title:
Interspace Pruning: Using Adaptive Filter Representations to Improve
  Training of Sparse CNNs

abstract:
  Unstructured pruning is well suited to reduce the memory footprint of
convolutional neural networks (CNNs), both at training and inference time. CNNs
contain parameters arranged in $K \times K$ filters. Standard unstructured
pruning (SP) reduces the memory footprint of CNNs by setting filter elements to
zero, thereby specifying a fixed subspace that constrains the filter.
Especially if pruning is applied before or during training, this induces a
strong bias. To overcome this, we introduce interspace pruning (IP), a general
tool to improve existing pruning methods. It uses filters represented in a
dynamic interspace by linear combinations of an underlying adaptive filter
basis (FB). For IP, FB coefficients are set to zero while un-pruned
coefficients and FBs are trained jointly. In this work, we provide mathematical
evidence for IP's superior performance and demonstrate that IP outperforms SP
on all tested state-of-the-art unstructured pruning methods. Especially in
challenging situations, like pruning for ImageNet or pruning to high sparsity,
IP greatly exceeds SP with equal runtime and parameter costs. Finally, we show
that advances of IP are due to improved trainability and superior
generalization ability.


title: Image Quality Assessment for Magnetic Resonance Imaging

abstract:
  Image quality assessment (IQA) algorithms aim to reproduce the human's
perception of the image quality. The growing popularity of image enhancement,
generation, and recovery models instigated the development of many methods to
assess their performance. However, most IQA solutions are designed to predict
image quality in the general domain, with the applicability to specific areas,
such as medical imaging, remaining questionable. Moreover, the selection of
these IQA metrics for a specific task typically involves intentionally induced
distortions, such as manually added noise or artificial blurring; yet, the
chosen metrics are then used to judge the output of real-life computer vision
models. In this work, we aspire to fill these gaps by carrying out the most
extensive IQA evaluation study for Magnetic Resonance Imaging (MRI) to date
(14,700 subjective scores). We use outputs of neural network models trained to
solve problems relevant to MRI, including image reconstruction in the scan
acceleration, motion correction, and denoising. Seven trained radiologists
assess these distorted images, with their verdicts then correlated with 35
different image quality metrics (full-reference, no-reference, and
distribution-based metrics considered). Our emphasis is on reflecting the
radiologist's perception of the reconstructed images, gauging the most
diagnostically influential criteria for the quality of MRI scans:
signal-to-noise ratio, contrast-to-noise ratio, and the presence of artifacts.


title: On boundary properties of asymptotically holomorphic functions

abstract:
  We prove a Fatou type theorem for bounded functions with d_J -bar
differential of a controled growth on smoothly bounded domains in an almost
complex manifold.


title:
Global properties of eigenvalues of parametric rank one perturbations
  for unstructured and structured matrices II

abstract:
  We present in this note a correction to Theorem 17 in our earlier paper
"Global properties of eigenvalues of parametric rank one perturbations for
structured and unstructured matrices" and sharpen the estimates for eigenvalues
of parametric rank one perturbations given in that theorem.


title: Single-photon scattering in a giant-molecule waveguide-QED system

abstract:
  We study the coherent single-photon scattering in a one-dimensional waveguide
coupled to a giant artificial molecule consisting of two coupled giant atoms.
Since each giant atom couples to the waveguide via two coupling points, the
couplings of the molecule with the waveguide have three different coupling
configurations: the separated-, braided-, and nested-coupling cases. We obtain
the exact expressions of the single-photon transmission and reflection
amplitudes with the real-space approach. It is found that the behavior of the
scattering spectra depends on the phase shift between two neighboring coupling
points, the coupling configuration, and the coupling between the two giant
atoms. Concretely, we study the photon scattering in both the Markovian and
non-Markovian regimes, in which the photon propagating time between two
neighboring coupling points is neglected and considered, respectively. Under
the Markovian limit, the asymmetric Fano lineshapes in different coupling
configurations of the giant-molecule waveguide-QED system can be obtained by
choosing proper phase shift, and the transmission window can be adjusted by the
coupling strength between the two giant atoms in these three coupling
configurations. In particular, multiple reflection peaks and dips in these
configurations are revived in the non-Markovian regime. This work will pave the
way for the study of controllable single-photon devices based on the
giant-molecule waveguide-QED systems.


title: The optimal approximation of qubit states with limited quantum states

abstract:
  Measuring the closest distance between two states is an alternative and
significant approach in the resource quantification, which is the core task in
the resource theory. Quite limited progress has been made for this approach
even in simple systems due to the various potential complexities. Here we
analytically solve the optimal scheme to find out the closest distance between
the objective qubit state and all the possible states convexly mixed by some
limited states, namely, to optimally construct the objective qubit state using
the quantum states within any given state set. In particular, we find the least
number of (not more than four) states within a given set to optimally construct
the objective state and also find that any state can be optimally established
by at most four quantum states of the set. The examples in various cases are
presented to verify our analytic solutions further.


title: Competition-Level Code Generation with AlphaCode

abstract:
  Programming is a powerful and ubiquitous problem-solving tool. Developing
systems that can assist programmers or even generate programs independently
could make programming more productive and accessible, yet so far incorporating
innovations in AI has proven challenging. Recent large-scale language models
have demonstrated an impressive ability to generate code, and are now able to
complete simple programming tasks. However, these models still perform poorly
when evaluated on more complex, unseen problems that require problem-solving
skills beyond simply translating instructions into code. For example,
competitive programming problems which require an understanding of algorithms
and complex natural language remain extremely challenging. To address this gap,
we introduce AlphaCode, a system for code generation that can create novel
solutions to these problems that require deeper reasoning. In simulated
evaluations on recent programming competitions on the Codeforces platform,
AlphaCode achieved on average a ranking of top 54.3% in competitions with more
than 5,000 participants. We found that three key components were critical to
achieve good and reliable performance: (1) an extensive and clean competitive
programming dataset for training and evaluation, (2) large and
efficient-to-sample transformer-based architectures, and (3) large-scale model
sampling to explore the search space, followed by filtering based on program
behavior to a small set of submissions.


title:
Adversarial Counterfactual Augmentation: Application in Alzheimer's
  Disease Classification

abstract:
  Data augmentation has been widely used in deep learning to reduce
over-fitting and improve the robustness of models. However, traditional data
augmentation techniques, e.g., rotation, cropping, flipping, etc., do not
consider \textit{semantic} transformations, e.g., changing the age of a brain
image. Previous works tried to achieve semantic augmentation by generating
\textit{counterfactuals}, but they focused on how to train deep generative
models and randomly created counterfactuals with the generative models without
considering which counterfactuals are most \textit{effective} for improving
downstream training. Different from these approaches, in this work, we propose
a novel adversarial counterfactual augmentation scheme that aims to find the
most \textit{effective} counterfactuals to improve downstream tasks with a
pre-trained generative model. Specifically, we construct an adversarial game
where we update the input \textit{conditional factor} of the generator and the
downstream \textit{classifier} with gradient backpropagation alternatively and
iteratively. The key idea is to find conditional factors that can result in
\textit{hard} counterfactuals for the classifier. This can be viewed as finding
the `\textit{weakness}' of the classifier and purposely forcing it to
\textit{overcome} its weakness via the generative model. To demonstrate the
effectiveness of the proposed approach, we validate the method with the
classification of Alzheimer's Disease (AD) as the downstream task based on a
pre-trained brain ageing synthesis model. We show the proposed approach
improves test accuracy and can alleviate spurious correlations. Code will be
released upon acceptance.


title:
The best approximation of a given qubit state with the limited
  pure-state set

abstract:
  The preparation of quantum states lies at the foundation in the quantum
information processing. The convex mixing of some existing quantum states is
one of the effective candidate. In this paper, we mainly study how a target
quantum state can be optimally prepared by not more than three given pure
states. The analytic optimal distance based on the fidelity is found. We also
show that the preparation with more than four states can be essentially
converted to the case with not more than four states, which can be similarly
solved as the case with three states. The validity is illustrated by the
comparison of our analytical and numerical results.


title: Search for the QCD Critical Point in High Energy Nuclear Collisions

abstract:
  QCD critical point is a landmark region in the QCD phase diagram outlined by
temperature as a function of baryon chemical potential. To the right of this
second-order phase transition point, one expects first order quark-hadron phase
transition boundary, towards the left a crossover region, top of it lies the
quark gluon plasma phase and below it the hadronic phase. Hence locating the
QCD critical point through relativistic heavy-ion collision experiments is an
active area of research. Cumulants of conserved quantities in strong
interaction, such as net-baryon, net-charge, and net-strangeness, are suggested
to be sensitive to the physics of QCD critical point and are therefore useful
observables in the study of the phase transition between quark-gluon plasma and
hadronic matter. We review the experimental status of the search for the QCD
critical point via the measurements of cumulants of net-particle distributions
in heavy ion collisions. We discuss various experimental challenges and
associated corrections in such fluctuation measurements. We also comment on the
physics implications of the measurements by comparing them with theoretical
calculations. This is followed by a discussion on future experiments and
measurements related to high baryonic density QCD matter.


title: CHANG-ES XXV: HI Imaging of Nearby Edge-on Galaxies -- Data Release 4

abstract:
  We present the HI distribution of galaxies from the Continuum Halos in Nearby
Galaxies - an EVLA Survey (CHANG-ES). Though the observational mode was not
optimized for detecting HI, we successfully produce HI cubes for 19 galaxies.
The moment-0 maps from this work are available on CHANG-ES data release
website, i.e., https://www.queensu.ca/changes. Our sample is dominated by
star-forming, HI-rich galaxies at distances from 6.27 to 34.1 Mpc. HI
interferometric images on two of these galaxies (NGC 5792 and UGC 10288) are
presented here for the first time, while 12 of our remaining sample galaxies
now have better HI spatial resolutions and/or sensitivities of intensity maps
than those in existing publications. We characterize the average scale heights
of the HI distributions for a subset of most inclined galaxies (inclination >
80 deg), and compare them to the radio continuum intensity scale heights, which
have been derived in a similar way. The two types of scale heights are well
correlated, with similar dependence on disk radial extension and star formation
rate surface density but different dependence on mass surface density. This
result indicates that the vertical distribution of the two components may be
governed by similar fundamental physics but with subtle differences.


