. We obtain $95{\%}$
confidence level sensitivities on the anomalous coupling parameters at the
three FCC-eh energies and various integrated luminosities. We find that our
limits are at least two orders of magnitude better than the current LHC
experimental results.


title:
AMCAD: Adaptive Mixed-Curvature Representation based Advertisement
  Retrieval System

abstract:
  Graph embedding based retrieval has become one of the most popular techniques
in the information retrieval community and search engine industry. The
classical paradigm mainly relies on the flat Euclidean geometry. In recent
years, hyperbolic (negative curvature) and spherical (positive curvature)
representation methods have shown their superiority to capture hierarchical and
cyclic data structures respectively. However, in industrial scenarios such as
e-commerce sponsored search platforms, the large-scale heterogeneous
query-item-advertisement interaction graphs often have multiple structures
coexisting. Existing methods either only consider a single geometry space, or
combine several spaces manually, which are incapable and inflexible to model
the complexity and heterogeneity in the real scenario. To tackle this
challenge, we present a web-scale Adaptive Mixed-Curvature ADvertisement
retrieval system (AMCAD) to automatically capture the complex and heterogeneous
graph structures in non-Euclidean spaces. Specifically, entities are
represented in adaptive mixed-curvature spaces, where the types and curvatures
of the subspaces are trained to be optimal combinations. Besides, an attentive
edge-wise space projector is designed to model the similarities between
heterogeneous nodes according to local graph structures and the relation types.
Moreover, to deploy AMCAD in Taobao, one of the largest ecommerce platforms
with hundreds of million users, we design an efficient two-layer online
retrieval framework for the task of graph based advertisement retrieval.
Extensive evaluations on real-world datasets and A/B tests on online traffic
are conducted to illustrate the effectiveness of the proposed system.


title: Investigating transactions in cryptocurrencies

abstract:
  This thesis presents techniques to investigate transactions in uncharted
cryptocurrencies and services. Cryptocurrencies are used to securely send
payments online. Payments via the first cryptocurrency, Bitcoin, use
pseudonymous addresses that have limited privacy and anonymity guarantees.
Research has shown that this pseudonymity can be broken, allowing users to be
tracked using clustering and tagging heuristics. Such tracking allows crimes to
be investigated. If a user has coins stolen, investigators can track addresses
to identify the destination of the coins. This, combined with an explosion in
the popularity of blockchain, has led to a vast increase in new coins and
services. These offer new features ranging from coins focused on increased
anonymity to scams shrouded as smart contracts. In this study, we investigated
the extent to which transaction privacy has improved and whether users can
still be tracked in these new ecosystems. We began by analysing the
privacy-focused coin Zcash, a Bitcoin-forked cryptocurrency, that is considered
to have strong anonymity properties due to its background in cryptographic
research. We revealed that the user anonymity set can be considerably reduced
using heuristics based on usage patterns. Next, we analysed cross-chain
transactions collected from the exchange ShapeShift, revealing that users can
be tracked as they move across different ledgers. Finally, we present a
measurement study on the smart-contract pyramid scheme Forsage, a scam that
cycled $267 million USD (of Ethereum) within its first year, showing that at
least 88% of the participants in the scheme suffered a loss. The significance
of this study is the revelation that users can be tracked in newer
cryptocurrencies and services by using our new heuristics, which informs those
conducting investigations and developing these technologies.


title:
HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed
  Training System

abstract:
  As giant dense models advance quality but require large-scale expensive GPU
clusters for training, the sparsely gated Mixture-of-Experts (MoE), a kind of
conditional computation architecture, are proposed to scale models while
keeping the computation constant. Specifically, the input data is routed by a
gate network and only activates a part of the expert network. Existing MoE
training systems only support part of mainstream MoE models (e.g. Top k)
training under expensive high-bandwidth GPU clusters. In this paper, we present
HetuMoE, a high-performance large-scale sparse MoE training system built on
Hetu. HetuMoE provides multiple gating strategies and efficient GPU kernel
implementations. To further improve the training efficiency on commodity GPU
clusters (e.g, with only 1 NiC), we introduce the hierarchical AllToAll
communication that combines hierarchical networks and aggregating messages.
Compared with existing state-of-the-art MoE systems, HetuMoE obtains at least
15% speedup. Specifically, HetuMoE outperforms DeepSpeed-MoE up to 8.1x under
the switch gate with a batch size of 32. The code is available at:
https://github.com/PKU-DAIR/Hetu.


title:
REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive
  Framework

abstract:
  In this work a general framework is proposed to support the development of
software systems that are able to adapt their behaviour according to the
operating environment changes. The proposed approach, named REPTILE, works in a
complete proactive manner and relies on Deep Reinforcement Learning-based
agents to react to events, referred as novelties, that can affect the expected
behaviour of the system. In our framework, two types of novelties are taken
into account: those related to the context/environment and those related to the
physical architecture itself. The framework, predicting those novelties before
their occurrence, extracts time-changing models of the environment and uses a
suitable Markov Decision Process to deal with the real-time setting. Moreover,
the architecture of our RL agent evolves based on the possible actions that can
be taken.


title: On the classification of low-degree ovoids of Q(4,q)

abstract:
  Ovoids of the non-degenerate quadric Q(4,q) of PG(4,q) have been studied
since the end of the '80s. They are rare objects and, beside the classical
example given by an elliptic quadric, only three classes are known for q odd,
one class for $q$ even, and a sporadic example for $\`iq=3^5. It is well known
that to any ovoid of Q(4,q) a bivariate polynomial f(x,y) can be associated. In
this paper we classify ovoids of Q(4,q) whose corresponding polynomial f(x,y)
has 'low degree' compared with q, in particular deg(f)<(q/6.3)^(3/13)-1.
Finally, as an application, {two classes} of permutation polynomials in
characteristic 3 are obtained.


