title:
A spin-optomechanical quantum interface enabled by an ultrasmall
  mechanical and optical mode volume cavity

abstract:
  We propose a coherent mechanical interface between defect centers in diamond
and telecom optical modes. Combining recent developments in spin-mechanical
devices and optomechanical crystals, we introduce a 1D diamond nanobeam with
embedded mechanical and electric field concentrator with mechanical and optical
mode volumes $V_\mathrm{mech}/\Lambda_\mathrm{p}^3\sim 10^{-5}$ and
$V_\mathrm{opt}/\lambda^3\sim 10^{-3} $, respectively. By placing a Group IV
vacancy in the concentrator we demonstrate exquisitely high spin-mechanical
coupling rates approaching 40 MHz, while retaining high acousto-optical
couplings. We theoretically show that such a device, used in an entanglement
heralding scheme, can provide high-fidelity Bell pairs between quantum
repeaters. Using the mechanical interface as an intermediary between the
optical and spin subsystems, we are able to directly use telecom optics,
bypassing the native wavelength requirements of the spin. As the spin is never
optically excited or addressed, we do not suffer from spectral diffusion and
can operate at higher temperatures (up to 40 K), limited only by thermal
losses. We estimate that based on these metrics, optomechanical devices with
high spin-mechanical coupling will be a useful architecture for near-term
quantum repeaters.


title: Degree and regularity of Eulerian ideals of hypergraphs

abstract:
  We define the Eulerian ideal of a $k$-uniform hypergraph and study its degree
and Castelnuovo--Mumford regularity. The main tool is a Gr\"obner basis of the
ideal obtained combinatorially from the hypergraph. We define the notion of
parity join in a hypergraph and show that the regularity of the Eulerian ideal
is equal to the maximum cardinality of such a set of edges. The formula for the
degree involves the cardinality of the set of sets of vertices, $T$, that admit
a $T$-join. We compute the degree and regularity explicity in the cases of a
complete $k$-partite hypergraph and a complete hypergraph of rank $3$.


title:
Handcrafted Histological Transformer (H2T): Unsupervised Representation
  of Whole Slide Images

abstract:
  Diagnostic, prognostic and therapeutic decision-making of cancer in pathology
clinics can now be carried out based on analysis of multi-gigapixel tissue
images, also known as whole-slide images (WSIs). Recently, deep convolutional
neural networks (CNNs) have been proposed to derive unsupervised WSI
representations; these are attractive as they rely less on expert annotation
which is cumbersome. However, a major trade-off is that higher predictive power
generally comes at the cost of interpretability, posing a challenge to their
clinical use where transparency in decision-making is generally expected. To
address this challenge, we present a handcrafted framework based on deep CNN
for constructing holistic WSI-level representations. Building on recent
findings about the internal working of the Transformer in the domain of natural
language processing, we break down its processes and handcraft them into a more
transparent framework that we term as the Handcrafted Histological Transformer
or H2T. Based on our experiments involving various datasets consisting of a
total of 5,306 WSIs, the results demonstrate that H2T based holistic WSI-level
representations offer competitive performance compared to recent
state-of-the-art methods and can be readily utilized for various downstream
analysis tasks. Finally, our results demonstrate that the H2T framework can be
up to 14 times faster than the Transformer models.


title: Separating monomials for diagonalizable actions

abstract:
  Sets of monomials separating Zariski closed orbits under diagonalizable group
actions are characterized in terms of the monoid of zero-sum sequences over the
character group. This is applied to compare the degree bounds for separating
invariants and generating invariants of diagonalizable group actions.


title:
Privacy-preserving estimation of an optimal individualized treatment
  rule : A case study in maximizing time to severe depression-related outcomes

abstract:
  Estimating individualized treatment rules - particularly in the context of
right-censored outcomes - is challenging because the treatment effect
heterogeneity of interest is often small, thus difficult to detect. While this
motivates the use of very large datasets such as those from multiple health
systems or centres, data privacy may be of concern with participating data
centres reluctant to share individual-level data. In this case study on the
treatment of depression, we demonstrate an application of distributed
regression for privacy protection used in combination with dynamic weighted
survival modelling (DWSurv) to estimate an optimal individualized treatment
rule whilst obscuring individual-level data. In simulations, we demonstrate the
flexibility of this approach to address local treatment practices that may
affect confounding, and show that DWSurv retains its double robustness even
when performed through a (weighted) distributed regression approach. The work
is motivated by, and illustrated with, an analysis of treatment for unipolar
depression using the United Kingdom's Clinical Practice Research Datalink.


title:
Environmental selection of the cosmological constant and electroweak
  scales

abstract:
  We investigate whether selfish/Goldilocks Higgs models can be extended to
accomodate vacua with both the right Higgs mass and cosmological constant.
Through the introduction of multiple four-form fields coupled with the Higgs
scalar boson we find that both the cosmological constant and the electroweak
hierarchy problems can be addressed simultaneously, without inserting the
relevant physical scales by hand and avoiding fine tuning. The resulting bounds
on the brane charges, the only dimensionful free parameter of the theory,
depend on the strength of the four-form scalar coupling and can be of the same
order of magnitude as those necessary for a solution to the cosmological
constant problem alone via the Bousso-Polchinski mechanism.


title: Continuously Generalized Ordinal Regression for Linear and Deep Models

abstract:
  Ordinal regression is a classification task where classes have an order and
prediction error increases the further the predicted class is from the true
class. The standard approach for modeling ordinal data involves fitting
parallel separating hyperplanes that optimize a certain loss function. This
assumption offers sample efficient learning via inductive bias, but is often
too restrictive in real-world datasets where features may have varying effects
across different categories. Allowing class-specific hyperplane slopes creates
generalized logistic ordinal regression, increasing the flexibility of the
model at a cost to sample efficiency. We explore an extension of the
generalized model to the all-thresholds logistic loss and propose a
regularization approach that interpolates between these two extremes. Our
method, which we term continuously generalized ordinal logistic, significantly
outperforms the standard ordinal logistic model over a thorough set of ordinal
regression benchmark datasets. We further extend this method to deep learning
and show that it achieves competitive or lower prediction error compared to
previous models over a range of datasets and modalities. Furthermore, two
primary alternative models for deep learning ordinal regression are shown to be
special cases of our framework.


title: A Survey of Visual Sensory Anomaly Detection

abstract:
  Visual sensory anomaly detection (AD) is an essential problem in computer
vision, which is gaining momentum recently thanks to the development of AI for
good. Compared with semantic anomaly detection which detects anomaly at the
label level (semantic shift), visual sensory AD detects the abnormal part of
the sample (covariate shift). However, no thorough review has been provided to
summarize this area for the computer vision community. In this survey, we are
the first one to provide a comprehensive review of visual sensory AD and
category into three levels according to the form of anomalies. Furthermore, we
classify each kind of anomaly according to the level of supervision. Finally,
we summarize the challenges and provide open directions for this community. All
resources are available at
https://github.com/M-3LAB/awesome-visual-sensory-anomaly-detection.


title: Late-Time X-ray Observations of the Transient Source Cygnus A-2

abstract:
  We examine Chandra observations of the powerful Fanaroff-Riley class II (FR
II) radio galaxy Cygnus A for an X-ray counterpart to the radio transient
Cygnus A-2 that was first detected in 2011. Observations are performed using
the High-Resolution Camera (HRC) instrument in order to spatially resolve
Cygnus A-2 and the central Active Galactic Nucleus (AGN) at a separation of
0.42 arcseconds. Simulated images are generated of the emission region, and
radial profiles for the region of interest are extracted. A comparison between
the simulations and observations reveals no X-ray detection of Cygnus A-2 to a
0.5-7.0 keV flux upper limit of $1.04 \times
10^{-12}\rm\,erg\,cm^{-2}\,s^{-1}$, or a rest-frame 2-10 keV luminosity of
$8.6\times 10^{42}\rm\,erg\,s^{-1}$. We estimate the black hole mass of Cygnus
A-2 based on our X-ray flux limit and find it to be consistent with a flaring
black hole rather than a steadily accreting source. The HRC observations are
additionally compared with archival ACIS data from 2016-2017, and both the
overall morphology and the flux limits of the AGN complex agree between the two
datasets. This consistency is despite the pile-up effect in ACIS which was
previously considered to bias the observed morphology of the AGN. The agreement
between the datasets demonstrates the viability of utilizing the archival
Chandra data of Cygnus A to analyze its AGN at an unprecedented level of
precision.


title: Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing

abstract:
  The rising popularity of self-driving cars has led to the emergence of a new
research field in the recent years: Autonomous racing. Researchers are
developing software and hardware for high performance race vehicles which aim
to operate autonomously on the edge of the vehicles limits: High speeds, high
accelerations, low reaction times, highly uncertain, dynamic and adversarial
environments. This paper represents the first holistic survey that covers the
research in the field of autonomous racing. We focus on the field of autonomous
racecars only and display the algorithms, methods and approaches that are used
in the fields of perception, planning and control as well as end-to-end
learning. Further, with an increasing number of autonomous racing competitions,
researchers now have access to a range of high performance platforms to test
and evaluate their autonomy algorithms. This survey presents a comprehensive
overview of the current autonomous racing platforms emphasizing both the
software-hardware co-evolution to the current stage. Finally, based on
additional discussion with leading researchers in the field we conclude with a
summary of open research challenges that will guide future researchers in this
field.


title: Realizing exceptional points of any order in the presence of symmetry

abstract:
  Exceptional points~(EPs) appear as degeneracies in the spectrum of
non-Hermitian matrices at which the eigenvectors coalesce. In general, an EP of
order $n$ may find room to emerge if $2(n-1)$ real constraints are imposed. Our
results show that these constraints can be expressed in terms of the
determinant and traces of the non-Hermitian matrix. Our findings further reveal
that the total number of constraints may reduce in the presence of unitary and
antiunitary symmetries. Additionally, we draw generic conclusions for the
low-energy dispersion of the EPs. Based on our calculations, we show that in
odd dimensions the presence of sublattice or pseudo-chiral symmetry enforces
$n$th order EPs to disperse with the $(n-1)$th root. For two-, three- and
four-band systems, we explicitly present the constraints needed for the
occurrence of EPs in terms of system parameters and classify EPs based on their
low-energy dispersion relations.