title: The generalized X-join of Cayley graphs

abstract:
  As a main result of this paper we give conditions under which the generalized
$X$-join of Cayley graphs is a Cayley graph. In particular, we show that
$X$-join of isomorphic Cayley graphs is a Cayley grpah. To do this, new
properties for a generalized wreath product of permutation groups are given in
the case where the base group acts regularly. These are used to give conditions
for the generalized wreath product to contain a regular subgroup, which are
then applied to generalized $X$-joins of Cayley graphs. Along the way, it is
shown that the generalized $X$-join of isomorphic Cayley graphs will always be
a vertex transitive graph.


title:
DFT-FE 1.0: A massively parallel hybrid CPU-GPU density functional
  theory code using finite-element discretization

abstract:
  We present DFT-FE 1.0, building on DFT-FE 0.6 [Comput. Phys. Commun. 246,
106853 (2020)], to conduct fast and accurate large-scale density functional
theory (DFT) calculations (reaching ~ $100,000$ electrons) on both many-core
CPU and hybrid CPU-GPU computing architectures. This work involves improvements
in the real-space formulation -- via an improved treatment of the electrostatic
interactions that substantially enhances the computational efficiency -- as
well high-performance computing aspects, including the GPU acceleration of all
the key compute kernels in DFT-FE. We demonstrate the accuracy by comparing the
ground-state energies, ionic forces and cell stresses on a wide-range of
benchmark systems against those obtained from widely used DFT codes. Further,
we demonstrate the numerical efficiency of our implementation, which yields
$\sim 20 \times$ CPU-GPU speed-up by using GPU acceleration on hybrid CPU-GPU
nodes. Notably, owing to the parallel-scaling of the GPU implementation, we
obtain wall-times of $80-140$ seconds for full ground-state calculations, with
stringent accuracy, on benchmark systems containing ~ $6,000-15,000$ electrons.


title:
Wiener-Hopf factorization indices of rational matrix functions with
  respect to the unit circle in terms of realization

abstract:
  As in the paper [G. Groenewald, M.A. Kaashoek, A.C.M. Ran, Wiener-Hopf
indices of unitary functions on the unit circle in terms of realizations and
related results on Toeplitz operators. \emph{Indag. Math.} 28 (2017) 694--710]
our aim is to obtain explicitly the Wiener-Hopf indices of a rational $m\times
m$ matrix function $R(z)$ that has no poles and no zeros on the unit circle
$\mathbb{T}$ but, in contrast with that paper, the function $R(z)$ is not
required to be unitary on the unit circle. On the other hand, using a
Douglas-Shapiro-Shields type of factorization, we show that $R(z)$ factors as
$R(z)=\Xi(z)\Psi(z)$, where $\Xi(z)$ and $\Psi(z)$ are rational $m\times m$
matrix functions, $\Xi(z)$ is unitary on the unit circle and $\Psi(z)$ is an
invertible outer function. Furthermore, the fact that $\Xi(z)$ is unitary on
the unit circle allows us to factor as $\Xi(z) =V(z)W^*(z)$ where $V(z)$ and
$W(z)$ are rational bi-inner $m\times m$ matrix functions. The latter allows us
to solve the Wiener-Hopf indices problem. To derive explicit formulas for the
functions $V(z)$ and $W(z)$ requires additional realization properties of the
function $\Xi(z)$ which are given in the last two sections.


title: On exchange matrices from string diagrams

abstract:
  Inspired by Fock-Goncharov's amalgamation procedure
\cite{Fock-Goncharov-2006}, Shen-Weng introduced string diagrams in
\cite{Shen-Weng-2021}, which are very useful to describe many interesting
skew-symmetrizable matrices closely related with Lie theory. In this paper, we
prove that the skew-symmetrizable matrices from string diagrams are in the
smallest class $\mathcal P^\prime$ of skew-symmetrizable matrices containing
the $1\times 1$ zero matrix and closed under mutations and source-sink
extensions. This result applies to the exchange matrices of cluster algebras
from double Bruhat cells, unipotent cells, double Bott-Samelson cells and so
on.
  Our main result can be used to explain why many skew-symmetrizable matrices
from Lie theory have reddening sequences. It can be also used to prove some
interesting results regarding non-degenerate potentials on many quivers from
Lie theory.


title:
Activated hopping transport in nematic conducting aerogel at low
  temperatures

abstract:
  The transport properties of nematic aerogels, which consist of highly
oriented Al$_2$O$_3\cdot$SiO$_2$ nanofibers coated with a graphene shell with a
large number of defects, are studied. The temperature dependences of the
electrical resistivity in the range of 9-40K strictly follow the formula
derived to describe the variable range hopping (VRH) conductivity, in which
exponent $\alpha$ changes from 0.4 to 0.9 when the number of layers in the
graphene shell decreases from 4-6 to 1-2. The dependence of $\alpha$ on the
shell thickness can be explained by a simultaneous change in the dimensionality
of hopping transport and the character of the energy dependence of the density
of localized states near the Fermi level. The fact that $\alpha$ approaches
unity at the minimum graphene shell thickness indicates a gradual transition
from VRH transport to nearest neighbor hopping (NNH) transport. The
magnetoresistance measured at T = 4.2 K is negative, increases significantly
with decreasing graphene shell thickness, and is approximated by a formula for
the case of weak localization with a good accuracy. The phase coherence lengths
are in a reasonable relation with the graphene grain sizes. The conducting
aerogels under study complement the well-known set of materials that exhibit
hopping electron transport at low temperatures, which is characteristic of
media with strong carrier localization, and also a negative magnetoresistance,
which usually manifests itself under weak localization conditions.


title:
SISL:Self-Supervised Image Signature Learning for Splicing Detection and
  Localization

abstract:
  Recent algorithms for image manipulation detection almost exclusively use
deep network models. These approaches require either dense pixelwise
groundtruth masks, camera ids, or image metadata to train the networks. On one
hand, constructing a training set to represent the countless tampering
possibilities is impractical. On the other hand, social media platforms or
commercial applications are often constrained to remove camera ids as well as
metadata from images. A self-supervised algorithm for training manipulation
detection models without dense groundtruth or camera/image metadata would be
extremely useful for many forensics applications. In this paper, we propose
self-supervised approach for training splicing detection/localization models
from frequency transforms of images. To identify the spliced regions, our deep
network learns a representation to capture an image specific signature by
enforcing (image) self consistency . We experimentally demonstrate that our
proposed model can yield similar or better performances of multiple existing
methods on standard datasets without relying on labels or metadata.


title:
SPA-VAE: Similar-Parts-Assignment for Unsupervised 3D Point Cloud
  Generation

abstract:
  This paper addresses the problem of unsupervised parts-aware point cloud
generation with learned parts-based self-similarity. Our SPA-VAE infers a set
of latent canonical candidate shapes for any given object, along with a set of
rigid body transformations for each such candidate shape to one or more
locations within the assembled object. In this way, noisy samples on the
surface of, say, each leg of a table, are effectively combined to estimate a
single leg prototype. When parts-based self-similarity exists in the raw data,
sharing data among parts in this way confers numerous advantages: modeling
accuracy, appropriately self-similar generative outputs, precise in-filling of
occlusions, and model parsimony. SPA-VAE is trained end-to-end using a
variational Bayesian approach which uses the Gumbel-softmax trick for the
shared part assignments, along with various novel losses to provide appropriate
inductive biases. Quantitative and qualitative analyses on ShapeNet demonstrate
the advantage of SPA-VAE.


title:
Discrete approximations to Dirac operators and norm resolvent
  convergence

abstract:
  We consider continuous Dirac operators defined on $\mathbf{R}^d$,
$d\in\{1,2,3\}$, together with various discrete versions of them. Both
forward-backward and symmetric finite differences are used as approximations to
partial derivatives. We also allow a bounded, H\"older continuous, and
self-adjoint matrix-valued potential, which in the discrete setting is
evaluated on the mesh.
  Our main goal is to investigate whether the proposed discrete models converge
in norm resolvent sense to their continuous counterparts, as the mesh size
tends to zero and up to a natural embedding of the discrete space into the
continuous one. In dimension one we show that forward-backward differences lead
to norm resolvent convergence, while in dimension two and three they do
\textit{not}. The same negative result holds in all dimensions when symmetric
differences are used. On the other hand, strong resolvent convergence holds in
all these cases. Nevertheless, and quite remarkably, a rather simple but
non-standard modification to the discrete models, involving the mass term,
ensures norm resolvent convergence in general.


title:
Structure and production mechanism of the enigmatic $X(3872)$ in
  high-energy hadronic reactions

abstract:
  We calculate the total cross section and transverse momentum distributions
for the production of enigmatic $\chi_{c,1}(3872)$ (or X(3872)) assuming
different scenarios: $c \bar c$ state and $D^{0*} {\bar D}^0 + D^0 {\bar
D}^{0*}$ molecule. The derivative of the $c \bar c$ wave function needed in the
first scenario is taken from a potential model calculations. Compared to
earlier calculation of molecular state we include not only single parton
scattering (SPS) but also double parton scattering (DPS) contribution. The
latter one seems to give smaller contribution than the SPS one. The upper limit
for the DPS production of $\chi_{c,1}(3872)$ is much below the CMS data. We
compare results of our calculations with existing experimental data of CMS,
ATLAS and LHCb collaborations. Reasonable cross sections can be obtained in
either $c \bar c$ or molecular $D {\bar D}^*$ scenarios for $X(3872)$. Also a
hybrid scenario is not excluded.


title:
Do BERTs Learn to Use Browser User Interface? Exploring Multi-Step Tasks
  with Unified Vision-and-Language BERTs

abstract:
  Pre-trained Transformers are good foundations for unified multi-task models
owing to their task-agnostic representation. Pre-trained Transformers are often
combined with text-to-text framework to execute multiple tasks by a single
model. Performing a task through a graphical user interface (GUI) is another
candidate to accommodate various tasks, including multi-step tasks with vision
and language inputs. However, few papers combine pre-trained Transformers with
performing through GUI. To fill this gap, we explore a framework in which a
model performs a task by manipulating the GUI implemented with web pages in
multiple steps. We develop task pages with and without page transitions and
propose a BERT extension for the framework. We jointly trained our BERT
extension with those task pages, and made the following observations. (1) The
model learned to use both task pages with and without page transition. (2) In
four out of five tasks without page transitions, the model performs greater
than 75% of the performance of the original BERT, which does not use browsers.
(3) The model did not generalize effectively on unseen tasks. These results
suggest that we can fine-tune BERTs to multi-step tasks through GUIs, and there
is room for improvement in their generalizability. Code will be available
online.