title: Training speaker recognition systems with limited data

abstract:
  This work considers training neural networks for speaker recognition with a
much smaller dataset size compared to contemporary work. We artificially
restrict the amount of data by proposing three subsets of the popular VoxCeleb2
dataset. These subsets are restricted to 50 k audio files (versus over 1 M
files available), and vary on the axis of number of speakers and session
variability. We train three speaker recognition systems on these subsets; the
X-vector, ECAPA-TDNN, and wav2vec2 network architectures. We show that the
self-supervised, pre-trained weights of wav2vec2 substantially improve
performance when training data is limited. Code and data subsets are available
at \url{https://github.com/nikvaessen/w2v2-speaker-few-samples}.


title: Extensions of the fundamental theorem of algebra

abstract:
  In this note, for the most part, we consider arbitrary real algebras of the
following kinds: finite-dimensional algebras, normed algebras, and locally
convex algebras, all containing a copy of complex numbers. First, we prove a
Fundamental Theorem of Algebra type result for such commutative and associative
algebras and for finite-dimensional commutative and alternative algebras. We
also prove the existence of right eigenvalues for matrices with entries in such
finite-dimensional associative algebras. Next, we present a Fundamental Theorem
of Algebra type result for both unital real and complex locally convex
associative algebras. We use this result to prove the existence of eigenvalues
for matrices with entries from arbitrary finite-dimensional complex unital
algebras as well as certain real unital algebras that contain a copy of complex
numbers.


title:
The incompressible $\alpha$--Euler equations in the exterior of a
  vanishing disk

abstract:
  In this article we consider the $\alpha$--Euler equations in the exterior of
a small fixed disk of radius $\epsilon$. We assume that the initial potential
vorticity is compactly supported and independent of $\epsilon$, and that the
circulation of the unfiltered velocity on the boundary of the disk does not
depend on $\epsilon$. We prove that the solution of this problem converges, as
$\epsilon \to 0$, to the solution of a modified $\alpha$--Euler equation in the
full plane where an additional Dirac located at the center of the disk is
imposed in the potential vorticity.


title: Sketch3T: Test-Time Training for Zero-Shot SBIR

abstract:
  Zero-shot sketch-based image retrieval typically asks for a trained model to
be applied as is to unseen categories. In this paper, we question to argue that
this setup by definition is not compatible with the inherent abstract and
subjective nature of sketches, i.e., the model might transfer well to new
categories, but will not understand sketches existing in different test-time
distribution as a result. We thus extend ZS-SBIR asking it to transfer to both
categories and sketch distributions. Our key contribution is a test-time
training paradigm that can adapt using just one sketch. Since there is no
paired photo, we make use of a sketch raster-vector reconstruction module as a
self-supervised auxiliary task. To maintain the fidelity of the trained
cross-modal joint embedding during test-time update, we design a novel
meta-learning based training paradigm to learn a separation between model
updates incurred by this auxiliary task from those off the primary objective of
discriminative learning. Extensive experiments show our model to outperform
state of-the-arts, thanks to the proposed test-time adaption that not only
transfers to new categories but also accommodates to new sketching styles.


title:
HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a
  Probabilistic Causal Approach

abstract:
  What-if (provisioning for an update to a database) and how-to (how to modify
the database to achieve a goal) analyses provide insights to users who wish to
examine hypothetical scenarios without making actual changes to a database and
thereby help plan strategies in their fields. Typically, such analyses are done
by testing the effect of an update in the existing database on a specific view
created by a query of interest. In real-world scenarios, however, an update to
a particular part of the database may affect tuples and attributes in a
completely different part due to implicit semantic dependencies. To allow for
hypothetical reasoning while accommodating such dependencies, we develop HypeR,
a framework that supports what-if and how-to queries accounting for
probabilistic dependencies among attributes captured by a probabilistic causal
model. We extend the SQL syntax to include the necessary operators for
expressing these hypothetical queries, define their semantics, devise efficient
algorithms and optimizations to compute their results using concepts from
causality and probabilistic databases, and evaluate the effectiveness of our
approach experimentally.


title:
Supersymmetric Quantum Mechanics: Light at the End of the (Quantum)
  Tunnel

abstract:
  In this project, we will develop the foundations of quantum mechanics using
the methods of supersymmetry. We will discuss the use of the superpotential to
derive the supersymmetric partner of a potential in one dimension, and explore
several key examples with an emphasis on shape invariant potentials. We will
then discuss the modeling of supersymmetric quantum systems using matrices and
operators, and how it relates to the eigenstate thermalization hypothesis.


title: Facial Action Unit Recognition Based on Transfer Learning

abstract:
  Facial action unit recognition is an important task for facial analysis.
Owing to the complex collection environment, facial action unit recognition in
the wild is still challenging. The 3rd competition on affective behavior
analysis in-the-wild (ABAW) has provided large amount of facial images with
facial action unit annotations. In this paper, we introduce a facial action
unit recognition method based on transfer learning. We first use available
facial images with expression labels to train the feature extraction network.
Then we fine-tune the network for facial action unit recognition.


title: Recruiting Software Engineers on Prolific

abstract:
  Recruiting participants for software engineering research has been a primary
concern of the human factors community. This is particularly true for
quantitative investigations that require a minimum sample size not to be
statistically underpowered. Traditional data collection techniques, such as
mailing lists, are highly doubtful due to self-selection biases. The
introduction of crowdsourcing platforms allows researchers to select informants
with the exact requirements foreseen by the study design, gather data in a
concise time frame, compensate their work with fair hourly pay, and most
importantly, have a high degree of control over the entire data collection
process. This experience report discusses our experience conducting sample
studies using Prolific, an academic crowdsourcing platform. Topics discussed
are the type of studies, selection processes, and power computation.


title: Entropy of Kerr--Newman--AdS black holes with torsion

abstract:
  The canonical approach to black hole entropy in Poincar\'e gauge theory
without matter is extended to include Maxwell field as a matter source. The new
formalism is used to calculate asymptotic charges and entropy of
Kerr--Newmann--AdS black holes with torsion. The result implies that the first
law, with a nontrivial contribution of the Maxwell field, takes the same form
as in general relativity.