title:
Statistical inference for intrinsic wavelet estimators of SPD matrices
  in a log-Euclidean manifold

abstract:
  In this paper we treat statistical inference for an intrinsic wavelet
estimator of curves of symmetric positive definite (SPD) matrices in a
log-Euclidean manifold. This estimator preserves positive-definiteness and
enjoys permutation-equivariance, which is particularly relevant for covariance
matrices. Our second-generation wavelet estimator is based on
average-interpolation and allows the same powerful properties, including fast
algorithms, known from nonparametric curve estimation with wavelets in standard
Euclidean set-ups.
  The core of our work is the proposition of confidence sets for our high-level
wavelet estimator in a non-Euclidean geometry. We derive asymptotic normality
of this estimator, including explicit expressions of its asymptotic variance.
This opens the door for constructing asymptotic confidence regions which we
compare with our proposed bootstrap scheme for inference. Detailed numerical
simulations confirm the appropriateness of our suggested inference schemes.


title:
Dynamic microscopic optical coherence tomography to visualize
  morphological and functional micro-anatomy of the airways

abstract:
  Imaging airway tissue, optical coherence tomography (OCT) provides
cross-sectional images of tissue structures, shows cilia movement and mucus
secretion, but does not provide sufficient contrast to differentiate individual
cells. By using fast sequences of microscopic resolution OCT (mOCT) images, OCT
can picture small signal fluctuations (dynamic) OCT and overcomes the lack in
contrast caused by speckle noise. In this way, OCT visualizes airway morphology
on a cellular level and allows to track the dynamic behavior of immune cells,
as well as mucus transport and secretion. Here, we demonstrate that mOCT, by
using temporal tissue fluctuation as contrast (dynamic OCT), provides the
possibility to study physiological and pathological tissue processes in vivo.


title:
Building Inspection Toolkit: Unified Evaluation and Strong Baselines for
  Damage Recognition

abstract:
  In recent years, several companies and researchers have started to tackle the
problem of damage recognition within the scope of automated inspection of built
structures. While companies are neither willing to publish associated data nor
models, researchers are facing the problem of data shortage on one hand and
inconsistent dataset splitting with the absence of consistent metrics on the
other hand. This leads to incomparable results. Therefore, we introduce the
building inspection toolkit -- bikit -- which acts as a simple to use data hub
containing relevant open-source datasets in the field of damage recognition.
The datasets are enriched with evaluation splits and predefined metrics,
suiting the specific task and their data distribution. For the sake of
compatibility and to motivate researchers in this domain, we also provide a
leaderboard and the possibility to share model weights with the community. As
starting point we provide strong baselines for multi-target classification
tasks utilizing extensive hyperparameter search using three transfer learning
approaches for state-of-the-art algorithms. The toolkit and the leaderboard are
available online.


title: Robust Policy Learning over Multiple Uncertainty Sets

abstract:
  Reinforcement learning (RL) agents need to be robust to variations in
safety-critical environments. While system identification methods provide a way
to infer the variation from online experience, they can fail in settings where
fast identification is not possible. Another dominant approach is robust RL
which produces a policy that can handle worst-case scenarios, but these methods
are generally designed to achieve robustness to a single uncertainty set that
must be specified at train time. Towards a more general solution, we formulate
the multi-set robustness problem to learn a policy robust to different
perturbation sets. We then design an algorithm that enjoys the benefits of both
system identification and robust RL: it reduces uncertainty where possible
given a few interactions, but can still act robustly with respect to the
remaining uncertainty. On a diverse set of control tasks, our approach
demonstrates improved worst-case performance on new environments compared to
prior methods based on system identification and on robust RL alone.


title:
Strategy Discovery and Mixture in Lifelong Learning from Heterogeneous
  Demonstration

abstract:
  Learning from Demonstration (LfD) approaches empower end-users to teach
robots novel tasks via demonstrations of the desired behaviors, democratizing
access to robotics. A key challenge in LfD research is that users tend to
provide heterogeneous demonstrations for the same task due to various
strategies and preferences. Therefore, it is essential to develop LfD
algorithms that ensure \textit{flexibility} (the robot adapts to personalized
strategies), \textit{efficiency} (the robot achieves sample-efficient
adaptation), and \textit{scalability} (robot reuses a concise set of strategies
to represent a large amount of behaviors). In this paper, we propose a novel
algorithm, Dynamic Multi-Strategy Reward Distillation (DMSRD), which distills
common knowledge between heterogeneous demonstrations, leverages learned
strategies to construct mixture policies, and continues to improve by learning
from all available data. Our personalized, federated, and lifelong LfD
architecture surpasses benchmarks in two continuous control problems with an
average 77\% improvement in policy returns and 42\% improvement in log
likelihood, alongside stronger task reward correlation and more precise
strategy rewards.


title:
Underdense relativistically thermal plasma produced by magnetically
  assisted direct laser acceleration

abstract:
  We introduce the first approach to volumetrically generate relativistically
thermal plasma at gas-jet--accessible density. Using fully kinetic simulations
and theory, we demonstrate that two stages of direct laser acceleration driven
by two laser pulses in an applied magnetic field can heat a significant plasma
volume to multi-MeV average energy. The highest-momentum feature is
2D-isotropic, persists after the interaction, and includes the majority of
electrons, enabling experimental access to bulk-relativistic,
high-energy-density plasma in an optically diagnosable regime for the first
time.


title: Discrete Adjoint Momentum-Weighted Interpolation Strategies

abstract:
  This Technical Note outlines an adjoint complement to a critical building
block of pressure-based Finite-Volume (FV) flow solvers that employ a
collocated variable arrangement to simulate virtually incompressible fluids.
The focal point is to strengthen the adjoint pressure-velocity coupling by
using an adjoint Momentum-Weighted Interpolation (MWI) strategy. To this end,
analogies of established primal MWI techniques are derived and investigated.
The study reveals the merits of an adjoint MWI but also highlights the
importance of its careful implementation.


title: An open-source modular framework for quantum computing

abstract:
  In this proceedings we describe the current development status and recent
technical achievements of Qibo, an open-source framework for quantum
simulation. After a concise overview of the project goal, we introduce the
modular layout for backend abstraction released in version 0.1.7. We discuss
the advantages of each backend choice with particular emphasis on hardware
accelerators for quantum state vector simulation. Finally, we summarize the
primitives and models currently available.


title: Singular fibrations over surfaces

abstract:
  Singular fibrations generalize achiral Lefschetz fibrations of 4-manifolds
over surfaces while sharing some of their properties. For instance, relatively
minimal singular fibrations are determined by their monodromy. We explain how
to construct examples of singular fibrations with a single singularity and
Matsumoto's construction of singular fibrations of the sphere $S^4$. Previous
results of Hirzebruch and Hopf on 2-plane fields with finitely many
singularities are outlined in connection with the work of Neumann and Rudolph
on the Hopf invariant. Eventually, we prove that closed orientable 4-manifolds
with large first Betti number and vanishing second Betti number do not admit
singular fibrations.


title: Sputtering power effects on the electrochromic properties of NiO films

abstract:
  The effect of sputtering power ($P$=60 W-180 W) on the electrochromic
properties of nickel oxide films deposited on ITO-coated glass substrates by
the RF magnetron sputtering technique was investigated. Crystalline structure
and morphology were assessed by X-ray diffraction (XRD) and scanning electron
microscopy (SEM), respectively. The effect of sputtering power on
electrochromism of the samples was evaluated with cyclic voltammetry and
chronoamperometry. We used LiClO$_4$ in propilene carbonate solution for Li
insertion/extraction. The chemical composition of the samples before and after
Li intercalation was analyzed by X-ray photoelectron spectroscopy (XPS). We
observed the cubic phase of NiO with sputtering power mainly affecting
crystallinity and grain size. These in turn affect the electrochromic
properties. Coloration efficiency reduces from 24.4 cm$^{2}$/C to (15.4, 13.7.
16.1) cm$^{2}$/C and the reversibility from 40% to 30% as sputtering power
increases from 60 W to 180 W. The impedance spectra indicates that ion
diffusion is larger for the film grown at 60 W and then goes down for the films
deposited at higher sputtering powers. XPS analysis reveals that higher values
of $P$ promote the formation of nickel hydroxides on the film surface. As
consequence of changes in crystallinity and morphology, the presence of nickel
hydroxides increases, showing that not only the electrochromic properties of
the samples are affected by the sputtering power but also their chemical
composition.


title: The Genesis of the No-Boundary Wave Function of the Universe

abstract:
  Brief recollections by the author about how he and Stephen Hawking arrived at
the theory of the No-Boundary Quantum State of the Universe

categories: physics.hist-ph astro-ph.CO gr-qc hep-th quant-ph


title:
QuadSim: A Quadcopter Rotational Dynamics Simulation Framework For
  Reinforcement Learning Algorithms

abstract:
  This study focuses on designing and developing a mathematically based
quadcopter rotational dynamics simulation framework for testing reinforcement
learning (RL) algorithms in many flexible configurations. The design of the
simulation framework aims to simulate both linear and nonlinear representations
of a quadcopter by solving initial value problems for ordinary differential
equation (ODE) systems. In addition, the simulation environment is capable of
making the simulation deterministic/stochastic by adding random Gaussian noise
in the forms of process and measurement noises. In order to ensure that the
scope of this simulation environment is not limited only with our own RL
algorithms, the simulation environment has been expanded to be compatible with
the OpenAI Gym toolkit. The framework also supports multiprocessing
capabilities to run simulation environments simultaneously in parallel. To test
these capabilities, many state-of-the-art deep RL algorithms were trained in
this simulation framework and the results were compared in detail.


title:
Recurrent Neural Networks for Dynamical Systems: Applications to
  Ordinary Differential Equations, Collective Motion, and Hydrological Modeling

abstract:
  Classical methods of solving spatiotemporal dynamical systems include
statistical approaches such as autoregressive integrated moving average, which
assume linear and stationary relationships between systems' previous outputs.
Development and implementation of linear methods are relatively simple, but
they often do not capture non-linear relationships in the data. Thus,
artificial neural networks (ANNs) are receiving attention from researchers in
analyzing and forecasting dynamical systems. Recurrent neural networks (RNN),
derived from feed-forward ANNs, use internal memory to process variable-length
sequences of inputs. This allows RNNs to applicable for finding solutions for a
vast variety of problems in spatiotemporal dynamical systems. Thus, in this
paper, we utilize RNNs to treat some specific issues associated with dynamical
systems. Specifically, we analyze the performance of RNNs applied to three
tasks: reconstruction of correct Lorenz solutions for a system with a
formulation error, reconstruction of corrupted collective motion trajectories,
and forecasting of streamflow time series possessing spikes, representing three
fields, namely, ordinary differential equations, collective motion, and
hydrological modeling, respectively. We train and test RNNs uniquely in each
task to demonstrate the broad applicability of RNNs in reconstruction and
forecasting the dynamics of dynamical systems.


