title: Differentially Private Subgraph Counting in the Shuffle Model

abstract:
  Subgraph counting is fundamental for analyzing connection patterns or
clustering tendencies in graph data. Recent studies have applied LDP (Local
Differential Privacy) to subgraph counting to protect user privacy even against
a data collector in both centralized and decentralized social networks.
However, existing local algorithms suffer from extremely large estimation
errors or assume multi-round interaction between users and the data collector,
which requires a lot of user effort and synchronization.
  In this paper, we focus on a one-round of interaction and propose accurate
subgraph counting algorithms by introducing a recently studied shuffle model.
We first propose a basic technique called wedge shuffling to send wedge
information, the main component of several subgraphs, with small noise. Then we
apply our wedge shuffling to counting triangles and 4-cycles -- basic subgraphs
for analyzing clustering tendencies -- with several additional techniques. We
also show upper bounds on the estimation error for each algorithm. We show
through comprehensive experiments that our one-round shuffle algorithms
significantly outperform the one-round local algorithms in terms of accuracy
and achieve small estimation errors with a reasonable privacy budget, e.g.,
smaller than 1 in edge DP.


title:
A Riccati-Lyapunov Approach to Nonfeedback Capacity of MIMO Gaussian
  Channels Driven by Stable and Unstable Noise

abstract:
  In this paper it is shown that the nonfeedback capacity of multiple-input
multiple-output (MIMO) additive Gaussian noise (AGN) channels, when the noise
is nonstationary and unstable, is characterized by an asymptotic optimization
problem that involves, a generalized matrix algebraic Riccati equation (ARE) of
filtering theory, and a matrix Lyapunov equation of stability theory of
Gaussian systems. Furthermore, conditions are identified such that, the
characterization of nonfeedback capacity corresponds to the uniform asymptotic
per unit time limit, over all initial distributions, of the characterization of
a finite block or transmission without feedback information (FTwFI) capacity,
which involves, two generalized matrix difference Riccati equations (DREs) and
a matrix difference Lyapunov equation.


title: Higher-order Dirac Semimetal in a Photonic Crystal

abstract:
  The recent discovery of higher-order topology has largely enriched the
classification of topological materials. Theoretical and experimental studies
have unveiled various higher-order topological insulators that exhibit
topologically protected corner or hinge states. More recently, higher-order
topology has been introduced to topological semimetals. Thus far, realistic
models and experimental verifications on higher-order topological semimetals
are still very limited. Here, we design and demonstrate a three-dimensional
photonic crystal that realizes a higher-order Dirac semimetal phase. Numerical
results on the band structure show that the designed three-dimensional photonic
crystal is able to host two four-fold Dirac points, the momentum-space
projections of which at an edge are connected by higher-order hinge states. The
higher-order topology can be characterised with the calculation of the \chi(6)
topological invariant at different values of k_z. An experiment at microwave
frequencies is also presented to measure the hinge state dispersion. Our work
demonstrates the physical realization of a higher-order Dirac semimetal phase
and paves the way to exploring higher-order topological semimetals phases in
three-dimensional photonic systems.


title:
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network
  Anomaly Detection

abstract:
  As the number of heterogenous IP-connected devices and traffic volume
increase, so does the potential for security breaches. The undetected
exploitation of these breaches can bring severe cybersecurity and privacy
risks. In this paper, we present a practical unsupervised anomaly-based deep
learning detection system called ARCADE (Adversarially Regularized
Convolutional Autoencoder for unsupervised network anomaly DEtection). ARCADE
exploits the property of 1D Convolutional Neural Networks (CNNs) and Generative
Adversarial Networks (GAN) to automatically build a profile of the normal
traffic based on a subset of raw bytes of a few initial packets of network
flows so that potential network anomalies and intrusions can be effectively
detected before they could cause any more damage to the network. A
convolutional Autoencoder (AE) is proposed that suits online detection in
resource-constrained environments, and can be easily improved for environments
with higher computational capabilities. An adversarial training strategy is
proposed to regularize and decrease the AE's capabilities to reconstruct
network flows that are out of the normal distribution, and thereby improve its
anomaly detection capabilities. The proposed approach is more effective than
existing state-of-the-art deep learning approaches for network anomaly
detection and significantly reduces detection time. The evaluation results show
that the proposed approach is suitable for anomaly detection on
resource-constrained hardware platforms such as Raspberry Pi.


title: Integrable fluctuations in the KPZ universality class

abstract:
  The KPZ fixed point is a scaling invariant Markov process which arises as the
universal scaling limit of a broad class of models of random interface growth
in one dimension, the one-dimensional KPZ universality class. In this survey we
review the construction of the KPZ fixed point and some of the history that led
to it, in particular through the exact solution of the totally asymmetric
simple exclusion process, a special solvable model in the class. We also
explain how the construction reveals the KPZ fixed point as a stochastic
integrable system, and how from this it follows that its finite dimensional
distributions satisfy a classical integrable dispersive PDE, the
Kadomtsev-Petviashvili (KP) equation.


title:
Reducing controls noise in gravitational wave detectors with
  interferometric local damping of suspended optics

abstract:
  Control noise is a limiting factor in the low-frequency performance of the
LIGO gravitational wave detectors. In this paper we model the effects of using
new sensors called HoQIs to control the suspension resonances. We show if we
were to use HoQIs, instead of the standard shadow sensors, we can suppress
resonance peaks up to tenfold more while simultaneously reducing the noise
injected by the damping system. Through a cascade of effects this will reduce
the resonant cross-coupling, allow for improved stability for feed-forward
control, and result in improved sensitivity of the detector in the 10-20 Hz
band. This analysis shows that local sensors such as HoQIs should be used in
current and future detectors to improve low-frequency performance.


title:
RLFlow: Optimising Neural Network Subgraph Transformation with World
  Models

abstract:
  We explored the use of reinforcement learning (RL) agents that can learn to
perform neural network subgraph transformations, without the need of expertly
designed heuristics to achieve a high level of performance. Reducing compute
requirements of deep learning models is a focus of extensive research and many
systems, optimisations and just-in-time (JIT) compilers have been proposed to
decrease runtime.
  Recent work has aimed to apply reinforcement learning to computer systems
with some success, especially using model-free RL techniques. Model-based
reinforcement learning methods have seen an increased focus in research as they
can be used to learn the transition dynamics of the environment; this can be
leveraged to train an agent using the hallucinogenic environment, thereby
increasing sample efficiency compared to model-free approaches. Furthermore,
when using a world model as a simulated environment, batch rollouts can occur
safely in parallel and, especially in systems environments, it overcomes the
latency impact of updating system environments that can take orders of
magnitude longer to perform an action compared to simple emulators for video
games.
  We propose a design for a model-based agent which learns to optimise the
architecture of neural networks by performing a sequence of subgraph
transformations to reduce model runtime. We show our approach can match the
performance of state of the art on common convolutional networks and outperform
those by up to 5% on transformer-style architectures.


title: Potential gains of long-distance trade in electricity

abstract:
  Electrification of all economic sectors and solar photovoltaics (PV) becoming
the lowest-cost electricity generation technology in ever more regions give
rise to new potential gains of trade. We develop a stylized analytical model to
minimize unit energy cost in autarky, open it to different trade
configurations, and evaluate it empirically. We identify large potential gains
from interhemispheric and global electricity trade by combining complementary
seasonal and diurnal cycles. The corresponding high willingness to pay for
large-scale transmission suggests far-reaching political economy and regulatory
implications.


title: Compatibility of muon $g-2$, $W$ mass anomaly in type-X 2HDM

abstract:
  Recently CDF II Collaboration reported that they measured W boson mass
precisely. The measurement is deviated from the Standard Model (SM) Prediction
at 7$\sigma$. Also, the recent FNAL measurement of the muon magnetic moment
shows a $4.2\sigma$ deviation. To resolve the W boson as well as the muon
$(g-2)$ anomalies, we explore the type-X two Higgs doublet model (2HDM). We
analyze the $(m_A, \tan\beta)$ parameter space of the type-X 2HDM consistent
with the muon $(g-2)$, lepton universality test and recent $W$ boson mass
measurments from the CDF II collaboration. We find that the measurement of the
$h\to AA$ branching ratio gives a strong lower mass bound on $m_H$.


title: Efficient and Convergent Federated Learning

abstract:
  Federated learning has shown its advances over the last few years but is
facing many challenges, such as how algorithms save communication resources,
how they reduce computational costs, and whether they converge. To address
these issues, this paper proposes a new federated learning algorithm (FedGiA)
that combines the gradient descent and the inexact alternating direction method
of multipliers. It is shown that FedGiA is computation and
communication-efficient and convergent linearly under mild conditions.


title:
Orbital Stability of Periodic Traveling Waves for the "abcd" Boussinesq
  Systems

abstract:
  New results concerning the orbital stability of periodic traveling wave
solutions for the "abcd" Boussinesq model will be shown in this manuscript. For
the existence of solutions, we use basic tools of ordinary differential
equations to show that the corresponding periodic wave depends on the Jacobi
elliptic function of cnoidal type. The spectral analysis for the associated
linearized operator is determined by using some tools concerning the Floquet
theory. The orbital stability is then established by applying the abstract
results [2] and [14] which give us sufficient conditions to the orbital
stability for a general class of evolution equations.


title:
How Are Communication Channels on GitHub Presented to Their Intended
  Audience? -- A Thematic Analysis

abstract:
  Communication is essential in software development, and even more in
distributed settings. Communication activities need to be organized and
coordinated to defend against the threat of productivity losses, increases in
cognitive load, and stress among team members. With a plethora of communication
channels that were identified by previous research in open-source projects,
there is a need to explore organizational issues in how these communication
channels are introduced, explained, and motivated for use among all project
members. In this study, we wanted to understand which communication channels
are used in GitHub projects and how they are presented to the GitHub project
audience. We employed thematic analysis to analyze 151 artifacts in 90 GitHub
projects. Our results revealed 32 unique communications channels that can be
divided into nine different types. Projects mostly provide channels of
different types, but for some types (e.g., chat) it is common to provide
several channels. Maintainers are aware that channels have different properties
and help the developers to decide which channel should be used in which case.
However, this is not true for all projects, and often we have not found any
explicit reasons why maintainers chose to provide one channel over another.
Different channels can be used for different purposes and have different
affordances, so maintainers have to decide wisely which channels they want to
provide and make clear which channel should be used in which case. Otherwise,
developers might feel overwhelmed of too many channels and information can get
fragmented over multiple channels.