title: Majorana-like Coulomb spectroscopy in the absence of zero bias peaks

abstract:
  Hybrid semiconductor-superconductor devices hold great promise for realizing
topological quantum computing with Majorana zero modes. However, multiple
claims of Majorana detection, based on either tunneling or Coulomb
spectroscopy, remain disputed. Here we devise an experimental protocol that
allows to perform both types of measurements on the same hybrid island by
adjusting its charging energy via tunable junctions to the normal leads. This
method reduces ambiguities of Majorana detections by checking the consistency
between Coulomb spectroscopy and zero bias peaks in non-blockaded transport.
Specifically we observe junction-dependent, even-odd modulated, single-electron
Coulomb peaks without concomitant low-bias peaks in tunneling spectroscopy. We
provide a theoretical interpretation of the experimental observations in terms
of low-energy longitudinally-confined island states rather than overlapping
Majorana modes. Our method highlights the importance of combined measurements
on the same device for the identification of topological Majorana bound states.


title:
A Survey of fault models and fault tolerance methods for 2D bus-based
  multi-core systems and TSV based 3D NOC many-core systems

abstract:
  Reliability has taken centre stage in the development of high-performance
computing processors. A Surge of interest is noticeable in recent times in
formulating fault and failure models, understanding failure mechanism and
strategizing fault mitigation methods for improving the reliability of the
system. The article presents a congregation of concepts illustrated one after
the other for a better understanding of damages caused by radiation, relevant
fault models, and effects of faults. We examine the state of art fault
mitigation techniques at the logical layer for digital CMOS based design and
SRAM based FPGA. CMOS SRAM structure is the same for both digital CMOS and
FPGA. Understanding of resilient SRAM based FPGA is necessary for developing
resilient prototypes and it facilitates a faster integration of digital CMOS
designs. At the micro-architectural and architectural layer, error detection
and recovery methods are discussed for bus-based multi-core systems. The
Through silicon via based 3D Network on chip is the prospective solution for
integrating many cores on single die. A suitable interconnection approach for
petascale computing on many-core systems. The article presents an elaborate
discussion on fault models, failure mechanisms, resilient 3D routers, defect
tolerance methods for the TSV based 3D NOC many-core systems. Core redundancy,
self-diagnosis and distributed diagnosis at the hardware level are examined for
many-core systems. The article presents a gamut of fault tolerance solutions
from logic level to processor core level in a multi-core and many-core
scenario.


title: Graph Neural Network Sensitivity Under Probabilistic Error Model

abstract:
  Graph convolutional networks (GCNs) can successfully learn the graph signal
representation by graph convolution. The graph convolution depends on the graph
filter, which contains the topological dependency of data and propagates data
features. However, the estimation errors in the propagation matrix (e.g., the
adjacency matrix) can have a significant impact on graph filters and GCNs. In
this paper, we study the effect of a probabilistic graph error model on the
performance of the GCNs. We prove that the adjacency matrix under the error
model is bounded by a function of graph size and error probability. We further
analytically specify the upper bound of a normalized adjacency matrix with
self-loop added. Finally, we illustrate the error bounds by running experiments
on a synthetic dataset and study the sensitivity of a simple GCN under this
probabilistic error model on accuracy.


title: Learning to Infer Belief Embedded Communication

abstract:
  In multi-agent collaboration problems with communication, an agent's ability
to encode their intention and interpret other agents' strategies is critical
for planning their future actions. This paper introduces a novel algorithm
called Intention Embedded Communication (IEC) to mimic an agent's language
learning ability. IEC contains a perception module for decoding other agents'
intentions in response to their past actions. It also includes a language
generation module for learning implicit grammar during communication with two
or more agents. Such grammar, by construction, should be compact for efficient
communication. Both modules undergo conjoint evolution - similar to an infant's
babbling that enables it to learn a language of choice by trial and error. We
utilised three multi-agent environments, namely predator/prey, traffic junction
and level-based foraging and illustrate that such a co-evolution enables us to
learn much quicker (50%) than state-of-the-art algorithms like MADDPG. Ablation
studies further show that disabling the inferring belief module, communication
module, and the hidden states reduces the model performance by 38%, 60% and
30%, respectively. Hence, we suggest that modelling other agents' behaviour
accelerates another agent to learn grammar and develop a language to
communicate efficiently. We evaluate our method on a set of cooperative
scenarios and show its superior performance to other multi-agent baselines. We
also demonstrate that it is essential for agents to reason about others' states
and learn this ability by continuous communication.


title:
One hundred years ago Alfred Land\'e unriddled the Anomalous Zeeman
  Effect and presaged Electron Spin

abstract:
  In order to commemorate Alfred Land\'e's unriddling of the anomalous Zeeman
Effect a century ago, we reconstruct his seminal contribution to atomic physics
in light of the atomic models available at the time. Land\'e recognized that
the coupling of quantized electronic angular momenta via their vector addition
within an atom was the origin of all the apparent mysteries of atomic structure
as manifested by the anomalous Zeeman effect. We show to which extent Land\'e's
ideas influenced the development of quantum physics, particularly Wolfgang
Pauli's path to the exclusion principle. We conclude with Land\'e's brief
biography.


title:
Scale Free Avalanches in Excitatory-Inhibitory Populations of Spiking
  Neurons with Conductance Based Synaptic Currents

abstract:
  We investigate spontaneous critical dynamics of excitatory and inhibitory
(EI) sparsely connected populations of spiking leaky integrate-and-fire neurons
with conductance-based synapses. We use a bottom-up approach to derive a single
neuron gain function and a linear Poisson neuron approximation which we use to
study mean-field dynamics of the EI population and its bifurcations. In the low
firing rate regime, the quiescent state loses stability due to saddle-node or
Hopf bifurcations. In particular, at the Bogdanov-Takens (BT) bifurcation point
which is the intersection of the Hopf bifurcation and the saddle-node
bifurcation lines of the 2D dynamical system, the network shows avalanche
dynamics with power-law avalanche size and duration distributions. This matches
the characteristics of low firing spontaneous activity in the cortex. By
linearizing gain functions and excitatory and inhibitory nullclines, we can
approximate the location of the BT bifurcation point. This point in the control
parameter phase space corresponds to the internal balance of excitation and
inhibition and a slight excess of external excitatory input to the excitatory
population. Due to the tight balance of average excitation and inhibition
currents, the firing of the individual cells is fluctuation-driven. Around the
BT point, the spiking of neurons is a Poisson process and the population
average membrane potential of neurons is approximately at the middle of the
operating interval $[V_{Rest}, V_{th}]$. Moreover, the EI network is close to
both oscillatory and active-inactive phase transition regimes.


title:
Trustworthy Deep Learning via Proper Calibration Errors: A Unifying
  Approach for Quantifying the Reliability of Predictive Uncertainty

abstract:
  With model trustworthiness being crucial for sensitive real-world
applications, practitioners are putting more and more focus on evaluating deep
neural networks in terms of uncertainty calibration. Calibration errors are
designed to quantify the reliability of probabilistic predictions but their
estimators are usually biased and inconsistent. In this work, we introduce the
framework of proper calibration errors, which relates every calibration error
to a proper score and provides a respective upper bound with optimal estimation
properties. This upper bound allows us to reliably estimate the calibration
improvement of any injective recalibration method in an unbiased manner. We
demonstrate that, in contrast to our approach, the most commonly used
estimators are substantially biased with respect to the true improvement of
recalibration methods.


title: Graph Pre-training for AMR Parsing and Generation

abstract:
  Abstract meaning representation (AMR) highlights the core semantic
information of text in a graph structure. Recently, pre-trained language models
(PLMs) have advanced tasks of AMR parsing and AMR-to-text generation,
respectively. However, PLMs are typically pre-trained on textual data, thus are
sub-optimal for modeling structural knowledge. To this end, we investigate
graph self-supervised training to improve the structure awareness of PLMs over
AMR graphs. In particular, we introduce two graph auto-encoding strategies for
graph-to-graph pre-training and four tasks to integrate text and graph
information during pre-training. We further design a unified framework to
bridge the gap between pre-training and fine-tuning tasks. Experiments on both
AMR parsing and AMR-to-text generation show the superiority of our model. To
our knowledge, we are the first to consider pre-training on semantic graphs.


title:
Pose-MUM : Reinforcing Key Points Relationship for Semi-Supervised Human
  Pose Estimation

abstract:
  A well-designed strong-weak augmentation strategy and the stable teacher to
generate reliable pseudo labels are essential in the teacher-student framework
of semi-supervised learning (SSL). Considering these in mind, to suit the
semi-supervised human pose estimation (SSHPE) task, we propose a novel approach
referred to as Pose-MUM that modifies Mix/UnMix (MUM) augmentation. Like MUM in
the dense prediction task, the proposed Pose-MUM makes strong-weak augmentation
for pose estimation and leads the network to learn the relationship between
each human key point much better than the conventional methods by adding the
mixing process in intermediate layers in a stochastic manner. In addition, we
employ the exponential-moving-average-normalization (EMAN) teacher, which is
stable and well-suited to the SSL framework and furthermore boosts the
performance. Extensive experiments on MS-COCO dataset show the superiority of
our proposed method by consistently improving the performance over the previous
methods following SSHPE benchmark.


title: Stability of and conduction in single-walled Si$_2$BN nanotubes

abstract:
  We explore the possibility and potential benefit of rolling a Si2BN sheet
into single-walled nanotubes (NTs). Using density functional theory (DFT), we
consider both structural stability and the impact on the nature of chemical
bonding and conduction. The structure is similar to carbon NTs and hexagonal
boron-nitride (hBN) NTs and we consider both armchair and zigzag Si2BN
configurations with varying diameters. The stability of these Si$_2$BN NTs is
confirmed by first-principles molecular dynamics calculations, by an exothermal
formation, an absence of imaginary modes in the phonon spectra. Also, we find
the nature of conduction varies semiconducting, from semi-metallic to metallic,
reflecting differences in armchair/zigzag-type structures, curvature effects,
and the effect of quantum confinement. We present the detailed characterization
of how these properties lead to differences in both the bonding nature and
electronic structures


title:
Comment on "HADOKEN: An open-source software package for predicting
  electron confinement effects in various nanowire geometries and
  configurations"

abstract:
  In a recent work [C. Chevalier, B. M. Wong, Comput. Phys. Commun. ${\bf
274}$, 108299 (2022); arXiv: 2203.05233 [cond-mat.mes-hall]] the interesting
and popular problem was considered. Authors attempted to solve the
self-consistent Schr\"{o}dinger-Poisson problem for an effective mass electron
in a core-shell semiconductor nanowire. The corresponding MATLAB-based software
package was presented. However, an incorrect solution of the Schr\"{o}dinger
equation invalidates the whole result. Here we point out the corresponding
error and possible ways to fix it.


