Abstract:
  In this paper, a sparse Kronecker-product (SKP) coding scheme is proposed for
unsourced multiple access. Specifically, the data of each active user is
encoded as the Kronecker product of two component codewords with one being
sparse and the other being forward-error-correction (FEC) coded. At the
receiver, an iterative decoding algorithm is developed, consisting of matrix
factorization for the decomposition of the Kronecker product and soft-in
soft-out decoding for the component sparse code and the FEC code. The cyclic
redundancy check (CRC) aided interference cancellation technique is further
incorporated for performance improvement. Numerical results show that the
proposed scheme outperforms the state-of-the-art counterparts, and approaches
the random coding bound within a gap of only 0.1 dB at the code length of 30000
when the number of active users is less than 75, and the error rate can be made
very small even if the number of active users is relatively large.


Abstract:
  Deep neural networks for medical image reconstruction are traditionally
trained using high-quality ground-truth images as training targets. Recent work
on Noise2Noise (N2N) has shown the potential of using multiple noisy
measurements of the same object as an alternative to having a ground-truth.
However, existing N2N-based methods are not suitable for learning from the
measurements of an object undergoing nonrigid deformation. This paper addresses
this issue by proposing the deformation-compensated learning (DeCoLearn) method
for training deep reconstruction networks by compensating for object
deformations. A key component of DeCoLearn is a deep registration module, which
is jointly trained with the deep reconstruction network without any
ground-truth supervision. We validate DeCoLearn on both simulated and
experimentally collected magnetic resonance imaging (MRI) data and show that it
significantly improves imaging quality.


Abstract:
  This paper develops a theory of polynomial maps from commutative semigroups
to arbitrary groups and proves that it has desirable formal properties when the
target group is locally nilpotent. We apply this theory to solve Waring's
Problem for Heisenberg groups in a sequel to this paper.


Abstract:
  Conditional image synthesis aims to create an image according to some
multi-modal guidance in the forms of textual descriptions, reference images,
and image blocks to preserve, as well as their combinations. In this paper,
instead of investigating these control signals separately, we propose a new
two-stage architecture, M6-UFC, to unify any number of multi-modal controls. In
M6-UFC, both the diverse control signals and the synthesized image are
uniformly represented as a sequence of discrete tokens to be processed by
Transformer. Different from existing two-stage autoregressive approaches such
as DALL-E and VQGAN, M6-UFC adopts non-autoregressive generation (NAR) at the
second stage to enhance the holistic consistency of the synthesized image, to
support preserving specified image blocks, and to improve the synthesis speed.
Further, we design a progressive algorithm that iteratively improves the
non-autoregressively generated image, with the help of two estimators developed
for evaluating the compliance with the controls and evaluating the fidelity of
the synthesized image, respectively. Extensive experiments on a newly collected
large-scale clothing dataset M2C-Fashion and a facial dataset Multi-Modal
CelebA-HQ verify that M6-UFC can synthesize high-fidelity images that comply
with flexible multi-modal controls.


Abstract:
  The scaling up of quantum hardware is the fundamental challenge ahead in
order to realize the disruptive potential of quantum technology in information
science. Among the plethora of hardware platforms, photonics stands out by
offering a modular approach, where the main challenge is to construct
sufficiently high-quality building blocks and develop methods to efficiently
interface them. Importantly, the subsequent scaling-up will make full use of
the mature integrated photonic technology provided by photonic foundry
infrastructure to produce small foot-print quantum processors of immense
complexity. A fully coherent and deterministic photon-emitter interface is a
key enabler of quantum photonics, and can today be realized with solid-state
quantum emitters with specifications reaching the quantitative benchmark
referred to as Quantum Advantage. This light-matter interaction primer realizes
a range of quantum photonic resources and functionalities, including on-demand
single-photon and multi-photon entanglement sources, and photon-photon
nonlinear quantum gates. We will present the current state-of-the-art in
single-photon quantum hardware and the main photonic building blocks required
in order to scale up. Furthermore, we will point out specific promising
applications of the hardware building blocks within quantum communication and
photonic quantum computing, laying out the road ahead for quantum photonics
applications that could offer a genuine quantum advantage.


Abstract:
  This paper presents an NLP (Natural Language Processing) approach to
detecting spoilers in book reviews, using the University of California San
Diego (UCSD) Goodreads Spoiler dataset. We explored the use of LSTM, BERT, and
RoBERTa language models to perform spoiler detection at the sentence-level.
This was contrasted with a UCSD paper which performed the same task, but using
handcrafted features in its data preparation. Despite eschewing the use of
handcrafted features, our results from the LSTM model were able to slightly
exceed the UCSD team's performance in spoiler detection.


Abstract:
  An iterative numerical method to compute the conformal mapping in the context
of propagating water waves over uneven topographies is investigated. The map
flattens the fluid domain onto a canonical strip in which computations are
performed. The accuracy of the method is tested by using the MATLAB
Schwarz-Christoffel toolbox mapping as a benchmark. Besides, we give a
numerical alternative to compute the inverse of the conformal map.


Abstract:
  We consider an investment process that includes a number of features, each of
which can be active or inactive. Our goal is to attribute or decompose an
achieved performance to each of these features, plus a baseline value. There
are many ways to do this, which lead to potentially different attributions in
any specific case. We argue that a specific attribution method due to Shapley
is the preferred method, and discuss methods that can be used to compute this
attribution exactly, or when that is not practical, approximately.


Abstract:
  The evolution of circumstellar discs is highly influenced by their
surroundings, in particular by external photoevaporation due to nearby stars
and dynamical truncations. The impact of these processes on disc populations
depends on the dynamical evolution of the star-forming region. Here we
implement a simple model of molecular cloud collapse and star formation to
obtain primordial positions and velocities of young stars and follow their
evolution in time, including that of their circumstellar discs. Our disc model
takes into account viscous evolution, internal and external photoevaporation,
dust evolution, and dynamical truncations. The disc evolution is resolved
simultaneously with the star cluster dynamics and stellar evolution. Our
results show that an extended period of star formation allows for massive discs
formed later in the simulations to survive for several million years. This
could explain massive discs surviving in regions of high UV radiation.


Abstract:
  This article proposes a framework for the study of periodic maps $T$ from a
(typically finite) set $X$ to itself when the set $X$ is equipped with one or
more real- or complex-valued functions. The main idea, inspired by the
time-evolution operator construction from ergodic theory, is the introduction
of a vector space that contains the given functions and is closed under
composition with $T$, along with a time-evolution operator on that vector
space. I show that the invariant functions and 0-mesic functions span
complementary subspaces associated respectively with the eigenvalue 1 and the
other eigenvalues. Alongside other examples, I give an explicit description of
the spectrum of the evolution operator when $X$ is the set of $k$-element
multisets with elements in $\{0,1,\dots,n-1\}$, $T$ increments each element of
a multiset by 1 mod $n$, and $g_i: X \rightarrow \mathbb{R}$ (with $1 \leq i
\leq k$) maps a multiset to its $i$th smallest element.


Abstract:
  In the upcoming decades large facilities, such as the SKA, will provide
resolved observations of the kinematics of millions of galaxies. In order to
assist in the timely exploitation of these vast datasets we explore the use of
a self-supervised, physics aware neural network capable of Bayesian kinematic
modelling of galaxies. We demonstrate the network's ability to model the
kinematics of cold gas in galaxies with an emphasis on recovering physical
parameters and accompanying modelling errors. The model is able to recover
rotation curves, inclinations and disc scale lengths for both CO and HI data
which match well with those found in the literature. The model is also able to
provide modelling errors over learned parameters thanks to the application of
quasi-Bayesian Monte-Carlo dropout. This work shows the promising use of
machine learning, and in particular self-supervised neural networks, in the
context of kinematically modelling galaxies. This work represents the first
steps in applying such models for kinematic fitting and we propose that
variants of our model would seem especially suitable for enabling emission-line
science from upcoming surveys with e.g. the SKA, allowing fast exploitation of
these large datasets.


Abstract:
  Context: Petri net slicing is a technique to reduce the size of a Petri net
so that it can ease the analysis or understanding of the original Petri net.
  Objective: Presenting two new Petri net slicing algorithms to isolate those
places and transitions of a Petri net (the slice) that may contribute tokens to
one or more places given (the slicing criterion).
  Method: The two algorithms proposed are formalized. The completeness of the
first algorithm and the minimality of the second algorithm are formally proven.
Both algorithms together with other three state-of-the-art algorithms have been
implemented and integrated into a single tool so that we have been able to
carry out a fair empirical evaluation.
  Results: Besides the two new Petri net slicing algorithms, a public, free,
and open-source implementation of five algorithms is reported. The results of
an empirical evaluation of the new algorithms and the slices that they produce
are also presented.
  Conclusions: The first algorithm collects all places and transitions that may
influence (in any computation) the slicing criterion, while the second
algorithm collects a minimum set of places and transitions that may influence
(in some computation) the slicing criterion. Therefore, the net computed by the
first algorithm can reproduce any computation that contributes tokens to any
place of interest. In contrast, the second algorithm loses this possibility but
it often produces a much more reduced subnet (which still can reproduce some
computations that contribute tokens to some places of interest). The first
algorithm is proven complete, and the second one is proven minimal.


Abstract:
  The recently developed generalized Fourier-Galerkin method is complemented by
a numerical continuation with respect to the kinetic energy, which extends the
framework to the investigation of modal interactions resulting in folds of the
nonlinear modes. In order to enhance the practicability regarding the
investigation of complex large-scale systems, it is proposed to provide
analytical gradients and exploit sparsity of the nonlinear part of the
governing algebraic equations. A novel reduced order model (ROM) is developed
for those regimes where internal resonances are absent. The approach allows for
an accurate approximation of the multi-harmonic content of the resonant mode
and accounts for the contributions of the off-resonant modes in their
linearized forms. The ROM facilitates the efficient analysis of self-excited
limit cycle oscillations, frequency response functions and the direct tracing
of forced resonances. The ROM is equipped with a large parameter space
including parameters associated with linear damping and near-resonant harmonic
forcing terms. An important objective of this paper is to demonstrate the broad
applicability of the proposed overall methodology. This is achieved by selected
numerical examples including finite element models of structures with strongly
nonlinear, non-conservative contact constraints.