title:
Exhaustivity and anti-exhaustivity in the RSA framework: Testing the
  effect of prior beliefs

abstract:
  During communication, the interpretation of utterances is sensitive to a
listener's probabilistic prior beliefs, something which is captured by one
currently influential model of pragmatics, the Rational Speech Act (RSA)
framework. In this paper we focus on cases when this sensitivity to priors
leads to counterintuitive predictions of the framework. Our domain of interest
is exhaustivity effects, whereby a sentence such as "Mary came" is understood
to mean that only Mary came. We show that in the baseline RSA model, under
certain conditions, anti-exhaustive readings are predicted (e.g., "Mary came"
would be used to convey that both Mary and Peter came). The specific question
we ask is the following: should exhaustive interpretations be derived as purely
pragmatic inferences (as in the classical Gricean view, endorsed in the
baseline RSA model), or should they rather be generated by an encapsulated
semantic mechanism (as argued in some of the recent formal literature)? To
answer this question, we provide a detailed theoretical analysis of different
RSA models and evaluate them against data obtained in a new study which tested
the effects of prior beliefs on both production and comprehension, improving on
previous empirical work. We found no anti-exhaustivity effects, but observed
that message choice is sensitive to priors, as predicted by the RSA framework
overall. The best models turn out to be those which include an encapsulated
exhaustivity mechanism (as other studies concluded on the basis of very
different data). We conclude that, on the one hand, in the division of labor
between semantics and pragmatics, semantics plays a larger role than is often
thought, but, on the other hand, the tradeoff between informativity and cost
which characterizes all RSA models does play a central role for genuine
pragmatic effects.


title:
Realising perfect derived categories of Auslander algebras of type A as
  Fukaya-Seidel categories

abstract:
  We prove that the Fukaya-Seidel categories of a certain family of Lefschetz
fibrations on $\mathbb{C}^2$ are equivalent to the perfect derived categories
of Auslander algebras of Dynkin type $\mathbb{A}$. We give an explicit
equivalence between these categories and the partially wrapped Fukaya
categories considered by Dyckerhoff-Jasso-Lekili. We provide a complete
description of the Milnor fibre of such fibrations.


title: Box Supervised Video Segmentation Proposal Network

abstract:
  Video Object Segmentation (VOS) has been targeted by various fully-supervised
and self-supervised approaches. While fully-supervised methods demonstrate
excellent results, self-supervised ones, which do not use pixel-level ground
truth, attract much attention. However, self-supervised approaches pose a
significant performance gap. Box-level annotations provide a balanced
compromise between labeling effort and result quality for image segmentation
but have not been exploited for the video domain. In this work, we propose a
box-supervised video object segmentation proposal network, which takes
advantage of intrinsic video properties. Our method incorporates object motion
in the following way: first, motion is computed using a bidirectional temporal
difference and a novel bounding box-guided motion compensation. Second, we
introduce a novel motion-aware affinity loss that encourages the network to
predict positive pixel pairs if they share similar motion and color. The
proposed method outperforms the state-of-the-art self-supervised benchmark by
16.4% and 6.9% $\mathcal{J}$ &$\mathcal{F}$ score and the majority of fully
supervised methods on the DAVIS and Youtube-VOS dataset without imposing
network architectural specifications. We provide extensive tests and ablations
on the datasets, demonstrating the robustness of our method.


title:
Analysis of Neural Fragility: Bounding the Norm of a Rank-One
  Perturbation Matrix

abstract:
  Over 15 million epilepsy patients worldwide do not respond to drugs and
require surgical treatment. Successful surgical treatment requires complete
removal, or disconnection of the epileptogenic zone (EZ), but without a
prospective biomarker of the EZ, surgical success rates vary between 30%-70%.
Neural fragility is a model recently proposed to localize the EZ. Neural
fragility is computed as the l2 norm of a structured rank-one perturbation of
an estimated linear dynamical system. However, an analysis of its numerical
properties have not been explored. We show that neural fragility is a
well-defined model given a good estimator of the linear dynamical system from
data. Specifically, we provide bounds on neural fragility as a function of the
underlying linear system and noise.


title: Conformally and Disformally Coupled Vector field Models of Dark Energy

abstract:
  Scalar fields coupled to dark matter by conformal or disformal
transformations give rise to a general class of scalar-tensor theories which
leads to a rich phenomenology in a cosmological setting. While this possibility
has been studied comprehensively in the literature for scalar fields, the
vector case has been hardly treated. We build hence models based on vector
fields conformally and disformally coupled to dark matter and derive explicitly
the general covariant form of the interaction term in an independent way of the
gravity theory, whereby this result can be applied to general vector-tensor
theories. For concreteness, the standard Proca theory with a vector exponential
potential is taken to describe the vector-tensor sector, and some specific
coupling functions are assumed to study the cosmological background dynamics by
dynamical system techniques. Despite of choosing such a minimalist form for the
underlying theory, the parameter space is considerably enriched compared to the
uncoupled case due to the novel interactions, leading to new branches of
solutions for the vector equation of motion. Thus, different trajectories can
exist in phase space depending on the coupling parameters associated to the
conformal and disformal functions. From here, new emerging vector-dark matter
scaling solutions, and renewed stable attractor points are found to drive the
late-time accelerated expansion of the universe. Numerical calculations are
performed as well to investigate more quantitatively the impact of the
conformal and disformal couplings on the cosmological background evolution.
These effects depend essentially on the strength on the coupling parameters
and, in some specific cases, on their associated signs.


title:
One Step at a Time: Long-Horizon Vision-and-Language Navigation with
  Milestones

abstract:
  We study the problem of developing autonomous agents that can follow human
instructions to infer and perform a sequence of actions to complete the
underlying task. Significant progress has been made in recent years, especially
for tasks with short horizons. However, when it comes to long-horizon tasks
with extended sequences of actions, an agent can easily ignore some
instructions or get stuck in the middle of the long instructions and eventually
fail the task. To address this challenge, we propose a model-agnostic
milestone-based task tracker (M-TRACK) to guide the agent and monitor its
progress. Specifically, we propose a milestone builder that tags the
instructions with navigation and interaction milestones which the agent needs
to complete step by step, and a milestone checker that systemically checks the
agent's progress in its current milestone and determines when to proceed to the
next. On the challenging ALFRED dataset, our M-TRACK leads to a notable 33% and
52% relative improvement in unseen success rate over two competitive base
models.


title: The Multi-Billion Dollar Software Supply Chain of Ethereum

abstract:
  The rise of blockchain technologies has triggered tremendous research
interests, coding efforts, and monetary investments in the last decade.
Ethereum is the largest programmable blockchain platform today. It features
cryptocurrency trading, digital art, and decentralized finance through smart
contracts. So-called Ethereum nodes operate the blockchain, relying on a vast
supply chain of third-party software dependencies maintained by diverse
organizations. These software suppliers have a direct impact on the reliability
and the security of Ethereum. In this article, we perform the first analysis of
the software supply chain of Java Ethereum nodes and distill the challenges of
maintaining and securing the Ethereum supply chain.


title:
Least energy solutions for affine $p$-Laplace equations involving
  subcritical and critical nonlinearities

abstract:
  The paper is concerned with Lane-Emden and Brezis-Nirenberg problems for a
nonlocal quasilinear operator $\Delta_p^{\cal A}$ coming from convex geometry,
called affine $p$-Laplace operator, which coincides with the corresponding
classical one on balls for radial functions. More specifically, $\Delta_p^{\cal
A}$ has been introduced in \cite{HJM5} driven by the affine $L^p$ energy ${\cal
E}_{p,\Omega}$ due to Lutwak, Yang and Zhang \cite{LYZ2}. We are particularly
interested in positive $C^1$ solutions of least energy type. The existence is
achieved via direct method to functionals with non-coercive geometry. Key
ingredients like compactness in $L^q(\Omega)$ of affine balls in
$W^{1,p}_0(\Omega)$, established by Tintarev \cite{T1}, and weak lower
semicontinuity on $W^{1,p}_0(\Omega)$ of ${\cal E}_{p,\Omega}$, among others,
play a fundamental role in our approach. Nonexistence results are obtained by
means of integral arguments.


title:
Transitions in Stochastic Non-equilibrium Systems: Efficient Reduction
  and Analysis

abstract:
  A central challenge in physics is to describe non-equilibrium systems driven
by randomness, such as a randomly growing interface, or fluids subject to
random fluctuations that account e.g. for local stresses and heat fluxes not
related to the velocity and temperature gradients. For deterministic systems
with infinitely many degrees of freedom, normal form and center manifold theory
have shown a prodigious efficiency to often completely characterize how the
onset of linear instability translates into the emergence of nonlinear
patterns. However, in presence of random fluctuations, this reduction procedure
is seriously challenged due to large excursions caused by the noise, and the
approach needs to be revisited.
  We present an alternative framework to cope with these difficulties
exploiting the approximation theory of stochastic invariant manifolds and
energy estimates measuring the defect of parameterization of the high-modes. To
operate for fluid problems, these error estimates are derived under assumptions
regarding dissipation effects brought by the high-modes that suitably
counterbalance the loss of regularity due to the nonlinear terms. The approach
enables us to predict, from the reduced equations, the occurrence in large
probability of a stochastic analogue to the pitchfork bifurcation, as long as
the noise's intensity and the eigenvalue's magnitude of the mildly unstable
mode scale accordingly.
  Our parameterization formulas involve non-Markovian coefficients, which
depend explicitly on the history of the noise path that drives the SPDE
dynamics, and their memory content is self-consistently determined by the
intensity of the random force and its interaction through the SPDE's nonlinear
terms. Applications to a stochastic Rayleigh-Benard problem are detailed, for
which conditions for a stochastic pitchfork bifurcation (in large probability)
to occur, are clarified.