title: Optimizing microservices with hyperparameter optimization

abstract:
  In the last few years, the cloudification of applications requires new
concepts and techniques to fully reap the benefits of the new computing
paradigm. Among them, the microservices architectural style, which is inspired
by service-oriented architectures, has gained attention from both industry and
academia. However, decomposing a monolith into multiple microservices also
creates several challenges across the application's lifecycle. In this work, we
focus on the operation aspect of microservices, and present our novel proposal
to enable self-optimizing microservices systems based on grid search and random
search techniques. The initial results show our approach is able to optimize
the latency performance of microservices to up to 10.56\%.


title:
Self Organized Criticality in a Mesoscopic Model of
  Excitatory-Inhibitory Neuronal Populations by Short-term and Long-term
  Synaptic Plasticity

abstract:
  In [1], we have shown that the dynamics of an interconnected population of
excitatory and inhibitory spiking neurons wandering around a Bogdanov-Takens
(BT)bifurcation point can generate the observed scale-free avalanches at the
population level and the highly variable spike patterns of individual neurons.
These characteristics match experimental findings for spontaneous intrinsic
activity in the brain. In this paper, we address the mechanisms causing the
system to get and remain near this BT point. We propose an effective stochastic
neural field model which captures the dynamics of the mean-field model. We show
how the network tunes itself through local long-term synaptic plasticity by
STDP and short-term synaptic depression to be close to this bifurcation point.
The mesoscopic model that we derive matches the directed percolation model at
the absorbing state phase transition.


title:
Static negative susceptibility in ferromagnetic material induced by
  domain wall motion: an aspect of superconductor state

abstract:
  Domain wall motion in magnetic materiel induces the negative susceptibility
leading to a perfect diamagnetism state. The local susceptibility is calculated
by the derivative of magnetization ($\vec{M}$) vector w.r.t. magnetic field
strength ($\vec{H}$) vector. In the transient region from the upward domain to
the downward domain (domain wall width), local $\vec{M}$ and $\vec{H}$ vectors
exhibit opposite slopes, which leads to a negative susceptibility value. A
negative susceptibility value induces the diamagnetism effect leading to a
relative permeability value $<$ 1 $\left(\mu _r < 1\right)$. This diamagnetism
sate originates due to the domain wall motion, which is an entirely different
mechanism from the electron motion's induced diamagnetism. Furthermore, the
strength of the diamagnetism state can be enhanced by tuning the gradient
energy of a domain that may correspond to a perfect diamagnetism state
$\left(\chi _v \approx -1 \Rightarrow \mu _r \rightarrow 0\right)$. Besides, we
believe that there may be a possibility of sustaining such a diamagnetic state
(domain wall induced) in a ferromagnetic material that is utterly contradictory
to the conventional theory.


title:
Locally refined quad meshing for linear elasticity problems based on
  convolutional neural networks

abstract:
  In this paper we propose a method to generate suitably refined finite element
meshes using neural networks. As a model problem we consider a linear
elasticity problem on a planar domain (possibly with holes) having a polygonal
boundary. We impose boundary conditions by fixing the position of a part of the
boundary and applying a force on another part of the boundary. The resulting
displacement and distribution of stresses depend on the geometry of the domain
and on the boundary conditions. When applying a standard Galerkin
discretization using quadrilateral finite elements, one usually has to perform
adaptive refinement to properly resolve maxima of the stress distribution. Such
an adaptive scheme requires a local error estimator and a corresponding local
refinement strategy. The overall costs of such a strategy are high. We propose
to reduce the costs of obtaining a suitable discretization by training a neural
network whose evaluation replaces this adaptive refinement procedure. We set up
a single network for a large class of possible domains and boundary conditions
and not on a single domain of interest. The computational domain and boundary
conditions are interpreted as images, which are suitable inputs for convolution
neural networks. We use the U-net architecture and we devise training
strategies by dividing the possible inputs into different categories based on
their overall geometric complexity. Thus, we compare different training
strategies based on varying geometric complexity. One of the advantages of the
proposed approach is the interpretation of input and output as images, which do
not depend on the underlying discretization scheme. Another is the
generalizability and geometric flexibility. The network can be applied to
previously unseen geometries, even with different topology and level of detail.
Thus, training can easily be extended to other classes of geometries.


title:
What is the best RNN-cell structure for forecasting each time series
  behavior?

abstract:
  It is unquestionable that time series forecasting is of paramount importance
in many fields. The most used machine learning models to address time series
forecasting tasks are Recurrent Neural Networks (RNNs). Typically, those models
are built using one of the three most popular cells, ELMAN, Long-Short Term
Memory (LSTM), or Gated Recurrent Unit (GRU) cells, each cell has a different
structure and implies a different computational cost. However, it is not clear
why and when to use each RNN-cell structure. Actually, there is no
comprehensive characterization of all the possible time series behaviors and no
guidance on what RNN cell structure is the most suitable for each behavior. The
objective of this study is two-fold: it presents a comprehensive taxonomy of
all-time series behaviors (deterministic, random-walk, nonlinear, long-memory,
and chaotic), and provides insights into the best RNN cell structure for each
time series behavior. We conducted two experiments: (1) The first experiment
evaluates and analyzes the role of each component in the LSTM-Vanilla cell by
creating 11 variants based on one alteration in its basic architecture
(removing, adding, or substituting one cell component). (2) The second
experiment evaluates and analyzes the performance of 20 possible RNN-cell
structures. Our results showed that the MGU-SLIM3 cell is the most recommended
for deterministic and nonlinear behaviors, the MGU-SLIM2 cell is the most
suitable for random-walk behavior, FB1 cell is advocated for long-memory
behavior, and LSTM-SLIM1 for chaotic behavior.


title:
Bamboo: Building Mega-Scale Vision Dataset Continually with
  Human-Machine Synergy

abstract:
  Large-scale datasets play a vital role in computer vision. Existing datasets
are either collected according to heuristic label systems or annotated blindly
without differentiation to samples, making them inefficient and unscalable. How
to systematically collect, annotate and build a mega-scale dataset remains an
open question. In this work, we advocate building a high-quality vision dataset
actively and continually on a comprehensive label system. Specifically, we
contribute Bamboo Dataset, a mega-scale and information-dense dataset for both
classification and detection. Bamboo aims to populate the comprehensive
categories with 69M image classification annotations and 170,586 object
bounding box annotations. Compared to ImageNet22K and Objects365, models
pre-trained on Bamboo achieve superior performance among various downstream
tasks (6.2% gains on classification and 2.1% gains on detection). In addition,
we provide valuable observations regarding large-scale pre-training from over
1,000 experiments. Due to its scalable nature on both label system and
annotation pipeline, Bamboo will continue to grow and benefit from the
collective efforts of the community, which we hope would pave the way for more
general vision models.


title: Recursive 3D Segmentation of Shoulder Joint with Coarse-scanned MR Image

abstract:
  For diagnosis of shoulder illness, it is essential to look at the morphology
deviation of scapula and humerus from the medical images that are acquired from
Magnetic Resonance (MR) imaging. However, taking high-resolution MR images is
time-consuming and costly because the reduction of the physical distance
between image slices causes prolonged scanning time. Moreover, due to the lack
of training images, images from various sources must be utilized, which creates
the issue of high variance across the dataset. Also, there are human errors
among the images due to the fact that it is hard to take the spatial
relationship into consideration when labeling the 3D image in low resolution.
In order to combat all obstacles stated above, we develop a fully automated
algorithm for segmenting the humerus and scapula bone from coarsely scanned and
low-resolution MR images and a recursive learning framework that iterative
utilize the generated labels for reducing the errors among segmentations and
increase our dataset set for training the next round network. In this study, 50
MR images are collected from several institutions and divided into five
mutually exclusive sets for carrying five-fold cross-validation. Contours that
are generated by the proposed method demonstrated a high level of accuracy when
compared with ground truth and the traditional method. The proposed neural
network and the recursive learning scheme improve the overall quality of the
segmentation on humerus and scapula on the low-resolution dataset and reduced
incorrect segmentation in the ground truth, which could have a positive impact
on finding the cause of shoulder pain and patient's early relief.


title: SCD: Self-Contrastive Decorrelation for Sentence Embeddings

abstract:
  In this paper, we propose Self-Contrastive Decorrelation (SCD), a
self-supervised approach. Given an input sentence, it optimizes a joint
self-contrastive and decorrelation objective. Learning a representation is
facilitated by leveraging the contrast arising from the instantiation of
standard dropout at different rates. The proposed method is conceptually simple
yet empirically powerful. It achieves comparable results with state-of-the-art
methods on multiple benchmarks without using contrastive pairs. This study
opens up avenues for efficient self-supervised learning methods that are more
robust than current contrastive methods.


title:
Perspectives on testing fundamental physics with highly charged ions in
  Penning traps

abstract:
  In Penning traps electromagnetic forces are used to confine charged particles
under well-controlled conditions for virtually unlimited time. Sensitive
detection methods have been developed to allow observation of single stored
ions. Various cooling methods can be employed to reduce the energy of the
trapped particle to nearly at rest. In this review we summarize how highly
charged ions offer unique possibilities for precision measurements in Penning
traps. Precision atomic and nuclear masses as well as magnetic moments of bound
electrons allow among others to determine fundamental constants like the mass
of the electron or to perform stringent tests of fundamental interactions like
bound-state quantum electrodynamics. Recent results and future perspectives in
high-precision Penning-trap spectroscopy with highly charged ions will be
discussed.