title:
Flash Colloidal Gold Nanoparticle Assembly in a Milli Flow System:
  Implications for Thermoplasmonic and for the Amplification of Optical Signals

abstract:
  The assembly and stabilization of a finite number of nanocrystals in contact
in water could maximize the optical absorption per unit of material. Some local
plasmonic properties exploited in applications, such as photothermia and
optical signal amplification, would also be maximized which is important in the
perspective of mass producing nanostructures at a lower cost. The main lock is
that bringing charged particles in close contact requires the charges to be
screened/suppressed, which leads to the rapid formation of micrometric
aggregates. In this article, we show that aggregates containing less than 60
particles in contact can be obtained with a milli-flow system composed of
turbulent mixers and flow reactors. This process allows to stop a fast
non-equilibrium colloidal aggregation process at millisecond times after the
initiation of the aggregation process which allows to control the aggregation
number. As a case study, we considered the rapid mixing of citrate coated gold
nanoparticles (NP) and AlCl3 in water to initiate a fast aggregation controlled
by diffusion. Injecting a solution of polycation using a second mixer allowed
us to arrest the aggregation process after a reaction time by formation of
overcharged cationic aggregates. We obtained within seconds stable dispersions
of a few milliliters composed of particle aggregates. Our main result is to
show that it is possible to master the average aggregation number between 2 and
60 NP per aggregate by varying the a reaction time between 10 ms and 1 s.


title:
Extracting structure from functional expressions for continuous and
  discrete relaxations of MINLP

abstract:
  In this paper, we develop new continuous and discrete relaxations for
nonlinear expressions in an MINLP. In contrast to factorable programming, our
techniques utilize the inner-function structure by encapsulating it in a
polyhedral set, using a technique first proposed in [12]. We tighten the
relaxations derived in [33,13] and obtain new relaxations for functions that
could not be treated using prior techniques. We develop new
discretization-based mixed-integer programming relaxations that yield tighter
relaxations than similar relaxations in the literature. These relaxations
utilize the simplotope that captures inner-function structure to generalize the
incremental formulation of [8] to multivariate functions. In particular, when
the outer-function is supermodular, our formulations require exponentially
fewer continuous variables than any previously known formulation.


title: Learning Coulomb Diamonds in Large Quantum Dot Arrays

abstract:
  We introduce an algorithm that is able to find the facets of Coulomb diamonds
in quantum dot arrays. We simulate these arrays using the constant-interaction
model, and rely only on one-dimensional raster scans (rays) to learn a model of
the device using regularized maximum likelihood estimation. This allows us to
determine, for a given charge state of the device, which transitions exist and
what the compensated gate voltages for these are. For smaller devices the
simulator can also be used to compute the exact boundaries of the Coulomb
diamonds, which we use to assess that our algorithm correctly finds the vast
majority of transitions with high precision.


title: Volatility Sensitive Bayesian Estimation of Portfolio VaR and CVaR

abstract:
  In this paper, a new way to integrate volatility information for estimating
value at risk (VaR) and conditional value at risk (CVaR) of a portfolio is
suggested. The new method is developed from the perspective of Bayesian
statistics and it is based on the idea of volatility clustering. By specifying
the hyperparameters in a conjugate prior based on two different rolling window
sizes, it is possible to quickly adapt to changes in volatility and
automatically specify the degree of certainty in the prior. This constitutes an
advantage in comparison to existing Bayesian methods that are less sensitive to
such changes in volatilities and also usually lack standardized ways of
expressing the degree of belief. We illustrate our new approach using both
simulated and empirical data. Compared to some other well known homoscedastic
and heteroscedastic models, the new method provides a good alternative for risk
estimation, especially during turbulent periods where it can quickly adapt to
changing market conditions.


title:
High-dimensional Asymptotics of Feature Learning: How One Gradient Step
  Improves the Representation

abstract:
  We study the first gradient descent step on the first-layer parameters
$\boldsymbol{W}$ in a two-layer neural network: $f(\boldsymbol{x}) =
\frac{1}{\sqrt{N}}\boldsymbol{a}^\top\sigma(\boldsymbol{W}^\top\boldsymbol{x})$,
where $\boldsymbol{W}\in\mathbb{R}^{d\times N},
\boldsymbol{a}\in\mathbb{R}^{N}$ are randomly initialized, and the training
objective is the empirical MSE loss: $\frac{1}{n}\sum_{i=1}^n
(f(\boldsymbol{x}_i)-y_i)^2$. In the proportional asymptotic limit where
$n,d,N\to\infty$ at the same rate, and an idealized student-teacher setting, we
show that the first gradient update contains a rank-1 "spike", which results in
an alignment between the first-layer weights and the linear component of the
teacher model $f^*$. To characterize the impact of this alignment, we compute
the prediction risk of ridge regression on the conjugate kernel after one
gradient step on $\boldsymbol{W}$ with learning rate $\eta$, when $f^*$ is a
single-index model. We consider two scalings of the first step learning rate
$\eta$. For small $\eta$, we establish a Gaussian equivalence property for the
trained feature map, and prove that the learned kernel improves upon the
initial random features model, but cannot defeat the best linear model on the
input. Whereas for sufficiently large $\eta$, we prove that for certain $f^*$,
the same ridge estimator on trained features can go beyond this "linear regime"
and outperform a wide range of random features and rotationally invariant
kernels. Our results demonstrate that even one gradient step can lead to a
considerable advantage over random features, and highlight the role of learning
rate scaling in the initial phase of training.


title:
A Cross-Company Ethnographic Study on Software Teams for DevOps and
  Microservices: Organization, Benefits, and Issues

abstract:
  Context: DevOps and microservices are acknowledged to be important new
paradigms to tackle contemporary software demands and provide capabilities for
rapid and reliable software development. Industrial reports show that they are
quickly adopted together in massive software companies. However, because of the
technical and organizational requirements, many difficulties against efficient
implementation of the both emerge in real software teams. Objectives: This
study aims to discover the organization, benefits and issues of software teams
using DevOps & microservices from an immersive perspective. Method: An
ethnographic study was carried out in three companies with different business,
size, products, customers, and degree of globalization. All the three companies
claimed their adoption of DevOps and microservices. Seven months (cumulative)
of participant observations and nine interviews with practitioners were
conducted to collect the data of software teams related to DevOps and
microservices. A cross-company empirical investigation using grounded theory
was done by analyzing the archive data. Results: The adoption of DevOps and
microservices brings benefits to rapid delivery, ability improvements and
burden reduction, whilst the high cost and lack of practical guidance were
emerged. Moreover, our observations and interviews reflect that in software
teams, the relationship between DevOps and microservices is not significant,
which differs from the relationship described in the previous studies. Four
lessons for practitioners and four implications for researchers were discussed
based on our findings. Conclusion: Our findings contribute to the understanding
of the organization, benefits and issues of adopting DevOps and microservices
from an immersive perspective of software teams.


title: Model-Free Opponent Shaping

abstract:
  In general-sum games, the interaction of self-interested learning agents
commonly leads to collectively worst-case outcomes, such as defect-defect in
the iterated prisoner's dilemma (IPD). To overcome this, some methods, such as
Learning with Opponent-Learning Awareness (LOLA), shape their opponents'
learning process. However, these methods are myopic since only a small number
of steps can be anticipated, are asymmetric since they treat other agents as
naive learners, and require the use of higher-order derivatives, which are
calculated through white-box access to an opponent's differentiable learning
algorithm. To address these issues, we propose Model-Free Opponent Shaping
(M-FOS). M-FOS learns in a meta-game in which each meta-step is an episode of
the underlying ("inner") game. The meta-state consists of the inner policies,
and the meta-policy produces a new inner policy to be used in the next episode.
M-FOS then uses generic model-free optimisation methods to learn meta-policies
that accomplish long-horizon opponent shaping. Empirically, M-FOS
near-optimally exploits naive learners and other, more sophisticated algorithms
from the literature. For example, to the best of our knowledge, it is the first
method to learn the well-known Zero-Determinant (ZD) extortion strategy in the
IPD. In the same settings, M-FOS leads to socially optimal outcomes under
meta-self-play. Finally, we show that M-FOS can be scaled to high-dimensional
settings.


title:
Approximate Selection with Unreliable Comparisons in Optimal Expected
  Time

abstract:
  Given $n$ elements, an integer $k$ and a parameter $\varepsilon$, we study to
select an element with rank in $(k-n\varepsilon,k+n\varepsilon]$ using
unreliable comparisons where the outcome of each comparison is incorrect
independently with a constant error probability, and multiple comparisons
between the same pair of elements are independent. In this fault model, the
fundamental problems of finding the minimum, selecting the $k$-th smallest
element and sorting have been shown to require $\Theta\big(n \log
\frac{1}{Q}\big)$, $\Theta\big(n\log \frac{\min\{k,n-k\}}{Q}\big)$ and
$\Theta\big(n\log \frac{n}{Q}\big)$ comparisons, respectively, to achieve
success probability $1-Q$. Recently, Leucci and Liu proved that the approximate
minimum selection problem ($k=0$) requires expected
$\Theta(\varepsilon^{-1}\log \frac{1}{Q})$ comparisons.
  We develop a randomized algorithm that performs expected
$O(\frac{k}{n}\varepsilon^{-2} \log \frac{1}{Q})$ comparisons to achieve
success probability at least $1-Q$. We also prove that any randomized algorithm
with success probability at least $1-Q$ performs expected
$\Omega(\frac{k}{n}\varepsilon^{-2}\log \frac{1}{Q})$ comparisons. Our results
indicate a clear distinction between approximating the minimum and
approximating the $k$-th smallest element, which holds even for the high
probability guarantee, e.g., if $k=\frac{n}{2}$ and $Q=\frac{1}{n}$,
$\Theta(\varepsilon^{-1}\log n)$ versus $\Theta(\varepsilon^{-2}\log n)$.
Moreover, if $\varepsilon=n^{-\alpha}$ for $\alpha \in (0,\frac{1}{2})$, the
asymptotic difference is almost quadratic, i.e., $\tilde{\Theta}(n^{\alpha})$
versus $\tilde{\Theta}(n^{2\alpha})$.