title:
Peripheral birationality for 3-dimensional convex co-compact
  $PSL_2\mathbb{C}$ varieties

abstract:
  Let $M$ be a hyperbolizable $3$-manifold with boundary, and let $\chi_0(M)$
be a component of the $PSL_2\mathbb{C}$-character variety of $M$ that contains
the convex co-compact characters. We show that the peripheral map
$i_*:\chi_0(M)\rightarrow\chi(\partial M)$ to the character variety of
$\partial M$ is a birational isomorphism with its image, and in particular is
generically a one-to-one map. This generalizes work of Dunfield (one cusped
hyperbolic $3$-manifolds) and Klaff-Tillmann (finite volume hyperbolic
$3$-manifolds). We use the Bonahon-Schl\"afli formula and volume rigidity of
discrete co-compact representations.


title:
Formation mechanism of the rotating spoke in partially magnetized
  plasmas

abstract:
  Rotating spokes commonly occur in partially magnetized plasmas devices. In
this paper, the driving mechanism behind the formation of an m=1 rotating spoke
mode in a magnetically enhanced hollow cathode arc discharge is investigated by
means of 2D radial-azimuthal particle-in-cell/Monte Carlo collision simulations
with a uniform axial magnetic field. We find that the formation of the spoke
potential hump region can be explained as a result of the positive anode sheath
collapse due to the lower hybrid type instability evolving into the long
wavelength regime. It is shown that an initial short-wavelength instability in
the non-neutral anode sheath undergoes a sequence of transitions into the large
scale modes. The sheath non-neutrality effect on the instability is considered
and incorporated in the two-fluid linear theory of the lower hybrid
instability. The unstable modes predicted by the theory in the linear phase and
nonlinear evolution are in good agreement with the fluctuation modes developed
in the particle simulations.


title: Slowing down light in a qubit metamaterial

abstract:
  The rapid progress in quantum information processing leads to a rising demand
for devices to control the propagation of electromagnetic wave pulses and to
ultimately realize a universal and efficient quantum memory. While in recent
years significant progress has been made to realize slow light and quantum
memories with atoms at optical frequencies, superconducting circuits in the
microwave domain still lack such devices. Here, we demonstrate slowing down
electromagnetic waves in a superconducting metamaterial composed of eight
qubits coupled to a common waveguide, forming a waveguide quantum
electrodynamics system. We analyze two complementary approaches, one relying on
dressed states of the Autler-Townes splitting, and the other based on a
tailored dispersion profile using the qubits tunability. Our time-resolved
experiments show reduced group velocities of down to a factor of about 1500
smaller than in vacuum. Depending on the method used, the speed of light can be
controlled with an additional microwave tone or an effective qubit detuning.
Our findings demonstrate high flexibility of superconducting circuits to
realize custom band structures and open the door to microwave dispersion
engineering in the quantum regime.


title: Testing the Tools of Systems Neuroscience on Artificial Neural Networks

abstract:
  Neuroscientists apply a range of common analysis tools to recorded neural
activity in order to glean insights into how neural circuits implement
computations. Despite the fact that these tools shape the progress of the field
as a whole, we have little empirical evidence that they are effective at
quickly identifying the phenomena of interest. Here I argue that these tools
should be explicitly tested and that artificial neural networks (ANNs) are an
appropriate testing grounds for them. The recent resurgence of the use of ANNs
as models of everything from perception to memory to motor control stems from a
rough similarity between artificial and biological neural networks and the
ability to train these networks to perform complex high-dimensional tasks.
These properties, combined with the ability to perfectly observe and manipulate
these systems, makes them well-suited for vetting the tools of systems and
cognitive neuroscience. I provide here both a roadmap for performing this
testing and a list of tools that are suitable to be tested on ANNs. Using ANNs
to reflect on the extent to which these tools provide a productive
understanding of neural systems -- and on exactly what understanding should
mean here -- has the potential to expedite progress in the study of the brain.


title:
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting
  Recognition from IMU-Enhanced Pens

abstract:
  Handwriting is one of the most frequently occurring patterns in everyday life
and with it come challenging applications such as handwriting recognition
(HWR), writer identification, and signature verification. In contrast to
offline HWR that only uses spatial information (i.e., images), online HWR
(OnHWR) uses richer spatio-temporal information (i.e., trajectory data or
inertial data). While there exist many offline HWR datasets, there is only
little data available for the development of OnHWR methods as it requires
hardware-integrated pens. This paper presents data and benchmark models for
real-time sequence-to-sequence (seq2seq) learning and single character-based
recognition. Our data is recorded by a sensor-enhanced ballpoint pen, yielding
sensor data streams from triaxial accelerometers, a gyroscope, a magnetometer
and a force sensor at 100Hz. We propose a variety of datasets including
equations and words for both the writer-dependent and writer-independent tasks.
We provide an evaluation benchmark for seq2seq and single character-based HWR
using recurrent and temporal convolutional networks and Transformers combined
with a connectionist temporal classification (CTC) loss and cross entropy
losses. Our methods do not resort to language or lexicon models.


title: Principal Manifold Flows

abstract:
  Normalizing flows map an independent set of latent variables to their samples
using a bijective transformation. Despite the exact correspondence between
samples and latent variables, their high level relationship is not well
understood. In this paper we characterize the geometric structure of flows
using principal manifolds and understand the relationship between latent
variables and samples using contours. We introduce a novel class of normalizing
flows, called principal manifold flows (PF), whose contours are its principal
manifolds, and a variant for injective flows (iPF) that is more efficient to
train than regular injective flows. PFs can be constructed using any flow
architecture, are trained with a regularized maximum likelihood objective and
can perform density estimation on all of their principal manifolds. In our
experiments we show that PFs and iPFs are able to learn the principal manifolds
over a variety of datasets. Additionally, we show that PFs can perform density
estimation on data that lie on a manifold with variable dimensionality, which
is not possible with existing normalizing flows.


title: Hatchery-induced transition of the effective size in a Pareto population

abstract:
  It seems paradoxical to have observed the absence of reduced effective
population sizes $N_{\mathrm{e}}$ under marine hatchery practices. This paper
studies the Ryman-Laikre, or two-demographic-component, model of the hatchery
impact related to inbreeding in a population with power-law family-size
distribution, where hatchery inputs are represented by a Dirac delta function.
By examining the asymptotic (i.e. large-population limit) behavior of the
normalized sizes (or weights) of families of the mixture population, I derive
the distribution properties of the average weight of families (i.e. the sum of
the squared weights, $Y$) over the population existing at any given time. The
reciprocal of the average weight $Y$ gives the effective number of families (or
reproducing lineages) in the population, $N_{\mathrm{e}}=1/Y$. When the
specific production in the hatchery (i.e. the number of offspring per
broodstock) is low, the most probable value of $N_{\mathrm{e}}$ is close to the
lower bound of the $N_{\mathrm{e}}$-distribution. When the specific
hatchery-production is increased to a critical value with fixed mixing
proportion of hatchery fish, a discontinuous transition takes place, so that
the most probable $N_{\mathrm{e}}$ jumps to the upper extreme of the
distribution. This hatchery-induced transition is attributed to the breaking of
reciprocal symmetry, i.e. the fact that the typical value of $Y$ and its
reciprocal (the typical $N_{\mathrm{e}}$) do not vary with the population size
in opposite ways. At a high specific hatchery-production, the symmetry breaking
disappears.


title:
Modeling Non-additive Effects in Neighboring Chemically Identical
  Fluorophores

abstract:
  Quantitative fluorescence analysis is often used to derive chemical
properties, including stoichiometries, of biomolecular complexes. One
fundamental underlying assumption in the analysis of fluorescence data --
whether it be the determination of protein complex stoichiometry by
super-resolution, or step-counting by photobleaching, or the determination of
RNA counts in diffraction-limited spots in RNA fluorescence {\it in situ}
hybridization (RNA-FISH) experiments -- is that fluorophores behave identically
and do not interact. However, recent experiments on fluorophore-labeled
DNA-origami structures such as fluorocubes have shed light on the nature of the
interactions between identical fluorophores as these are brought closer
together, thereby raising questions on the validity of the modeling assumption
that fluorophores do not interact. Here, we analyze photon arrival data under
pulsed illumination from fluorocubes where distances between dyes range from
2-10 nm. We discuss the implications of non-additivity of brightness on
quantitative fluorescence analysis.


title: A Census of X-ray Millisecond Pulsars in Globular Clusters

abstract:
  We present a comprehensive census of X-ray millisecond pulsars (MSPs) in 29
Galactic globular clusters (GCs), including 68 MSPs with confirmed X-ray
luminosities and 107 MSPs with X-ray upper limits. We compile previous X-ray
studies of GC MSPs, and add new analyses of six MSPs (PSRs J1326$-$4728A,
J1326$-$4728B, J1518$+$0204C, J1717$+$4308A, J1737$-$0314A, and J1807$-$2459A)
discovered in five GCs. Their X-ray spectra are well described by a single
blackbody model, a single power-law model, or a combination of them, with X-ray
luminosities ranging from 1.9$\times$10$^{30}$ erg s$^{-1}$ to
8.3$\times$10$^{31}$ erg s$^{-1}$. We find that most detected X-ray MSPs have
luminosities between $\sim 10^{30}$ erg s$^{-1}$ to $3 \times 10^{31}$ erg
s$^{-1}$. Redback pulsars are a relatively bright MSP population with X-ray
luminosities of $\sim2\times10^{31}$--$3\times10^{32}$ erg s$^{-1}$. Black
widows show a bi-modal distribution in X-ray luminosities, with eclipsing black
widows between $\sim 7\times10^{30}$ and $2\times10^{31}$ erg s$^{-1}$, while
the two confirmed non-eclipsing black widows are much fainter, with $L_X$ of
$1.5-3\times10^{30}$ erg s$^{-1}$, suggesting an intrinsic difference in the
populations. We estimate the total number of MSPs in 36 GCs by considering the
correlation between the number of MSPs and stellar encounter rate in GCs, and
suggest that between 600--1500 MSPs exist in these 36 GCs. Finally, we estimate
the number of X-ray detectable MSPs in the Galactic bulge, finding that 1--86
MSPs with $L_X > 10^{33}$ erg s$^{-1}$, and 20--900 MSPs with $L_X > 10^{32}$
erg s$^{-1}$, should be detectable there.