Abstract:
  In this position paper, we explore the adoption of a Smart City with a
socio-technical perspective. A Smart city is a transformational technological
process leading to profound modifications of existing urban regimes and
infrastructure components. In this study, we consider a Smart City as a
socio-technical system where the interplay between technologies and users
ensures the sustainable development of smart city initiatives that improve the
quality of life and solve important socio-economic problems. The adoption of a
Smart City required a participative approach where users are involved during
the adoption process to joint optimise both systems. Thus, we contribute to
socio-technical research showing how a participative approach based on press
relationships to facilitate information exchange between municipal actors and
citizens worked as a success factor for the smart city adoption. We also
discuss the limitations of this approach.


Abstract:
  We investigate the role played by density inhomogeneities and dissipation on
the final outcome of collapse of a self-gravitating sphere. By imposing a
perturbative scheme on the thermodynamical variables and gravitational
potentials we track the evolution of the collapse process starting off with an
initially static perfect fluid sphere which is shear-free. The collapsing core
dissipates energy in the form of a radial heat flux with the exterior spacetime
being filled with a superposition of null energy and an anisotropic string
distribution. The ensuing dynamical process slowly evolves into a shear-like
regime with contributions from the heat flux and density fluctuations. We show
that the anisotropy due to the presence of the strings drives the stellar fluid
towards instability with this effect being enhanced by the density
inhomogeneity. An interesting and novel consequence of this collapse scenario
is the delay in the formation of the horizon.


Abstract:
  Shapley Values, a solution to the credit assignment problem in cooperative
game theory, are a popular type of explanation in machine learning, having been
used to explain the importance of features, embeddings, and even neurons. In
NLP, however, leave-one-out and attention-based explanations still predominate.
Can we draw a connection between these different methods? We formally prove
that -- save for the degenerate case -- attention weights and leave-one-out
values cannot be Shapley Values. $\textit{Attention flow}$ is a post-processed
variant of attention weights obtained by running the max-flow algorithm on the
attention graph. Perhaps surprisingly, we prove that attention flows are indeed
Shapley Values, at least at the layerwise level. Given the many desirable
theoretical qualities of Shapley Values -- which has driven their adoption
among the ML community -- we argue that NLP practitioners should, when
possible, adopt attention flow explanations alongside more traditional ones.


Abstract:
  Currently there has been increasing demand for real-time training on
resource-limited IoT devices such as smart sensors, which realizes standalone
online adaptation for streaming data without data transfers to remote servers.
OS-ELM (Online Sequential Extreme Learning Machine) has been one of promising
neural-network-based online algorithms for on-chip learning because it can
perform online training at low computational cost and is easy to implement as a
digital circuit. Existing OS-ELM digital circuits employ fixed-point data
format and the bit-widths are often manually tuned, however, this may cause
overflow or underflow which can lead to unexpected behavior of the circuit. For
on-chip learning systems, an overflow/underflow-free design has a great impact
since online training is continuously performed and the intervals of
intermediate variables will dynamically change as time goes by. In this paper,
we propose an overflow/underflow-free bit-width optimization method for
fixed-point digital circuits of OS-ELM. Experimental results show that our
method realizes overflow/underflow-free OS-ELM digital circuits with 1.0x -
1.5x more area cost compared to the baseline simulation method where overflow
or underflow can happen.


Abstract:
  In 1914 Bohr proved that there is an $r_0 \in(0,1)$ such that if a power
series $\sum_{m=0}^\infty c_m z^m$ is convergent in the open unit disc and
$|\sum_{m=0}^\infty c_m z^m|<1$ then, $\sum_{m=0}^\infty |c_m z^m|<1$ for
$|z|<r_0$. The largest value of such $r_0$ is called the Bohr radius. In this
article, we find Bohr radius for some univalent harmonic mappings having
different dilatations and in addition, also compute Bohr radius for the
functions convex in one direction.


Abstract:
  Edge computing was introduced as a technical enabler for the demanding
requirements of new network technologies like 5G. It aims to overcome
challenges related to centralized cloud computing environments by distributing
computational resources to the edge of the network towards the customers. The
complexity of the emerging infrastructures increases significantly, together
with the ramifications of outages on critical use cases such as self-driving
cars or health care. Artificial Intelligence for IT Operations (AIOps) aims to
support human operators in managing complex infrastructures by using machine
learning methods. This paper describes the system design of an AIOps platform
which is applicable in heterogeneous, distributed environments. The overhead of
a high-frequency monitoring solution on edge devices is evaluated and
performance experiments regarding the applicability of three anomaly detection
algorithms on edge devices are conducted. The results show, that it is feasible
to collect metrics with a high frequency and simultaneously run specific
anomaly detection algorithms directly on edge devices with a reasonable
overhead on the resource utilization.


Abstract:
  Pushing is an essential non-prehensile manipulation skill used for tasks
ranging from pre-grasp manipulation to scene rearrangement, reasoning about
object relations in the scene, and thus pushing actions have been widely
studied in robotics. The effective use of pushing actions often requires an
understanding of the dynamics of the manipulated objects and adaptation to the
discrepancies between prediction and reality. For this reason, effect
prediction and parameter estimation with pushing actions have been heavily
investigated in the literature. However, current approaches are limited because
they either model systems with a fixed number of objects or use image-based
representations whose outputs are not very interpretable and quickly accumulate
errors. In this paper, we propose a graph neural network based framework for
effect prediction and parameter estimation of pushing actions by modeling
object relations based on contacts or articulations. Our framework is validated
both in real and simulated environments containing different shaped multi-part
objects connected via different types of joints and objects with different
masses. Our approach enables the robot to predict and adapt the effect of a
pushing action as it observes the scene. Further, we demonstrate 6D effect
prediction in the lever-up action in the context of robot-based hard-disk
disassembly.


Abstract:
  The Ly$\alpha$ emission line is one of the most promising probes of cosmic
reionisation but isolating the signature of a change in the ionisation state of
the IGM is challenging because of intrinsic evolution and internal radiation
transfer effects. We present the first study of the evolution of Ly$\alpha$
emitters (LAE) during the epoch of reionisation based on a full
radiation-hydrodynamics cosmological simulation that is able to capture both
the large-scale process of reionisation and the small-scale properties of
galaxies. We predict the Ly$\alpha$ emission of galaxies in the $10^3$ cMpc$^3$
SPHINX simulation at $6\leq z\leq9$ by computing the full Ly$\alpha$ radiation
transfer from ISM to IGM scales. SPHINX is able to reproduce many observational
constraints such as the UV/Ly$\alpha$ luminosity functions and stellar mass
functions at z $\geq$ 6 for the dynamical range probed by our simulation
($M_{\rm 1500}\gtrsim-18$, $L_{\rm Ly\alpha}\lesssim10^{42}$ erg/s,
$M_{\star}\lesssim10^9$ M$_{\odot}$). As intrinsic Ly$\alpha$ emission and
internal Ly$\alpha$ escape fractions barely evolve from $z=6$ to 9, the
observed suppression of Ly$\alpha$ luminosities with increasing redshift is
fully attributed to IGM absorption. For most observable galaxies ($M_{\rm
1500}\lesssim-16$), the Ly$\alpha$ line profiles are slightly shifted to the
red due to internal radiative transfer effects which mitigates the effect of
IGM absorption. Overall, the enhanced Ly$\alpha$ suppression during
reionisation traces the IGM neutral fraction $x_{\rm HI}$ well but the
predicted amplitude of this reduction is a strong function of the Ly$\alpha$
peak shift, which is set at ISM/CGM scales. We find that a large number of LAEs
could be detectable in very deep surveys during reionisation when $x_{\rm HI}$
is still $\approx 50\%$.


Abstract:
  We present our 500 pc distance-limited study of stellar fares using the Dark
Energy Camera as part of the Deeper, Wider, Faster Program. The data was
collected via continuous 20-second cadence g band imaging and we identify
19,914 sources with precise distances from Gaia DR2 within twelve, ~3
square-degree, fields over a range of Galactic latitudes. An average of ~74
minutes is spent on each field per visit. All light curves were accessed
through a novel unsupervised machine learning technique designed for anomaly
detection. We identify 96 flare events occurring across 80 stars, the majority
of which are M dwarfs. Integrated are energies range from $\sim
10^{31}-10^{37}$ erg, with a proportional relationship existing between
increased are energy with increased distance from the Galactic plane,
representative of stellar age leading to declining yet more energetic are
events. In agreement with previous studies we observe an increase in flaring
fraction from M0 -> M6 spectral types. Furthermore, we find a decrease in the
flaring fraction of stars as vertical distance from the galactic plane is
increased, with a steep decline present around ~100 pc. We find that ~70% of
identified flares occur on short timescales of ~8 minutes. Finally we present
our associated are rates, finding a volumetric rate of $2.9 \pm 0.3 \times
10^{-6}$ flares pc$^{-3}$ hr$^{-1}$.


Abstract:
  We combine NLO predictions with full top-quark mass dependence with
approximate NNLO predictions for Higgs-boson pair production in gluon fusion,
including the possibility to vary coupling parameters within a non-linear
Effective Field Theory framework containing five anomalous couplings for this
process. We study the impact of the anomalous couplings on various observables,
and present Higgs-pair invariant-mass distributions at seven benchmark points
characterising different $m_{hh}$ shape types. We also provide numerical
coefficients for the approximate NNLO cross section as a function of the
anomalous couplings at $\sqrt{s}=14$ TeV.


Abstract:
  Detection and classification of ships based on their silhouette profiles in
natural imagery is an important undertaking in computer science. This problem
can be viewed from a variety of perspectives, including security, traffic
control, and even militarism. Therefore, in each of the aforementioned
applications, specific processing is required. In this paper, by applying the
"bag of words" (BoW), a new method is presented that its words are the features
that are obtained using pre-trained models of deep convolutional networks. ,
Three VGG models are utilized which provide superior accuracy in identifying
objects. The regions of the image that are selected as the initial proposals
are derived from a greedy algorithm on the key points generated by the Scale
Invariant Feature Transform (SIFT) method. Using the deep features in the BOW
method provides a good improvement in the recognition and classification of
ships. Eventually, we obtained an accuracy of 91.8% in the classification of
the ships which shows the improvement of about 5% compared to previous methods.