title: Mechanically Tunable Radiative Cooling for Adaptive Thermal Control

abstract:
  Passive radiative cooling is currently the frontier technology in
renewable-energy research. In terms of extraterrestrial applications, radiative
cooling is a critical component to the thermal management system of a
spacecraft, where the extreme environment of space can cause large temperature
variations that can break and damage equipment. For terrestrial applications,
nocturnal or daytime radiative cooling is expected to lead to cost-effective
passive heat management without the need of inefficient and costly artificial
refrigeration technologies. However, most currently available radiative cooling
systems cannot be changed dynamically and radiate a constant static amount of
thermal power. Dynamically tunable adaptive radiative cooling systems will be a
critical development to prolong the lifetime of spacecraft or improve the
efficiency of terrestrial cooling systems. Here we propose stretchable
radiative cooling designs that can be substantially tuned by using the simple
physical mechanism of mechanical strain. When their structure is stretched, the
radiated power is significantly reduced. We develop a modeling method that can
simulate mechanical stretching combined with electromagnetic response to
compute the tunable thermal emission of these new adaptive radiative cooling
systems. The presented photonically engineered structures can be used as
coatings to achieve efficient adaptive thermal control of various objects in a
cost-effective and environmentally friendly way. The proposed designs are much
simpler to be realized than others found in the literature and the best design
achieves a high thermal emission power with a tunable range on the order of 132
W/m2.


title:
LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR
  Point Clouds

abstract:
  Existing motion capture datasets are largely short-range and cannot yet fit
the need of long-range applications. We propose LiDARHuman26M, a new human
motion capture dataset captured by LiDAR at a much longer range to overcome
this limitation. Our dataset also includes the ground truth human motions
acquired by the IMU system and the synchronous RGB images. We further present a
strong baseline method, LiDARCap, for LiDAR point cloud human motion capture.
Specifically, we first utilize PointNet++ to encode features of points and then
employ the inverse kinematics solver and SMPL optimizer to regress the pose
through aggregating the temporally encoded features hierarchically.
Quantitative and qualitative experiments show that our method outperforms the
techniques based only on RGB images. Ablation experiments demonstrate that our
dataset is challenging and worthy of further research. Finally, the experiments
on the KITTI Dataset and the Waymo Open Dataset show that our method can be
generalized to different LiDAR sensor settings.


title:
Towards Stable Interstellar Flight: Levitation of a Laser-Propelled
  Sailcraft

abstract:
  Exploring and traveling to distant stars has long fascinated humanity but has
been limited due to the vast distances. The Breakthrough Starshot Program aims
at eliminating this limitation by traveling to Alpha Centauri, which is 4.37
light-years away. Thus, it is only possible if a vehicle travels at a
substantial fraction of the speed of light. The Breakthrough Starshot Program
initiatives to develop a proof-of-concept that is accelerating a sail to
relativistic speeds using a laser beam aimed at the sail. At this high speed,
while a stable beam riding is one of the crucial concerns of this concept, the
dynamic stability analysis of a sail is hardly present in the previous
literature. Furthermore, it is important to investigate the dynamic stability
in the experiment before driving the sail to relativistic speeds. As a proof of
concept, we study the dynamic stability of the sail levitated at a certain
height by a laser beam. The sail's dynamics are modeled as a rigid body whose
shape is parameterized by a sweep function. We estimate the region of
attraction (ROA) for dynamic stability analysis using Lyapunov theory and
Sum-of-square (SOS) programming. The ROA confirms how much a levitated sail can
tolerate the transverse and angular perturbations. We also conclude on some of
the important parameters of the sail that affects the dynamic stability.
Simulation results validate our theoretical analysis.


title:
Xenon-Doped Liquid Argon TPCs as a Neutrinoless Double Beta Decay
  Platform

abstract:
  Searches for neutrinoless double-beta decay continue to expand our
understanding of the lepton sector, with experiments now pursuing ton-scale
target masses with sensitivity to $m_{\beta\beta}$ covering the allowed
parameter space for the inverted neutrino mass ordering. Continued searches for
this rare decay will require scalable detector technologies to achieve
significant increases in the target mass beyond the ton scale, in order to
probe the normal ordering region. This work explores the concept of searching
for neutrinoless double-beta decay in a 10 kton scale liquid argon time
projection chamber (LArTPC) located deep underground and doped with
percent-level quantities of xenon. We discuss the design requirements,
background mitigation and detector R&D needs, and considerations for deployment
in a modified DUNE far detector module. We find that such a detector could
reach $m_{\beta\beta}$ sensitivity at the 2-4 meV range with xenon doping at 2%
if significant background reductions can be achieved.


title: On Weakly S-primary Submodules

abstract:
  Let $R$ be a commutative ring with a non-zero identity, $S$ be a
multiplicatively closed subset of $R$ and $M$ be a unital $R$-module. In this
paper, we define a submodule $N$ of $M$ with $(N:_{R}M)\cap S=\emptyset$ to be
weakly $S$-primary if there exists $s\in S$ such that whenever $a\in R$ and
$m\in M$ with $0\neq am\in N$, then either $sa\in\sqrt{(N:_{R}M)}$ or $sm\in
N$. We present various properties and characterizations of this concept
(especially in finitely generated faithful multiplication modules). Moreover,
the behavior of this structure under module homomorphisms, localizations,
quotient modules, cartesian product and idealizations is investigated. Finally,
we determine some conditions under which two kinds of submodules of the
amalgamation module along an ideal are weakly $S$-primary.