title: Parabolic methods for ultraspherical interpolation inequalities

abstract:
  The carr\'e du champ method is a powerful technique for proving interpolation
inequalities with explicit constants in presence of a non-trivial metric on a
manifold. The method applies to some classical Gagliardo-Nirenberg-Sobolev
inequalities on the sphere, with optimal constants. Very nonlinear regimes
close to the critical Sobolev exponent can be covered using nonlinear parabolic
flows of porous medium or fast diffusion type. Considering power law weights is
a natural question in relation with symmetry breaking issues for
Caffarelli-Kohn-Nirenberg inequalities, but regularity estimates for a complete
justification of the computation are missing. We provide the first example of a
complete parabolic proof based on a nonlinear flow by regularizing the
singularity induced by the weight. Our result is established in the simplified
framework of a diffusion built on the ultraspherical operator, which amounts to
reduce the problem to functions on the sphere with simple symmetry properties.


title:
Tensor Network Renormalization Study on the Crossover in Classical
  Heisenberg and $\mathrm{RP^2}$ Models in Two Dimensions

abstract:
  We study the classical two-dimensional $\mathrm{RP^2}$ and Heisenberg models,
using the Tensor-Network Renormalization (TNR) method. The determination of the
phase diagram of these models has been challenging and controversial, owing to
the very large correlation lengths at low temperatures. The finite-size
spectrum of the transfer matrix obtained by TNR is useful in identifying the
conformal field theory describing a possible critical point. Our results
indicate that the ultraviolet fixed point for the Heisenberg model and the
ferromagnetic $\mathrm{RP^2}$ model in the zero temperature limit corresponds
to a conformal field theory with central charge $c=2$, in agreement with two
independent would-be Nambu-Goldstone modes. On the other hand, the ultraviolet
fixed point in the zero temperature limit for the antiferromagnetic
Lebwohl-Lasher model, which is a variant of the $\mathrm{RP^2}$ model, seems to
have a larger central charge. This is consistent with $c=4$ expected from the
effective SO(5) symmetry. At $T >0$, the convergence of the spectrum is not
good in both the Heisenberg and ferromagnetic $\mathrm{RP^2}$ models. Moreover,
there seems no appropriate candidate of conformal field theory matching the
spectrum, which shows the effective central charge $c \sim 1.9$. These suggest
that both models have a single disordered phase at finite temperatures,
although the ferromagnetic $\mathrm{RP^2}$ model exhibits a strong crossover at
the temperature where the dissociation of $\mathbb{Z}_2$ vortices has been
reported.


title:
Ionization yield measurement in a germanium CDMSlite detector using
  photo-neutron sources

abstract:
  Two photo-neutron sources, $^{88}$Y$^{9}$Be and $^{124}$Sb$^{9}$Be, have been
used to investigate the ionization yield of nuclear recoils in the CDMSlite
germanium detectors by the SuperCDMS collaboration. This work evaluates the
yield for nuclear recoil energies between 1 keV and 7 keV at a temperature of
$\sim$ 50 mK. We use a Geant4 simulation to model the neutron spectrum assuming
a charge yield model that is a generalization of the standard Lindhard model
and consists of two energy dependent parameters. We perform a likelihood
analysis using the simulated neutron spectrum, modeled background, and
experimental data to obtain the best fit values of the yield model. The
ionization yield between recoil energies of 1 keV and 7 keV is shown to be
significantly lower than predicted by the standard Lindhard model for
germanium. There is a general lack of agreement among different experiments
using a variety of techniques studying the low-energy range of the nuclear
recoil yield, which is most critical for interpretation of direct dark matter
searches. This suggests complexity in the physical process that many direct
detection experiments use to model their primary signal detection mechanism and
highlights the need for further studies to clarify underlying systematic
effects that have not been well understood up to this point.


title:
Effect of medium on fundamental interactions in gravity and condensed
  matter

abstract:
  Recently, it was shown that the gravitational field undergoes exponential
cutoff at large cosmological scales due to the presence of background matter.
In this article, we demonstrate that there is a close mathematical analogy
between this effect and the behavior of the magnetic field induced by a
solenoid placed in a superconductor.

categories: gr-qc astro-ph.CO cond-mat.supr-con hep-th


title:
Statistical estimation of spatial wave extremes for tropical cyclones
  from small data samples: validation of the STM-E approach using long-term
  synthetic cyclone data for the Caribbean Sea

abstract:
  Occurrences of tropical cyclones at a location are rare, and for many
locations, only short periods of observations or hindcasts are available.
Hence, estimation of return values (corresponding to a period considerably
longer than that for which data is available) for cyclone-induced significant
wave height (SWH) from small samples is challenging. The STM-E (space-time
maximum and exposure) model was developed to provide reduced bias in estimates
of return values compared to competitor approaches in such situations, and
realistic estimates of return value uncertainty. STM-E exploits data from a
spatial neighbourhood satisfying certain conditions, rather than data from a
single location, for return value estimation.
  This article provides critical assessment of the STM-E model for tropical
cyclones in the Caribbean Sea near Guadeloupe for which a large database of
synthetic cyclones is available, corresponding to more than 3,000 years of
observation. Results indicate that STM-E yields values for the 500-year return
value of SWH and its variability, estimated from 200 years of cyclone data,
consistent with direct empirical estimates obtained by sampling 500 years of
data from the full synthetic cyclone database; similar results were found for
estimation of the 100-year return value from samples corresponding to
approximately 50 years of data. In general, STM-E also provides reduced bias
and more realistic uncertainty estimates for return values relative to single
location analysis.


title: Radiative natural supersymmetry emergent from the string landscape

abstract:
  In string theory with flux compactifications, anthropic selection for
structure formation from a discretuum of vacuum energy values provides at
present our only understanding of the tiny yet positive value of the
cosmological constant. We apply similar reasoning to a toy model of the
multiverse restricted to vacua with the MSSM as the low energy effective
theory. Here, one expects a statistical selection favoring large soft SUSY
breaking terms leading to a derived value of the weak scale in each pocket
universe (with appropriate electroweak symmetry breaking) which differs from
the weak scale as measured in our universe. In contrast, the SUSY preserving
\mu parameter is selected uniformly on a log scale as is consistent with the
distribution of SM fermion masses: this favors smaller values of \mu. An
anthropic selection of the weak scale to within a factor of a few of our
measured value -- in order to produce complex nuclei as we know them (atomic
principle) -- provides statistical predictions for Higgs and sparticle masses
in accord with LHC measurements. The statistical selection then more often
leads to (radiatively-driven) {\it natural} SUSY models over the Standard Model
or finely-tuned SUSY models such as mSUGRA/CMSSM, split, mini-split, spread,
high scale or PeV SUSY. The predicted Higgs and superparticle spectra might be
testable at HL-LHC via higgsino pair production but is certainly testable at
higher energy hadron colliders with \sqrt{s}~ 30-100 TeV.


title:
Vector Coded Caching Multiplicatively Boosts the Throughput of Realistic
  Downlink Systems

abstract:
  The recent introduction of vector coded caching has revealed that multi-rank
transmissions in the presence of receiver-side cache content can dramatically
ameliorate the file-size bottleneck of coded caching and substantially boost
performance in error-free wire-like channels. We here employ large-matrix
analysis to explore the effect of vector coded caching in realistic wireless
multi-antenna downlink systems. Our analysis answers a simple question: Under a
fixed set of antenna and SNR resources, and a given downlink MISO system which
can already enjoy an optimized exploitation of multiplexing and beamforming
gains, what is the multiplicative boost in the throughput when we are now
allowed to occasionally add content inside reasonably-sized receiver-side
caches? The derived closed-form expressions capture various linear precoders,
and a variety of practical considerations such as power dissemination across
signals, realistic SNR values, as well as feedback costs. The schemes are very
simple (we simply collapse precoding vectors into a single vector), and the
recorded gains are notable. For example, for 32 transmit antennas, a received
SNR of 20 dB, a coherence bandwidth of 300 kHz, a coherence period of 40 ms,
and under realistic file-size and cache-size constraints, vector coded caching
is here shown to offer a multiplicative throughput boost of about 310% with
ZF/RZF precoding and a 430% boost in the performance of already optimized
MF-based systems. Interestingly, vector coded caching also accelerates channel
hardening to the benefit of feedback acquisition, often surpassing 540% gains
over traditional hardening-constrained downlink systems.


title:
The Supersymmetry Breaking Soft Terms, and Fermion Masses and Mixings in
  the Supersymmetric Pati-Salam Model from Intersecting D6-branes

abstract:
  A supersymmetric Pati-Salam model with wrapping number equal to 5 has been
constructed in Type IIA orientifolds on $\mathbf{T}^6/(\mathbb{Z}_2\times
\mathbb{Z}_2)$ with intersecting D6-branes recently. In particular, the
string-scale gauge coupling unification can be achieved due to the
intermediate-scale vector-like particles from $\mathcal{N}=2$ sector. We
calculate the supersymmetry breaking soft terms, and study the Standard Model
(SM) fermion masses and mixings. There are nine pairs of Higgs doublets from
$\mathcal{N}=2$ sector. Interestingly, we can explain the SM quark masses and
mixings, as well as the charged leptons masses from three point and four-point
Yukawa interactions. Moreover, we calculate the supersymmetry breaking soft
terms in a previous model with gauge coupling unification since we find a typo
in the previous study.


title:
LIDAR data based Segmentation and Localization using Open Street Maps
  for Rural Roads

abstract:
  Accurate pose estimation is a fundamental ability that all mobile robots must
posses in order to traverse robustly in a given environment. Much like a human,
this ability is dependent on the robot's understanding of a given scene. For
Autonomous Vehicles (AV's), detailed 3D maps created beforehand are widely used
to augment the perceptive abilities and estimate pose based on current sensor
measurements. This approach however is less suited for rural communities that
are sparsely connected and cover large areas. To deal with the challenge of
localizing a vehicle in a rural setting, this paper presents a data-set of
rural road scenes, along with an approach for fast segmentation of roads using
LIDAR point clouds. The segmented point cloud in concert with road network
information from Open Street Maps (OSM) is used for pose estimation. We propose
two measurement models which are compared with state of the art methods for
localization on OSM for tracking as well as global localization. The results
show that the proposed algorithm is able to estimate pose within a 2 sq. km
area with mean accuracy of 6.5 meters.