Abstract:
  Recent data on the nuclear modification of W and Z boson production measured
by the ATLAS collaboration in PbPb collisions at $\sqrt{s_{\rm nn}}=5.02$ TeV
show an enhancement in peripheral collisions, seemingly contradicting
predictions of the Glauber model. The data were previously explained by arguing
that the nucleon-nucleon cross section may be shadowed in nucleus-nucleus
collisions, and hence suppressed compared to the proton-proton cross section at
the same collision energy. This interpretation has quite significant
consequences for the understanding of heavy-ion data, in particular in the
context of the Glauber model. Instead, we provide an alternative explanation of
the data by assuming that there is a mild bias present in the centrality
determination of the measurement; on the size of the related systematic
uncertainty. Using this assumption, we show that the data is in agreement with
theoretical calculations using nuclear parton distribution functions. Finally,
we speculate that the centrality dependence of the W$^-$/W$^{+}$ ratio may
point to the relevance of a larger skin thickness of the Pb nucleus, which, if
present, would result in a few percent larger PbPb cross section than currently
accounted for in the Glauber model and may hence be the root of the centrality
bias.


Abstract:
  A complete understanding of solar radio bursts requires developing numerical
techniques which can connect large-scale activities with kinetic plasma
processes. As a starting point, this study presents a numerical scheme
combining three different techniques: (1) extrapolation of magnetic field
overlying a specific active region in order to derive the background field, (2)
guiding-center simulation of dynamics of millions of particles within a
selected loop to reveal the integral velocity distribution function (VDF)
around certain sections of the loop, and (3) particle-in-cell (PIC) simulation
of kinetic instabilities driven by energetic electrons initiated by the
obtained distributions. Scattering effects at various levels (weak, moderate,
and strong) due to wave/turbulence-particle interaction are considered using
prescribed time scales of scattering. It was found that the obtained VDFs
contain strip-like and loss-cone features with positive gradient, and both
features are capable of driving electron cyclotron maser emission (ECME), which
is a viable radiation mechanism for some solar radio bursts, in particular,
solar radio spikes. The strip-like feature is important in driving the harmonic
X mode, while the loss-cone feature can be important in driving the fundamental
X mode. In the weak-scattering case, the rate of energy conversion from
energetic electrons to X2 can reach up to ~2.9 * 10^-3 Ek0, where Ek0 is the
initial kinetic energy of energetic electrons. The study demonstrates a novel
way of exciting X2 mode in the corona during solar flares, and provides new
sight into how escaping radiation can be generated within a coronal loop during
solar flares.


Abstract:
  We show that the QRAT simulation algorithm of $\forall$Exp+Res from [B. Kiesl
and M. Seidl, 2019] cannot be lifted to IR-calc.


Abstract:
  William Cranch Bond, director of the Harvard College Observatory in mid-19th
century, carried out detailed sunspot observations during the period 1847-1849.
We highlight Bond was the observer with the highest daily number of sunspot
groups observed in Solar Cycle 9 recording 18 groups on 26 December 1848
according to the current sunspot group database. However, we have detected
significant mistakes in these counts due to the use of sunspot position tables
instead of solar drawings. Therefore, we have revisited the sunspot
observations made by Bond, establishing a new group counting. Our new counts of
the sunspot groups from Bond's drawings indicate that solar activity was
previously overestimated. Moreover, after this new counting, Bond would not be
the astronomer who recorded the highest daily group number for Solar Cycle 9
but Schmidt with 16 groups on 14 February 1849. We have also indicated the new
highest annual group numbers recorded by any observer for the period 1847-1849
in order to correct those values applied in the "brightest star" method, which
is used as a rough indicator of the solar activity level. Furthermore, a
comparison between Bond's sunspot records and the sunspot observations made by
Schwabe and Wolf is shown. We conclude that the statistics of Wolf and Bond are
similar regarding to the group count. Additionally, Schwabe was able to observe
smaller groups than Bond.


Abstract:
  Implementing embedded neural network processing at the edge requires
efficient hardware acceleration that couples high computational performance
with low power consumption. Driven by the rapid evolution of network
architectures and their algorithmic features, accelerator designs are
constantly updated and improved. To evaluate and compare hardware design
choices, designers can refer to a myriad of accelerator implementations in the
literature. Surveys provide an overview of these works but are often limited to
system-level and benchmark-specific performance metrics, making it difficult to
quantitatively compare the individual effect of each utilized optimization
technique. This complicates the evaluation of optimizations for new accelerator
designs, slowing-down the research progress. This work provides a survey of
neural network accelerator optimization approaches that have been used in
recent works and reports their individual effects on edge processing
performance. It presents the list of optimizations and their quantitative
effects as a construction kit, allowing to assess the design choices for each
building block separately. Reported optimizations range from up to 10'000x
memory savings to 33x energy reductions, providing chip designers an overview
of design choices for implementing efficient low power neural network
accelerators.


Abstract:
  The speed-accuracy Pareto curve of object detection systems have advanced
through a combination of better model architectures, training and inference
methods. In this paper, we methodically evaluate a variety of these techniques
to understand where most of the improvements in modern detection systems come
from. We benchmark these improvements on the vanilla ResNet-FPN backbone with
RetinaNet and RCNN detectors. The vanilla detectors are improved by 7.7% in
accuracy while being 30% faster in speed. We further provide simple scaling
strategies to generate family of models that form two Pareto curves, named
RetinaNet-RS and Cascade RCNN-RS. These simple rescaled detectors explore the
speed-accuracy trade-off between the one-stage RetinaNet detectors and
two-stage RCNN detectors. Our largest Cascade RCNN-RS models achieve 52.9% AP
with a ResNet152-FPN backbone and 53.6% with a SpineNet143L backbone. Finally,
we show the ResNet architecture, with three minor architectural changes,
outperforms EfficientNet as the backbone for object detection and instance
segmentation systems.


Abstract:
  We are concerned with the linear stability of the Couette flow for the
non-isentropic compressible Navier-Stokes equations with vanished shear
viscosity in a domain $\mathbb{T}\times \mathbb{R}$. For a general initial data
settled in Sobolev spaces, we obtain a Lyapunov type instability of the
density, the temperature, the compressible part of the velocity field, and also
obtain an inviscid damping for the incompressible part of the velocity field.
Moreover, if the initial density, the initial temperature and the
incompressible part of the initial velocity field satisfy some quality
relation, we can prove the enhanced dissipation phenomenon for the velocity
field.


Abstract:
  In 2021, the Sombor index was introduced by Gutman, which is a new
degree-based topological molecular descriptors. The Sombor index of a graph $G$
is defined as $SO(G) =\sum_{uv\in E(G)}\sqrt{d^2_G(u)+d^2_G(v)}$, where
$d_G(v)$ is the degree of the vertex $v$ in $G$. Let $\mathscr{T}_{n,m}$ and
$\mathscr{U}_{n,m}$ be the set of trees and unicyclic graphs on $n$ vertices
with fixed matching number $m$, respectively. In this paper, the tree and the
unicyclic graph with the maximum Sombor index are determined among
$\mathscr{T}_{n,m}$ and $\mathscr{U}_{n,m}$, respectively.


Abstract:
  Purpose: Diffusion MRI (dMRI) suffers from eddy currents induced by strong
diffusion gradients, which introduce artefacts that can impair subsequent
diffusion metric analysis. Existing retrospective correction techniques that
correct for diffusion gradient induced eddy currents do not account for eddy
current decay, which is generally effective for traditional Pulsed Gradient
Spin Echo (PGSE) diffusion encoding. However, these techniques do not
necessarily apply to advanced forms of dMRI that require substantial gradient
slewing, such as Oscillating Gradient Spin Echo (OGSE).
  Methods: An in-house algorithm (TVEDDY), that for the first time
retrospectively models eddy current decay, was tested on PGSE and OGSE brain
images acquired at 7T. Correction performance was compared to conventional
correction methods by evaluating the mean-squared error (MSE) between
diffusion-weighted images acquired with opposite polarity diffusion gradients.
As a ground truth comparison, images were corrected using field dynamics up to
third order in space measured using a field monitoring system.
  Results: Time-varying eddy currents were observed for OGSE, which introduced
blurring that was not reduced using the traditional approach but was diminished
considerably with TVEDDY and model-based reconstruction. No MSE difference was
observed between the conventional approach and TVEDDY for PGSE, but for OGSE
TVEDDY resulted in significantly lower MSE than the conventional approach. The
field-monitoring-informed model-based reconstruction had the lowest MSE for
both PGSE and OGSE.
  Conclusion: This work establishes that it is possible to estimate
time-varying eddy currents from the diffusion data itself, which provides
substantial image quality improvements for gradient-intensive dMRI acquisitions
like OGSE.


Abstract:
  We consider two popular Graph Representation Learning (GRL) methods: message
passing for node classification and network embedding for link prediction. For
each, we pick a popular model that we: (i) linearize and (ii) and switch its
training objective to Frobenius norm error minimization. These simplifications
can cast the training into finding the optimal parameters in closed-form. We
program in TensorFlow a functional form of Truncated Singular Value
Decomposition (SVD), such that, we could decompose a dense matrix $\mathbf{M}$,
without explicitly computing $\mathbf{M}$. We achieve competitive performance
on popular GRL tasks while providing orders of magnitude speedup. We
open-source our code at http://github.com/samihaija/tf-fsvd


Abstract:
  We describe and contrast two distinct problem areas for statistical
causality: studying the likely effects of an intervention ("effects of
causes"), and studying whether there is a causal link between the observed
exposure and outcome in an individual case ("causes of effects"). For each of
these, we introduce and compare various formal frameworks that have been
proposed for that purpose, including the decision-theoretic approach,
structural equations, structural and stochastic causal models, and potential
outcomes. It is argued that counterfactual concepts are unnecessary for
studying effects of causes, but are needed for analysing causes of effects.
They are however subject to a degree of arbitrariness, which can be reduced,
though not in general eliminated, by taking account of additional structure in
the problem.