title:
Bi-level Doubly Variational Learning for Energy-based Latent Variable
  Models

abstract:
  Energy-based latent variable models (EBLVMs) are more expressive than
conventional energy-based models. However, its potential on visual tasks are
limited by its training process based on maximum likelihood estimate that
requires sampling from two intractable distributions. In this paper, we propose
Bi-level doubly variational learning (BiDVL), which is based on a new bi-level
optimization framework and two tractable variational distributions to
facilitate learning EBLVMs. Particularly, we lead a decoupled EBLVM consisting
of a marginal energy-based distribution and a structural posterior to handle
the difficulties when learning deep EBLVMs on images. By choosing a symmetric
KL divergence in the lower level of our framework, a compact BiDVL for visual
tasks can be obtained. Our model achieves impressive image generation
performance over related works. It also demonstrates the significant capacity
of testing image reconstruction and out-of-distribution detection.


title: Chemical Composition of a Palomar 12 Blue Straggler

abstract:
  With the equivalent area of a 16m telescope, ESPRESSO in 4UT mode allows to
inaugurate high resolution spectroscopy for solar-type stars belonging to
extragalactic globular clusters. We determine the chemical composition of an
extragalactic blue straggler. The star has a G magnitude of 19.01 and belongs
to the globular cluster Pal12, that is associated to the Sagittarius dwarf
galaxy. Abundances are computed by using high resolution spectroscopy and LTE
analysis. Two 50 minutes ESPRESSO spectra, co-added, provide a Signal to Noise
Ratio of 25 with a resolving power R=70000. This allows us to measure with good
precision abundance of several (13) elements. Li could help to distinguish
between formation models of Blue Stragglers; we are able to set a 3 sigma upper
limit of Li=3.1, which is still too high to discriminate between competing
models. The abundances we retrieve for the BS are compatible with those of
giant stars of Pal 12 published in literature, re-analyzed by us using the same
procedure and line list. Small differences are present, that can be ascribed to
NLTE effects, but for Mg the BS shows a large under-abundance. The most likely
explanation is that the BS atmosphere is dominated by gas processed through the
Mg-Al cycle, but we have no suitable Al or Na lines to confirm this hypothesis.
We show that ESPRESSO with 4UT can be used to derive precise abundances for
solar-type stars fainter than magnitude 19. At these magnitudes a proper sky
subtraction is needed and in crowded field the targets must be chosen with
outmost care, to avoid contamination of the sky fibre from nearby stars.


title: Spin Physics Detector at NICA

abstract:
  The Spin Physics Detector is one of two large setups at the NICA collider
under construction at JINR (Dubna). The ultimate goal of the studies at SPD is
the measurement of different spin observables in polarized proton-proton and
deuteron-deuteron collisions sensitive to the polarized gluon structure of the
nucleon at the luminosity up to 10$^{32}$ $cm^{-2}\cdot s^{-1}$ and $\sqrt{s}$
up to 27 GeV. SPD will consist of the superconducting magnetic system, silicon
tracker, straw mini-drift tubes tracker, time-of-flight system, electromagnetic
"shashlyk"- type calorimeter, muon (range) and local-polarimetry systems. The
high performance free-streaming DAQ system will be able to operate at the
collision rate up to 4 MHz.


title: Walking droplets as a damped-driven system

abstract:
  We consider the dynamics of a droplet on a vibrating fluid bath. This
hydrodynamic quantum analog system is shown to elicit the canonical behavior of
damped-driven systems, including a period doubling route to chaos. By
approximating the system as a compositional map between the gain and loss
dynamics, the underlying nonlinear dynamics can be shown to be driven by energy
balances in the systems. The gain-loss iterative mapping is similar to a normal
form encoding for the pattern forming instabilities generated in such
spatially-extended system. Similar to mode-locked lasers and rotating
detonation engines, the underlying bifurcations persist for general forms of
the loss and gain, both of which admit explicit representations in our
approximation. Moreover, the resulting geometrical description of the
particle-wave interaction completely characterizes the instabilities observed
in experiments.


title:
Application-driven engagement with universities, synergies with other
  funding agencies

abstract:
  In this whitepaper, we argue that nurturing HEP Lab-Engineering cooperation
through established collaboration and support mechanisms will advance the
scientific mission of the labs significantly, while at the same time giving the
laboratories a stronger position in influencing the next generation workforce
of engineers that will provide their services towards the unique computing and
technology needs of the HEP community. The authors of this whitepaper are
electronics and computer engineers and so, naturally, the arguments herein are
made from their perspective. However, these arguments are only strengthened by
the simple fact that they could also have been made from the perspective of
mechanical engineers, civil engineers or numerous other technologists. At the
same time, this document serves as a summary of discussions that occurred
during the Joint Instrumentation Frontier & Community Engagement Frontier
Townhall meeting on November 10, 2020


title: The Constructor-Blocker Game

abstract:
  We study the following game version of generalized graph Tur\'an problems.
For two fixed graphs $F$ and $H$, two players, Constructor and Blocker,
alternately claim unclaimed edges of the complete graph $K_n$. Constructor can
only claim edges so that he never claims all edges of any copy of $F$, i.e.his
graph must remain $F$-free, while Blocker can claim unclaimed edges without
restrictions. The game ends when Constructor cannot claim further edges or when
all edges have been claimed. The score of the game is the number of copies of
$H$ with all edges claimed by Constructor. Constructor's aim is to maximize the
score, while Blocker tries to keep the score as low as possible. We denote by
$g(n,H,F)$ the score of the game when both players play optimally and
Constructor starts the game.
  In this paper, we obtain the exact value of $g(n,H,F)$ when both $F$ and $H$
are stars and when $F=P_4$, $H=P_3$. We determine the asymptotics of $g(n,H,F)$
when $F$ is a star and $H$ is a tree and when $F=P_5$, $H=K_3$, and we derive
upper and lower bounds on $g(n,P_4,P_5)$.