title:
Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential
  Solutions for Fake-Normal Attacks: A Short Review

abstract:
  Smart grid systems are critical to the power industry, however their
sophisticated architectural design and operations expose them to a number of
cybersecurity threats, such as data tampering, data eavesdropping, and Denial
of Service, among others. Artificial Intelligence (AI)-based technologies are
becoming increasingly popular for detecting cyber assaults in a variety of
computer settings, and several efforts have been made to secure various
systems. The present AI systems are being exposed and vanquished because of the
recent emergence of sophisticated adversarial systems such as Generative
Adversarial Networks (GAN). The purpose of this short review is to outline some
of the initiatives to protect smart grid systems, their obstacles, and what
might be a potential future AI research direction


title: Random Expansive Measures

abstract:
  The notions of expansive measures; expansive, positively expansive,
measure-expansive, countably expansive and continuum-wise homeomorphisms are
well known for the deterministic case. In this paper, we extend these notions
to the random context and prove that some of them are particular cases of
others and some of them are equivalents. Also, we show that every ergodic
probability with positive entropy is positively random expansive.


title: Orthogonalising gradients to speed up neural network optimisation

abstract:
  The optimisation of neural networks can be sped up by orthogonalising the
gradients before the optimisation step, ensuring the diversification of the
learned representations. We orthogonalise the gradients of the layer's
components/filters with respect to each other to separate out the intermediate
representations. Our method of orthogonalisation allows the weights to be used
more flexibly, in contrast to restricting the weights to an orthogonalised
sub-space. We tested this method on ImageNet and CIFAR-10 resulting in a large
decrease in learning time, and also obtain a speed-up on the semi-supervised
learning BarlowTwins. We obtain similar accuracy to SGD without fine-tuning and
better accuracy for na\"ively chosen hyper-parameters.


title: Rank-one Boolean tensor factorization and the multilinear polytope

abstract:
  We consider the NP-hard problem of approximating a tensor with binary entries
by a rank-one tensor, referred to as rank-one Boolean tensor factorization
problem. We formulate this problem, in an extended space of variables, as the
problem of minimizing a linear function over a highly structured multilinear
set. Leveraging on our prior results regarding the facial structure of
multilinear polytopes, we propose novel linear programming relaxations for
rank-one Boolean tensor factorization. To analyze the performance of the
proposed linear programs, we consider a random corruption model for the input
tensor. We first consider the original NP-hard problem and establish
information theoretic limits under the random model. Next, we obtain sufficient
conditions under which the proposed linear programming relaxations recover the
ground truth with high probability. Our theoretical results as well as
numerical simulations indicate that certain facets of the multilinear polytope
significantly improve the recovery properties of linear programming relaxations
for rank-one Boolean tensor factorization.


title:
Universal Adversarial Examples in Remote Sensing: Methodology and
  Benchmark

abstract:
  Deep neural networks have achieved great success in many important remote
sensing tasks. Nevertheless, their vulnerability to adversarial examples should
not be neglected. In this study, we systematically analyze the universal
adversarial examples in remote sensing data for the first time, without any
knowledge from the victim model. Specifically, we propose a novel black-box
adversarial attack method, namely Mixup-Attack, and its simple variant
Mixcut-Attack, for remote sensing data. The key idea of the proposed methods is
to find common vulnerabilities among different networks by attacking the
features in the shallow layer of a given surrogate model. Despite their
simplicity, the proposed methods can generate transferable adversarial examples
that deceive most of the state-of-the-art deep neural networks in both scene
classification and semantic segmentation tasks with high success rates. We
further provide the generated universal adversarial examples in the dataset
named UAE-RS, which is the first dataset that provides black-box adversarial
samples in the remote sensing field. We hope UAE-RS may serve as a benchmark
that helps researchers to design deep neural networks with strong resistance
toward adversarial attacks in the remote sensing field. Codes and the UAE-RS
dataset will be available online.


title:
Fourier-Stieltjes transform defined by induced representation on locally
  compact groups

abstract:
  In this work we extend the Fourier-Stieltjes transform of a vector measure
and a continuous function defined on compact groups to locally compact groups.
To do so, we consider a representation L of a normal compact subgroup K of a
locally compact group G, and we use a representation of G induced by that of L.
Then, we define the Fourier-Stieltjes transform of a vector measure and that of
a continuous function with compact support defined on G from the representation
of G. Then, we extend the Shur orthogonality relation established for compact
groups to locally compact groups by using the representations of G induced by
the unitary representations of one of its normal compact subgroups. This
extension enables us to develop a Fourier-Stieltjes transform in series form
that is linear, continuous, and invertible.


title: Cluster outskirts and their connection to the cosmic web

abstract:
  We review the latest developments in our X-ray observational and theoretical
understanding of the outskirts of galaxy clusters, and their connection to the
cosmic web. The faint cluster outskirts are challenging regions to observe in
X-rays, requiring highly sensitive telescopes with low and stable background
levels. We present our latest understanding of the thermodynamic profiles of
clusters in the outskirts, and the biases that gas clumping and non-thermal
pressure support can introduce. Features in the outskirts due to merging
activity are discussed, along with the chemical enrichment of the outskirts
ICM. We describe future prospects for X-ray observations to explore further out
in the cluster outskirts and probe their connections to the cosmic web.


title: Bases equivalent to the unit vector basis of $c_0$ or $\ell_p$

abstract:
  We will show that an unconditional basis in a Banach space is equivalent to
the unit vector basis of $c_0$ or $\ell_p$ for $1\le p < \infty$ if and only if
all finitely supported blocks of the basis generated by a unit vector and its
dual basis are uniformly equivalent to the basis or all such blocks are
uniformly complemented.


title:
Features of Linear Models that May Compromise Model-Based, Plant-Wide
  Control Techniques. The Case of the Tennessee Eastman Plant

abstract:
  This work examines a set of features that impact the reliability of linear
models within the context of plant-wide control design (PWC). The study case is
the Tennessee-Eastman (TE) plant. This benchmark problem is well-known for
challenging many control design approaches. Analyses involve eigenvalues,
average errors between simulations, condition numbers, and loss of rank across
frequencies. These studies offer guidance for designing an effective plant-wide
control system based on linear models.


title: Atomistic Models of Amorphous Metal-Organic Frameworks

abstract:
  There is an increasing interest in the amorphous states of metal-organic
frameworks (MOFs) and porous coordination polymers, which can be produced by
pressure-induced amorphization, temperature-induced amorphization,
melt-quenching, ball-milling, irradiation, etc. They can exhibit useful
physical and chemical properties, distinct from those achievable in the
crystalline states, along with greater ease of processing, and intrinsic
advantages over crystals and powders, such as high transparency and mechanical
robustness. However, these amorphous states are particularly challenging to
characterize, and the determination of their framework structure at the
microscopic scale is difficult, with only indirect structural information
available from diffraction experiments. In this Perspective, we review and
compare the existing methodologies available for the determination of
microscopic models of amorphous MOFs, based on both experimental data and
simulation methods. In particular, we present the atomistic models that can be
obtained using Reverse Monte Carlo (RMC) methods, Continuous Random Networks
(CRN), classical and \emph{ab initio} molecular dynamics, reactive force
fields, and simulated assembly/polymerization algorithms.


title:
Scrambling Dynamics and Out-of-Time Ordered Correlators in Quantum
  Many-Body Systems: a Tutorial

abstract:
  This tutorial article introduces the physics of quantum information
scrambling in quantum many-body systems. The goals are to understand how to
quantify the spreading of quantum information precisely and how causality
emerges in complex quantum systems. We introduce the general framework to study
the dynamics of quantum information, including detection and decoding. We show
that the dynamics of quantum information is closely related to operator
dynamics in the Heisenberg picture, and, in certain circumstances, can be
precisely quantified by the so-called out-the-time ordered correlator (OTOC).
The general behavior of OTOC is discussed based on several toy models,
including the Sachdev-Ye-Kitaev model, random circuit models, and Brownian
models, in which OTOC is analytically tractable. We introduce numerical
methods, both exact diagonalization and tensor network methods, to calculate
OTOC for generic quantum many-body systems. We also survey current experiment
schemes to measure OTOC in various quantum simulators.


title:
Topological Phases of Mn$A_{2}$$X_{4}$ (A=Bi, Sb; X = Se, Te) under
  Magnetic Field

abstract:
  The concept of electronic topology and the associated topological protection
brings excellent opportunities for developing next-generation devices. Ideally,
magnetic topological materials (MTM) should have their Dirac/Weyl points and/or
associated mass gaps at the Fermi energy (EF) or be readily tunable such that
they can be placed at EF via external perturbations such as electric field
gating, chemical substitutions, or doping. Three-dimensional antiferromagnetic
(AFM) materials like Mn$Bi_{2}$$X_{4}$ (X=Se, Te) that have strong spin-orbit
coupling (SOC) and broken time-reversal symmetry (TRS) due to magnetic ordering
have been the subject of enormous interest.. In this work, using density
functional theory (DFT), we have studied the electronic properties and
topological phases of the first intrinsic magnetic topological insulator family
Mn$Bi_{2}$$Te_{4}$ (MBT) in the presence of an external magnetic field. Our
calculations reveal that the topological phase of bulk rhombohedral
(R$\overline{3}$m) Mn$A_{2}$$X_{4}$ (A = Sb, Bi; X=Se, Te) depends on the spin
direction and the chemistry. The antiferromagnetic (AFM) ground state of
Mn$Sb_{2}$$Se_{4}$ (MSS) is a trivial insulator, whereas the AFM ground state
of Mn$Bi_{2}$$Se_{4}$(MBS) is an Axion insulator. Both materials become nodal
point or nodal line Weyl semimetals in the presence of a sufficiently strong
external magnetic field. The AFM ground state of Mn$Sb_{2}$$Te_{4}$ (MST) is an
Axion insulator. MST is a type-II Weyl semimetal with spins aligned in the
$\hat{Z}$-direction, but becomes insulating with an inverted band gap for spins
in-plane. Similarly, the AFM phase of MBT is an Axion insulator, but remains
insulating with an inverted gap in the ferromagnetic phase. Additionally, we
demonstrated the evolution of the topological phase of Mn$Bi_{2}$$Te_{4}$ (MBT)
by substituting the Bi atoms with the Sb atoms.


title: The core of a Grassmannian frame

abstract:
  Let $X=\{x_i\}_{i=1}^m$ be a set of unit vectors in $\RR^n$. The coherence of
$X$ is $\coh(X):=\max_{i\not=j}|\langle x_i, x_j\rangle|$. A vector $x\in X$ is
said to be isolable if there are no unit vectors $x'$ arbitrarily close to $x$
such that $|\langle x', y\rangle|<\coh(X)$ for all other vectors $y$ in $X$. We
define the {\bf core} of a Grassmannian frame $X=\{x_i\}_{i=1}^m$ in $\RR^n$ at
angle $\alpha$ as a maximal subset of $X$ which has coherence $\alpha$ and has
no isolable vectors. In other words, if $Y$ is a subset of $X$,
$\coh(Y)=\alpha$, and $Y$ has no isolable vectors, then $Y$ is a subset of the
core. We will show that every Grassmannian frame of $m>n$ vectors for $\RR^n$
has the property that each vector in the core makes angle $\alpha$ with a
spanning family from the core. Consequently, the core consists of $\ge n+1$
vectors. We then develop other properties of Grassmannian frames and of the
core.