title:
Asymmetric phenomenon of flow and heat transfer in charging process of
  thermal energy storage based on an entire domain model

abstract:
  Phase change energy storage is getting increasing attention as a
representative technology to achieve carbon neutrality. The phase change
process exists typical phenomenon of asymmetry that affects the energy storage
performance. However, the mechanism of asymmetry is currently lack of
elaboration because the half domain model is always used to simplify the
numerical simulation and avoid the appearance of asymmetry. In this study, the
entire domain model and boundary conditions were adopted and numerically
simulated for the melting process of paraffin wax, i.e., the charging process
of energy storage. The nonlinear dynamics method was applied to explain the
asymmetric flow and heat transfer phenomenon. The three-stage characteristic
based on charging speed was proposed for charging process and was explained by
thermal conduction or natural convection. The asymmetric phenomenon was
elaborated on the cause of formation, change mechanism, and effect evaluation.
Results show that natural convection accounts for the multiple solutions
(including the asymmetric ones) of charging process. It is also pointed out
that asymmetric solutions can exist under symmetric geometry structure. To
accurately solve the charging process, it is necessary to use the entire domain
model. Both the charging speed and energy storage capacity have a positive
correlation with asymmetry. Thus, a potential idea for energy storage
application was proposed to increase the charging speed; that is, adding
thermal disturbance during the melting process to destroy flow stability and to
enhance convective heat transfer. The research methods and findings will
support the development of energy storage and numerical investigation in other
related areas.


title: The colormagnetic confinement in QCD

abstract:
  Colormagnetic confinement as a natural component of the QCD confinement is
explained and treated in the framework of the Field Correlator Method. For
quarks and gluons in hadrons the effects of the colormagnetic confinement are
discussed at zero temperature, where it contributes to the spectrum properties
and can create its own bound states, while at nonzero temperature in the EoS of
the quark gluon plasma the colormagnetic confinement plays a dominating role.
Its properties in the QCD thermodynamics are discussed in detail.


title:
Standing spin wave excitation in Bi:YIG films via temperature induced
  anisotropy changes and magnetoacoustic coupling

abstract:
  Based on micromagnetic simulations and experimental observations of the
magnetization and lattice dynamics following the direct optical excitation of
the magnetic insulator Bi:YIG or indirect excitation via an optically opaque
Pt/Cu double layer, we disentangle the dynamical effects of magnetic anisotropy
and magnetoelastic coupling. The strain and temperature of the lattice are
quantified via modeling ultrafast x-ray diffraction data. Measurements of the
time-resolved magneto-optical Kerr effect agree well with the magnetization
dynamics simulated according to the excitation via two mechanisms: The
magneto-acoustic coupling to the experimentally verified strain dynamics and
the ultrafast temperature-induced transient change in the magnetic anisotropy.
The numerical modeling proves that for direct excitation both mechanisms drive
the fundamental mode with opposite phase. The relative ratio of standing
spin-wave amplitudes of higher order modes indicates that both mechanisms are
substantially active.


title: Block-Recurrent Transformers

abstract:
  We introduce the Block-Recurrent Transformer, which applies a transformer
layer in a recurrent fashion along a sequence, and has linear complexity with
respect to sequence length. Our recurrent cell operates on blocks of tokens
rather than single tokens, and leverages parallel computation within a block in
order to make efficient use of accelerator hardware. The cell itself is
strikingly simple. It is merely a transformer layer: it uses self-attention and
cross-attention to efficiently compute a recurrent function over a large set of
state vectors and tokens. Our design was inspired in part by LSTM cells, and it
uses LSTM-style gates, but it scales the typical LSTM cell up by several orders
of magnitude.
  Our implementation of recurrence has the same cost in both computation time
and parameter count as a conventional transformer layer, but offers
dramatically improved perplexity in language modeling tasks over very long
sequences. Our model out-performs a long-range Transformer XL baseline by a
wide margin, while running twice as fast. We demonstrate its effectiveness on
PG19 (books), arXiv papers, and GitHub source code.


title: Concentration Properties of Random Codes

abstract:
  This paper studies the concentration properties of random codes.
Specifically, we show that, for discrete memoryless channels, the error
exponent of a randomly generated code with pairwise-independent codewords
converges in probability to its expectation -- the typical error exponent. For
high rates, the result is a consequence of the fact that the random-coding
error exponent and the sphere-packing error exponent coincide. For low rates,
instead, the convergence is based on the fact that the union bound accurately
characterizes the probability of error. The paper also zooms into the behavior
at asymptotically low rates and shows that the error exponent converges in
distribution to a Gaussian-like distribution. Finally, we present several
results on the convergence of the error probability and error exponent for
generic ensembles and channels.


title: Light ray fluctuations in simplicial quantum gravity

abstract:
  A non-perturbative study on the quantum fluctuations of light ray propagation
through a quantum region of spacetime is long overdue. Within the theory of
Lorentzian simplicial quantum gravity, we compute the probabilities for a test
light ray to land at different locations after travelling through a
symmetry-reduced box region in 2,3 and 4 spacetime dimensions. It is found that
for fixed boundary conditions, light ray fluctuations are generically large
when all coupling constants are relatively small in absolute value. For fixed
coupling constants, as the boundary size is decreased light ray fluctuations
first increase and then decrease in a 2D theory with the cosmological constant,
Einstein-Hilbert and R-squared terms. While in 3D and 4D theories with the
cosmological constant and Einstein-Hilbert terms, as the boundary size is
decreased light ray fluctuations just increase. Incidentally, when studying 2D
quantum gravity we show that the global time-space duality with the
cosmological constant and Einstein-Hilbert terms noted previously also holds
when arbitrary even powers of the Ricci scalar are added. We close by
discussing how light ray fluctuations can be used in obtaining the continuum
limit of non-perturbative Lorentzian quantum gravity.


title: Mu3e Integration Run 2021

abstract:
  The Mu3e experiment at the Paul Scherrer Institute searches for the charged
lepton flavor violating decay $\mu^+ \rightarrow e^+ e^+ e^-$. The experiment
aims for an ultimate sensitivity of one in $10^{16}$ $\mu$ decays. The first
phase of the experiment, currently under construction, will reach a branching
ratio sensitivity of $2.5\times10^{-15}$ by observing $10^{8}$ $\mu$ decays per
second over a year of data taking. The highly granular detector based on thin
high-voltage monolithic active pixel sensors (HV-MAPS) and scintillating timing
detectors will produce about 100 GB/s of data at these particle rates. The
Field Programmable Gate Array (FPGA) based Mu3e Data Acquisition System will
read out this data from the detector and reduce the event rate to 100 MB/s by
selecting interesting events using a filter farm of graphics processing units.
The paper presents the status of the data acquisition system (DAQ) and first
results from the 2021 integration run, which for the first time operated a
slice of the Mu3e detector at the $\pi$E5 muon beam line at the Paul Scherrer
Institute (PSI).


title:
Improved Multi-label Classification under Temporal Concept Drift:
  Rethinking Group-Robust Algorithms in a Label-Wise Setting

abstract:
  In document classification for, e.g., legal and biomedical text, we often
deal with hundreds of classes, including very infrequent ones, as well as
temporal concept drift caused by the influence of real world events, e.g.,
policy changes, conflicts, or pandemics. Class imbalance and drift can
sometimes be mitigated by resampling the training data to simulate (or
compensate for) a known target distribution, but what if the target
distribution is determined by unknown future events? Instead of simply
resampling uniformly to hedge our bets, we focus on the underlying optimization
algorithms used to train such document classifiers and evaluate several
group-robust optimization algorithms, initially proposed to mitigate
group-level disparities. Reframing group-robust algorithms as adaptation
algorithms under concept drift, we find that Invariant Risk Minimization and
Spectral Decoupling outperform sampling-based approaches to class imbalance and
concept drift, and lead to much better performance on minority classes. The
effect is more pronounced the larger the label set.


title:
Combating fluctuations in relaxation times of fixed-frequency transmon
  qubits with microwave-dressed states

abstract:
  With the long coherence time, fixed-frequency transmon qubit is a promising
qubit modality for quantum computing. Currently, diverse qubit architectures
that utilize fixed-frequency transmon qubits have been demonstrated with
high-fidelity gate performance. Nevertheless, the relaxation times of transmon
qubits can have large temporal fluctuations, causing instabilities in gate
performance. The fluctuations are often believed to be caused by nearly
on-resonance couplings with sparse two-level-system (TLS) defects. To mitigate
their impact on qubit coherence and gate performance, one direct approach is to
tune the qubits away from these TLSs. In this work, to combat the potential
TLS-induced performance fluctuations in a tunable-bus architecture unitizing
fixed-frequency transmon qubits, we explore the possibility of using an
off-resonance microwave drive to effectively tuning the qubit frequency through
the ac-Stark shift while implementing universal gate operations on the
microwave-dressed qubit. We show that the qubit frequency can be tuned up to 20
MHz through the ac-stark shift while keeping minimal impacts on the qubit
control. Besides passive approaches that aim to remove these TLSs through more
careful treatments of device fabrications, this work may offer an active
approach towards mitigating the TLS-induced performance fluctuations in
fixed-frequency transmon qubit devices.


title: A Survey of Non-Rigid 3D Registration

abstract:
  Non-rigid registration computes an alignment between a source surface with a
target surface in a non-rigid manner. In the past decade, with the advances in
3D sensing technologies that can measure time-varying surfaces, non-rigid
registration has been applied for the acquisition of deformable shapes and has
a wide range of applications. This survey presents a comprehensive review of
non-rigid registration methods for 3D shapes, focusing on techniques related to
dynamic shape acquisition and reconstruction. In particular, we review
different approaches for representing the deformation field, and the methods
for computing the desired deformation. Both optimization-based and
learning-based methods are covered. We also review benchmarks and datasets for
evaluating non-rigid registration methods, and discuss potential future
research directions.


title:
Construction and commissioning of the collinear laser spectroscopy
  system at BRIF

abstract:
  We have constructed a collinear laser spectroscopy (CLS) system installed at
the Beijing Radioactive Ion-beam Facility (BRIF), aiming to investigate the
nuclear properties of unstable nuclei. The first on-line commissioning
experiment of this system was performed using the continuous stable ($^{39}$K)
and unstable ($^{38}$K) ion beams produced by impinging a 100-MeV proton beam
on a CaO target. Hyperfine structure spectra of these two isotopes are
reasonably reproduced, and the extracted magnetic dipole hyperfine parameters
and isotope shift agree with the literature values. The on-line experiment
demonstrates the overall functioning of this CLS system, opening new
opportunities for laser spectroscopy measurement of unstable isotopes at BRIF
and other radioactive ion beam facilities in China.