title: Object Memory Transformer for Object Goal Navigation

abstract:
  This paper presents a reinforcement learning method for object goal
navigation (ObjNav) where an agent navigates in 3D indoor environments to reach
a target object based on long-term observations of objects and scenes. To this
end, we propose Object Memory Transformer (OMT) that consists of two key ideas:
1) Object-Scene Memory (OSM) that enables to store long-term scenes and object
semantics, and 2) Transformer that attends to salient objects in the sequence
of previously observed scenes and objects stored in OSM. This mechanism allows
the agent to efficiently navigate in the indoor environment without prior
knowledge about the environments, such as topological maps or 3D meshes. To the
best of our knowledge, this is the first work that uses a long-term memory of
object semantics in a goal-oriented navigation task. Experimental results
conducted on the AI2-THOR dataset show that OMT outperforms previous approaches
in navigating in unknown environments. In particular, we show that utilizing
the long-term object semantics information improves the efficiency of
navigation.


title:
MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction
  Detection

abstract:
  Human-Object Interaction (HOI) detection is the task of identifying a set of
<human, object, interaction> triplets from an image. Recent work proposed
transformer encoder-decoder architectures that successfully eliminated the need
for many hand-designed components in HOI detection through end-to-end training.
However, they are limited to single-scale feature resolution, providing
suboptimal performance in scenes containing humans, objects and their
interactions with vastly different scales and distances. To tackle this
problem, we propose a Multi-Scale TRansformer (MSTR) for HOI detection powered
by two novel HOI-aware deformable attention modules called Dual-Entity
attention and Entity-conditioned Context attention. While existing deformable
attention comes at a huge cost in HOI detection performance, our proposed
attention modules of MSTR learn to effectively attend to sampling points that
are essential to identify interactions. In experiments, we achieve the new
state-of-the-art performance on two HOI detection benchmarks.


title: Hierarchical Transformer Model for Scientific Named Entity Recognition

abstract:
  The task of Named Entity Recognition (NER) is an important component of many
natural language processing systems, such as relation extraction and knowledge
graph construction. In this work, we present a simple and effective approach
for Named Entity Recognition. The main idea of our approach is to encode the
input subword sequence with a pre-trained transformer such as BERT, and then,
instead of directly classifying the word labels, another layer of transformer
is added to the subword representation to better encode the word-level
interaction. We evaluate our approach on three benchmark datasets for
scientific NER, particularly in the computer science and biomedical domains.
Experimental results show that our model outperforms the current
state-of-the-art on SciERC and TDM datasets without requiring external
resources or specific data augmentation. Code is available at
\url{https://github.com/urchade/HNER}.


title:
Towards Formal Verification of HotStuff-based Byzantine Fault Tolerant
  Consensus in Agda: Extended Version

abstract:
  LibraBFT is a Byzantine Fault Tolerant (BFT) consensus protocol based on
HotStuff. We present an abstract model of the protocol underlying HotStuff /
LibraBFT, and formal, machine-checked proofs of their core correctness (safety)
property and an extended condition that enables non-participating parties to
verify committed results. (Liveness properties would be proved for specific
implementations, not for the abstract model presented in this paper.)
  A key contribution is precisely defining assumptions about the behavior of
honest peers, in an abstract way, independent of any particular implementation.
Therefore, our work is an important step towards proving correctness of an
entire class of concrete implementations, without repeating the hard work of
proving correctness of the underlying protocol. The abstract proofs are for a
single configuration (epoch); extending these proofs across configuration
changes is future work. Our models and proofs are expressed in Agda, and are
available in open source.


title:
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding
  Procedural Activities

abstract:
  Assembly101 is a new procedural activity dataset featuring 4321 videos of
people assembling and disassembling 101 "take-apart" toy vehicles. Participants
work without fixed instructions, and the sequences feature rich and natural
variations in action ordering, mistakes, and corrections. Assembly101 is the
first multi-view action dataset, with simultaneous static (8) and egocentric
(4) recordings. Sequences are annotated with more than 100K coarse and 1M
fine-grained action segments, and 18M 3D hand poses. We benchmark on three
action understanding tasks: recognition, anticipation and temporal
segmentation. Additionally, we propose a novel task of detecting mistakes. The
unique recording format and rich set of annotations allow us to investigate
generalization to new toys, cross-view transfer, long-tailed distributions, and
pose vs. appearance. We envision that Assembly101 will serve as a new challenge
to investigate various activity understanding problems.


title: Image-text Retrieval: A Survey on Recent Research and Development

abstract:
  In the past few years, cross-modal image-text retrieval (ITR) has experienced
increased interest in the research community due to its excellent research
value and broad real-world application. It is designed for the scenarios where
the queries are from one modality and the retrieval galleries from another
modality. This paper presents a comprehensive and up-to-date survey on the ITR
approaches from four perspectives. By dissecting an ITR system into two
processes: feature extraction and feature alignment, we summarize the recent
advance of the ITR approaches from these two perspectives. On top of this, the
efficiency-focused study on the ITR system is introduced as the third
perspective. To keep pace with the times, we also provide a pioneering overview
of the cross-modal pre-training ITR approaches as the fourth perspective.
Finally, we outline the common benchmark datasets and valuation metric for ITR,
and conduct the accuracy comparison among the representative ITR approaches.
Some critical yet less studied issues are discussed at the end of the paper.


title: Primitive-based Shape Abstraction via Nonparametric Bayesian Inference

abstract:
  3D shape abstraction has drawn great interest over the years. Apart from
low-level representations such as meshes and voxels, researchers also seek to
semantically abstract complex objects with basic geometric primitives. Recent
deep learning methods rely heavily on datasets, with limited generality to
unseen categories. Furthermore, abstracting an object accurately yet with a
small number of primitives still remains a challenge. In this paper, we propose
a novel non-parametric Bayesian statistical method to infer an abstraction,
consisting of an unknown number of geometric primitives, from a point cloud. We
model the generation of points as observations sampled from an infinite mixture
of Gaussian Superquadric Taper Models (GSTM). Our approach formulates the
abstraction as a clustering problem, in which: 1) each point is assigned to a
cluster via the Chinese Restaurant Process (CRP); 2) a primitive representation
is optimized for each cluster, and 3) a merging post-process is incorporated to
provide a concise representation. We conduct extensive experiments on various
datasets. The results indicate that our method outperforms the state-of-the-art
in terms of accuracy and is generalizable to various types of objects.