Abstract:
  The breaking of chiral symmetry in holographic light-front QCD is encoded in
its longitudinal dynamics with its chiral limit protected by the superconformal
algebraic structure which governs its transverse dynamics. The scale in the
longitudinal light-front Hamiltonian determines the confinement strength in
this direction: It is also responsible for most of the light meson ground state
mass consistent with the Gell-Mann-Oakes-Renner constraint. Longitudinal
confinement and the breaking of chiral symmetry are found to be different
manifestations of the same underlying dynamics as found in 't Hooft large $N_C$
QCD(1 + 1) model.


Abstract:
  We study the benefits of complex-valued weights for neural networks. We prove
that shallow complex neural networks with quadratic activations have no
spurious local minima. In contrast, shallow real neural networks with quadratic
activations have infinitely many spurious local minima under the same
conditions. In addition, we provide specific examples to demonstrate that
complex-valued weights turn poor local minima into saddle points. The
activation function CReLU is also discussed to illustrate the superiority of
analytic activations in complex-valued neural networks.


Abstract:
  In the development of governmental policy for artificial intelligence (AI)
that is informed by ethics, one avenue currently pursued is that of drawing on
AI Ethics Principles. However, these AI Ethics Principles often fail to be
actioned in governmental policy. This paper proposes a novel framework for the
development of Actionable Principles for AI. The approach acknowledges the
relevance of AI Ethics Principles and homes in on methodological elements to
increase their practical implementability in policy processes. As a case study,
elements are extracted from the development process of the Ethics Guidelines
for Trustworthy AI of the European Commissions High Level Expert Group on AI.
Subsequently, these elements are expanded on and evaluated in light of their
ability to contribute to a prototype framework for the development of
Actionable Principles for AI. The paper proposes the following three
propositions for the formation of such a prototype framework: (1) preliminary
landscape assessments; (2) multi-stakeholder participation and cross-sectoral
feedback; and, (3) mechanisms to support implementation and
operationalizability.


Abstract:
  We describe the asymptotic behavior of positive solutions $u_\epsilon$ of the
equation $-\Delta u + au = 3\,u^{5-\epsilon}$ in $\Omega\subset\mathbb{R}^3$
with a homogeneous Dirichlet boundary condition. The function $a$ is assumed to
be critical in the sense of Hebey and Vaugon and the functions $u_\epsilon$ are
assumed to be an optimizing sequence for the Sobolev inequality. Under a
natural nondegeneracy assumption we derive the exact rate of the blow-up and
the location of the concentration point, thereby proving a conjecture of
Br\'ezis and Peletier (1989). Similar results are also obtained for solutions
of the equation $-\Delta u + (a+\epsilon V) u = 3\,u^5$ in $\Omega$.


Abstract:
  The multiple-input multiple-output (MIMO) detection problem, a fundamental
problem in modern digital communications, is to detect a vector of transmitted
symbols from the noisy outputs of a fading MIMO channel. The maximum likelihood
detector can be formulated as a complex least-squares problem with discrete
variables, which is NP-hard in general. Various semidefinite relaxation (SDR)
methods have been proposed in the literature to solve the problem due to their
polynomial-time worst-case complexity and good detection error rate
performance. In this paper, we consider two popular classes of SDR-based
detectors and study the conditions under which the SDRs are tight and the
relationship between different SDR models. For the enhanced complex and real
SDRs proposed recently by Lu et al., we refine their analysis and derive the
necessary and sufficient condition for the complex SDR to be tight, as well as
a necessary condition for the real SDR to be tight. In contrast, we also show
that another SDR proposed by Mobasher et al. is not tight with high probability
under mild conditions. Moreover, we establish a general theorem that shows the
equivalence between two subsets of positive semidefinite matrices in different
dimensions by exploiting a special "separable" structure in the constraints.
Our theorem recovers two existing equivalence results of SDRs defined in
different settings and has the potential to find other applications due to its
generality.


Abstract:
  The joint detection of the gravitational wave GW170817, of the short
$\gamma$-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the
the binary neutron star merger observed on August 17, 2017, is a milestone in
multimessenger astronomy and provides new constraints on the neutron star
equation of state. We perform Bayesian inference and model selection on
AT2017gfo using semi-analytical, multi-components models that also account for
non-spherical ejecta. Observational data favor anisotropic geometries to
spherically symmetric profiles, with a log-Bayes' factor of ${\sim}10^{4}$, and
favor multi-component models against single-component ones. The best fitting
model is an anisotropic three-component composed of dynamical ejecta plus
neutrino and viscous winds. Using the dynamical ejecta parameters inferred from
the best-fitting model and numerical-relativity relations connecting the ejecta
properties to the binary properties, we constrain the binary mass ratio to
$q<1.54$ and the reduced tidal parameter to $120<\tilde\Lambda<1110$. Finally,
we combine the predictions from AT2017gfo with those from GW170817,
constraining the radius of a neutron star of $1.4~{\rm M}_\odot$ to
$12.2\pm0.5~{\rm km}$ ($1\sigma$ level). This prediction could be further
strengthened by improving kilonova models with numerical-relativity
information.


Abstract:
  In this paper, we discuss possible color palletes, prediction and analysis of
originality of the colors that Artists used on the Renaissance oil paintings.
This framework goal is to help to use the color symbology and image enhancement
tools, to predict the historical color palletes of the Renaissance oil
artworks. This work is only the start of a development to explore the
possibilities of prediction of color palletes of the Renaissance oil artworks.
We believe that framework might be very useful in the prediction of color
palletes of the Renaissance oil artworks and other artworks. The images in
number 105 have been taken from the paintings of three well-known artists,
Rafael, Leonardo Da Vinci, and Rembrandt that are available in the Olga's
Gallery. Images are processed in the frequency domain to enhance a quality of
images and ratios of primary colors are calculated and analyzed by using new
measurements of color-ratios.


Abstract:
  The control of domain walls is central to nearly all magnetic technologies,
particularly for information storage and spintronics. Creative attempts to
increase storage density need to overcome volatility due to thermal
fluctuations of nanoscopic domains and heating limitations. Topological
defects, such as solitons, skyrmions, and merons, may be much less susceptible
to fluctuations, owing to topological constraints, while also being
controllable with low current densities. Here, we present the first evidence
for soliton/soliton and soliton/antisoliton domain walls in the hexagonal
chiral magnet Mn1/3NbS2 that respond asymmetrically to magnetic fields and
exhibit pair-annihilation. This is important because it suggests the
possibility of controlling the occurrence of soliton pairs and the use of small
fields or small currents to control nanoscopic magnetic domains. Specifically,
our data suggest that either soliton/soliton or soliton/antisoliton pairs can
be stabilized by tuning the balance between intrinsic exchange interactions and
long-range magnetostatics in restricted geometries


Abstract:
  How many neurons are needed to approximate a target probability distribution
using a neural network with a given input distribution and approximation error?
This paper examines this question for the case when the input distribution is
uniform, and the target distribution belongs to the class of histogram
distributions. We obtain a new upper bound on the number of required neurons,
which is strictly better than previously existing upper bounds. The key
ingredient in this improvement is an efficient construction of the neural nets
representing piecewise linear functions. We also obtain a lower bound on the
minimum number of neurons needed to approximate the histogram distributions.


Abstract:
  The development of recommender systems that optimize multi-turn interaction
with users, and model the interactions of different agents (e.g., users,
content providers, vendors) in the recommender ecosystem have drawn increasing
attention in recent years. Developing and training models and algorithms for
such recommenders can be especially difficult using static datasets, which
often fail to offer the types of counterfactual predictions needed to evaluate
policies over extended horizons. To address this, we develop RecSim NG, a
probabilistic platform for the simulation of multi-agent recommender systems.
RecSim NG is a scalable, modular, differentiable simulator implemented in
Edward2 and TensorFlow. It offers: a powerful, general probabilistic
programming language for agent-behavior specification; tools for probabilistic
inference and latent-variable model learning, backed by automatic
differentiation and tracing; and a TensorFlow-based runtime for running
simulations on accelerated hardware. We describe RecSim NG and illustrate how
it can be used to create transparent, configurable, end-to-end models of a
recommender ecosystem, complemented by a small set of simple use cases that
demonstrate how RecSim NG can help both researchers and practitioners easily
develop and train novel algorithms for recommender systems.


Abstract:
  Experimentally synthesized perovskite-type YRh$_{3}$B with a $Pm\bar{3}m$
type structure was proposed as a novel topological material (TM) via
first-principles calculations and the low-energy $k\cdot p$ effective
Hamiltonian, which has a quadratic contact triple point (QCTP) at point
$\Gamma$ and six pairs of open nodal lines (NLs) of the hybrid type. Clear
surface states observed in the surface spectrum confirmed the topological
states. When spin-orbit coupling was considered, the QCTP at $\Gamma$
transferred to the quadratic-type Dirac nodal point (NP). Under 1$\%$
tetragonal strained lattice constants, YRh$_{3}$B hosted richer topological
states, including a quadratic-type two-fold degenerate NP, six pairs of open
NLs of the hybrid type, and two closed NLs of type I and hybrid type. Moreover,
it was proved that the NLs of YRh$_{3}$B at its strained lattice constants
contain all types of band-crossing points (BCPs) (i.e., type I, type II, and
critical type). Such rich types of NP and NL states in one compound make it
potentially applicable for multifunctional electronic devices as well as an
appropriate platform to study entanglement among topological states.