title:
Does a Program Yield the Right Distribution? Verifying Probabilistic
  Programs via Generating Functions

abstract:
  We study discrete probabilistic programs with potentially unbounded looping
behaviors over an infinite state space. We present, to the best of our
knowledge, the first decidability result for the problem of determining whether
such a program generates exactly a specified distribution over its outputs
(provided the program terminates almost surely). The class of distributions
that can be specified in our formalism consists of standard distributions
(geometric, uniform, etc.) and finite convolutions thereof. Our method relies
on representing these (possibly infinite-support) distributions as probability
generating functions which admit effective arithmetic operations. We have
automated our techniques in a tool called prodigy, which supports automatic
invariance checking, compositional reasoning of nested loops, and efficient
queries on various quantities of to the output distribution, as demonstrated by
experiments.


title: New Monitoring Interface for the AMS Experiment

abstract:
  The Alpha Magnetic Spectrometer (AMS) is constantly exposed to harsh
condition on the ISS. As such, there is a need to constantly monitor and
perform adjustments to ensure the AMS operates safely and efficiently. With the
addition of the Upgraded Tracker Thermal Pump System, the legacy monitoring
interface was no longer suitable for use. This paper describes the new AMS
Monitoring Interface (AMI). The AMI is built with state-of-the-art time series
database and analytics software. It uses a custom feeder program to process AMS
Raw Data as time series data points, feeds them into InfluxDB databases, and
uses Grafana as a visualization tool. It follows modern design principles,
allowing client CPUs to handle the processing work, distributed creation of AMI
dashboards, and up-to-date security protocols. In addition, it offers a more
simple way of modifying the AMI and allows the use of APIs to automate backup
and synchronization. The new AMI has been in use since January 2020 and was a
crucial component in remote shift taking during the COVID-19 pandemic.


title:
Comparison of the star formation of X-ray selected active galactic
  nuclei in eFEDS with star-forming galaxies

abstract:
  We use $\sim 1800$ X-ray Active Galactic Nuclei (AGN) in the eROSITA Final
Equatorial-Depth Survey (eFEDS), that span over two orders of magnitude in
X-ray luminosity, $\rm L_{X,2-10keV} \approx 10^{43-45}\,ergs^{-1}$, and
compare their star-formation rate (SFR) relative to that of non-AGN
star-forming systems, at $\rm 0.5<z<1.5$. For that purpose, we compile a
reference galaxy catalogue with $\sim 17000$ sources. Both samples have the
same photometric coverage, from optical to far-infrared. We construct the
spectral energy distributions (SEDs) of all sources and fit them using the
CIGALE code, utilizing the same templates and parametric grid for both samples.
We account for the mass incompleteness and exclude quiescent systems from both
datasets. These allow us to compare the SFR of the two populations in a uniform
manner, minimising systematic effects. Based on our analysis, AGN at low and
moderate L$_X$ ($\rm L_{X,2-10keV} < 10^{44}\,ergs^{-1}$), have SFR that is
lower, or at most, equal to that of star-forming galaxies, in agreement with
previous studies. The large number of luminous X-ray AGN available in the eFEDS
dataset, enable us to expand our investigations at higher L$_X$ to test
previous, tentative results. At $\rm L_{X,2-10keV} > 10^{44.2}\,ergs^{-1}$, the
SFR of AGN appears enhanced, by $\sim 30\%$, compared to that of star-forming
sources, for systems with stellar mass, $\rm 10.5 < log\,[M_*(M_\odot)] <
11.5$, confirming indications found in previous studies. The most massive
sources $\rm log\,[M_*(M_\odot)] > 11.5$, present a flat SFR$_{norm}$-L$_X$
relation up to $\rm L_{X,2-10keV} \sim 10^{44.5}\,ergs^{-1}$, with SFR similar
to that of star-forming galaxies. However, at higher L$_X$ ($\rm L_{X,2-10keV}
\sim 10^{45}\,ergs^{-1}$), we find indications that the SFR of these massive
AGN hosts may be enhanced compared to that of non-AGN systems.


title:
On the Issue of Textured Crystallization of Ba(NO$_3$)$_2$ in Mesoporous
  SiO$_2$: Raman Spectroscopy and Lattice Dynamics Analysis

abstract:
  The lattice dynamics of preferentially aligned nanocrystals formed upon
drying of aqueous Ba(NO$_3$)$_2$ solutions in a mesoporous silica glass
traversed by tubular pores of approximately 12 nm are explored by Raman
scattering. To interpret the experiments on the confined nanocrystals polarized
Raman spectra of bulk single crystals and X-ray diffraction experiments are
also performed. Since a cubic symmetry is inherent to Ba(NO$_3$)$_2$, a special
Raman scattering geometry was utilized to separate the phonon modes of A$_g$
and E$_g$ species. Combining group-theory analysis and \textit{ab initio}
lattice dynamics calculations a full interpretation of all Raman lines of the
bulk single crystal is achieved. Apart from a small confinement-induced line
broadening, the peak positions and normalized peak intensities of the Raman
spectra of the nanoconfined and macroscopic crystals are identical.
Interestingly, the Raman scattering experiment indicates the existence of
comparatively large, $\sim$10-20 $\mu$m, single-crystalline regions of
Ba(NO$_3$)$_2$ embedded in the porous host, near three orders of magnitude
larger than the average size of single nanopores. This is contrast to the
initial assumption of non-interconnected pores. It rather indicates an
inter-pore propagation of the crystallization front, presumably via
microporosity in the pore walls.

categories: cond-mat.mtrl-sci cond-mat.mes-hall physics.app-ph physics.chem-ph physics.optics


title: Understanding the Moments of Tabulation Hashing via Chaoses

abstract:
  Simple tabulation hashing dates back to Zobrist in 1970 and is defined as
follows: Each key is viewed as $c$ characters from some alphabet $\Sigma$, we
have $c$ fully random hash functions $h_0, \ldots, h_{c - 1} \colon \Sigma \to
\{0, \ldots, 2^l - 1\}$, and a key $x = (x_0, \ldots, x_{c - 1})$ is hashed to
$h(x) = h_0(x_0) \oplus \ldots \oplus h_{c - 1}(x_{c - 1})$ where $\oplus$ is
the bitwise XOR operation. The previous results on tabulation hashing by P{\v
a}tra{\c s}cu and Thorup~[J.ACM'11] and by Aamand et al.~[STOC'20] focused on
proving Chernoff-style tail bounds on hash-based sums, e.g., the number keys
hashing to a given value, for simple tabulation hashing, but their bounds do
not cover the entire tail.
  Chaoses are random variables of the form $\sum a_{i_0, \ldots, i_{c - 1}}
X_{i_0} \cdot \ldots \cdot X_{i_{c - 1}}$ where $X_i$ are independent random
variables. Chaoses are a well-studied concept from probability theory, and
tight analysis has been proven in several instances, e.g., when the independent
random variables are standard Gaussian variables and when the independent
random variables have logarithmically convex tails. We notice that hash-based
sums of simple tabulation hashing can be seen as a sum of chaoses that are not
independent. This motivates us to use techniques from the theory of chaoses to
analyze hash-based sums of simple tabulation hashing.
  In this paper, we obtain bounds for all the moments of hash-based sums for
simple tabulation hashing which are tight up to constants depending only on
$c$. In contrast with the previous attempts, our approach will mostly be
analytical and does not employ intricate combinatorial arguments. The improved
analysis of simple tabulation hashing allows us to obtain bounds for the
moments of hash-based sums for the mixed tabulation hashing introduced by
Dahlgaard et al.~[FOCS'15].


title: Analogue Penrose process in rotating acoustic black Hole

abstract:
  Analogue gravity stands today as an important tool for the investigation of
gravitational phenomena that would be otherwise out of reach considering our
technology. We consider here the analogue Penrose process in a rotating
acoustic black hole based on a quantum fluid described by the draining bathtub
model. Because of the rotating nature of this acoustic spacetime, a particle,
that is a phonon travelling on the quantum fluid, will experience the well
known frame dragging effect. By defining the effective Komar mass and angular
momentum of this acoustic black hole, we found that energy can be extracted
from it. At the same time, we show that the black hole angular momentum and
mass are reduced, in complete analogy with the Penrose effect.


title: Our Galaxy's youngest disc

abstract:
  We investigate the structure of our Galaxy's young stellar disc by fitting
the distribution functions (DFs) of a new family to five-dimensional Gaia data
for a sample of $47\,000$ OB stars. Tests of the fitting procedure show that
the young disc's DF would be strongly constrained by Gaia data if the
distribution of Galactic dust were accurately known. The DF that best fits the
real data accurately predicts the kinematics of stars at their observed
locations, but it predicts the spatial distribution of stars poorly, almost
certainly on account of errors in the best-available dust map. We argue that
dust models could be greatly improved by modifying the dust model until the
spatial distribution of stars predicted by a DF agreed with the data. The
surface density of OB stars is predicted to peak at $R\simeq5.5\mbox{kpc}$,
slightly outside the reported peak in the surface density of molecular gas; we
suggest that the latter radius may have been under-estimated through the use of
poor kinematic distances. The velocity distributions predicted by the best-fit
DF for stars with measured line-of-sight velocities $v_\parallel$ reveal that
the outer disc is disturbed at the level of 10 $\mbox{km}~\mbox{s}^{-1}$ in
agreement with earlier studies, and that the measured values of $v_\parallel$
have significant contributions from the orbital velocities of binaries. Hence
the outer disc is colder than it is sometimes reported to be.


title: Schur properties of randomly perturbed sets

abstract:
  A set $A$ of integers is said to be Schur if any two-colouring of $A$ results
in monochromatic $x,y$ and $z$ with $x+y=z$. We study the following problem:
how many random integers from $[n]$ need to be added to some $A\subseteq [n]$
to ensure with high probability that the resulting set is Schur? Hu showed in
1980 that when $|A|> \lceil\tfrac{4n}{5}\rceil$, no random integers are needed,
as $A$ is already guaranteed to be Schur. Recently, Aigner-Horev and Person
showed that for any dense set of integers $A\subseteq [n]$, adding
$\omega(n^{1/3})$ random integers suffices, noting that this is optimal for
sets $A$ with $|A|\leq \lceil\tfrac{n}{2}\rceil$. We close the gap between
these two results by showing that if $A\subseteq [n]$ with
$|A|=\lceil\tfrac{n}{2}\rceil+t<\lceil\tfrac{4n}{5}\rceil$, then adding
$\omega(\min\{n^{1/3},nt^{-1}\})$ random integers will with high probability
result in a set that is Schur. Our result is optimal for all $t$, and we
further provide a stability result showing that one needs far fewer random
integers when $A$ is not close in structure to the extremal examples. We also
initiate the study of perturbing sparse sets of integers $A$ by using
algorithmic arguments and the theory of hypergraph containers to provide
nontrivial upper and lower bounds.