title: Tunable bilayer dielectric metasurface via stacking magnetic mirrors

abstract:
  Functional tunability, environmental adaptability, and easy fabrication are
highly desired properties in metasurfaces. Here we provide a tunable bilayer
metasurface composed of two stacked identical dielectric magnetic mirrors,
which are excited by the dominant electric dipole and other magnetic
multipoles, exhibiting nonlocal electric field enhancement near the interface
and high reflection. Differ from the tunability through the direct
superposition of two structures with different functionalities, we achieve the
reversible conversion between high reflection and high transmission by
manipulating the interlayer coupling near the interface between the two
magnetic mirrors. The magnetic mirror effect boosts the interlayer coupling
when the interlayer spacing is small. Decreasing the interlayer spacing of the
bilayer metasurface leads to stronger interlayer coupling and scattering
suppression of the meta-atom, which results in high transmission. On the
contrary, increasing the spacing leads to weaker interlayer coupling and
scattering enhancement, which results in high reflection. The high transmission
of the bilayer metasurface has good robustness due to that the meta-atom with
interlayer coupling can maintain the scattering suppression against adjacent
meta-atom movement and disordered position perturbation. This work provides a
straightforward method (i.e. stacking magnetic mirrors) to design tunable
metasurface, shed new light on high-performance optical switches applied in
communication and sensing.


title: Walsh's conformal map onto lemniscatic domains for polynomial pre-images

abstract:
  We consider Walsh's conformal map from the exterior of a compact set $E
\subseteq \mathbb{C}$ onto a lemniscatic domain. If $E$ is simply connected,
the lemniscatic domain is the exterior of a circle, while if $E$ has several
components, the lemniscatic domain is the exterior of a generalized lemniscate
and is determined by the logarithmic capacity of $E$ and by the exponents and
centers of the generalized lemniscate. For general $E$, we characterize the
exponents in terms of the Green function of $E^c$. Under additional symmetry
conditions on $E$, we also locate the centers of the lemniscatic domain. For
polynomial pre-images $E = P^{-1}(\Omega)$ of a simply-connected infinite
compact set $\Omega$, we explicitly determine the exponents in the lemniscatic
domain and derive a set of equations to determine the centers of the
lemniscatic domain. Finally, we present several examples where we explicitly
obtain the exponents and centers of the lemniscatic domain, as well as the
conformal map.


title:
A novel evolutionary-based neuro-fuzzy task scheduling approach to
  jointly optimize the main design challenges of heterogeneous MPSoCs

abstract:
  In this paper, an online task scheduling and mapping method based on a fuzzy
neural network (FNN) learned by an evolutionary multi-objective algorithm
(NSGA-II) to jointly optimize the main design challenges of heterogeneous
MPSoCs is proposed. In this approach, first, the FNN parameters are trained
using an NSGA-II-based optimization engine by considering the main design
challenges of MPSoCs including temperature, power consumption, failure rate,
and execution time on a training dataset consisting of different application
graphs of various sizes. Next, the trained FNN is employed as an online task
scheduler to jointly optimize the main design challenges in heterogeneous
MPSoCs. Due to the uncertainty in sensor measurements and the difference
between computational models and reality, applying the fuzzy neural network is
advantageous in online scheduling procedures. The performance of the method is
compared with some previous heuristic, meta-heuristic, and rule-based
approaches in several experiments. Based on these experiments our proposed
method outperforms the related studies in optimizing all design criteria. Its
improvement over related heuristic and meta-heuristic approaches are estimated
10.58% in temperature, 9.22% in power consumption, 39.14% in failure rate, and
12.06% in execution time, averagely. Moreover, considering the interpretable
nature of the FNN, the frequently fired extracted fuzzy rules of the proposed
approach are demonstrated.


title: Testing optomechanical microwave oscillators for SATCOM application

abstract:
  The realization of photonic microwave oscillators using optomechanical
cavities has recently become a reality. By pumping the cavity with a
blue-detuned laser, the so-called phonon lasing regime - in which a mechanical
resonance is amplified beyond losses - can be reached and the input signal gets
modulated by highly-coherent tones at integer multiples of the mechanical
resonance. \textcolor{Red}{Implementing optomechanical cavities on released
films with high index of refraction can lead to optical modes at telecom
wavelengths and mechanical resonances in the GHz scale, resulting in
highly-stable signals in the microwave domain upon photodetection}. Owing to
the extreme compactness of such cavities, application in satellite
communications (SATCOM) seems highly appropriate, but no experiments have been
reported so far. In this paper, an optomechanical microwave oscillator (OMO)
built on a micron-scale silicon optomechanical crystal cavity is characterized
and tested in a real SATCOM testbed. Using a blue-detuned laser, the OMO is
driven into a phonon lasing state where multiple harmonics are generated,
reaching tones up to 20 GHz. Under this regime, its practical applicability,
remarkably addressing its performance as a photonic local oscillator, has been
validated. The results, in addition with the advantages of extreme compactness
and silicon-technology compatibility, make OMOs very promising candidates to
build \textcolor{Red}{ultra-low} weight photonics-based microwave oscillators
for SATCOM applications.