Abstract:
  A couple of dozen Earth-like planets orbiting M dwarfs have been discovered
so far. Some of them have attracted interest because of their potential
long-term habitability; such a possibility is currently vigorously debated in
the literature. I show that post-Keplerian (pK) orbit precessions may impact
the habitability of a fictitious telluric planet orbiting an oblate late-type M
dwarf of spectral class M9V with $M_\star=0.08\,M_\odot$ at
$a=0.02\,\mathrm{au}$, corresponding to an orbital period $P_\mathrm{b}\simeq
4\,\mathrm{d}$, inducing long-term variations of the planetary obliquity
$\varepsilon$ which, under certain circumstances, may not be deemed as
negligible from the point of view of life's sustainability. I resume the
analytical orbit-averaged equations of the pK precessions, both classical and
general relativistic, of the unit vectors
$\boldsymbol{\hat{S}},\,\boldsymbol{\hat{h}}$ of both the planet's spin and
orbital angular momenta $\boldsymbol S,\,\boldsymbol{L}$ entering
$\varepsilon$, and numerically integrate them by producing time series of the
pK changes $\Delta\varepsilon(t)$ of the obliquity. For rapidly rotating M
dwarfs with rotational periods of the order of $P_\star \simeq
0.1-1\,\mathrm{d}$, the planet's obliquity $\varepsilon$ can undergo classical
pK large variations $\Delta\varepsilon(t)$ up to tens of degrees over
timescales $\Delta t \simeq 20-200\,\mathrm{kyr}$, depending on the mutual
orientations of the star's spin ${\boldsymbol J}_\star$, of $\boldsymbol S$,
and of $\boldsymbol L$. Instead, $\Delta\varepsilon(t)$ are $\lesssim
1-1.5^\circ$ for the planet b of the Teegarden's Star. In certain
circumstances, the M dwarf's oblateness $J_2^\star$ should be considered as one
of the key dynamical features to be taken into account in compiling budgets of
the long-term habitability of rocky planets around fast spinning late M dwarfs.
(Abridged)


Abstract:
  Multipartite entangled states are significant resources for both quantum
information processing and quantum metrology. In particular, non-Gaussian
entangled states are predicted to achieve a higher sensitivity of precision
measurements than Gaussian states. On the basis of metrological sensitivity,
the conventional linear Ramsey squeezing parameter (RSP) efficiently
characterises the Gaussian entangled atomic states but fails for much wider
classes of highly sensitive non-Gaussian states. These complex non-Gaussian
entangled states can be classified by the nonlinear squeezing parameter (NLSP),
as a generalisation of the RSP with respect to nonlinear observables, and
identified via the Fisher information. However, the NLSP has never been
measured experimentally. Using a 19-qubit programmable superconducting
processor, here we report the characterisation of multiparticle entangled
states generated during its nonlinear dynamics. First, selecting 10 qubits, we
measure the RSP and the NLSP by single-shot readouts of collective spin
operators in several different directions. Then, by extracting the Fisher
information of the time-evolved state of all 19 qubits, we observe a large
metrological gain of 9.89$^{+0.28}_{-0.29}$ dB over the standard quantum limit,
indicating a high level of multiparticle entanglement for quantum-enhanced
phase sensitivity. Benefiting from high-fidelity full controls and addressable
single-shot readouts, the superconducting processor with interconnected qubits
provides an ideal platform for engineering and benchmarking non-Gaussian
entangled states that are useful for quantum-enhanced metrology.


Abstract:
  Convolutional Neural Networks (CNN) are used mainly to treat problems with
many images characteristic of Deep Learning. In this work, we propose a hybrid
image classification model to take advantage of quantum and classical
computing. The method will use the potential that convolutional networks have
shown in artificial intelligence by replacing classical filters with
variational quantum filters. Similarly, this work will compare with other
classification methods and the system's execution on different servers. The
algorithm's quantum feasibility is modelled and tested on Amazon Braket
Notebook instances and experimented on the Pennylane's philosophy and
framework.


Abstract:
  We derive a distribution function for the position of a tagged active
particle in a slowly varying in space external potential, in a system of
interacting active particles. The tagged particle distribution has the form of
the Boltzmann distribution but with an effective temperature that replaces the
temperature of the heat bath. We show that the effective temperature that
enters the tagged particle distribution is the same as the effective
temperature defined through the Einstein relation, i.e. it is equal to the
ratio of the self-diffusion and tagged particle mobility coefficients. This
shows that this effective temperature, which is defined through a
fluctuation-dissipation ratio, is relevant beyond the linear response regime.
We verify our theoretical findings through computer simulations. Our theory
fails when an additional large length scale appears in our active system. This
length scale is associated with long-wavelength density fluctuations that
emerge upon approaching motility-induced phase separation.


Abstract:
  We study all the possible spin asymmetries that can arise in back-to-back
electron-jet production, $ep\rightarrow e+\text{jet}+X$, as well as the
associated jet fragmentation process, $ep\rightarrow e+ \text{jet} (h)+X$, in
electron-proton collisions. We derive the factorization formalism for these
spin asymmetries and perform the corresponding phenomenology for the kinematics
relevant to the future electron ion collider. In the case of unpolarized
electron-proton scattering, we also give predictions for azimuthal asymmetries
for the HERA experiment. This demonstrates that electron-jet production is an
outstanding process for probing unpolarized and polarized transverse momentum
dependent parton distribution functions and fragmentation functions.


Abstract:
  In this paper, combinatorial quantitative group testing (QGT) with noisy
measurements is studied. The goal of QGT is to detect defective items from a
data set of size $n$ with counting measurements, each of which counts the
number of defects in a selected pool of items. While most literatures consider
either probabilistic QGT with random noise or combinatorial QGT with noiseless
measurements, our focus is on the combinatorial QGT with noisy measurements
that might be adversarially perturbed by additive bounded noises. Since perfect
detection is impossible, a partial detection criterion is adopted. With the
adversarial noise being bounded by $d_n = \Theta(n^\delta)$ and the detection
criterion being to ensure no more than $k_n = \Theta(n^\kappa)$ errors can be
made, our goal is to characterize the fundamental limit on the number of
measurement, termed \emph{pooling complexity}, as well as provide explicit
construction of measurement plans with optimal pooling complexity and efficient
decoding algorithms. We first show that the fundamental limit is
$\frac{1}{1-2\delta}\frac{n}{\log n}$ to within a constant factor not depending
on $(n,\kappa,\delta)$ for the non-adaptive setting when $0<2\delta\leq \kappa
<1$, sharpening the previous result by Chen and Wang [1]. We also provide an
explicit construction of a non-adaptive deterministic measurement plan with
$\frac{1}{1-2\delta}\frac{n}{\log_{2} n}$ pooling complexity up to a constant
factor, matching the fundamental limit, with decoding complexity being
$o(n^{1+\rho})$ for all $\rho > 0$, nearly linear in $n$, the size of the data
set.


Abstract:
  In SPECT, list-mode (LM) format allows storing data at higher precision
compared to binned data. There is significant interest in investigating whether
this higher precision translates to improved performance on clinical tasks.
Towards this goal, in this study, we quantitatively investigated whether
processing data in LM format, and in particular, the energy attribute of the
detected photon, provides improved performance on the task of absolute
quantification of region-of-interest (ROI) uptake in comparison to processing
the data in binned format. We conducted this evaluation study using a DaTscan
brain SPECT acquisition protocol, conducted in the context of imaging patients
with Parkinson's disease. This study was conducted with a synthetic phantom. A
signal-known exactly/background-known-statistically (SKE/BKS) setup was
considered. An ordered-subset expectation-maximization algorithm was used to
reconstruct images from data acquired in LM format, including the
scatter-window data, and including the energy attribute of each LM event. Using
a realistic 2-D SPECT system simulation, quantification tasks were performed on
the reconstructed images. The results demonstrated improved quantification
performance when LM data was used compared to binning the attributes in all the
conducted evaluation studies. Overall, we observed that LM data, including the
energy attribute, yielded improved performance on absolute quantification tasks
compared to binned data.


Abstract:
  In the last few years, the interest in knowledge bases has grown
exponentially in both the research community and the industry due to their
essential role in AI applications. Entity alignment is an important task for
enriching knowledge bases. This paper provides a comprehensive tutorial-type
survey on representative entity alignment techniques that use the new approach
of representation learning. We present a framework for capturing the key
characteristics of these techniques, propose two datasets to address the
limitation of existing benchmark datasets, and conduct extensive experiments
using the proposed datasets. The framework gives a clear picture of how the
techniques work. The experiments yield important results about the empirical
performance of the techniques and how various factors affect the performance.
One important observation not stressed by previous work is that techniques
making good use of attribute triples and relation predicates as features stand
out as winners.


Abstract:
  Micrometer sized alkane-in-water emulsion drops, stabilized by appropriate
long-chain surfactants, spontaneously break symmetry upon cooling and transform
consecutively into series of regular shapes (Denkov et al., Nature 2015, 528,
392). Two mechanisms were proposed to explain this phenomenon of drop
"self-shaping". One of these mechanisms assumes that thin layers of plastic
rotator phase form at the drop surface around the freezing temperature of the
oil. This mechanism has been supported by several indirect experimental
findings but direct structural characterization has not been reported so far.
We combine small- and wide-angle X-ray scattering (SAXS/WAXS) with optical
microscopy and DSC measurements of self-shaping drops in emulsions. In the
emulsions exhibiting drop self-shaping, the scattering spectra reveal the
formation of intermediate, metastable rotator phases in the alkane drops before
their crystallization. In addition, shells of rotator phase were observed to
form in hexadecane drops, stabilized by C16EO10 surfactant. This rotator phase
melts at ca. 16.6 {\deg}C which is significantly lower than the melting
temperature of crystalline hexadecane, 18 {\deg}C. The scattering results are
in a very good agreement with the complementary optical observations and DSC
measurements.


Abstract:
  We present a method that generalizes the periodic orbit dividing surface
construction for Hamiltonian systems with three or more degrees of freedom. We
construct a torus using as a basis a periodic orbit and we extend this to a
$2n-2$ dimensional object in the $2n-1$ dimensional energy surface. We present
our methods using benchmark examples for two and three degree of freedom
Hamiltonian systems to illustrate the corresponding algorithm for this
construction. Towards this end we use the normal form quadratic Hamiltonian
system with two and three degrees of freedom. We found that the periodic orbit
dividing surface can provide us the same dynamical information as the dividing
surface constructed using normally hyperbolic invariant manifolds. This is
significant because, in general, computations of normally hyperbolic invariant
manifolds are very difficult in Hamiltonian systems with three or more degrees
of freedom. However, our method avoids this computation and the only
information that we need is the location of one periodic orbit.