title: Efficient implementation of incremental proximal-point methods

abstract:
  Model training algorithms which observe a small portion of the training set
in each computational step are ubiquitous in practical machine learning, and
include both stochastic and online optimization methods. In the vast majority
of cases, such algorithms typically observe the training samples via the
gradients of the cost functions the samples incur. Thus, these methods exploit
are the \emph{slope} of the cost functions via their first-order
approximations.
  To address limitations of gradient-based methods, such as sensitivity to
step-size choice in the stochastic setting, or inability to exploit small
function variability in the online setting, several streams of research attempt
to exploit more information about the cost functions than just their gradients
via the well-known proximal framework of optimization. However, implementing
such methods in practice poses a challenge, since each iteration step boils
down to computing a proximal operator, which may not be easy. In this work we
provide efficient algorithms and corresponding implementations of proximal
operators in order to make experimentation with incremental proximal
optimization algorithms accessible to a larger audience of researchers and
practitioners, and in particular to promote additional theoretical research
into these methods by closing the gap between their theoretical description in
research papers and their use in practice. The corresponding code is published
at https://github.com/alexshtf/inc_prox_pt.


title: Frequency-Selective Geometry Upsampling of Point Clouds

abstract:
  The demand for high-resolution point clouds has increased throughout the last
years. However, capturing high-resolution point clouds is expensive and thus,
frequently replaced by upsampling of low-resolution data. Most state-of-the-art
methods are either restricted to a rastered grid, incorporate normal vectors,
or are trained for a single use case. We propose to use the frequency
selectivity principle, where a frequency model is estimated locally that
approximates the surface of the point cloud. Then, additional points are
inserted into the approximated surface. Our novel frequency-selective geometry
upsampling shows superior results in terms of subjective as well as objective
quality compared to state-of-the-art methods for scaling factors of 2 and 4. On
average, our proposed method shows a 4.4 times smaller point-to-point error
than the second best state-of-the-art PU-Net for a scale factor of 4.


title:
Improvement of a conserved current density versus adding a total
  derivative to a Lagrangian density

abstract:
  For classical relativistic field theory in Minkowski space-time, the addition
of a superpotential term to a conserved current density is trivial in the sense
that it does not modify the local conservation law nor change the conserved
charge, though it may allow us to obtain a current density with some improved
properties. The addition of a total derivative term to a Lagrangian density is
also trivial in the sense that it does not modify the equations of motion of
the theory. These facts suggest that both operations are related and possibly
equivalent to each other for any global symmetry of an action functional. We
address this question following the study of two quite different (and well
known) instances: the Callan-Coleman-Jackiw improvement of the canonical
energy-momentum tensor for scalar and vector fields (providing an on-shell
traceless energy-momentum tensor) and the construction of a current density
satisfying a zero curvature condition for two-dimensional sigma models on
deformed spaces (notably the squashed three-sphere and warped AdS spaces).
These instances correspond to fairly different implementations of the general
results. An appendix addresses the precise relationship between the approaches
to local conservation laws based on active and passive symmetry
transformations, respectively.


title: 3D Semantic Scene Perception using Distributed Smart Edge Sensors

abstract:
  We present a system for 3D semantic scene perception consisting of a network
of distributed smart edge sensors. The sensor nodes are based on an embedded
CNN inference accelerator and RGB-D and thermal cameras. Efficient vision CNN
models for object detection, semantic segmentation, and human pose estimation
run on-device in real time. 2D human keypoint estimations, augmented with the
RGB-D depth estimate, as well as semantically annotated point clouds are
streamed from the sensors to a central backend, where multiple viewpoints are
fused into an allocentric 3D semantic scene model. As the image interpretation
is computed locally, only semantic information is sent over the network. The
raw images remain on the sensor boards, significantly reducing the required
bandwidth, and mitigating privacy risks for the observed persons. We evaluate
the proposed system in challenging real-world multi-person scenes in our lab.
The proposed perception system provides a complete scene view containing
semantically annotated 3D geometry and estimates 3D poses of multiple persons
in real time.


title:
Tunable Non-equilibrium Phase Transitions between Spatial and Temporal
  Order through Dissipation

abstract:
  We propose an experiment with a driven quantum gas coupled to a dissipative
optical cavity that realizes a novel kind of far-from-equilibrium phase
transition between spatial and temporal order. The control parameter of the
transition is the detuning between the drive frequency and the cavity
resonance. For negative detunings, the system features a spatially ordered
phase, while positive detunings lead to a phase with both spatial order and
persistent oscillations, which we call dissipative spatio-temporal lattice. We
give numerical and analytical evidence for this superradiant phase transition
and show that the spatio-temporal lattice originates from cavity dissipation.
In both regimes the atoms are subject to an accelerated transport, either via a
uniform acceleration or via abrupt transitions to higher momentum states. Our
work provides perspectives for temporal phases of matter that are not possible
at equilibrium.


title: Deep learning of quantum entanglement from incomplete measurements

abstract:
  The quantification of the entanglement present in a physical system is of
paramount importance for fundamental research and many cutting-edge
applications. Currently, achieving this goal requires very demanding
experimental procedures such as full state tomography. Here, we demonstrate
that by employing neural networks we can quantify the degree of entanglement
without needing to know the full description of the quantum state. Our method
allows for direct quantification of the quantum correlations using an
incomplete set of local measurements. Despite using under-sampled measurements,
we achieve an estimation error of up to an order of magnitude lower than the
state-of-the-art quantum tomography. Furthermore, we achieve this result
employing networks trained using exclusively simulated data. Finally, we derive
a method based on a convolutional network input that can accept data from
various measurement scenarios and perform, to some extent, independently of the
measurement device.


title:
Discrete Nonlocal Nonlinear Schroedinger equation on Metric Graphs:
  Dynamics of PT-Symmetric Solitons in Discrete Networks

abstract:
  We consider PT-symmetric, discrete nonlocal nonlinear Schr\"{o}dinger
equation on metric graphs. Soliton solutions are obtained for simplest graph
topologies, such as star and tree graphs. Integrability of the problem is shown
by proving existence of infinite number of conservation laws.


title: Inducing and Using Alignments for Transition-based AMR Parsing

abstract:
  Transition-based parsers for Abstract Meaning Representation (AMR) rely on
node-to-word alignments. These alignments are learned separately from parser
training and require a complex pipeline of rule-based components,
pre-processing, and post-processing to satisfy domain-specific constraints.
Parsers also train on a point-estimate of the alignment pipeline, neglecting
the uncertainty due to the inherent ambiguity of alignment. In this work we
explore two avenues for overcoming these limitations. First, we propose a
neural aligner for AMR that learns node-to-word alignments without relying on
complex pipelines. We subsequently explore a tighter integration of aligner and
parser training by considering a distribution over oracle action sequences
arising from aligner uncertainty. Empirical results show this approach leads to
more accurate alignments and generalization better from the AMR2.0 to AMR3.0
corpora. We attain a new state-of-the art for gold-only trained models,
matching silver-trained performance without the need for beam search on AMR3.0.


title: Sub-lattice entanglement in an exactly solvable anyon-like spin ladder

abstract:
  We introduce an integrable spin ladder model and study its exact solution,
correlation functions, and entanglement properties. The model supports two
particle types (corresponding to the even and odd sub-lattices), such that the
scattering phases are constants: particles of the same type scatter as free
fermions, whereas the inter-particle phase shift is a constant tuned by an
interaction parameter. Therefore, the spin ladder bears similarities with
anyonic models. We present exact results for the spectrum and correlation
functions, and we study the sub-lattice entanglement by numerical means.


title:
Fundamental effective temperature measurements for eclipsing binary
  stars -- III. SPIRou near-infrared spectroscopy and CHEOPS photometry of the
  benchmark G0V star EBLM J0113+31

abstract:
  EBLM J0113+31 is moderately bright (V=10.1), metal-poor ([Fe/H]$\approx-0.3$)
G0V star with a much fainter M dwarf companion on a wide, eccentric orbit
(=14.3 d). We have used near-infrared spectroscopy obtained with the SPIRou
spectrograph to measure the semi-amplitude of the M dwarf's spectroscopic
orbit, and high-precision photometry of the eclipse and transit from the CHEOPS
and TESS space missions to measure the geometry of this binary system. From the
combined analysis of these data together with previously published observations
we obtain the following model-independent masses and radii: $M_1 = 1.029 \pm
0.025 M_{\odot}$, $M_2 = 0.197 \pm 0.003 M_{\odot}$, $R_1 = 1.417 \pm 0.014
R_{\odot}$, $R_2 = 0.215 \pm 0.002 R_{\odot}$. Using $R_1$ and the parallax
from Gaia EDR3 we find that this star's angular diameter is $\theta = 0.0745
\pm 0.0007$ mas. The apparent bolometric flux of the G0V star corrected for
both extinction and the contribution from the M dwarf ($<0.2$ per cent) is
${\mathcal F}_{\oplus,0} = (2.62\pm 0.05)\times10^{-9}$ erg.cm$^{-2}$.s$^{-1}$.
Hence, this G0V star has an effective temperature $T_{\rm eff,1} = 6124{\rm\,K}
\pm 40{\rm \,K\,(rnd.)} \pm 10 {\rm \,K\,(sys.)}$. EBLM J0113+31 is an ideal
benchmark star that can be used for "end-to-end" tests of the stellar
parameters measured by large-scale spectroscopic surveys, or stellar parameters
derived from asteroseismology with PLATO. The techniques developed here can be
applied to many other eclipsing binaries in order to create a network of such
benchmark stars.


title: On the Effect of Information Asymmetry in Human-AI Teams

abstract:
  Over the last years, the rising capabilities of artificial intelligence (AI)
have improved human decision-making in many application areas. Teaming between
AI and humans may even lead to complementary team performance (CTP), i.e., a
level of performance beyond the ones that can be reached by AI or humans
individually. Many researchers have proposed using explainable AI (XAI) to
enable humans to rely on AI advice appropriately and thereby reach CTP.
However, CTP is rarely demonstrated in previous work as often the focus is on
the design of explainability, while a fundamental prerequisite -- the presence
of complementarity potential between humans and AI -- is often neglected.
Therefore, we focus on the existence of this potential for effective human-AI
decision-making. Specifically, we identify information asymmetry as an
essential source of complementarity potential, as in many real-world
situations, humans have access to different contextual information. By
conducting an online experiment, we demonstrate that humans can use such
contextual information to adjust the AI's decision, finally resulting in CTP.