title:
Tackling the Crowdsourced Delivery Problem at Scale through a
  Set-Partitioning Formulation and Novel Decomposition Heuristic

abstract:
  This paper presents a set-partitioning formulation and a novel decomposition
heuristic (D-H) solution algorithm to solve large-scale instances of the urban
crowdsourced shared-trip package delivery problem. The D-H begins by dividing
the packages between shared personal vehicles (SPVs) and dedicated vehicles
(DVs). For package-assignment to SPVs, this paper enumerates the set of routes
each SPV can traverse and constructs a package-SPV route assignment problem.
For package-assignment to DVs and routing, the paper first obtains DV routes by
solving a conventional vehicle routing problem and then seeks potential
solution improvements by switching packages from SPVs to DVs. The switching
process is cost driven. The D-H significantly outperforms a commercial solver
in terms of computational efficiency, while obtaining near-optimal solutions
for small problem instances. This paper presents a city-scale case study to
analyze the important service design factors that impact the efficiency of
crowdsourced shared-trip delivery. The paper further analyzes the impact of
three important service design factors on system performance, namely (i) the
number of participating SPVs, (ii) the maximum detour willingness of SPVs, and
(iii) the depot locations. The results and findings provide meaningful insights
for industry practice, while the algorithms illustrate promise for large
real-world systems.


title:
Brownian reservoir computing realized using geometrically confined
  skyrmions

abstract:
  Reservoir computing (RC) has been considered as one of the key computational
principles beyond von-Neumann computing. Magnetic skyrmions, topological
particle-like spin textures in magnetic films are particularly promising for
implementing RC, since they respond strongly nonlinear to external stimuli and
feature inherent multiscale dynamics. However, despite several theoretical
proposals that exist for skyrmion reservoir computing, experimental
realizations have been elusive until now. Here, we propose and experimentally
demonstrate a conceptually new approach to skyrmion RC that leverages the
thermally activated diffusive motion of skyrmions. By confining the
electrically gated and thermal skyrmion motion, we find that already a single
skyrmion in a confined geometry suffices to realize non-linearly separable
functions, which we demonstrate for the XOR gate along with all other Boolean
logic gate operations. Besides this universality, the reservoir computing
concept ensures low training costs and ultra-low power operation with current
densities orders of magnitude smaller than those used in existing spintronic
reservoir computing demonstrations. Our proposed concept can be readily
extended by linking multiple confined geometries and/or by including more
skyrmions in the reservoir, suggesting high potential for scalable and
low-energy reservoir computing.


title:
Heterogeneous Federated CubeSat System: problems, constraints and
  capabilities

abstract:
  Different arguments were being presented in the last decade about CubeSats
and their applications. Some of them address wireless communication (5G and 6G
technologies) trying to achieve better characteristics as coverage and
connectivity. Some arrived with terms as IoST (Internet of Space Things),
Internet of Satellites (IoSat), DSS (Distributed Space Systems), and FSS
(Federated Satellite Systems). All of them aim to use Small/NanoSatellites as
constellations/swarms is to provide specific services, share unused resources,
and evolve the concept of satellites-as-a-service (SaS). This paper aims to
emophasize performance attributes of such cyber-physical systems, model their
inherent operational constraints and at the very end, evaluate the quality of
service in terms of figures of merit for the entering/leaving of new
heterogeneous constituent systems, a.k.a satellites, to the constellation. This
"whitepaper"-styled work focuses on presenting the definitions of this
heterogeneous constellation problem, aims at its main capabilities and
constraints, and proposes modeling approaches for this system representation
and evaluation.


title:
Constraining the parameterized neutron star equation of state with
  astronomical observations

abstract:
  We utilise the phenomenologically parameterized piecewise polytropic
equations of state to study various neutron star properties. We investigate the
compliance of these equations of state with several astronomical observations.
We also demonstrate that the theoretical estimates of the fractional moment of
inertia cannot explain all the pulsar glitches observed. We model the crust as
a solid spheroidal shell to calculate the fractional moment of inertia of
fast-spinning neutron stars. We also show that the braking index obtained in a
simple magnetic dipole radiation model with a varying moment of inertia
deviates significantly from the observed data. Future developments in both
theory and observations may allow us to use the fractional moment of inertia
and braking index as observational constraints for neutron star equation of
state.


title: $R$-matrix analysis of $^{22}$Ne($\alpha$, n)$^{25}$Mg reaction

abstract:
  The $^{22}$Ne($\alpha$, n)$^{25}$Mg plays an major role on neutron flux in
weak s-process nucleosynthesis path in AGB stars of mass (M$\geq$ 8M$_\odot$).
The recent evaluation by Philip $\it{et al.}$ of $^{22}$Ne($\alpha$,
n)$^{25}$Mg reaction rate using updated nuclear data of $^{26}$Mg from number
of sources shows sizable uncertainty and significant discrepancy with
literature at low temperature. Also Philip $\it{et al.}$ suggested that
$R$-matrix modeling will required to estimate $^{26}$Mg resonance states
parameters as well as study the interference patterns between distant levels.
Hear $R$-matrix analysis has been performed to study interference effects and
constrain spin, parity of resonances by fitting the direct measurement data.
The resonance states parameters of Philip $\it{et al.}$ are not explain well
due to strong interference between same spin, parity states. By changing spin
parity of some states, experimental data are nicely explain by present
$R$-matrix calculation for 0.8 to 1.45 MeV energy range.


title:
The effect of local universe constraints on halo abundance and
  clustering

abstract:
  Cosmological $N$-body simulations of the dark matter component of the
universe typically use initial conditions with a fixed power spectrum and
random phases of the density field, leading to structure consistent with the
local distribution of galaxies only in a statistical sense. It is, however,
possible to infer the initial phases which lead to the configuration of
galaxies and clusters that we see around us. We analyse the CSiBORG suite of
101 simulations, formed by constraining the density field within 155 Mpc$/h$
with dark matter particle mass $4.38\times10^9 M_\odot$, to quantify the degree
to which constraints imposed on 2.65 Mpc$/h$ scales reduce variance in the halo
mass function and halo-halo cross-correlation function on a range of scales.
This is achieved by contrasting CSiBORG with a subset of the unconstrained
Quijote simulations and expectations for the $\Lambda$CDM average. Using the
FOF, PHEW and HOP halofinders, we show that the CSiBORG suite beats cosmic
variance at large mass scales ($\gtrsim 10^{14}M_{\odot}/h$), which are most
strongly constrained by the initial conditions, and exhibits a significant
halo-halo cross-correlation out to $\sim30$ Mpc$/h$. Moreover, the effect of
the constraints percolates down to lower mass objects and to scales below those
on which they are imposed. Finally, we develop an algorithm to "twin" halos
between realisations and show that approximately 50% of halos with mass greater
than $10^{15}M_{\odot}/h$ can be identified in all realisations of the CSiBORG
suite. We make the CSiBORG halo catalogues publicly available for future
applications requiring knowledge of the local halo field.