Abstract:
  We perform the maximal twist of eleven-dimensional supergravity. This twist
is partially topological and exists on manifolds of $G_2 \times SU(2)$
holonomy. Our derivation starts with an explicit description of the
Batalin-Vilkovisky complex associated to the three-form multiplet in the pure
spinor superfield formalism. We then determine the $L_\infty$ module structure
of the supersymmetry algebra on the component fields. We twist the theory by
modifying the differential of the Batalin-Vilkovisky complex to incorporate the
action of a scalar supercharge. We find that the resulting free twisted theory
is given by the tensor product of the de Rham and Dolbeault complexes of the
respective $G_2$ and $SU(2)$ holonomy manifolds as conjectured by Costello.


Abstract:
  A numerical method is developed to solve linear semi-infinite programming
problem (LSIP) in which the iterates produced by the algorithm are feasible for
the original problem. This is achieved by constructing a sequence of standard
linear programming problems with respect to the successive discretization of
the index set such that the approximate regions are included in the original
feasible region. The convergence of the approximate solutions to the solution
of the original problem is proved and the associated optimal objective function
values of the approximate problems are monotonically decreasing and converge to
the optimal value of LSIP. An adaptive refinement procedure is designed to
discretize the index set and update the constraints for the approximate
problem. Numerical experiments demonstrate the performance of the proposed
algorithm.


Abstract:
  Searches for the lepton number violating $K^{+} \rightarrow \pi^{-} \mu^{+}
e^{+}$ decay and the lepton flavour violating $K^{+} \rightarrow \pi^{+}
\mu^{-} e^{+}$ and $\pi^{0} \rightarrow \mu^{-} e^{+}$ decays are reported
using data collected by the NA62 experiment at CERN in $2017$-$2018$. No
evidence for these decays is found and upper limits of the branching ratios are
obtained at 90% confidence level:
$\mathcal{B}(K^{+}\rightarrow\pi^{-}\mu^{+}e^{+})<4.2\times 10^{-11}$,
$\mathcal{B}(K^{+}\rightarrow\pi^{+}\mu^{-}e^{+})<6.6\times10^{-11}$ and
$\mathcal{B}(\pi^{0}\rightarrow\mu^{-}e^{+})<3.2\times 10^{-10}$. These results
improve by one order of magnitude over previous results for these decay modes.


Abstract:
  The phenomenon of population interference, where a treatment assigned to one
experimental unit affects another experimental unit's outcome, has received
considerable attention in standard randomized experiments. The complications
produced by population interference in this setting are now readily recognized,
and partial remedies are well known. Much less understood is the impact of
population interference in panel experiments where treatment is sequentially
randomized in the population, and the outcomes are observed at each time step.
This paper proposes a general framework for studying population interference in
panel experiments and presents new finite population estimation and inference
results. Our findings suggest that, under mild assumptions, the addition of a
temporal dimension to an experiment alleviates some of the challenges of
population interference for certain estimands. In contrast, we show that the
presence of carryover effects -- that is, when past treatments may affect
future outcomes -- exacerbates the problem. Revisiting the special case of
standard experiments with population interference, we prove a central limit
theorem under weaker conditions than previous results in the literature and
highlight the trade-off between flexibility in the design and the interference
structure.


Abstract:
  One of the difficulties related to the COVID-19 pandemic is the shifting from
face-to-face to distance teaching. Both schools and universities had suddenly
to organize on-line lectures. To perform laboratory practice even in this
period, easily accessible materials, smartphones physics apps, on-line tools
and devices can be used. In this paper a method to measure the gravitational
acceleration studying the free falling body using Arduino board is presented.


Abstract:
  The presence of multiple talkers in the surrounding environment poses a
difficult challenge for real-time speech communication systems considering the
constraints on network size and complexity. In this paper, we present
Personalized PercepNet, a real-time speech enhancement model that separates a
target speaker from a noisy multi-talker mixture without compromising on
complexity of the recently proposed PercepNet. To enable speaker-dependent
speech enhancement, we first show how we can train a perceptually motivated
speaker embedder network to produce a representative embedding vector for the
given speaker. Personalized PercepNet uses the target speaker embedding as
additional information to pick out and enhance only the target speaker while
suppressing all other competing sounds. Our experiments show that the proposed
model significantly outperforms PercepNet and other baselines, both in terms of
objective speech enhancement metrics and human opinion scores.


Abstract:
  Composite quantum compounds (CQC) are classic example of quantum materials
which host more than one apparently distinct quantum phenomenon in physics.
Magnetism, topological superconductivity, Rashba physics etc. are few such
quantum phenomenon which are ubiquitously observed in several functional
materials and can co-exist in CQCs. In this letter, we use {\it ab-initio}
calculations to predict the co-existence of two incompatible phenomena, namely
topologically non-trivial Weyl semimetal and spin gapless semiconducting (SGS)
behavior, in a single crystalline system. SGS belong to a special class of
spintronics material which exhibit a unique band structure involving a
semiconducting state for one spin channel and a gapless state for the other. We
report such a SGS behavior in conjunction with the topologically non-trivial
multi-Weyl Fermions in MnPO$_4$. Interestingly, these Weyl nodes are located
very close to the Fermi level with the minimal trivial band density. A drumhead
like surface state originating from a nodal loop around Y-point in the
Brillouin zone is observed. A large value of the simulated anomalous Hall
conductivity (1265 $\Omega^{-1} cm^{-1}$) indirectly reflects the topological
non-trivial behavior of this compound. Such co-existent quantum phenomena are
not common in condensed matter systems and hence it opens up a fertile ground
to explore and achieve newer functional materials.


Abstract:
  We present the structural and magnetic properties of KNaCuP$_2$O$_7$
investigated via x-ray diffraction, magnetization, specific heat, and $^{31}$P
NMR and $^{23}$Na NMR measurements and complementary electronic structure
calculations. The temperature dependent magnetic susceptibility and $^{31}$P
NMR shift could be modeled very well by the uniform spin-$1/2$ Heisenberg
antiferromagnetic chain model with nearest-neighbour interaction $J/k_{\rm
B}\simeq 58.7$ K. The corresponding mapping using first principles electronic
structure calculations leads to $J^{\rm DFT}/k_{\rm B} \simeq 59$ K with
negligibly small inter-chain couplings ($J^{\prime}/k_{\rm B}$, $J^{\prime
\prime}/k_{\rm B} < 0.1$ K), further confirming that the system is indeed an
one-dimensional uniform spin-$1/2$ Heisenberg antiferromagnet. The
temperature-dependent unit cell volume could be described well using the Debye
approximation with a Debye temperature of $\Theta_{\rm D} \simeq 294$ K,
consistent with the heat capacity data. The diverging trend of the NMR
spin-lattice relaxation rates ($^{31}1/T_1$ and $^{23}1/T_1$) imply the onset
of a magnetic long-range-ordering at very low temperatures supporting the
anticipated $T_{\rm N} \simeq 0.38$ K from the inter-chain couplings. Moreover,
the NMR spin-lattice relaxation rates show the dominant contributions from
uniform ($q=0$) and staggered ($q = \pm \pi/a$) spin fluctuations in the high
and low temperature regimes, respectively mimicking one-dimensionality of the
spin-lattice. We have also demonstrated that $^{31}1/T_1$ in high temperatures
varies linearly with $1/\sqrt{H}$ reflecting the effect of spin diffusion on
the dynamic susceptibility. Further, the inter-chain frustration also
substantially impede the magnetic ordering rendering the spin-lattice a perfect
one-dimensional uniform spin-$1/2$ Heisenberg antiferromagnet over a wide
temperature range.


Abstract:
  Internet of Things (IoT) devices are becoming ubiquitous in our lives, with
applications spanning from the consumer domain to commercial and industrial
systems. The steep growth and vast adoption of IoT devices reinforce the
importance of sound and robust cybersecurity practices during the device
development life-cycles. IoT-related vulnerabilities, if successfully exploited
can affect, not only the device itself, but also the application field in which
the IoT device operates. Evidently, identifying and addressing every single
vulnerability is an arduous, if not impossible, task. Attack taxonomies can
assist in classifying attacks and their corresponding vulnerabilities. Security
countermeasures and best practices can then be leveraged to mitigate threats
and vulnerabilities before they emerge into catastrophic attacks and ensure
overall secure IoT operation. Therefore, in this paper, we provide an attack
taxonomy which takes into consideration the different layers of IoT stack,
i.e., device, infrastructure, communication, and service, and each layer's
designated characteristics which can be exploited by adversaries. Furthermore,
using nine real-world cybersecurity incidents, that had targeted IoT devices
deployed in the consumer, commercial, and industrial sectors, we describe the
IoT-related vulnerabilities, exploitation procedures, attacks, impacts, and
potential mitigation mechanisms and protection strategies. These (and many
other) incidents highlight the underlying security concerns of IoT systems and
demonstrate the potential attack impacts of such connected ecosystems, while
the proposed taxonomy provides a systematic procedure to categorize attacks
based on the affected layer and corresponding impact.


Abstract:
  Many deep learning based methods are designed to remove non-uniform
(spatially variant) motion blur caused by object motion and camera shake
without knowing the blur kernel. Some methods directly output the latent sharp
image in one stage, while others utilize a multi-stage strategy (\eg
multi-scale, multi-patch, or multi-temporal) to gradually restore the sharp
image. However, these methods have the following two main issues: 1) The
computational cost of multi-stage is high; 2) The same convolution kernel is
applied in different regions, which is not an ideal choice for non-uniform
blur. Hence, non-uniform motion deblurring is still a challenging and open
problem. In this paper, we propose a new architecture which consists of
multiple Atrous Spatial Pyramid Deformable Convolution (ASPDC) modules to
deblur an image end-to-end with more flexibility. Multiple ASPDC modules
implicitly learn the pixel-specific motion with different dilation rates in the
same layer to handle movements of different magnitude. To improve the training,
we also propose a reblurring network to map the deblurred output back to the
blurred input, which constrains the solution space. Our experimental results
show that the proposed method outperforms state-of-the-art methods on the
benchmark datasets.


Abstract:
  Constant function market makers (CFMMs) such as Uniswap, Balancer, Curve, and
mStable, among many others, make up some of the largest decentralized exchanges
on Ethereum and other blockchains. Because all transactions are public in
current implementations, a natural next question is if there exist similar
decentralized exchanges which are privacy-preserving; i.e., if a transaction's
quantities are hidden from the public view, then an adversary cannot correctly
reconstruct the traded quantities from other public information. In this note,
we show that privacy is impossible with the usual implementations of CFMMs
under most reasonable models of an adversary and provide some mitigating
strategies.