title:
Layer-by-layer growth of bilayer graphene single-crystals enabled by
  self-transmitting catalytic activity

abstract:
  Direct growth of large-area vertically stacked two-dimensional (2D) van der
Waal (vdW) materials is a prerequisite for their high-end applications in
integrated electronics, optoelectronics and photovoltaics. Currently,
centimetre- to even metre-scale monolayers of single-crystal graphene (MLG) and
hexagonal boron nitride (h-BN) have been achieved by epitaxial growth on
various single-crystalline substrates. However, in principle, this success in
monolayer epitaxy seems extremely difficult to be replicated to bi- or
few-layer growth, as the full coverage of the first layer was believed to
terminate the reactivity of those adopting catalytic metal surfaces. Here, we
report an exceptional layer-by-layer chemical vapour deposition (CVD) growth of
large size bi-layer graphene single-crystals, enabled by self-transmitting
catalytic activity from platinum (Pt) surfaces to the outermost graphene
layers. In-situ growth and real-time surveillance experiments, under
well-controlled environments, unambiguously verify that the growth does follow
the layer-by-layer mode on open surfaces of MLG/Pt(111). First-principles
calculations indicate that the transmittal of catalytic activity is allowed by
an appreciable electronic hybridisation between graphene overlayers and Pt
surfaces, enabling catalytic dissociation of hydrocarbons and subsequently
direct graphitisation of their radicals on the outermost sp2 carbon surface.
This self-transmitting catalytic activity is also proven to be robust for
tube-furnace CVD in fabricating single-crystalline graphene bi-, tri- and
tetra-layers, as well as h-BN few-layers. Our findings offer an exceptional
strategy for potential controllable, layer-by-layer and wafer-scale growth of
vertically stacked few-layered 2D single crystals.


title: On the Convergence of Fictitious Play: A Decomposition Approach

abstract:
  Fictitious play (FP) is one of the most fundamental game-theoretical learning
frameworks for computing Nash equilibrium in $n$-player games, which builds the
foundation for modern multi-agent learning algorithms. Although FP has provable
convergence guarantees on zero-sum games and potential games, many real-world
problems are often a mixture of both and the convergence property of FP has not
been fully studied yet. In this paper, we extend the convergence results of FP
to the combinations of such games and beyond. Specifically, we derive new
conditions for FP to converge by leveraging game decomposition techniques. We
further develop a linear relationship unifying cooperation and competition in
the sense that these two classes of games are mutually transferable. Finally,
we analyze a non-convergent example of FP, the Shapley game, and develop
sufficient conditions for FP to converge.


title:
Revisiting Communication-Efficient Federated Learning with Balanced
  Global and Local Updates

abstract:
  In federated learning (FL), a number of devices train their local models and
upload the corresponding parameters or gradients to the base station (BS) to
update the global model while protecting their data privacy. However, due to
the limited computation and communication resources, the number of local
trainings (a.k.a. local update) and that of aggregations (a.k.a. global update)
need to be carefully chosen. In this paper, we investigate and analyze the
optimal trade-off between the number of local trainings and that of global
aggregations to speed up the convergence and enhance the prediction accuracy
over the existing works. Our goal is to minimize the global loss function under
both the delay and the energy consumption constraints. In order to make the
optimization problem tractable, we derive a new and tight upper bound on the
loss function, which allows us to obtain closed-form expressions for the number
of local trainings and that of global aggregations. Simulation results show
that our proposed scheme can achieve a better performance in terms of the
prediction accuracy, and converge much faster than the baseline schemes.


title:
A Search for Exoplanets in Open Clusters and Young Associations based on
  TESS Objects of Interest

abstract:
  We report the results of our search of planet candidates in Open Clusters and
Young Stellar Associations based on the TESS Objects of Interest Catalog. We
find one confirmed planet, one promising candidate, one brown dwarf, and three
unverified planet candidates in a sample of 1229 Open Clusters from the second
Gaia data release. We discuss individual planet-star systems based on their
basic parameters, membership probability, and the observation notes from the
ExoFOP-TESS website. We also find ten planet candidates (P $>$ 95%) in Young
Stellar Associations by using the BANYAN $\Sigma$ Bayesian Algorithm. Among the
ten candidates, five are known planet systems. We estimate the rotation periods
of the host stars using the TESS light curves and estimate their ages based on
gyrochronology. Two candidates with periodic variations are likely to be young
planets, but their exact memberships to Young Stellar Associations remain
unknown.


title:
Learning Economic Indicators by Aggregating Multi-Level Geospatial
  Information

abstract:
  High-resolution daytime satellite imagery has become a promising source to
study economic activities. These images display detailed terrain over large
areas and allow zooming into smaller neighborhoods. Existing methods, however,
have utilized images only in a single-level geographical unit. This research
presents a deep learning model to predict economic indicators via aggregating
traits observed from multiple levels of geographical units. The model first
measures hyperlocal economy over small communities via ordinal regression. The
next step extracts district-level features by summarizing interconnection among
hyperlocal economies. In the final step, the model estimates economic
indicators of districts via aggregating the hyperlocal and district
information. Our new multi-level learning model substantially outperforms
strong baselines in predicting key indicators such as population, purchasing
power, and energy consumption. The model is also robust against data shortage;
the trained features from one country can generalize to other countries when
evaluated with data gathered from Malaysia, the Philippines, Thailand, and
Vietnam. We discuss the multi-level model's implications for measuring
inequality, which is the essential first step in policy and social science
research on inequality and poverty.


title:
A whole-body multi-scale mathematical model for dynamic simulation of
  the metabolism in man

abstract:
  We propose a whole-body model of the metabolism in man as well as a
generalized approach for modeling metabolic networks. Using this approach, we
are able to write a large metabolic network in a systematic and compact way. We
demonstrate the approach using a whole-body model of the metabolism of the
three macronutrients, carbohydrates, proteins and lipids. The model contains 7
organs, 16 metabolites and 31 enzymatic reactions. All reaction rates are
described by Michaelis-Menten kinetics with an addition of a hormonal regulator
based on the two hormones insulin and glucagon. We incorporate ingestion of
food in order to simulate metabolite concentrations during the feed-fast cycle.
The model can simulate several days due to the inclusion of storage forms
(glycogen, muscle protein and lipid droplets), that can be depleted if food is
not ingested regularly. A physiological model incorporating complex cellular
metabolism and whole-body mass dynamics can be used in virtual clinical trials.
Such trials can be used to improve the development of medicine, treatment
strategies such as control algorithms, and increase the likelihood of a
successful clinical trial.


title: The rheology of confined colloidal hard discs

abstract:
  Colloids may be treated as `big atoms' so that they are good models for
atomic and molecular systems. Colloidal hard disks are therefore good models
for 2d materials and although their phase behavior is well characterized,
rheology has received relatively little attention. Here we exploit a novel,
particle-resolved, experimental set-up and complementary computer simulations
to measure the shear rheology of quasi-hard-disc colloids in extreme
confinement. In particular, we confine quasi-2d hard discs in a circular
`corral' comprised of 27 particles held in optical traps. Confinement and shear
suppress hexagonal ordering that would occur in the bulk and create a layered
fluid. We measure the rheology of our system by balancing drag and driving
forces on each layer. Given the extreme confinement, it is remarkable that our
system exhibits rheological behavior very similar to unconfined 2d and 3d hard
particle systems, characterized by a dynamic yield stress and shear-thinning of
comparable magnitude. By quantifying particle motion perpendicular to shear, we
show that particles become more tightly confined to their layers with no
concomitant increase in density upon increasing shear rate. Shear thinning is
therefore a consequence of a reduction in dissipation due to a weakening in
interactions between layers as the shear rate increases. We reproduce our
experiments with Brownian Dynamics simulations with Hydrodynamic Interactions
(HI) included at the level of the Rotne-Prager tensor. That the inclusion of HI
is necessary to reproduce our experiments is evidence of their importance in
transmission of momentum through the system.


title:
Picophotonics -- Subatomic Optical Resolution Beyond Thermal
  Fluctuations

abstract:
  Despite recent tremendous progress in optical imaging and metrology, the
resolution gap between atomic scale transmission electron microscopy and
optical techniques has not been closed. Is optical imaging and metrology of
nanostructures exhibiting Brownian motion possible with resolution beyond
thermal fluctuations? Here we report on an experiment in which the average
position of a nanowire with a thermal oscillation amplitude of ~150 pm is
resolved in single-shot measurements with absolute error of ~30 pm using light
at a wavelength of {\lambda} = 488 nm, providing the first example of such
sub-Brownian metrology with {\lambda}/10,000 resolution. To localize the
nanowire, we employ a deep learning analysis of the scattering of topologically
structured light, which is highly sensitive to the nanowire's position. As a
non-invasive optical metrology with sub-Brownian absolute errors, down to a
fraction of the typical size of an atom (Si: 220 pm diameter), it opens the
exciting field of picophotonics.


title: Real-Time Streaming and Event-driven Control of Scientific Experiments

abstract:
  Advancements in scientific instrument sensors and connected devices provide
unprecedented insight into ongoing experiments and present new opportunities
for control, optimization, and steering. However, the diversity of sensors and
heterogeneity of their data result in make it challenging to fully realize
these new opportunities. Organizing and synthesizing diverse data streams in
near-real-time requires both rich automation and Machine Learning (ML). To
efficiently utilize ML during an experiment, the entire ML lifecycle must be
addressed, including refining experiment configurations, retraining models, and
applying decisions-tasks that require an equally diverse array of computational
resources spanning centralized HPC to the accelerators at the edge. Here we
present the Manufacturing Data and Machine Learning platform (MDML). The MDML
is designed to standardize the research and operational environment for
advanced data analytics and ML-enabled automated process optimization by
providing the cyberinfrastructure to integrate sensor data streams and AI in
cyber-physical systems for in-situ analysis. To achieve this, the MDML provides
a fabric to receive and aggregate IoT data and simultaneously orchestrate
remote computation across the computing continuum. In this paper we describe
the MDML and show how it is used in advanced manufacturing to act on IoT data
and orchestrate distributed ML to guide experiments.