title:
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models
  Robust with Little Cost

abstract:
  State-of-the-art NLP systems represent inputs with word embeddings, but these
are brittle when faced with Out-of-Vocabulary (OOV) words. To address this
issue, we follow the principle of mimick-like models to generate vectors for
unseen words, by learning the behavior of pre-trained embeddings using only the
surface form of words. We present a simple contrastive learning framework,
LOVE, which extends the word representation of an existing pre-trained language
model (such as BERT), and makes it robust to OOV with few additional
parameters. Extensive evaluations demonstrate that our lightweight model
achieves similar or even better performances than prior competitors, both on
original datasets and on corrupted variants. Moreover, it can be used in a
plug-and-play fashion with FastText and BERT, where it significantly improves
their robustness.


title:
Don't Get Me Wrong: How to apply Deep Visual Interpretations to Time
  Series

abstract:
  The correct interpretation and understanding of deep learning models is
essential in many applications. Explanatory visual interpretation approaches
for image and natural language processing allow domain experts to validate and
understand almost any deep learning model. However, they fall short when
generalizing to arbitrary time series data that is less intuitive and more
diverse. Whether a visualization explains the true reasoning or captures the
real features is difficult to judge. Hence, instead of blind trust we need an
objective evaluation to obtain reliable quality metrics. We propose a framework
of six orthogonal metrics for gradient- or perturbation-based post-hoc visual
interpretation methods designed for time series classification and segmentation
tasks. An experimental study includes popular neural network architectures for
time series and nine visual interpretation methods. We evaluate the visual
interpretation methods with diverse datasets from the UCR repository and a
complex real-world dataset, and study the influence of common regularization
techniques during training. We show that none of the methods consistently
outperforms any of the others on all metrics while some are ahead at times. Our
insights and recommendations allow experts to make informed choices of suitable
visualization techniques for the model and task at hand.


title: Visualization of airborne ultrasound field using thermal images

abstract:
  Thermoacoustics, the interaction between heat and acoustic waves, is used in
a wide range of advanced technological applications. Conversion of ultrasound
waves into heat is employed in medical applications, including tumor ablation.
However, thermal responses to airborne ultrasounds have not been extensively
studied. Herein, we show that thermal responses to high-intensity airborne
ultrasounds above 1000 Pa can be used for acoustic measurements. Thermal images
on a mesh screen enabled the real-time visualization of three-dimensional
acoustic fields. Based on the temperature distribution on a surface that
reflects ultrasound waves, it was possible to determine the ultrasound focus
position with a resolution much finer than the wavelength. Our results
demonstrate that thermography observations based on ultrasound-temperature
conversion can be used to visualize acoustic fields, which are conventionally
difficult to observe. Our findings will advance technologies that use strong
airborne ultrasound, such as mid-air haptics and acoustic levitation, and
provide new scientific and technical tools for detecting surface properties and
composition, including cells and fragile materials.


title: An asymptotic approximation for the Riemann zeta function revisited

abstract:
  We revisit a representation for the Riemann zeta function $\zeta(s)$
expressed in terms of normalised incomplete gamma functions given by the author
and S. Cang in Methods Appl. Anal. {\bf 4} (1997) 449--470. Use of the uniform
asymptotics of the incomplete gamma function produces an asymptotic-like
expansion for $\zeta(s)$ on the critical line $s=1/2+it$ as $t\to+\infty$. The
main term involves the original Dirichlet series smoothed by a complementary
error function of appropriate argument together with a series of correction
terms. It is the aim here to present these correction terms in a more
user-friendly format by expressing then in inverse powers of $\omega$, where
$\omega^2=\pi s/(2i)$, multiplied by coefficients involving trigonometric
functions of argument $\omega$.


title:
Snowmass2021 Cosmic Frontier White Paper: 21cm Radiation as a Probe of
  Physics Across Cosmic Ages

abstract:
  The 21cm line refers to a forbidden transition in neutral hydrogen associated
with alignment of spins of the proton and electron. It is a very low energy
transition that is emitted whenever there is neutral hydrogen in the Universe.
Since baryons are mostly (~75%) hydrogen, one can in principle detect this
emission throughout much of the history of the Universe. The dominant emission
mechanism is different across cosmic ages. Before the photons decouple from
matter, hydrogen is in an ionized state and does not emit in 21cm. After
recombination and during the Dark Ages, at z ~ 30-1000, the 21cm emission is
associated with density fluctuations in the neutral hydrogen medium. After the
first stars turn on and galaxies begin to form, the 21cm emission traces
bubbles of ionized hydrogen in the sea of the neutral medium. This epoch,
spanning z ~ 6-30, is often referred to as cosmic dawn and the Epoch of
Reionization (EoR). At redshifts below z<6, the intergalactic medium is largely
ionized, but pockets of self-shielded neutral gas form in dense galactic
environments and 21cm emission traces the distribution of galaxies. The vastly
different emission mechanisms allow us to probe very different physics at
different redshifts, corresponding to different observational frequencies. The
instrumental challenges, namely building very sensitive and exquisitely
calibrated radio telescopes, however, share many commonalities across frequency
bands. The potential of the 21cm probe has been recognized by the Decadal
Survey of Astronomy & Astrophysics, whose Panel on Cosmology identified the
Dark Ages as its sole discovery area. We argue that HEP should recognize the
potential of 21cm as a probe of fundamental physics across many axes and invest
in the technology development that will enable full exploitation of this rich
technique.


title: Characteristics-driven returns in equilibrium

abstract:
  We reverse-engineer the equilibrium construction process of asset prices in
order to obtain returns which depend on firm characteristics, possibly in a
linear fashion. One key requirement is that agents must have demands that rely
separately on firm characteristics and on the log-price of assets. Market
clearing via exogenous (non-factor driven) supply, combined with linear demands
in characteristics, yields the sought form. The coefficients in the resulting
linear expressions are scaled net aggregate demands for characteristics, as
well as their variations, and both can be jointly estimated via panel
regressions. Conditions underpinning asset pricing anomalies are derived and
underline the theoretical importance of the links between characteristics.
Empirically, when the number of characteristics is small, the value and
momentum anomalies are mostly driven by firm-specific fixed-effects, i.e.,
latent demands, which highlights the shortcomings of low-dimensional models.


title: Demonstrating the Concept of Parallax with James Webb Space Telescope

abstract:
  We measured the parallax of the James Webb Space Telescope based on near
simultaneous observations using the Lulin One-meter Telescope and the GROWTH
India Telescope, separated at a distance of ~4214 km. This serves a great
demonstration for the concept of parallax commonly taught in introductory
astronomy courses.


title: An on-chip astrophotonic spectrograph with a resolving power of 12,000

abstract:
  With the upcoming extremely large telescopes (ELTs), the volume, mass, and
cost of the associated spectrographs will scale with the telescope diameter.
Astrophotonics offers a unique solution to this problem in the form of
single-mode fiber-fed diffraction-limited spectrographs on a chip. These highly
miniaturized chips offer great flexibility in terms of coherent manipulation of
photons. Such photonic spectrographs are well-suited to disperse the light from
directly imaged planets (post-coronagraph, collected using a single-mode fiber)
to characterize exoplanet atmospheres. Here we present the results from a
proof-of-concept high-resolution astrophotonic spectrograph using the arrayed
waveguide gratings (AWG) architecture. This chip uses the low-loss SiN platform
(SiN core, SiO$_2$ cladding) with square waveguides (800 nm $\times$ 800 nm).
The AWG has a measured resolving power ($\lambda/\delta\lambda$) of $\sim$
12,000 and a free spectral range (FSR) of 2.8 nm. While the FSR is small, the
chip operates over a broad band (1200 $-$ 1700 nm). The peak on-chip throughput
(excluding the coupling efficiency) is $\sim$40\% (- 4 dB) and the overall
throughput (including the coupling loss) is $\sim$ 11\% (- 9.6 dB) in the TE
mode. Thanks to the high-confinement waveguide geometry, the chip is highly
miniaturized with a size of only 7.4 mm $\times$ 2 mm.
  This demonstration highlights the utility of SiN platform for astrophotonics,
particularly, the capability of commercial SiN foundries to fabricate
ultra-small, high-resolution, high-throughput AWG spectrographs on a chip
suitable for both ground- and space-based telescopes.


title: Development of an integrated near-IR astrophotonic spectrograph

abstract:
  Here, we present an astrophotonic spectrograph in the near-IR H-band (1.45
-1.65 $\mu m$) and a spectral resolution ($\lambda/\delta\lambda$) of 1500. The
main dispersing element of the spectrograph is a photonic chip based on
Arrayed-Waveguide-Grating technology. The 1D spectrum produced on the focal
plane of the AWG contains overlapping spectral orders, each spanning a 10 nm
band in wavelength. These spectral orders are cross-dispersed in the
perpendicular direction using a cross-dispersion setup which consists of
collimating lenses and a prism and the 2D spectrum is thus imaged onto a
near-IR detector. Here, as a proof of concept, we use a few-mode photonic
lantern to capture the light and feed the emanating single-mode outputs to the
AWG chip for dispersion. The total size of the setup is 50$\times$30$\times$20
cm$^3$, nearly the size of a shoebox. This spectrograph will pave the way for
future miniaturized integrated photonic spectrographs on large telescopes,
particularly for building future photonic multi-object spectrographs.


title: Finding Many Sparse Cuts Using Entropy Maximization

abstract:
  A randomized algorithm for finding sparse cuts is given which is based on
constructing a dual markov chain called multiscale rings process(MRP) and a new
concept of entropy. It is shown how the time to absorption of the dual process
measures the connectedness of the graph and mixing of the corresponding markov
process which is then utilized to do clustering. The second algorithm uses the
entropy which provides a new methodology and a set of tools to think about
sparse cuts as well as sparsification of a graph.


abstract:
  We prove Fermat's Last Theorem over ${\mathbb Q}(\sqrt{5})$ and ${\mathbb
Q}(\sqrt{17})$ for prime exponents $p \ge 5$ in certain congruence classes
modulo $48$ by using a combination of the modular method and Brauer-Manin
obstructions explicitly given by quadratic reciprocity constraints. The
reciprocity constraint used to treat the case of ${\mathbb Q}(\sqrt{5})$ is a
generalization to a real quadratic base field of the one used by Chen-Siksek.
For the case of ${\mathbb Q}(\sqrt{17})$, this is insufficient, and we
generalize a reciprocity constraint of Bennett-Chen-Dahmen-Yazdani using
Hilbert symbols from the rational field to certain real quadratic fields.