title:
Cosmic-ray propagation and production of secondary particles in the
  Galaxy

abstract:
  Cosmic rays are nowadays a crucial tool to study the astrophysics of extreme
objects in the Universe, the cosmic environmental plasma (both Galactic and
extra-galactic), the physics of nuclear interactions or the properties of
elementary particles at very high energies and even cosmological problems such
as the dark matter puzzle. In this thesis, the phenomenology on the transport
of Galactic cosmic rays is studied in light of the most recent experimental
data in the field and new analyses are presented in order to obtain better
constraints. Throughout the thesis, secondary particles produced from
collisions of cosmic rays with the interstellar gas, such as secondary cosmic
ray nuclei (B, Be and Li), antiprotons and gamma rays, are treated in order to
adjust and test our models and probe different scenarios, such as possible
signatures of dark matter decay or annihilation. A preliminary version of the
upcoming DRAGON2 code has been used to perform the propagation computations.
With the increasing accuracy of CR data a number of anomalies have appear with
respect to the standard paradigm of propagation of charged particles in the
Galaxy. In this thesis, we demonstrate that some of these anomalies disappear
by taking into account the systematic uncertainties involving these kind of
analyses, specially cross sections uncertainties.


title:
Towards hardware Implementation of WTA for CPG-based control of a
  Spiking Robotic Arm

abstract:
  Biological nervous systems typically perform the control of numerous degrees
of freedom for example in animal limbs. Neuromorphic engineers study these
systems by emulating them in hardware for a deeper understanding and its
possible application to solve complex problems in engineering and robotics.
Central-Pattern-Generators (CPGs) are part of neuro-controllers, typically used
at their last steps to produce rhythmic patterns for limbs movement. Different
patterns and gaits typically compete through winner-take-all (WTA) circuits to
produce the right movements. In this work we present a WTA circuit implemented
in a Spiking-Neural-Network (SNN) processor to produce such patterns for
controlling a robotic arm in real-time. The robot uses spike-based
proportional-integrativederivative (SPID) controllers to keep a commanded joint
position from the winner population of neurons of the WTA circuit. Experiments
demonstrate the feasibility of robotic control with spiking circuits following
brain-inspiration.


title:
Automatic Generation of Individual Fuzzy Cognitive Maps from
  Longitudinal Data

abstract:
  Fuzzy Cognitive Maps (FCMs) are computational models that represent how
factors (nodes) change over discrete interactions based on causal impacts
(weighted directed edges) from other factors. This approach has traditionally
been used as an aggregate, similarly to System Dynamics, to depict the
functioning of a system. There has been a growing interest in taking this
aggregate approach at the individual-level, for example by equipping each agent
of an Agent-Based Model with its own FCM to express its behavior. Although
frameworks and studies have already taken this approach, an ongoing limitation
has been the difficulty of creating as many FCMs as there are individuals.
Indeed, current studies have been able to create agents whose traits are
different, but whose decision-making modules are often identical, thus limiting
the behavioral heterogeneity of the simulated population. In this paper, we
address this limitation by using Genetic Algorithms to create one FCM for each
agent, thus providing the means to automatically create a virtual population
with heterogeneous behaviors. Our algorithm builds on prior work from Stach and
colleagues by introducing additional constraints into the process and applying
it over longitudinal, individual-level data. A case study from a real-world
intervention on nutrition confirms that our approach can generate heterogeneous
agents that closely follow the trajectories of their real-world human
counterparts. Future works include technical improvements such as lowering the
computational time of the approach, or case studies in computational
intelligence that use our virtual populations to test new behavior change
interventions.


title:
An ALMA search for high albedo objects among the mid-sized Jupiter
  Trojan population

abstract:
  We use ALMA measurements of 870 $\mu$m thermal emission from a sample of
mid-sized (15-40 km diameter) Jupiter Trojan asteroids to search for high
albedo objects in this population. We calculate the diameters and albedos of
each object using a thermal model which also incorporates {contemporaneous}
Zwicky Transient Facility photometry to accurately measure the absolute
magnitude at the time of the ALMA observation. We find that while many albedos
are lower than reported from WISE, several small Trojans have high albedos
independently measured both from ALMA and from WISE. The number of these high
albedo objects is approximately consistent with expectations of the number of
objects that recently have undergone large-scale impacts, suggesting that the
interiors of freshly-crated Jupiter Trojans could contain high albedo materials
such as ices.


title: Torsion theories of simplicial groups with truncated Moore complex

abstract:
  We introduce a linearly ordered lattice $\mu(Grp)$ of torsion theories in
simplicial groups. The torsion theories are defined where the
torsion/torsion-free subcategories are given by the simplicial groups with
bounded above/below Moore complex, respectively. These torsion theories extend
naturally the torsion theories in internal groupoids in groups. Connections of
this lattice with the homotopy groups are established since the homotopy groups
of a simplicial group can be calculated as the quotients of torsion subojects.


title: Motivating Physical Activity via Competitive Human-Robot Interaction

abstract:
  This project aims to motivate research in competitive human-robot interaction
by creating a robot competitor that can challenge human users in certain
scenarios such as physical exercise and games. With this goal in mind, we
introduce the Fencing Game, a human-robot competition used to evaluate both the
capabilities of the robot competitor and user experience. We develop the robot
competitor through iterative multi-agent reinforcement learning and show that
it can perform well against human competitors. Our user study additionally
found that our system was able to continuously create challenging and enjoyable
interactions that significantly increased human subjects' heart rates. The
majority of human subjects considered the system to be entertaining and
desirable for improving the quality of their exercise.


title: Quantalic Behavioural Distances

abstract:
  Behavioural distances measure the deviation between states in quantitative
systems, such as probabilistic or weighted systems. There is growing interest
in generic approaches to behavioural distances. In particular, coalgebraic
methods capture variations in the system type (nondeterministic, probabilistic,
game-based etc.), and the notion of quantale abstracts over actual values
distances take, thus covering, e.g., two-valued equivalences, metrics, and
probabilistic metrics. Coalgebraic behavioural distances have variously been
based on liftings of $\mathsf{Set}$-functors to categories of metric spaces; on
modalities modeled as predicate liftings, via a generalised Kantorovich
construction; and on lax extensions of $\mathsf{Set}$-functors to categories of
quantitative relations. Every lax extension induces a functor lifting in a
straightforward manner. Moreover, it has recently been shown that every lax
extension is Kantorovich, i.e. induced by a suitable choice of monotone
predicate liftings. In the present work, we complete this picture by
determining, in coalgebraic and quantalic generality, when a functor lifting is
induced by a class of predicate liftings or by a lax extension. We subsequently
show coincidence of the respective induced notions of behavioural distances, in
a unified approach via double categories that applies even more widely, e.g. to
(quasi)uniform spaces.


title: Sequential Monte Carlo With Model Tempering

abstract:
  Modern macroeconometrics often relies on time series models for which it is
time-consuming to evaluate the likelihood function. We demonstrate how Bayesian
computations for such models can be drastically accelerated by reweighting and
mutating posterior draws from an approximating model that allows for fast
likelihood evaluations, into posterior draws from the model of interest, using
a sequential Monte Carlo (SMC) algorithm. We apply the technique to the
estimation of a vector autoregression with stochastic volatility and a
nonlinear dynamic stochastic general equilibrium model. The runtime reductions
we obtain range from 27% to 88%.


title:
A Unified Perspective on Value Backup and Exploration in Monte-Carlo
  Tree Search

abstract:
  Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex
decision-making problems through the synergy of Monte-Carlo planning and
Reinforcement Learning (RL). The highly combinatorial nature of the problems
commonly addressed by MCTS requires the use of efficient exploration strategies
for navigating the planning tree and quickly convergent value backup methods.
These crucial problems are particularly evident in recent advances that combine
MCTS with deep neural networks for function approximation. In this work, we
propose two methods for improving the convergence rate and exploration based on
a newly introduced backup operator and entropy regularization. We provide
strong theoretical guarantees to bound convergence rate, approximation error,
and regret of our methods. Moreover, we introduce a mathematical framework
based on the use of the $\alpha$-divergence for backup and exploration in MCTS.
We show that this theoretical formulation unifies different approaches,
including our newly introduced ones, under the same mathematical framework,
allowing to obtain different methods by simply changing the value of $\alpha$.
In practice, our unified perspective offers a flexible way to balance between
exploration and exploitation by tuning the single $\alpha$ parameter according
to the problem at hand. We validate our methods through a rigorous empirical
study from basic toy problems to the complex Atari games, and including both
MDP and POMDP problems.


title:
X-ray irradiation of the stellar wind in HMXBs with B supergiants:
  Implications for ULXs

abstract:
  Wind-fed high-mass X-ray binaries are powered by accretion of the radiatively
driven wind on the compact star. Accretion-generated X-rays alter the
ionization state of the wind. Because higher ionization states drive the wind
less effectively, X-ray ionization may brake acceleration of the wind. This
causes a decrease in the wind terminal velocity and mass flux in the direction
toward the X-ray source. We study the effect of X-ray ionization on the stellar
wind of B supergiants. We determine the binary parameters for which the X-ray
irradiation significantly influences the stellar wind. This can be studied in
diagrams that plot the optical depth parameter versus the X-ray luminosity. For
low optical depths or for high X-ray luminosities, X-ray ionization leads to a
disruption in the wind aimed toward the X-ray source. Observational parameters
of high-mass X-ray binaries with B-supergiant components appear outside the
wind disruption zone. The X-ray feedback determines the resulting X-ray
luminosity. For low X-ray luminosities, ionization is weak, and the wind is not
disrupted by X-rays and flows at large velocities, consequently the accretion
rate is relatively low. On the other hand, for high X-ray luminosities, the
X-ray ionization disrupts the flow braking the acceleration, the wind velocity
is low, and the accretion rate becomes high. These effects determine the X-ray
luminosity of individual binaries. Accounting for the X-ray feedback, estimated
X-ray luminosities reasonably agree with observational values. We study the
effect of small-scale wind inhomogeneities, showing that they weaken the effect
of X-ray ionization by increasing recombination and the mass-loss. This effect
is particularly important in the region of the bistability jump. We show that
ultraluminous X-ray binaries with $L_x<10^{40}$ erg/s may be powered by
accretion of a B-supergiant wind on a massive black hole.