title: TASEH: A haloscope axion search experiment

abstract:
  We report on a holoscope axion search experiment near $20\ {\rm \mu eV}$ from
the TASEH collaboration. The experiment is carried out via a frequency-tunable
cavity detector with a volume $V = 0.234\ {\rm liter}$ in a magnetic field $B_0
= 8\ {\rm T}$. With a signal receiver that has a system noise temperature
$T_{\rm sys} \cong 2.2\ {\rm K}$ and experiment time about 1 month, the search
excludes values of the axion-photon coupling constant $g_{\rm a\gamma\gamma}
\gtrsim 8.2 \times 10^{-14} \ {\rm GeV}^{-1}$, a factor of 11 above the KSVZ
model, at the 95\% confidence level in the mass range of $19.4687-19.8436\ {\rm
\mu eV}$. We present the experimental setup and procedures to accomplish this
search.


title:
Eigenvector centrality for multilayer networks with dependent node
  importance

abstract:
  We present an approach for computing eigenvector centrality for multilayer
networks with interlayer constraints on node importance. Specifically, we
consider a multilayer network defined by multiple edge-weighted, potentially
directed, graphs over the same set of nodes with each graph representing one
layer of the network. In this scenario, edges between layers are not allowed.
As in the standard eigenvector centrality construction, the importance each
node in a given layer is based on the weighted sum of the importance of
adjacent nodes in that same layer. Unlike standard eigenvector centrality, we
assume that the adjacency relationship and the importance of adjacent nodes may
be based on distinct layers. This form of centrality constraint between the
layers leads to eigenvector centrality values defined by a system of
interdependent eigenvalue problems, whose solution can be efficiently realized
using an interleaved power iteration algorithm.


title: Dwork-type congruences and $p$-adic KZ connection

abstract:
  We show that the $p$-adic KZ connection associated with the family of curves
$y^q=(t-z_1)\dots (t-z_{qg+1})$ has an invariant subbundle of rank $g$, while
the corresponding complex KZ connection has no nontrivial proper subbundles due
to the irreducibility of its monodromy representation. The construction of the
invariant subbundle is based on new Dwork--type congruences for associated
Hasse--Witt matrices.


title:
Residual Graph Convolutional Recurrent Networks For Multi-step Traffic
  Flow Forecasting

abstract:
  Traffic flow forecasting is essential for traffic planning, control and
management. The main challenge of traffic forecasting tasks is accurately
capturing traffic networks' spatial and temporal correlation. Although there
are many traffic forecasting methods, most of them still have limitations in
capturing spatial and temporal correlations. To improve traffic forecasting
accuracy, we propose a new Spatial-temporal forecasting model, namely the
Residual Graph Convolutional Recurrent Network (RGCRN). The model uses our
proposed Residual Graph Convolutional Network (ResGCN) to capture the
fine-grained spatial correlation of the traffic road network and then uses a
Bi-directional Gated Recurrent Unit (BiGRU) to model time series with spatial
information and obtains the temporal correlation by analysing the change in
information transfer between the forward and reverse neurons of the time series
data. Our comparative experimental results on two real datasets show that RGCRN
improves on average by 20.66% compared to the best baseline model. You can get
our source code and data through https://github.com/zhangshqii/RGCRN.


title:
Remote distribution of non-classical correlations over 1250 modes
  between a telecom photon and a $^{171}$Yb$^{3+}$:Y$_2$SiO$_{5}$ crystal

abstract:
  Quantum repeaters based on heralded entanglement require quantum nodes that
are able to generate multimode quantum correlations between memories and
telecommunication photons. The communication rate scales linearly with the
number of modes, yet highly multimode quantum storage remains challenging. In
this work, we demonstrate an atomic frequency comb quantum memory with a
time-domain mode capacity of 1250 modes and a bandwidth of 100 MHz, to our
knowledge the largest number of modes stored in the quantum regime. The memory
is based on a $Y_{2}SiO_{5}$ crystal doped with $^{171}Yb^{3+}$ ions, with a
memory wavelength of 979 nm. The memory is interfaced with a source of
non-degenerate photon pairs at 979 and 1550 nm, bandwidth-matched to the
quantum memory. We obtain strong non-classical second-order cross correlations
over all modes, for storage times of up to $25$ $\mu s$. The telecommunication
photons propagated through 5 km of fiber before the release of the memory
photons, a key capability for quantum repeaters based on heralded entanglement
and feed-forward operations. Building on this experiment should allow
distribution of entanglement between remote quantum nodes, with enhanced rates
owing to the high multimode capacity.


title: Hardness Results for Weaver's Discrepancy Problem

abstract:
  Marcus, Spielman and Srivastava (Annals of Mathematics 2014) solved the
Kadison--Singer Problem by proving a strong form of Weaver's conjecture: they
showed that for all $\alpha > 0$ and all lists of vectors of norm at most
$\sqrt{\alpha}$ whose outer products sum to the identity, there exists a signed
sum of those outer products with operator norm at most $\sqrt{8 \alpha} + 2
\alpha.$ We prove that it is NP-hard to distinguish such a list of vectors for
which there is a signed sum that equals the zero matrix from those in which
every signed sum has operator norm at least $\kappa \sqrt{\alpha}$, for some
absolute constant $\kappa > 0.$ Thus, it is NP-hard to construct a signing that
is a constant factor better than that guaranteed to exist.
  For $\alpha = 1/4$, we prove that it is NP-hard to distinguish whether there
is a signed sum that equals the zero matrix from the case in which every signed
sum has operator norm at least $1/4$.


title: Hilbert Expansion for the Relativistic Landau Equation

abstract:
  In this paper, we study the local-in-time validity of the Hilbert expansion
for the relativistic Landau equation. We justify that solutions of the
relativistic Landau equation converge to small classical solutions of the
limiting relativistic Euler equations as the Knudsen number shrinks to zero in
a weighted Sobolev space. The key difficulty comes from the temporal and
spatial derivatives of the local Maxwellian, which produce momentum growth
terms and are uncontrollable by the standard $L^2$-based energy and
dissipation. We introduce novel time-dependent weight functions to generate
additional dissipation terms to suppress the large momentum. The argument
relies on a hierarchy of energy-dissipation structures with or without weights.
As far as the authors are aware of, this is the first result of the Hilbert
expansion for the Landau-type equation.


title:
A method for determining the transition energies of $^{\mathrm{83m}}$Kr
  at the KATRIN experiment

abstract:
  The neutrino mass experiment KATRIN uses conversion electrons from the
32.2-keV transition of the nuclear isomer $^{\mathrm{83m}}$Kr for calibration.
Comparing the measured energy of the conversion electrons to the literature
values allows for an independent evaluation of KATRIN's energy scale, but the
comparison is limited by the uncertainties in the literature values of the
transition energies. In this paper, a novel method to perform precision
spectroscopy of the transition energies is presented. Notably, the method uses
conversion electrons from the 41.6-keV direct transition of $^{\mathrm{83m}}$Kr
to its ground state in addition to conversion electrons from the much more
frequent cascade of a 32.2-keV and a 9.4-keV transition. By implementing this
method, KATRIN may be able to deliver order-of-magnitude improvements in
precision over current $^{\mathrm{83m}}$Kr transition energy literature values.
Such improvements would benefit not only KATRIN, but potentially also other
experiments which use $^{\mathrm{83m}}$Kr as a calibration source.


title:
Optimal $(r,\delta)$-LRCs from zero-dimensional affine variety codes and
  their subfield-subcodes

abstract:
  We introduce zero-dimensional affine variety codes (ZAVCs) which can be
regarded as $(r,\delta)$-locally recoverable codes (LRCs). These codes come
with a natural bound for their minimum distance and we determine those giving
rise to $(r,\delta)$-optimal LRCs for that distance, which are in fact
$(r,\delta)$-optimal. A large subfamily of ZAVCs admit subfield-subcodes with
the same parameters of the optimal codes but over smaller supporting fields.
This fact allows us to determine infinitely many sets of new
$(r,\delta)$-optimal LRCs and their parameters.


title: Scalable Regularised Joint Mixture Models

abstract:
  In many applications, data can be heterogeneous in the sense of spanning
latent groups with different underlying distributions. When predictive models
are applied to such data the heterogeneity can affect both predictive
performance and interpretability. Building on developments at the intersection
of unsupervised learning and regularised regression, we propose an approach for
heterogeneous data that allows joint learning of (i) explicit multivariate
feature distributions, (ii) high-dimensional regression models and (iii) latent
group labels, with both (i) and (ii) specific to latent groups and both
elements informing (iii). The approach is demonstrably effective in high
dimensions, combining data reduction for computational efficiency with a
re-weighting scheme that retains key signals even when the number of features
is large. We discuss in detail these aspects and their impact on modelling and
computation, including EM convergence. The approach is modular and allows
incorporation of data reductions and high-dimensional estimators that are
suitable for specific applications. We show results from extensive simulations
and real data experiments, including highly non-Gaussian data. Our results
allow efficient, effective analysis of high-dimensional data in settings, such
as biomedicine, where both interpretable prediction and explicit feature space
models are needed but hidden heterogeneity may be a concern.


title: On the $1$d cubic NLS with a non-generic potential

abstract:
  We consider the $1d$ cubic nonlinear Schr\"odinger equation with an external
potential $V$ that is non-generic. Without making any parity assumption on the
data, but assuming that the zero energy resonance of the associated
Schr\"odinger operator is either odd or even, we prove global-in-time
quantitative bounds and asymptotics for small solutions.
  First, we use a simple modification of the basis for the distorted Fourier
transform (dFT) to resolve the (possible) discontinuity at zero energy due to
the presence of a resonance and the absence of symmetry of the solution. We
then use a refined analysis of the low frequency structure of the (modified)
nonlinear spectral distribution, and employ smoothing estimates in the setting
of non-generic potentials.


title:
On the stability of strong-stability-preserving modified Patankar
  Runge-Kutta schemes

abstract:
  In this paper, we perform stability analysis for a class of second and third
order accurate strong-stability-preserving modified Patankar Runge-Kutta
(SSPMPRK) schemes, which were introduced in [4,5] and can be used to solve
convection equations with stiff source terms, such as reactive Euler equations,
with guaranteed positivity under the standard CFL condition due to the
convection terms only. The analysis allows us to identify the range of free
parameters in these SSPMPRK schemes in order to ensure stability. Numerical
experiments are provided to demonstrate the validity of the analysis.