Abstract:
  Motivated by recent results of Corwin and Knizel on stationary measures for
the open KPZ equation on the spatial interval [0, 1], we study a pair of Markov
processes with Laplace transforms that have dual representations, with the
arguments of the Laplace transforms and the time parameters of the processes
swapped. Combined with the results of Corwin and Knizel, our formula identifies
the law of the stationary solutions for the open KPZ in terms of a Markov
process which is a Doob's h transform of the Brownian motion killed at an
exponential rate.


Abstract:
  The entropy principle shows that, for self-gravitating perfect fluid, the
Einstein field equations can be derived from the extrema of the total entropy,
and the thermodynamical stability criterion are equivalent to the dynamical
stability criterion. In this paper, we recast the dynamical criterion for the
charged self-gravitating perfect fluid in Einstein-Maxwell theory, and further
give the criterion of the star with barotropic condition. In order to obtain
the thermodynamical stability criterion, first we get the general formula of
the second variation of the total entropy for charged perfect fluid case, and
then obtain the thermodynamical criterion for radial perturbation. We show that
these two stability criterion are the same, which suggest that the inherent
connection between gravity and thermodynamic even when the electric field is
taken into account.


Abstract:
  In this paper, we first consider two scalar nonlocal diffusion problems with
a free boundary and a fixed boundary. We obtain the global existence,
uniqueness and longtime behaviour of solution of these two problems. The
spreading-vanishing dichotomy and sharp criteria for spreading and vanishing
are established. We also prove that accelerated spreading could happen if and
only if a threshold condition is violated by kernel function. Then we discuss a
classical Lotka-Volterra predator-prey model with nonlocal diffusions and a
free boundary which can be seen as nonlocal diffusion counterpart of the model
in the work of Wang (2014 J. Differential Equations \textbf{256}, 3365-3394).


Abstract:
  We provide a setting and a general approach to fair online learning with
stochastic sensitive and non-sensitive contexts. The setting is a repeated game
between the Player and Nature, where at each stage both pick actions based on
the contexts. Inspired by the notion of unawareness, we assume that the Player
can only access the non-sensitive context before making a decision, while we
discuss both cases of Nature accessing the sensitive contexts and Nature
unaware of the sensitive contexts. Adapting Blackwell's approachability theory
to handle the case of an unknown contexts' distribution, we provide a general
necessary and sufficient condition for learning objectives to be compatible
with some fairness constraints. This condition is instantiated on (group-wise)
no-regret and (group-wise) calibration objectives, and on demographic parity as
an additional constraint. When the objective is not compatible with the
constraint, the provided framework permits to characterise the optimal
trade-off between the two.


Abstract:
  In this paper, we propose a new deep neural network classifier that
simultaneously maximizes the inter-class separation and minimizes the
intra-class variation by using the polyhedral conic classification function.
The proposed method has one loss term that allows the margin maximization to
maximize the inter-class separation and another loss term that controls the
compactness of the class acceptance regions. Our proposed method has a nice
geometric interpretation using polyhedral conic function geometry. We tested
the proposed method on various visual classification problems including
closed/open set recognition and anomaly detection. The experimental results
show that the proposed method typically outperforms other state-of-the art
methods, and becomes a better choice compared to other tested methods
especially for open set recognition type problems.


Abstract:
  We embed natural inflation in an explict string theory model and derive
observables in cosmology. We achieve this by compactifying the type IIB string
on a Calabi-Yau orientifold, stabilizing moduli via the Large Volume Scenario,
and configuring axions using D7-brane stacks. In order to obtain a large
effective decay constant, we employ the Kim-Nilles-Peloso alignment mechanism,
with the required multiple axions arising naturally from anisotropic bulk
geometries. The bulk volumes, and hence the axion decay constants, are
stabilized by generalized one-loop corrections and subject to various
conditions: the K\"ahler cone condition on the string geometry; the convex hull
condition of the weak gravity conjecture; and the constraint from the power
spectrum of scalar perturbations. We find that all constraints can be satisfied
in a geometry with relatively small volume and thus heavy bulk axion mass. We
also covariantize the convex hull condition for the axion-dilaton-instanton
system and verify the normalization of the extremal bound.


Abstract:
  The authors of the article have reviewed the scientific literature on the
development of the Russian-Chinese cooperation in the field of combining
economic and logistics projects of the Eurasian Economic Union and the Silk
Road Economic Belt. The opinions of not only Russian, but also Chinese experts
on these projects are indicated, which provides the expansion of the vision of
the concept of the New Silk Road in both countries.


Abstract:
  [This paper was initially published in PHME conference in 2016, selected for
further publication in International Journal of Prognostics and Health
Management.]
  This paper describes an Autoregressive Partially-hidden Markov model (ARPHMM)
for fault detection and prognostics of equipments based on sensors' data. It is
a particular dynamic Bayesian network that allows to represent the dynamics of
a system by means of a Hidden Markov Model (HMM) and an autoregressive (AR)
process. The Markov chain assumes that the system is switching back and forth
between internal states while the AR process ensures a temporal coherence on
sensor measurements. A sound learning procedure of standard ARHMM based on
maximum likelihood allows to iteratively estimate all parameters
simultaneously. This paper suggests a modification of the learning procedure
considering that one may have prior knowledge about the structure which becomes
partially hidden. The integration of the prior is based on the Theory of
Weighted Distributions which is compatible with the Expectation-Maximization
algorithm in the sense that the convergence properties are still satisfied. We
show how to apply this model to estimate the remaining useful life based on
health indicators. The autoregressive parameters can indeed be used for
prediction while the latent structure can be used to get information about the
degradation level. The interest of the proposed method for prognostics and
health assessment is demonstrated on CMAPSS datasets.


Abstract:
  We study the limit behaviour of upper and lower bounds on expected time
averages in imprecise Markov chains; a generalised type of Markov chain where
the local dynamics, traditionally characterised by transition probabilities,
are now represented by sets of `plausible' transition probabilities. Our first
main result is a necessary and sufficient condition under which these upper and
lower bounds, called upper and lower expected time averages, will converge as
time progresses towards infinity to limit values that do not depend on the
process' initial state. Our condition is considerably weaker than that needed
for ergodic behaviour; a similar notion which demands that marginal upper and
lower expectations of functions at a single time instant converge to so-called
limit-or steady state-upper and lower expectations. For this reason, we refer
to our notion as `weak ergodicity'. Our second main result shows that, as far
as this weakly ergodic behaviour is concerned, one should not worry about which
type of independence assumption to adopt-epistemic irrelevance, complete
independence or repetition independence. The characterisation of weak
ergodicity as well as the limit values of upper and lower expected time
averages do not depend on such a choice. Notably, this type of robustness is
not exhibited by the notion of ergodicity and the related inferences of limit
upper and lower expectations. Finally, though limit upper and lower
expectations are often used to provide approximate information about the limit
behaviour of time averages, we show that such an approximation is sub-optimal
and that it can be significantly improved by directly using upper and lower
expected time averages.


Abstract:
  We consider a generalization of the recursive utility model by adding a new
component that represents utility of investment gains and losses. We also study
the utility process in this generalized model with constant elasticity of
intertemporal substitution and relative risk aversion degree, and with infinite
time horizon. In a specific, finite-state Markovian setting, we prove that the
utility process uniquely exists when the agent derives nonnegative gain-loss
utility, and that it can be non-existent or non-unique otherwise. Moreover, we
prove that the utility process, when it uniquely exists, can be computed by
starting from any initial guess and applying the recursive equation that
defines the utility process repeatedly. We then consider a portfolio selection
problem with gain-loss utility and solve it by proving that the corresponding
dynamic programming equation has a unique solution. Finally, we extend certain
previous results to the case in which the state space is infinite.


Abstract:
  A stochastic sewing lemma which is applicable for processes taking values in
Banach spaces is introduced. Applications to additive functionals of fractional
Brownian motion of distributional type are discussed.


Abstract:
  Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions
of state and object visual primitives seen during training. A problem with
standard CZSL is the assumption of knowing which unseen compositions will be
available at test time. In this work, we overcome this assumption operating on
the open world setting, where no limit is imposed on the compositional space at
test time, and the search space contains a large number of unseen compositions.
To address this problem, we propose a new approach, Compositional Cosine Graph
Embeddings (Co-CGE), based on two principles. First, Co-CGE models the
dependency between states, objects and their compositions through a graph
convolutional neural network. The graph propagates information from seen to
unseen concepts, improving their representations. Second, since not all unseen
compositions are equally feasible, and less feasible ones may damage the
learned representations, Co-CGE estimates a feasibility score for each unseen
composition, using the scores as margins in a cosine similarity-based loss and
as weights in the adjacency matrix of the graphs. Experiments show that our
approach achieves state-of-the-art performances in standard CZSL while
outperforming previous methods in the open world scenario.


Abstract:
  In this paper, we present a technique for balancing predictive relevance
models related to supervised modelling ligand biochemical activities to
biological targets. We train uncalibrated models employing conventional
supervised machine learning technique, namely Support Vector Machines.
Unfortunately, SVMs have a serious drawback. They are sensitive to imbalanced
datasets, outliers and high multicollinearity among training samples, which
could be a cause of preferencing one group over another. Thus, an additional
calibration could be required for balancing a predictive relevance of models.
As a technique for this balancing, we propose the Platt's scaling. The achieved
results were demonstrated on single-target models trained on datasets exported
from the ExCAPE database. Unlike traditional used machine techniques, we focus
on decreasing uncertainty employing deterministic solvers.


Abstract:
  We use symplectic self-dual additive codes over $\mathbb{F}_4$ obtained from
metacirculant graphs to construct, for the first time, $[[\ell, 0, d ]]$ qubit
codes with parameters $(\ell,d) \in \{(78, 20), (90, 21), (91, 22),
(93,21),(96,21)\}$. Secondary constructions applied to the qubit codes result
in many qubit codes that perform better than the previous best-known.