title: A Corrected Simplified Proof of Chen's Theorem

abstract:
  In 1973, J.-R. Chen showed that every large even integer is a sum of a prime
and a product of at most two primes. In this paper, the author indicates and
fixes the issues in a simplified proof of this result given by Pan et al.


title: New quantum neural network designs

abstract:
  Quantum computers promise improving machine learning. We investigated the
performance of new quantum neural network designs. Quantum neural networks
currently employed rely on a feature map to encode the input into a quantum
state. This state is then evolved via a parameterized variational circuit.
Finally, a measurement is performed and post-processed on a classical computer
to extract the prediction of the quantum model. We develop a new technique,
where we merge feature map and variational circuit into a single parameterized
circuit and post-process the results using a classical neural network. On a
variety of real and generated datasets, we show that the new, combined approach
outperforms the separated feature map & variational circuit method. We achieve
lower loss, better accuracy, and faster convergence.


title:
Asymptotic Fermat for signatures $(p,p,2)$ and $(p,p,3)$ over totally
  real fields

abstract:
  Let $K$ be a totally real number field and consider a Fermat-type equation
$Aa^p+Bb^q=Cc^r$ over $K$. We call the triple of exponents $(p,q,r)$ the
signature of the equation. We prove various results concerning the solutions to
the Fermat equation with signature $(p,p,2)$ and $(p,p,3)$ using a method
involving modularity, level lowering and image of inertia comparison. These
generalize and extend the recent work of I\c{s}ik, Kara and Ozman. For example,
consider $K$ a totally real field of degree $n$ with $2 \nmid h_K^+$ and $2$
inert. Moreover, suppose there is a prime $q\geq 5$ which totally ramifies in
$K$ and satisfies $\gcd(n,q-1)=1$, then we know that the equation $a^p+b^p=c^2$
has no primitive, non-trivial solutions $(a,b,c) \in \mathcal{O}_K^3$ with $2 |
b$ for $p$ sufficiently large.


title: Low-pressure THGEM-based operation with Ne+H2 Penning mixtures

abstract:
  Operation of Thick Gas Electron Multipliers (THGEMs) in Time Projection
Chambers (TPCs) using various gas mixtures find applications in various nuclear
physics and particle detection experiments. Of particular interest are use of
neon-based gas mixtures at low pressures in TPCs as active targets for
radioactive beams. Here, we report on the low-pressure operation of THGEMs in
Ne+H2 based gas mixtures, where neon in this mixture is an important target for
fusion studies with TPCs. We show that since the Ne+H2 forms a Penning pair,
higher gains are achievable compared to pure neon gas. Moreover, H2 as a
quench-gas produces minimal background in fusion reactions compared to
carbon-based neon gas mixtures. Detailed electron transport and amplification
simulations have been performed and they qualitatively agree with the increased
gain H2 provides in the Ne:H2 mixture. This allows for sufficiently high gains
for the use of Ne gas with THGEMs that will enable the measurement of heavy-ion
tracks for nuclear physics experiments at modest gas pressures.


title:
Regret Bounds for Expected Improvement Algorithms in Gaussian Process
  Bandit Optimization

abstract:
  The expected improvement (EI) algorithm is one of the most popular strategies
for optimization under uncertainty due to its simplicity and efficiency.
Despite its popularity, the theoretical aspects of this algorithm have not been
properly analyzed. In particular, whether in the noisy setting, the EI strategy
with a standard incumbent converges is still an open question of the Gaussian
process bandit optimization problem. We aim to answer this question by
proposing a variant of EI with a standard incumbent defined via the GP
predictive mean. We prove that our algorithm converges, and achieves a
cumulative regret bound of $\mathcal O(\gamma_T\sqrt{T})$, where $\gamma_T$ is
the maximum information gain between $T$ observations and the Gaussian process
model. Based on this variant of EI, we further propose an algorithm called
Improved GP-EI that converges faster than previous counterparts. In particular,
our proposed variants of EI do not require the knowledge of the RKHS norm and
the noise's sub-Gaussianity parameter as in previous works. Empirical
validation in our paper demonstrates the effectiveness of our algorithms
compared to several baselines.


title:
The cosmic microwave background and mass power spectrum profiles for a
  novel and efficient model of dark energy

abstract:
  In a previous work it was shown that by considering the quantum nature of the
gravitational field mediator, it is possible to introduce the momentum energy
of the graviton into the Einstein equations as an effective cosmological
constant. The Compton Mass Dark Energy (CMaDE) model proposes that this
momentum can be interpreted as dark energy, with a Compton wavelength given by
the size of the observable universe $R_H$, implying that the dark energy varies
depending on this size. The main result of this previous work is the existence
of an effective cosmological constant $\Lambda=2\pi^2/\lambda^2$ that varies
very slowly, being $\lambda=(c/H_0) R_H$ the graviton Compton wavelength. In
the present work we use that the dark energy density parameter is given by
$\Omega_\Lambda=2\pi^2/3/R_H^2$, it only has the curvature $\Omega_k$ as a free
constant and depends exclusively on the radiation density parameter $\Omega_r$.
Using $\Omega_{0r} = 9.54\times10^{- 5}$, the theoretical prediction for a flat
universe of the dark energy density parameter is $\Omega_{0\Lambda} = 0.6922$.
We perform a general study for a non-flat universe, using the Planck data and a
modified version of the CLASS code we find an excellent concordance with the
Cosmic Microwave Background and Mass Power Spectrum profiles, provided that the
Hubble parameter today is $H_0 = 72.6$ km/s/Mpc for an universe with curvature
$\Omega_{0k}=-0.003$. We conclude that the CMaDE model provides a natural
explanation for the accelerated expansion and the coincidence problem of the
universe.


title:
Dise\~no de un espectrofot\'ometro UV-VIS de bajo costo para la
  industria bioqu\'imica: Una Revisi\'on

abstract:
  The spectrophotometry systems commonly required both in research areas and in
the chemical and materials industries turn out to be expensive laboratory
equipment, in many cases the acquisition of these becomes unfeasible; besides,
in several applications portability is also required to perform measurements in
the field and that they could be broad spectrum devices. In this work, a review
of various development projects for spectrophotometry devices was carried out,
ranging from academic prototypes to industrial products using different
implementation strategies in order to know the feasibility of creating portable
and broad-spectrum devices or specific products for a particular sector, but
with the intention of achieving high precision and functionality. The research
allowed to conclude with a novel contribution for the development of powerful
spectrophotometry prototypes for specific, low-cost industries or sectors.


title:
Direct numerical simulation of forced thermal convection in square ducts
  up to $Re_\tau \approx 2000$

abstract:
  We carry out direct numerical simulation (DNS) of flow in a turbulent square
duct by focusing on heat transfer effects, considering the case of unit Prandtl
number. Reynolds numbers up to $Re_\tau \approx 2000$ are considered which are
much higher than in previous studies, and which yield clear scale separation
between inner- and outer-layer dynamics. Close similarity between the behavior
of the temperature and the streamwise velocity fields is confirmed as in
previous studies related to plane channels and pipes. Just like the mean
velocity, the mean temperature is found to exhibit logarithmic layers as a
function of the nearest wall, however with a different slope. The most
important practical implication is the validity of the traditional hydraulic
diameter as the correct reference length for reporting heat transfer data, as
we rigorously show here. Temperature and velocity fluctuations also have
similar behavior, but apparently logarithmic growth of their inner-scaled peak
variances is not observed here unlike in canonical wall-bounded flows. Analysis
of the split contributions to the heat transfer coefficient shows that mean
cross-stream convection associated with secondary motions is responsible for
about $5\%$ of the total. Finally, we use the DNS database to highlight
shortcomings of traditional linear closures for the turbulent heat flux, and
show that substantial modeling improvement may be in principle obtained by
retaining at least the three terms in the vector polynomial integrity basis
expansion.


title: Superconducting qubits as musical synthesizers for live performance

abstract:
  In the frame of a year-long artistic residency at the Yale Quantum Institute
in 2019, artist and technologist Spencer Topel and quantum physicists Kyle
Serniak and Luke Burkhart collaborated to create Quantum Sound, the first-ever
music created and performed directly from measurements of superconducting
quantum devices. Using analog- and digital-signal-processing sonification
techniques, the team transformed GHz-frequency signals from experiments inside
dilution refrigerators into audible sounds. The project was performed live at
the International Festival of Arts and Ideas in New Haven, Connecticut on June
14, 2019 as a structured improvisation using the synthesis methods described in
this chapter. At the interface between research and art, Quantum Sound
represents an earnest attempt to produce a sonic reflection of the quantum
realm.


title:
Formation of CuO$_2$ sublattices by suppression of interlattice
  correlations in tetragonal CuO

abstract:
  We investigate the tetragonal phase of the binary transition metal oxide CuO
(t-CuO) within the context of cellular dynamical mean-field theory. Due to its
strong antiferromagnetic correlations and simple structure, analysing the
physics of t-CuO is of high interest as it may pave the way towards a more
complete understanding of high temperature superconductivity in hole-doped
antiferromagnets. In this work we give a formal justification for the weak
coupling assumption that has previously been made for the interconnected
sublattices within a single layer of t-CuO by studying the non-local
self-energies of the system. We compute momentum-resolved spectral functions
using a Matrix Product State (MPS)-based impurity solver directly on the real
axis, which does not require any numerically ill-conditioned analytic
continuation. The agreement with photoemission spectroscopy indicates that a
single band Hubbard model is sufficient to capture the material's low energy
physics. We perform calculations on a range of different temperatures, finding
two magnetic regimes, for which we identify the driving mechanism behind their
respective insulating state. Finally, we show that in the hole-doped regime the
sublattice structure of t-CuO has interesting consequences on the symmetry of
the superconducting state.