title:
Computing the jump-term in space-time FEM for arbitrary temporal
  interpolation

abstract:
  One approach with rising popularity in analyzing time-dependent problems in
science and engineering is the so-called space-time finite-element method that
utilized finiteelements in both space and time. A common ansatz in this context
is to divide the mesh in temporal direction into so-called space-time slabs,
which are subsequently weakly connected in time with a Discontinuous Galerkin
approach. The corresponding jumpterm, which is responsible for imposing the
weak continuity across space-time slabs can be challenging to compute, in
particular in the context of deforming domains. Ensuring a conforming
discretization of the space-time slab at the top and bottom in time direction
simplifies the handling of this term immensely. Otherwise, a computationally
expensive and error prone projection of the solution from one time-level to
another is necessary. However, when it comes to simulations with deformable
domains, e.g. for free-surface flows, ensuring conforming meshes is quite
laborious. A possible solution to this challenge is to extrude a spatial mesh
in time at each time-step resulting in the so-called time-discontinuous
prismatic space-time (D-PST) method. However, this procedure is restricted to
finite-elements of 1st order in time. We present a novel algorithmic approach
for arbitrarily discretized meshes by flipping the mesh in time-direction for
each time-step. This ansatz allows for a simple evaluation of the jump-term as
the mesh is always conforming. The cost of flipping the mesh around its
symmetry plane in time scales with the number of nodes, which makes it
computationally cheaper than an additional update of the mesh to enforce
conformity or the evaluation of a projection. We validate the approach on
various physical problems with and without deforming domains.


title: Subspace Diffusion Generative Models

abstract:
  Score-based models generate samples by mapping noise to data (and vice versa)
via a high-dimensional diffusion process. We question whether it is necessary
to run this entire process at high dimensionality and incur all the
inconveniences thereof. Instead, we restrict the diffusion via projections onto
subspaces as the data distribution evolves toward noise. When applied to
state-of-the-art models, our framework simultaneously improves sample quality
-- reaching an FID of 2.17 on unconditional CIFAR-10 -- and reduces the
computational cost of inference for the same number of denoising steps. Our
framework is fully compatible with continuous-time diffusion and retains its
flexible capabilities, including exact log-likelihoods and controllable
generation. Code is available at
https://github.com/bjing2016/subspace-diffusion.


title:
A Comprehensive Survey of Image Augmentation Techniques for Deep
  Learning

abstract:
  Deep learning has been achieving decent performance in computer vision
requiring a large volume of images, however, collecting images is expensive and
difficult in many scenarios. To alleviate this issue, many image augmentation
algorithms have been proposed as effective and efficient strategies.
Understanding current algorithms is essential to find suitable methods or
develop novel techniques for given tasks. In this paper, we perform a
comprehensive survey on image augmentation for deep learning with a novel
informative taxonomy. To get the basic idea why we need image augmentation, we
introduce the challenges in computer vision tasks and vicinity distribution.
Then, the algorithms are split into three categories; model-free, model-based,
and optimizing policy-based. The model-free category employs image processing
methods while the model-based method leverages trainable image generation
models. In contrast, the optimizing policy-based approach aims to find the
optimal operations or their combinations. Furthermore, we discuss the current
trend of common applications with two more active topics, leveraging different
ways to understand image augmentation, such as group and kernel theory, and
deploying image augmentation for unsupervised learning. Based on the analysis,
we believe that our survey gives a better understanding helpful to choose
suitable methods or design novel algorithms for practical applications.


title:
A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor
  Embedding (SNE)

abstract:
  We propose a unified view on two widely used data visualization techniques:
Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show
that they can both be derived from a common mathematical framework. Leveraging
this formulation, we propose to compare SOM and SNE quantitatively on two
datasets, and discuss possible avenues for future work to take advantage of
both approaches.


title: On the uncertainty principle of neural networks

abstract:
  Despite the successes in many fields, it is found that neural networks are
vulnerability and difficult to be both accurate and robust (robust means that
the prediction of the trained network stays unchanged for inputs with
non-random perturbations introduced by adversarial attacks). Various empirical
and analytic studies have suggested that there is more or less a trade-off
between the accuracy and robustness of neural networks. If the trade-off is
inherent, applications based on the neural networks are vulnerable with
untrustworthy predictions. It is then essential to ask whether the trade-off is
an inherent property or not. Here, we show that the accuracy-robustness
trade-off is an intrinsic property whose underlying mechanism is deeply related
to the uncertainty principle in quantum mechanics. We find that for a neural
network to be both accurate and robust, it needs to resolve the features of the
two conjugated parts $x$ (the inputs) and $\Delta$ (the derivatives of the
normalized loss function $J$ with respect to $x$), respectively. Analogous to
the position-momentum conjugation in quantum mechanics, we show that the inputs
and their conjugates cannot be resolved by a neural network simultaneously.


title: Universality in the two-dimensional dilute Baxter-Wu model

abstract:
  We study the question of universality in the two-dimensional spin-$1$
Baxter-Wu model in the presence of a crystal field $\Delta$. We employ
extensive numerical simulations of two types, providing us with complementary
results: Wang-Landau sampling at fixed values of $\Delta$ and a parallelized
variant of the multicanonical approach performed at constant temperature $T$. A
detailed finite-size scaling analysis in the regime of second-order phase
transitions in the $(\Delta, T)$ phase diagram indicates that the transition
belongs to the universality class of the $4$-state Potts model. Previous
controversies with respect to the nature of the transition are discussed and
possibly attributed to the presence of strong finite-size effects, especially
as one approaches the pentacritical point of the model.


title: Graviton partial waves and causality in higher dimensions

abstract:
  Do gravitational interactions respect the basic principles of relativity and
quantum mechanics? We show that any graviton S-matrix that satisfies these
assumptions cannot significantly differ from General Relativity at low
energies. We provide sharp bounds on the size of potential corrections in terms
of the mass M of new higher-spin states, in spacetime dimensions D>=5 where the
S-matrix does not suffer from infrared ambiguities. The key novel ingredient is
the full set of SO(D-1) partial waves for this process, which we show how to
efficiently compute with Young tableau manipulations. We record new bounds on
the central charges of holographic conformal theories.


title:
Variational properties of the first curve of the Fu\v{c}\'{\i}k spectrum
  for elliptic operators

abstract:
  In this paper we present a new variational characteriztion of the first
nontrival curve of the Fu\v{c}\'{\i}k spectrum for elliptic operators with
Dirichlet boundary conditions. Moreover, we describe the asymptotic behaviour
and some properties of this curve and of the corresponding eigenfunctions. In
particular, this new characterization allows us to compare the first curve of
the Fu\v{c}\'{\i}k spectrum with the infinitely many curves we obtained in
previous works (see R. Molle, D. Passaseo, New properties of the Fu\v{c}\'{\i}k
spectrum. C. R. Math. Acad. Sci. Paris 351 (2013), no. 17/18, 681--685 and R.
Molle, D. Passaseo, Infinitely many new curves of the Fu\v{c}\'{\i}k spectrum.
Ann. I. H. Poincar\'e - AN (2014),
http://dx.doi.org/10.1016/j.anihpc.2014.05.007): for example, we show that
these curves are all asymptotic to the same lines as the first curve, but they
are all distinct from such a curve.


title: Semantic Diversity in Dialogue with Natural Language Inference

abstract:
  Generating diverse, interesting responses to chitchat conversations is a
problem for neural conversational agents. This paper makes two substantial
contributions to improving diversity in dialogue generation. First, we propose
a novel metric which uses Natural Language Inference (NLI) to measure the
semantic diversity of a set of model responses for a conversation. We evaluate
this metric using an established framework (Tevet and Berant, 2021) and find
strong evidence indicating NLI Diversity is correlated with semantic diversity.
Specifically, we show that the contradiction relation is more useful than the
neutral relation for measuring this diversity and that incorporating the NLI
model's confidence achieves state-of-the-art results. Second, we demonstrate
how to iteratively improve the semantic diversity of a sampled set of responses
via a new generation procedure called Diversity Threshold Generation, which
results in an average 137% increase in NLI Diversity compared to standard
generation procedures.


title:
Carrier Doping Modulates 2D Intrinsic Ferromagnetic Mn2Ge2Te6 Monolayer
  High Curie Temperature, Large Magnetic Crystal Anisotropy

abstract:
  The Mn2Ge2Te6 shows intrinsic ferromagnetic (FM) order, with Curie
temperature (Tc) of 316 K. The FM order origins from superexchange interaction
between Mn and Te atoms. Mn2Ge2Te6 is half-metal (HM), and spin-\b{eta}
electron is a semiconductor with gap of 1.462 eV. Mn2Ge2Te6 tends in-plane
anisotropy (IPA), with magnetic anisotropy energy (MAE) of -13.2 meV/f.u.. The
Mn2Ge2Te6 shows good dynamical and thermal stability. Moreover, Mn2Ge2Te6
presents good ferromagnetic and half-metallic stability under charge doping.
The carriers doping could effectively tune magnetic and electronic properties.
Specifically, the magnetic moment, exchange parameter, and MAE could be
efficiently tuned. The total magnetic moment changes linearly with charges
doping. The exchange parameters could be controlled by the doping carriers. The
carriers doping could modulate MAE to -18.4 (+0.4 e), -0.85 (-1.6 e), 1.31
(-2.4 e) meV/f.u., by changing hybridization between Te atoms' py and pz
orbitals. Mn2Ge2Te6 with intrinsic ferromagnetism, high tunable MAE, good
stability of ferromagnetism and half-metallicity could help researchers to
investigate its wide application in the electronics and spintronics.


title:
A hierarchical Bayesian non-asymptotic extreme value model for spatial
  data

abstract:
  Spatial maps of extreme precipitation are crucial in flood protection. With
the aim of producing maps of precipitation return levels, we propose a novel
approach to model a collection of spatially distributed time series where the
asymptotic assumption, typical of the traditional extreme value theory, is
relaxed. We introduce a Bayesian hierarchical model that accounts for the
possible underlying variability in the distribution of event magnitudes and
occurrences, which are described through latent temporal and spatial processes.
Spatial dependence is characterized by geographical covariates and effects not
fully described by the covariates are captured by spatial structure in the
hierarchies. The performance of the approach is illustrated through simulation
studies and an application to daily rainfall extremes across North Carolina
(USA). The results show that we significantly reduce the estimation uncertainty
with respect to state of the art techniques.


title: Meta Learning for Natural Language Processing: A Survey

abstract:
  Deep learning has been the mainstream technique in natural language
processing (NLP) area. However, the techniques require many labeled data and
are less generalizable across domains. Meta-learning is an arising field in
machine learning studying approaches to learn better learning algorithms.
Approaches aim at improving algorithms in various aspects, including data
efficiency and generalizability. Efficacy of approaches has been shown in many
NLP tasks, but there is no systematic survey of these approaches in NLP, which
hinders more researchers from joining the field. Our goal with this survey
paper is to offer researchers pointers to relevant meta-learning works in NLP
and attract more attention from the NLP community to drive future innovation.
This paper first introduces the general concepts of meta-learning and the
common approaches. Then we summarize task construction settings and application
of meta-learning for various NLP problems and review the development of
meta-learning in NLP community.