Abstract:
  The bidomain equations have been widely used to mathematically model the
electrical activity of the cardiac tissue. In this work, we present a potential
theory-based Cartesian grid method which is referred as the kernel-free
boundary integral (KFBI) method which works well on complex domains to
efficiently simulate the linear diffusion part of the bidomain equation. After
a proper temporal discretization, the KFBI method is applied to solve the
resulting homogeneous Neumann boundary value problems with a second-order
accuracy. According to the potential theory, the boundary integral equations
reformulated from the boundary value problems can be solved iteratively with
the simple Richardson iteration or the Krylov subspace iteration method. During
the iteration, the boundary and volume integrals are evaluated by limiting the
structured grid-based discrete solutions of the equivalent interface problems
at quasi-uniform interface nodes without the need to know the analytical
expression of Green's functions. In particular, the discrete linear system of
the equivalent interface problem obtained from the standard finite difference
schemes or the finite element schemes can be efficiently solved by fast
elliptic solvers such as the fast Fourier transform based solvers or those
based on geometric multigrid iterations after an appropriate modification at
the irregular grid nodes. Numerical results for solving the FitzHugh-Nagumo
bidomain equations in both two- and three-dimensional spaces are presented to
demonstrate the numerical performance of the KFBI method such as the
second-order accuracy and the propagation and scroll wave of the voltage
simulated on the real human left ventricle model.


Abstract:
  Beginning programmers struggle with the complex grammar of modern programming
languages like Java, and make lot of syntax errors. The diagnostic syntax error
messages from compilers and IDEs are sometimes useful, but often the messages
are cryptic and puzzling. Students could be helped, and instructors' time
saved, by automated repair suggestions when dealing with syntax errors. Large
samples of student errors and fixes are now available, offering the possibility
of data-driven machine-learning approaches to help students fix syntax errors.
Current machine-learning approaches do a reasonable job fixing syntax errors in
shorter programs, but don't work as well even for moderately longer programs.
We introduce SYNFIX, a machine-learning based tool that substantially improves
on the state-of-the-art, by learning to use compiler diagnostics, employing a
very large neural model that leverages unsupervised pre-training, and relying
on multi-label classification rather than autoregressive synthesis to generate
the (repaired) output. We describe SYNFIX's architecture in detail, and provide
a detailed evaluation. We have built SYNFIX into a free, open-source version of
Visual Studio Code; we make all our source code and models freely available.


Abstract:
  Multiple-input multiple-output (MIMO) is an enabling technology to meet the
growing demand for faster and more reliable communications in wireless networks
with a large number of terminals, but it can also be applied for position
estimation of a terminal exploiting multipath propagation from multiple
antennas. In this paper, we investigate new convolutional neural network (CNN)
structures for exploiting MIMO-based channel state information (CSI) to improve
indoor positioning. We evaluate and compare the performance of three variants
of the proposed CNN structure to five NN structures proposed in the scientific
literature using the same sets of training-evaluation data. The results
demonstrate that the proposed residual convolutional NN structure improves the
accuracy of position estimation and keeps the total number of weights lower
than the published NN structures. The proposed CNN structure yields from 2cm to
10cm better position accuracy than known NN structures used as a reference.


Abstract:
  In this paper, one uses a damped potential to present a description of the
running coupling constant of QCD in the confinement phase. Based on a
phenomenological perspective for the Debye screening length, one compares the
running coupling obtained here with both the Brodsky-de T\'eramond-Deur and the
Richardson approaches. The results seem to indicate the model introduced here
corroborate the Richardson approach. Moreover, the Debye screening mass in the
confinement phase depends on a small parameter, which tends to vanish in the
non-confinement phase of QCD.


Abstract:
  Measurements of single Higgs production and its decays are in good agreement
with the Standard Model. There is still room for large modifications in double
Higgs production at LHC, though these effects may be correlated with large
corrections to other observables, in particular single Higgs production. In
this work we address the issue of enhancing double Higgs production in the
presence of scalar leptoquarks while satisfying all experimental constraints.
We show at leading order that including more than one species of leptoquarks,
large cubic interactions with the Higgs can lead to sizable enhancement of
di-Higgs production cross section at LHC, while at the same time keeping other
Higgs observables and precision measurements under control. For masses above
800 GeV these corrections are in general below 30%, whereas in a viable
scenario in which one of the leptoquarks can be light, specifically in the mass
range $400-600$ GeV, we show that it is possible to roughly double the SM cross
section for di-Higgs production, implying that possible first hints of it may
be probed at the high luminosity LHC at $\mathcal{L}\sim 2$ ab$^{-1}$.


Abstract:
  We investigate a quantum non-relativistic system describing the interaction
of two particles with spin 1/2 and spin 0, respectively. Assuming that the
Hamiltonian is rotationally invariant and parity conserving we identify all
such systems which allow additional (pseudo)tensor integrals of motion that are
second order matrix polynomials in the momenta. Previously we found all the
(pseudo)scalar and (axial)vector integrals of motion. No non-obvious tensor
integrals exist. However, nontrivial pseudo-tensor integrals do exist. Together
with our earlier results we give a complete list of such superintegrable
Hamiltonian systems allowing second-order integrals of motion.


Abstract:
  Graph coloring is often used in parallelizing scientific computations that
run in distributed and multi-GPU environments; it identifies sets of
independent data that can be updated in parallel. Many algorithms exist for
graph coloring on a single GPU or in distributed memory, but to the best of our
knowledge, hybrid MPI+GPU algorithms have been unexplored until this work. We
present several MPI+GPU coloring approaches based on the distributed coloring
algorithms of Gebremedhin et al. and the shared-memory algorithms of Deveci et
al. . The on-node parallel coloring uses implementations in KokkosKernels,
  which provide parallelization for both multicore CPUs and GPUs. We further
extend our approaches to compute distance-2 and partial distance-2 colorings,
giving the first known distributed, multi-GPU algorithm for these problems. In
addition, we propose a novel heuristic to reduce communication for recoloring
in distributed graph coloring. Our experiments show that our approaches operate
efficiently on inputs too large to fit on a single GPU and scale up to graphs
with 76.7 billion edges running on 128 GPUs.


Abstract:
  It is often assumed that atoms are hard spheres in the estimation of local
lattice distortion (LLD) in high-entropy alloys (HEAs). However, our study
demonstrates that the hard sphere model misses the key effect, charge transfer
among atoms with different electronegativities, in the understanding of the
stabilization of severely-distorted HEAs. Through the characterization and
simulations of the local structure of the HfNbTiZr HEA, we found that the
charge transfer effect competes with LLD to significantly reduce the average
atomic-size mismatch. Our finding may form the basis for the design of severely
distorted, but stable HEAs.


Abstract:
  Byzantine fault-tolerant systems have been researched for more than four
decades, and although shown possible early, the solutions were impractical for
a long time. With PBFT the first practical solution was proposed in 1999 and
spawned new research which culminated in novel applications using it today.
Although the safety and liveness properties of PBFT-type protocols have been
rigorously analyzed, when it comes to practical performance only empirical
results - often in artificial settings - are known and imperfections on the
communication channels are not specifically considered. In this work we present
the first performance model for PBFT specifically considering the impact of
unreliable channels and the use of different transport protocols over them. We
also did extensive simulations to verify the model and to gain more insight on
the impact of deployment parameters on the overall transaction time. We show
that the usage of UDP can lead to significant speedup for PBFT protocols
compared to TCP when tuned accordingly even over lossy channels. Finally, we
compared the simulation to a real implementation and measure the benefits of a
developed improvement directly. We found that the impact on the design of the
network layer has been overlooked in the past but offers some additional room
for improvement when it comes to practical performance. In this work we are
focusing on the optimistic case with no node failures, as this is hopefully the
most relevant situation.


Abstract:
  The polarization of Cosmic Microwave Background (CMB) photons is rotated as
they pass through (ultralight-) axion string loops. Studying this birefringence
can reveal valuable information about the axion-photon coupling and the
structure of the string network. We develop an approximate analytic formalism
and identify a kernel function that can be used to calculate the two-point
correlation function for CMB birefringence induced by an arbitrary axion string
network. Using this formalism, we evaluate the birefringence signal for some
simple loop distributions (including scaling and network collapse). We find
that the angular correlation function has a characteristic angular scale set by
$\theta_\mathrm{min}$, which corresponds to the angular extent of the loops at
the time of recombination. This results in a peak in the birefringence power
spectrum around $\ell_p \sim 1/\theta_\mathrm{min}$. An additional scale,
controlled by the axion's mass, is introduced if the network collapses before
today.


Abstract:
  High-power and narrow-linewidth laser light is a vital tool for atomic
physics, being used for example in laser cooling and trapping and precision
spectroscopy. Here we produce Watt-level laser radiation at 457.49 nm and
460.86 nm of respective relevance for the cooling transitions of cadmium and
strontium atoms. This is achieved via the frequency doubling of a kHz-linewidth
vertical-external-cavity surface-emitting laser (VECSEL), which is based on a
novel gain chip design enabling lasing at > 2 W in the 915-928 nm region.
Following an additional doubling stage, spectroscopy of the $^1S_0\to{}^1P_1$
cadmium transition at 228.89 nm is performed on an atomic beam, with all the
transitions from all eight natural isotopes observed in a single continuous
sweep of more than 4 GHz in the deep ultraviolet. The absolute value of the
transition frequency of Cd-114 and the isotope shifts relative to this
transition are determined, with values for some of these shifts provided for
the first time


Abstract:
  Holographic acoustical tweezers (HAT) based on Archimedes-Fermat spiraling
InterDigitated Transducers (S-IDTs) are a versatile tool for the selective
manipulation of microparticles [Baudoin et. al., Sci. Adv., 5: eaav1967 (2019)]
and cells [Baudoin et. al., Nat. Commu., 11, 4244 (2020)] in a standard
microfluidic environment. These binary active holograms produce some focused
helical wave, with the ability to trap particles at the vortex core. Yet, all
the studies conducted with S-IDTs have so far been restricted to 2D
manipulation only. Here we show (i) that 3D radiation trap for microparticles
and cells can be obtained with spiraling tweezers with sufficiently large
aperture and (ii) that the particles can be displaced axially by simply tuning
the driving frequency, without any motion of the transducer. This work opens
perspectives for 3D cells and microparticles manipulation with single-beam
acoustical tweezers.