title:
LiP-Flow: Learning Inference-time Priors for Codec Avatars via
  Normalizing Flows in Latent Space

abstract:
  Neural face avatars that are trained from multi-view data captured in camera
domes can produce photo-realistic 3D reconstructions. However, at inference
time, they must be driven by limited inputs such as partial views recorded by
headset-mounted cameras or a front-facing camera, and sparse facial landmarks.
To mitigate this asymmetry, we introduce a prior model that is conditioned on
the runtime inputs and tie this prior space to the 3D face model via a
normalizing flow in the latent space. Our proposed model, LiP-Flow, consists of
two encoders that learn representations from the rich training-time and
impoverished inference-time observations. A normalizing flow bridges the two
representation spaces and transforms latent samples from one domain to another,
allowing us to define a latent likelihood objective. We trained our model
end-to-end to maximize the similarity of both representation spaces and the
reconstruction quality, making the 3D face model aware of the limited driving
signals. We conduct extensive evaluations where the latent codes are optimized
to reconstruct 3D avatars from partial or sparse observations. We show that our
approach leads to an expressive and effective prior, capturing facial dynamics
and subtle expressions better.


title:
The Convergence Problem in Mean Field Games with Neumann Boundary
  Conditions

abstract:
  In this article we study the convergence of the Nash Equilibria in a N-player
differential game towards the optimal strategies in the Mean Field Games, when
the dynamic of the generic player includes a reflection process which
guarantees the invariance of the state space. The well-posedness of the Master
Equation allows us to use its solution U in order to construct finite
dimensional projections, which will converge, in some suitable spaces, to the
solution of the Nash system.


title: Global fit of 2HDM with future collider results

abstract:
  In this work, we summarize a global fit study of Type-II two Higgs doublet
models (2HDM), and explore the impact of future SM-like Higgs and Z-pole
precision measurements on the allowed parameter space. The work is based on the
study results of a global fit of 2HDMs with the tool GAMBIT, utilising various
current constraints including theoretical constraints (unitarity,
perturbativity and vacuum stability), Higgs searches at colliders, electroweak
physics and flavour constraints. We further investigate the ability of future
facilities, such as the HL-LHC, CEPC, ILC and FCC-ee to explore the 2HDM
parameter space.


title:
Efficient Training of the Memristive Deep Belief Net Immune to
  Non-Idealities of the Synaptic Devices

abstract:
  The tunability of conductance states of various emerging non-volatile
memristive devices emulates the plasticity of biological synapses, making it
promising in the hardware realization of large-scale neuromorphic systems. The
inference of the neural network can be greatly accelerated by the vector-matrix
multiplication (VMM) performed within a crossbar array of memristive devices in
one step. Nevertheless, the implementation of the VMM needs complex peripheral
circuits and the complexity further increases since non-idealities of
memristive devices prevent precise conductance tuning (especially for the
online training) and largely degrade the performance of the deep neural
networks (DNNs). Here, we present an efficient online training method of the
memristive deep belief net (DBN). The proposed memristive DBN uses
stochastically binarized activations, reducing the complexity of peripheral
circuits, and uses the contrastive divergence (CD) based gradient descent
learning algorithm. The analog VMM and digital CD are performed separately in a
mixed-signal hardware arrangement, making the memristive DBN high immune to
non-idealities of synaptic devices. The number of write operations on
memristive devices is reduced by two orders of magnitude. The recognition
accuracy of 95%~97% can be achieved for the MNIST dataset using pulsed synaptic
behaviors of various memristive synaptic devices.

categories: eess.SP cond-mat.dis-nn cs.SY eess.SY physics.app-ph


title:
Data Storage for HEP Experiments in the Era of High-Performance
  Computing

abstract:
  As particle physics experiments push their limits on both the energy and the
intensity frontiers, the amount and complexity of the produced data are also
expected to increase accordingly. With such large data volumes, next-generation
efforts like the HL-LHC and DUNE will rely even more on both high-throughput
(HTC) and high-performance (HPC) computing clusters. Full utilization of HPC
resources requires scalable and efficient data-handling and I/O. For the last
few decades, ROOT has been used by most HEP experiments to store data. However,
other storage technologies like HDF5 may perform better in HPC environments.
Initial explorations with HDF5 have begun using ATLAS, CMS and DUNE data; the
DUNE experiment has also adopted HDF5 for its data-acquisition system. This
paper presents the future outlook of the HEP computing and the role of HPC, and
a summary of ongoing and future works to use HDF5 as a possible data storage
technology for the HEP experiments to use in HPC environments.


title: Lyman-alpha forest tomography and cross-correlation with cosmic voids

abstract:
  The Lyman-alpha forest is a unique probe of large-scale matter density
fluctuations at high redshift (z > 2). It is possible to obtain 3D maps of the
matter distribution from Lyman-alpha data, using tomographic reconstruction
methods. Here, we present the largest tomographic map of matter fluctuations at
z > 2, over the Gpc^3 volume covered by Lyman-alpha forest from SDSS-IV quasar
spectra in the Stripe 82 field. We present a catalog of high-redshift voids
constructed from this map. The measurement of the cross-correlation between
these voids and the Lyman-alpha forest provides the first observation of the
matter velocity flow around voids, through the RSD effect, at such high
redshift. The data is in good agreement with simulations and is well adjusted
with a linear, Kaiser velocity model.


title: Self-duality of multidimensional continued fractions

abstract:
  F.~Schweiger introduced the fibred system in \cite{Schweiger-MCF}, to unify
and generalize many known continued fraction algorithms. An advantage of a
fibred system is that it often provides a systematic construction of absolutely
continuous invariant density. In this paper, we define and study the
self-duality of fibred systems, a strong symmetry of a given system. We show
that explicit algebraic self-duality holds in many systems and presents a
curious system with "partial" self-duality.


title: The Need for Further Development of Magnetrons as RF Sources for HEP

abstract: Proposal to develop magnetrons as RF sources for HEP


title: Comparing two samples through stochastic dominance: a graphical approach

abstract:
  Non-deterministic measurements are common in real-world scenarios: the
performance of a stochastic optimization algorithm or the total reward of a
reinforcement learning agent in a chaotic environment are just two examples in
which unpredictable outcomes are common. These measures can be modeled as
random variables and compared among each other via their expected values or
more sophisticated tools such as null hypothesis statistical tests. In this
paper, we propose an alternative framework to visually compare two samples
according to their estimated cumulative distribution functions. First, we
introduce a dominance measure for two random variables that quantifies the
proportion in which the cumulative distribution function of one of the random
variables scholastically dominates the other one. Then, we present a graphical
method that decomposes in quantiles i) the proposed dominance measure and ii)
the probability that one of the random variables takes lower values than the
other. With illustrative purposes, we re-evaluate the experimentation of an
already published work with the proposed methodology and we show that
additional conclusions (missed by the rest of the methods) can be inferred.
Additionally, the software package RVCompare was created as a convenient way of
applying and experimenting with the proposed framework.


title:
K-VQG: Knowledge-aware Visual Question Generation for Common-sense
  Acquisition

abstract:
  Visual Question Generation (VQG) is a task to generate questions from images.
When humans ask questions about an image, their goal is often to acquire some
new knowledge. However, existing studies on VQG have mainly addressed question
generation from answers or question categories, overlooking the objectives of
knowledge acquisition. To introduce a knowledge acquisition perspective into
VQG, we constructed a novel knowledge-aware VQG dataset called K-VQG. This is
the first large, humanly annotated dataset in which questions regarding images
are tied to structured knowledge. We also developed a new VQG model that can
encode and use knowledge as the target for a question. The experiment results
show that our model outperforms existing models on the K-VQG dataset.


title: On the solvability of a parameter-dependent cantilever-type BVP

abstract:
  We discuss the solvability of a parameter dependent cantilever-type boundary
value problem. We provide an existence and localization result for the positive
solutions via a Birkhoff-Kellogg type theorem. We also obtain, under additional
growth conditions, upper and lower bounds for the involved parameters. An
example is presented in order to illustrate the theoretical results.


title:
Autonomous atomic Hamiltonian construction and active sampling of x-ray
  absorption spectroscopy by adversarial Bayesian optimization

abstract:
  X-ray absorption spectroscopy (XAS) is a well-established method for in-depth
characterization of the electronic structure due to its sensitivity to the
local coordination and electronic states of the active ions. In practice
hundreds of energy points should be sampled during the XAS measurement, most of
which are redundant and do not contain important information. In addition, it
is also a tedious procedure to estimate reasonable parameters in the atomic
Hamiltonian for mechanistic understanding. We implemented an Adversarial
Bayesian optimization (ABO) algorithm comprising two coupled Gaussian processes
(GPs) to automatically fit the multiplet model Hamiltonian and meanwhile to
sample effectively based on active learning. Taking NiO as an example, we found
that less than 30 sampling points are enough to obtain the complete XAS with
the corresponding crystal field and charge transfer models, which can be
selected based on intuitive hypothesis learning. Further application on the
experimental data revealed that less than 80 sampling points can already give
reasonable XAS and reliable atomic model parameters. Our ABO algorithm has a
great potential for future application in automated physics-driven XAS analysis
and active learning sampling.


title:
Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear
  Guarded Attribute Information

abstract:
  We describe a simple and effective method (Spectral Attribute removaL; SAL)
to remove guarded information from neural representations. Our method uses
singular value decomposition and eigenvalue decomposition to project the input
representations into directions with reduced covariance with the guarded
information rather than maximal covariance as normally these factorization
methods are used. We begin with linear information removal and proceed to
generalize our algorithm to the case of nonlinear information removal through
the use of kernels. Our experiments demonstrate that our algorithm retains
better main task performance after removing the guarded information compared to
previous methods. In addition, our experiments demonstrate that we need a
relatively small amount of guarded attribute data to remove information about
these attributes, which lowers the exposure to such possibly sensitive data and
fits better low-resource scenarios.


title: Lab at Home of fluid draining with Tracker

abstract:
  The pandemic caused by the SARS-CoV-2 virus has threaten the face-to-face
teaching activity in both schools and universities. The transformation from
face-to-face classes to online activities yielded inefficient outcomes from the
activities. Particularly, a key issue was organizing laboratory activities
without accessing the labs. In this paper, we propose how to overcome this
problem by enabling the students to perform physics experiments of fluid
draining at home. In other words, the use of equipment commonly available at
home or that can be purchased at a low price is practically plausible. In the
present work, we do an experimental investigation of liquid draining through a
hole of a container. From an analyzing step, we introduce a freely-accessible
video analysis software, Tracker, to obtain accurate results. Interestingly, we
observe the effects of viscosity causing a delay of draining time of liquids.
Our study shows that a ratio of open-space radius of the container to the hole
radius, $\sqrt{\lambda}$, can be used to determine a draining time.