title: Tempered, Anti-trunctated, Multiple Importance Sampling

abstract:
  We propose a Tempered Anti-truncated Adaptive Multiple Importance Sampling
(TAMIS) algorithm to solve the initialization difficulty of the adaptive
importance sampling algorithms, without introducing too many evaluations of the
target density. We combine a tempering scheme and a new nonlinear
transformation of the weights named anti-truncation. As a result, our proposal
is an automatically tuned sequential algorithm that is robust to poor initial
proposals, doesn't require gradient computations and scales well with the
dimension.


title: Lines of polynomials with alternating Galois group

abstract:
  We study the problem of existence of one-parameter, linear families of
polynomials of degree n all of whose polynomials have Galois group A_n. The
methods we use have a strong geometric flavour.


title:
Uniform Vs. Non-Uniform Coarse Quantization in Mutual Information
  Maximizing LDPC Decoding

abstract:
  Recently, low-resolution LDPC decoders have been introduced that perform
mutual information maximizing signal processing. However, the optimal
quantization in variable and check nodes requires expensive non-uniform
operations. Instead, we propose to use uniform quantization with a simple
hardware structure, which reduces the complexity of individual node operations
approximately by half and shortens the decoding delay significantly. Our
analysis shows that the loss of preserved mutual information resulting from
restriction to uniform quantization is very small. Furthermore, the error rate
simulations with regular LDPC codes confirm that the uniformly quantized
decoders cause only minor performance degradation within 0.01 dB compared to
the non-uniform alternatives. Due to the complexity reduction, especially the
proposed 3-bit decoder is a promising candidate to replace 4-bit conventional
decoders.


title:
Modeling directional hardening and intrinsic size effects using a
  dislocation density-based strain gradient plasticity framework

abstract:
  This work proposes a dislocation density-based strain gradient $J_2$
plasticity framework that models the strength contribution due to Geometrically
Necessary Dislocations (GNDs) using a lower order, Taylor hardening backstress
model. An anisotropy factor is introduced to phenomenologically represent the
differential hardening between grains in this $J_2$ plasticity framework. An
implicit numerical algorithm is implemented for the time integration of the
finite deformation plasticity model. The framework is first used to predict
directional hardening due to the GND-induced backstress during cyclic loading.
Deformation contours are studied to understand the substructure attributes
contributing to directional hardening. The framework is then used to predict
the intrinsic, grain size-dependent strengthening of polygrain ensembles. Model
predictions of simulations with different grain sizes are shown to agree with
the Hall-Petch effect and also with Ashby's model of hardening due to GNDs in
polygrain ensembles.


title:
Private Matrix Multiplication From MDS-Coded Storage With Colluding
  Servers

abstract:
  In this paper, we study the two problems of Private and Secure Matrix
Multiplication and Fully Private Matrix Multiplication from MDS-coded storage
with Colluding servers, referred to as MDS-C-PSMM and MDS-C-FPMM respectively,
on a distributed computing system with a master node and multiple servers.
Specifically, in the MDS-C-PSMM problem, the master wants to compute the
product of its owned confidential matrix $\mathbf{A}$ with one out of a batch
of public matrices that is stored across distributed servers according to an
MDS code, without revealing any information about the matrix $\mathbf{A}$ and
the index of another interested matrix to a certain number of colluding
servers. In the second MDS-C-FPMM problem, the matrix $\mathbf{A}$ is also
selected from another batch of public matrices that is stored at the servers in
MDS-coded form. In this case, the indices of the two interested matrices should
be kept private from the colluding servers. We construct computation strategies
for both MDS-C-PSMM and MDS-C-FPMM problems by exploiting the structure
inspired by the encoding functions of related secure matrix multiplication
strategies, yielding flexible tradeoffs among recovery threshold, i.e., the
number of servers required to recover desired product, computation overhead,
i.e., the computation complexity of distributed system, and communication
overhead, i.e., the amount of communication bits between the master and the
servers.


title: BasqueParl: A Bilingual Corpus of Basque Parliamentary Transcriptions

abstract:
  Parliamentary transcripts provide a valuable resource to understand the
reality and know about the most important facts that occur over time in our
societies. Furthermore, the political debates captured in these transcripts
facilitate research on political discourse from a computational social science
perspective. In this paper we release the first version of a newly compiled
corpus from Basque parliamentary transcripts. The corpus is characterized by
heavy Basque-Spanish code-switching, and represents an interesting resource to
study political discourse in contrasting languages such as Basque and Spanish.
We enrich the corpus with metadata related to relevant attributes of the
speakers and speeches (language, gender, party...) and process the text to
obtain named entities and lemmas. The obtained metadata is then used to perform
a detailed corpus analysis which provides interesting insights about the
language use of the Basque political representatives across time, parties and
gender.


title:
The effects of Galactic model uncertainties on LISA observations of
  double neutron stars

abstract:
  The upcoming space-based gravitational wave observatory, LISA, will detect
binaries containing pairs of neutron stars. LISA observations of double neutron
stars, particularly in combination with electromagnetic observations, have the
potential to improve our understanding of neutron star physics and binary
evolution. In this work we assess the effect of changing the model of the Milky
Way on predictions of the population of double neutron star binaries that will
be detected and resolved by LISA. We conclude that the spatial and
gravitational-wave frequency distributions of these binaries are insensitive to
the choice of galactic models, compared to the stochastic variation induced by
the small sample size. In particular, the time-consuming computation of the
binaries' Galactic orbits is not necessary. However, the distribution of
eccentricities is strongly affected by the choice of star-formation history.
Binaries with eccentricities e>0.1 that can be measured by LISA observations
are all younger than 100 Myr. We caution that comparisons between different
predictions for LISA observations need to use consistent star formation
histories, and that the Galactic star formation history should be taken into
account in the analysis of the observations themselves.


title: Compact Neural Networks via Stacking Designed Basic Units

abstract:
  Unstructured pruning has the limitation of dealing with the sparse and
irregular weights. By contrast, structured pruning can help eliminate this
drawback but it requires complex criterion to determine which components to be
pruned. To this end, this paper presents a new method termed TissueNet, which
directly constructs compact neural networks with fewer weight parameters by
independently stacking designed basic units, without requiring additional
judgement criteria anymore. Given the basic units of various architectures,
they are combined and stacked in a certain form to build up compact neural
networks. We formulate TissueNet in diverse popular backbones for comparison
with the state-of-the-art pruning methods on different benchmark datasets.
Moreover, two new metrics are proposed to evaluate compression performance.
Experiment results show that TissueNet can achieve comparable classification
accuracy while saving up to around 80% FLOPs and 89.7% parameters. That is,
stacking basic units provides a new promising way for network compression.


title:
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated
  Learning

abstract:
  Federated learning (FL) has been widely employed for medical image analysis
to facilitate multi-client collaborative learning without sharing raw data.
Despite great success, FL's performance is limited for multiple sclerosis (MS)
lesion segmentation tasks, due to variance in lesion characteristics imparted
by different scanners and acquisition parameters. In this work, we propose the
first FL MS lesion segmentation framework via two effective re-weighting
mechanisms. Specifically, a learnable weight is assigned to each local node
during the aggregation process, based on its segmentation performance. In
addition, the segmentation loss function in each client is also re-weighted
according to the lesion volume for the data during training. Comparison
experiments on two FL MS segmentation scenarios using public and clinical
datasets have demonstrated the effectiveness of the proposed method by
outperforming other FL methods significantly. Furthermore, the segmentation
performance of FL incorporating our proposed aggregation mechanism can exceed
centralised training with all the raw data. The extensive evaluation also
indicated the superiority of our method when estimating brain volume
differences estimation after lesion inpainting.


title: ExSpliNet: An interpretable and expressive spline-based neural network

abstract:
  In this paper we present ExSpliNet, an interpretable and expressive neural
network model. The model combines ideas of Kolmogorov neural networks,
ensembles of probabilistic trees, and multivariate B-spline representations. We
give a probabilistic interpretation of the model and show its universal
approximation properties. We also discuss how it can be efficiently encoded by
exploiting B-spline properties. Finally, we test the effectiveness of the
proposed model on synthetic approximation problems and classical machine
learning benchmark datasets.


title: On $P\mathcal{M}$-factorizable topological groups

abstract:
  A topological group $G$ is called $P\mathcal{M}$-factorizable if for every
continuous real-valued function $f$ on $G$, there exists a perfect homomorphism
$\pi :G\rightarrow H$ onto a metrizable topological group $H$ such that
$f=g\circ \pi$, for some continuous real-valued function $g$ on $H$. It is
proved that a topological group is $P\mathcal{M}$-factorizable if and only if
it is feathered $\mathcal{M}$-factorizable, and a topological group is
$P\mathbb{R}$-factorizable if and only if it is $P\mathcal{M}$-factorizable and
$\omega$-narrow. The two results deduce that a topological group $G$ is
$P\mathbb{R}$-factorizable if and only if $G$ is feathered
$\mathbb{R}$-factorizable. Moreover, some properties about products of
$P\mathcal{M}$-factorizable topological groups are investigated. In particular,
some interesting properties of $\mathcal{M}$-factorizable topological groups in
[8, 10] are strengthened to $P\mathcal{M}$-factorizable topological groups.


title: Fair Feature Subset Selection using Multiobjective Genetic Algorithm

abstract:
  The feature subset selection problem aims at selecting the relevant subset of
features to improve the performance of a Machine Learning (ML) algorithm on
training data. Some features in data can be inherently noisy, costly to
compute, improperly scaled, or correlated to other features, and they can
adversely affect the accuracy, cost, and complexity of the induced algorithm.
The goal of traditional feature selection approaches has been to remove such
irrelevant features. In recent years ML is making a noticeable impact on the
decision-making processes of our everyday lives. We want to ensure that these
decisions do not reflect biased behavior towards certain groups or individuals
based on protected attributes such as age, sex, or race. In this paper, we
present a feature subset selection approach that improves both fairness and
accuracy objectives and computes Pareto-optimal solutions using the NSGA-II
algorithm. We use statistical disparity as a fairness metric and F1-Score as a
metric for model performance. Our experiments on the most commonly used
fairness benchmark datasets with three different machine learning algorithms
show that using the evolutionary algorithm we can effectively explore the
trade-off between fairness and accuracy.