title: Discriminability-enforcing loss to improve representation learning

abstract:
  During the training process, deep neural networks implicitly learn to
represent the input data samples through a hierarchy of features, where the
size of the hierarchy is determined by the number of layers. In this paper, we
focus on enforcing the discriminative power of the high-level representations,
that are typically learned by the deeper layers (closer to the output). To this
end, we introduce a new loss term inspired by the Gini impurity, which is aimed
at minimizing the entropy (increasing the discriminative power) of individual
high-level features with respect to the class labels. Although our Gini loss
induces highly-discriminative features, it does not ensure that the
distribution of the high-level features matches the distribution of the
classes. As such, we introduce another loss term to minimize the
Kullback-Leibler divergence between the two distributions. We conduct
experiments on two image classification data sets (CIFAR-100 and Caltech 101),
considering multiple neural architectures ranging from convolutional networks
(ResNet-17, ResNet-18, ResNet-50) to transformers (CvT). Our empirical results
show that integrating our novel loss terms into the training objective
consistently outperforms the models trained with cross-entropy alone, without
increasing the inference time at all.


title: Benchmarking Robot Manipulation with the Rubik's Cube

abstract:
  Benchmarks for robot manipulation are crucial to measuring progress in the
field, yet there are few benchmarks that demonstrate critical manipulation
skills, possess standardized metrics, and can be attempted by a wide array of
robot platforms. To address a lack of such benchmarks, we propose Rubik's cube
manipulation as a benchmark to measure simultaneous performance of precise
manipulation and sequential manipulation. The sub-structure of the Rubik's cube
demands precise positioning of the robot's end effectors, while its highly
reconfigurable nature enables tasks that require the robot to manage pose
uncertainty throughout long sequences of actions. We present a protocol for
quantitatively measuring both the accuracy and speed of Rubik's cube
manipulation. This protocol can be attempted by any general-purpose
manipulator, and only requires a standard 3x3 Rubik's cube and a flat surface
upon which the Rubik's cube initially rests (e.g. a table). We demonstrate this
protocol for two distinct baseline approaches on a PR2 robot. The first
baseline provides a fundamental approach for pose-based Rubik's cube
manipulation. The second baseline demonstrates the benchmark's ability to
quantify improved performance by the system, particularly that resulting from
the integration of pre-touch sensing. To demonstrate the benchmark's
applicability to other robot platforms and algorithmic approaches, we present
the functional blocks required to enable the HERB robot to manipulate the
Rubik's cube via push-grasping.


title:
Regional Differences in Information Privacy Concerns After the
  Facebook-Cambridge Analytica Data Scandal

abstract:
  While there is increasing global attention to data privacy, most of their
current theoretical understanding is based on research conducted in a few
countries. Prior work argues that people's cultural backgrounds might shape
their privacy concerns; thus, we could expect people from different world
regions to conceptualize them in diverse ways. We collected and analyzed a
large-scale dataset of tweets about the #CambridgeAnalytica scandal in Spanish
and English to start exploring this hypothesis. We employed word embeddings and
qualitative analysis to identify which information privacy concerns are present
and characterize language and regional differences in emphasis on these
concerns. Our results suggest that related concepts, such as regulations, can
be added to current information privacy frameworks. We also observe a greater
emphasis on data collection in English than in Spanish. Additionally, data from
North America exhibits a narrower focus on awareness compared to other regions
under study. Our results call for more diverse sources of data and nuanced
analysis of data privacy concerns around the globe.


title: Collimated versatile atomic beam source with alkali dispensers

abstract:
  Alkali metal dispensers have become an indispensable tool in the production
of atomic vapors for magnetometry, alkali vapor cell clocks, and laser cooling
experiments. A primary advantage of these dispensers is that they contain
alkali metal in an inert form that can be exposed to air without hazard.
However, their high temperature of operation (>600 C) is undesirable for many
applications, as it shifts the atomic speed distribution to higher values and
presents a radiative heat source that can raise the temperature of its
surroundings. For this reason, dispensers are typically not used in
line-of-sight applications such as atomic beam generation. In this work, we
present an integrated rubidium dispenser collimating device with a thickness of
only 2 mm that produces a beam of atoms traveling primarily in the forward
direction. We find that the collimator plate serves to both shield the
dispenser's radiation as well as to moderate the velocity of the atomic beam so
that the measured longitudinal speed distribution is comparable to that of an
ordinary alkali oven at only a slightly elevated temperature of 200 C. To
confirm our theory, we also constructed another compact apparatus consisting of
a dispenser and a silicon collimator and the measurements support our
conclusion. Our integrated dispenser collimator will particularly be useful in
integrated photonics and cavity QED on chip, where a localized, directed source
of Rb vapor in small quantities is needed.

categories: physics.atom-ph physics.app-ph physics.ins-det quant-ph


title:
Fast and accurate dose predictions for novel radiotherapy treatments in
  heterogeneous phantoms using conditional 3D-UNet generative adversarial
  networks

abstract:
  Novel radiotherapy techniques like synchrotron X-ray microbeam radiation
therapy (MRT), require fast dose distribution predictions that are accurate at
the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo
physics simulations are recognised to be one of the most accurate tools to
predict the dose delivered in a target tissue but can be very time consuming
and therefore prohibitive for treatment planning. Faster dose prediction
algorithms are usually developed for clinically deployed treatments only. In
this work, we explore a new approach for fast and accurate dose estimations
suitable for novel treatments using digital phantoms used in pre-clinical
development and modern machine learning techniques. We develop a generative
adversarial network (GAN) model, which is able to emulate the equivalent Geant4
Monte Carlo simulation with adequate accuracy, and use it to predict the
radiation dose delivered by a broad synchrotron beam to various phantoms. The
energy depositions used for the training of the GAN are obtained using full
Geant4 Monte Carlo simulations of a synchrotron radiation broad beam passing
through the phantoms. The energy deposition is scored and predicted in voxel
matrices of size 140x18x18 with a voxel edge length of 1 mm. The GAN model
consists of two competing 3D convolutional neural networks, which are
conditioned on the photon beam and phantom properties. The energy deposition
predictions inside all phantom geometries under investigation show deviations
of less than 3% of the maximum deposited energy from the simulation for roughly
99% of the voxels in the field of the beam. The computing time for a single
prediction is reduced from several hundred hours using Geant4 simulation to
less than a second using the GAN model.


title:
A Queueing Model of Patient Flow for Stroke Networks to Estimate Acute
  Stroke Transfer Capacity

abstract:
  Background: Most acute stroke (AS) patients in the United States are
initially evaluated at a primary stroke center (PSC) and a significant
proportion requires transfer to a comprehensive stroke center (CSC) for
advanced treatment. A CSC typically accepts patients from multiple PSCs in its
network, leading to capacity limits. This study uses a queueing model to
estimate impacts on CSC capacity due to transfers from PSCs.
  Methods: The model assumes that the number of AS patients arriving at each
PSC, proportion of AS patients transferred, and length of stay in the CSC
Neurologic Intensive Care Unit (Neuro-ICU) by type of AS are random, while the
transfer rates of ischemic and hemorrhagic AS patients are control variables.
The main outcome measure is the "overflow" probability, namely, the probability
of a CSC not having capacity (unavailability of a Neuro-ICU bed) to accept a
transfer. Data simulations of the model, using a base case and an expanded
case, were performed to illustrate the effects of changing key parameters, such
as transfer rates from PSCs and CSC Neuro-ICU capacity on overflow capacity.
  Results: Data simulations of the model using a base case show that an
increase of a PSC's ischemic stroke transfer rate from 15% to 55% raises the
overflow probability from 30.62% to 36.13%. Further simulations of the expanded
case show that to maintain an a priori CSC overflow probability of 30.62% when
adding a PSC with a AS transfer rate of 15% to the network, other PSCs would
need to decrease their transfer rate by 12.5% or the CSC Neuro-ICU would need
to add 2 beds.
  Discussion: A queuing model can be used to estimate the effects of change in
the size of a PSC-CSC network, change in AS transfer rates, or change in number
of CSC Neuro-ICU beds of a CSC on its capacity on the overflow probability in
the CSC.


title: Synthetically Controlled Bandits

abstract:
  This paper presents a new dynamic approach to experiment design in settings
where, due to interference or other concerns, experimental units are coarse.
`Region-split' experiments on online platforms are one example of such a
setting. The cost, or regret, of experimentation is a natural concern here. Our
new design, dubbed Synthetically Controlled Thompson Sampling (SCTS), minimizes
the regret associated with experimentation at no practically meaningful loss to
inferential ability. We provide theoretical guarantees characterizing the
near-optimal regret of our approach, and the error rates achieved by the
corresponding treatment effect estimator. Experiments on synthetic and real
world data highlight the merits of our approach relative to both fixed and
`switchback' designs common to such experimental settings.


title:
A low-threshold ultrahigh-energy neutrino search with the Askaryan Radio
  Array

abstract:
  In the pursuit of the measurement of the still-elusive ultrahigh-energy (UHE)
neutrino flux at energies of order EeV, detectors using the in-ice Askaryan
radio technique have increasingly targeted lower trigger thresholds. This has
led to improved trigger-level sensitivity to UHE neutrinos. Working with data
collected by the Askaryan Radio Array (ARA), we search for neutrino candidates
at the lowest threshold achieved to date, leading to improved analysis-level
sensitivities. A neutrino search on a data set with 208.7~days of livetime from
the reduced-threshold fifth ARA station is performed, achieving a 68\% analysis
efficiency over all energies on a simulated mixed-composition neutrino flux
with an expected background of $0.10_{-0.04}^{+0.06}$ events passing the
analysis. We observe one event passing our analysis and proceed to set a
neutrino flux limit using a Feldman-Cousins construction. We show that the
improved trigger-level sensitivity can be carried through an analysis,
motivating the Phased Array triggering technique for use in future
radio-detection experiments. We also include a projection using all available
data from this detector. Finally, we find that future analyses will benefit
from studies of events near the surface to fully understand the background
expected for a large-scale detector.