title: vTTS: visual-text to speech

abstract:
  This paper proposes visual-text to speech (vTTS), a method for synthesizing
speech from visual text (i.e., text as an image). Conventional TTS converts
phonemes or characters into discrete symbols and synthesizes a speech waveform
from them, thus losing the visual features that the characters essentially
have. Therefore, our method synthesizes speech not from discrete symbols but
from visual text. The proposed vTTS extracts visual features with a
convolutional neural network and then generates acoustic features with a
non-autoregressive model inspired by FastSpeech2. Experimental results show
that 1) vTTS is capable of generating speech with naturalness comparable to or
better than a conventional TTS, 2) it can transfer emphasis and emotion
attributes in visual text to speech without additional labels and
architectures, and 3) it can synthesize more natural and intelligible speech
from unseen and rare characters than conventional TTS.


title:
Learning to estimate UAV created turbulence from scene structure
  observed by onboard cameras

abstract:
  Controlling UAV flights precisely requires a realistic dynamic model and
accurate state estimates from onboard sensors like UAV, GPS and visual
observations. Obtaining a precise dynamic model is extremely difficult, as
important aerodynamic effects are hard to model, in particular ground effect
and other turbulences. While machine learning has been used in the past to
estimate UAV created turbulence, this was restricted to flat grounds or diffuse
in-flight air turbulences, both without taking into account obstacles. In this
work we address the complex problem of estimating in-flight turbulences caused
by obstacles, in particular the complex structures in cluttered environments.
We learn a mapping from control input and images captured by onboard cameras to
turbulence. In a large-scale setting, we train a model over a large number of
different simulated photo-realistic environments loaded into the Habitat.AI
simulator augmented with a dynamic UAV model and an analytic ground effect
model. We transfer the model from simulation to a real environment and evaluate
on real UAV flights from the EuRoC-MAV dataset, showing that the model is
capable of good sim2real generalization performance. The dataset will be made
publicly available upon acceptance.


title: Deep ASKAP EMU Survey of the GAMA23 field: Properties of radio sources

abstract:
  We present the Australian Square Kilometre Array Pathfinder (ASKAP)
observations of the Galaxy and Mass Assembly (GAMA)-23h field. The survey was
carried out at 887.5 MHz and covers a 83 square degree field. We imaged the
calibrated visibility data, taken as part of the Evolutionary Mapping of
Universe (EMU) Early Science Programme, using the latest version of the
ASKAPSoft pipeline. The final mosaic has an angular resolution of 10 arcsec and
a central rms noise of around 38 $\mu$Jy beam$^{-1}$. The derived radio source
catalogue has 39812 entries above a peak flux density threshold of 5$\sigma$.
We searched for the radio source host galaxy counterparts using the GAMA
spectroscopic (with an i-band magnitude limit of 19.2 mag) and multi-wavelength
catalogues that are available as part of the collaboration. We identified hosts
with GAMA spectroscopic redshifts for 5934 radio sources. We describe the data
reduction, imaging, and source identification process, and present the source
counts. Thanks to the wide area covered by our survey, we obtain very robust
counts down to 0.2 mJy. ASKAP's exceptional survey speed, providing efficient,
sensitive and high resolution mapping of large regions of the sky in
conjunction with the multi-wavelength data available for the GAMA23 field,
allowed us to discover 63 giant radio galaxies. The data presented here
demonstrate the excellent capabilities of ASKAP in the pre-SKA era.


title:
Precursors of the RNA-world in space: Detection of ($Z$)-1,2-ethenediol
  in the interstellar medium, a key intermediate in sugar formation

abstract:
  We present the first detection of ($Z$)-1,2-ethenediol, (CHOH)$_2$, the enol
form of glycolaldehyde, in the interstellar medium towards the G+0.693-0.027
molecular cloud located in the Galactic Center. We have derived a column
density of (1.8$\pm$0.1)$\times$10$^{13}$ cm$^{-2}$, which translates into a
molecular abundance with respect to molecular hydrogen of
1.3$\times$10$^{-10}$. The abundance ratio between glycolaldehyde and
($Z$)-1,2-ethenediol is $\sim$5.2. We discuss several viable formation routes
through chemical reactions from precursors such as HCO, H$_2$CO, CHOH or
CH$_2$CHOH. We also propose that this species might be an important precursor
in the formation of glyceraldehyde (HOCH$_2$CHOHCHO) in the interstellar medium
through combination with the hydroxymethylene (CHOH) radical.


title: Topological and frame properties of certain pathological $C^*$-algebras

abstract:
  We introduce a classification of locally compact Hausdorff topological spaces
with respect to the behavior of $\sigma$-compact subsets, and relying on this
classification we study properties of corresponding $C^*$-algebras in terms of
frame theory and the theory of $\mathcal A$-compact operators in Hilbert
$C^*$-modules, some pathological examples are constructed.


title: Shifts in BCFW method for QED

abstract:
  We study the application of BCFW recursion relations to the QED processes
$0\to e^- e^+ n \gamma$. Based on 6-point amplitudes (both MHVA and NMHVA)
computed from Feynman diagrams in the Berends-Giele gauge, we conduct a
comprehensive study on all different shifts. Then we propose a new shift (LLYZ
shift) which can lead to the full amplitudes for these processes and can have
some realistic computation advantages. We compare the number of terms and the
independent amplitudes of this novel shift with a few typical shifts.