title: The Structural Power of Reconfigurable Circuits in the Amoebot Model

abstract:
  The amoebot model [Derakhshandeh et al., 2014] has been proposed as a model
for programmable matter consisting of tiny, robotic elements called amoebots.
We consider the reconfigurable circuit extension [Feldmann et al., JCB 2022] of
the geometric (variant of the) amoebot model that allows the amoebot structure
to interconnect amoebots by so-called circuits. A circuit permits the
instantaneous transmission of signals between the connected amoebots. In this
paper, we examine the structural power of the reconfigurable circuits.
  We start with some fundamental problems like the stripe computation problem
where, given any connected amoebot structure $S$, an amoebot $u$ in $S$, and
some axis $X$, all amoebots belonging to axis $X$ through $u$ have to be
identified. Second, we consider the global maximum problem, which identifies an
amoebot at the highest possible position with respect to some direction in some
given amoebot (sub)structure. A solution to this problem can then be used to
solve the skeleton problem, where a (not necessarily simple) cycle of amoebots
has to be found in the given amoebot structure which contains all boundary
amoebots. A canonical solution to that problem can then be used to come up with
a canonical path, which provides a unique characterization of the shape of the
given amoebot structure. Constructing canonical paths for different directions
will then allow the amoebots to set up a spanning tree and to check symmetry
properties of the given amoebot structure.
  The problems are important for a number of applications like rapid shape
transformation, energy dissemination, and structural monitoring. Interestingly,
the reconfigurable circuit extension allows polylogarithmic-time solutions to
all of these problems.


title: On conformal points of area preserving maps and related topics

abstract:
  In this article we consider area preserving diffeomorphisms of planar
domains, and we are interested in their conformal points, i.e., points at which
the derivative is a similarity. We present some conditions that guarantee
existence of conformal points for the infinitesimal problem of Hamiltonian
vector fields as well as for what we call moderate symplectomorphisms of simply
connected domains. We also link this problem to the Carath\'eodory and Loewner
conjectures.


title:
Coupler curves of moving graphs and counting realizations of rigid
  graphs

abstract:
  A calligraph is a graph that for almost all edge length assignments moves
with one degree of freedom in the plane, if we fix an edge and consider the
vertices as revolute joints. The trajectory of a distinguished vertex of the
calligraph is called its coupler curve. To each calligraph we uniquely assign a
vector consisting of three integers. This vector bounds the degrees and
geometric genera of irreducible components of the coupler curve. A graph, that
up to rotations and translations admits finitely many, but at least two,
realizations into the plane for almost all edge length assignments, is a union
of two calligraphs. We show that this number of realizations is equal to a
certain inner product of the vectors associated to these two calligraphs. As an
application we obtain an improved algorithm for counting numbers of
realizations, and by counting realizations we characterize invariants of
coupler curves.


title:
Exploiting Global and Local Hierarchies for Hierarchical Text
  Classification

abstract:
  Hierarchical text classification aims to leverage label hierarchy in
multi-label text classification. Existing methods encode label hierarchy in a
global view, where label hierarchy is treated as the static hierarchical
structure containing all labels. Since global hierarchy is static and
irrelevant to text samples, it makes these methods hard to exploit hierarchical
information. Contrary to global hierarchy, local hierarchy as the structured
target labels hierarchy corresponding to each text sample is dynamic and
relevant to text samples, which is ignored in previous methods. To exploit
global and local hierarchies, we propose Hierarchy-guided BERT with Global and
Local hierarchies (HBGL), which utilizes the large-scale parameters and prior
language knowledge of BERT to model both global and local hierarchies.
Moreover, HBGL avoids the intentional fusion of semantic and hierarchical
modules by directly modeling semantic and hierarchical information with BERT.
Compared with the state-of-the-art method HGCLR, our method achieves
significant improvement on three benchmark datasets.


title: Very Pliable Index Coding

abstract:
  In the pliable variant of index coding, receivers are allowed to decode any
new message not known a priori. Optimal code design for this variant involves
identifying each receiver's choice of a new message that minimises the overall
transmission rate. This paper proposes a formulation that further relaxes the
decoding requirements of pliable index coding by allowing receivers to decode
different new messages depending on message realisations. Such relaxation is
shown to offer no rate benefit when linear codes are used, but can achieve
strictly better rates in general. Scenarios are demonstrated for which the
transmission rates are better when the message size is finite than when it is
asymptotically large. This is in stark contrast to traditional communication
setups.


title: Dynamical symmetries of homogeneous minisuperspace models

abstract:
  We investigate the phase space symmetries and conserved charges of
homogeneous gravitational minisuperspaces. These (0+1)-dimensional reductions
of general relativity are defined by spacetime metrics in which the dynamical
variables depend only on a time coordinate, and are formulated as mechanical
systems with a non-trivial field space metric (or supermetric) and effective
potential. We show how to extract conserved charges for those minisuperspaces
from the homothetic Killing vectors of the field space metric. In the case of
two-dimensional field spaces, we exhibit a universal 8-dimensional symmetry
algebra given by the semi-direct sum of
$\mathfrak{sl}(2,\mathbb{R})\oplus\mathbb{R}$ with the two-dimensional
Heisenberg algebra $\mathfrak{h}_2\simeq\mathbb{R}^4$. We apply this to the
systematic study of the Bianchi models for homogeneous cosmology. This extends
previous results on the $\mathfrak{sl}(2,\mathbb{R})$ CVH algebra for FLRW
cosmology, and the Poincar\'e symmetry for Kantowski-Sachs metrics describing
the black hole interior. The presence of this rich symmetry structure already
in minisuperspace models opens new doors towards quantization and the study of
solution generating mechanisms.


title: Convolutional Auto-Encoders for Drift Chamber data de-noising for CLAS12

abstract:
  In this article, we present the results of using Convolutional Auto-Encoders
for de-noising raw data for CLAS12 drift chambers. The de-noising neural
network provides increased efficiency in track reconstruction and also improved
performance for high luminosity experimental data collection. The de-noising
neural network used in conjunction with the previously developed track
classifier neural network \cite{Gavalian:2022hfa} lead to a significant track
reconstruction efficiency increase for current luminosity
($0.6\times10^{35}~cm^{-2}~sec^{-1}$ ). The increase in experimentally measured
quantities will allow running experiments at twice the luminosity with the same
track reconstruction efficiency. This will lead to huge savings in accelerator
operational costs, and large savings for Jefferson Lab and collaborating
institutions.


title: COMBSS: Best Subset Selection via Continuous Optimization

abstract:
  We consider the problem of best subset selection in linear regression, where
the goal is to find for every model size $k$, that subset of $k$ features that
best fits the response. This is particularly challenging when the total
available number of features is very large compared to the number of data
samples. We propose COMBSS, a novel continuous optimization based method that
directly solves the best subset selection problem in linear regression. COMBSS
turns out to be very fast, potentially making best subset selection possible
when the number of features is well in excess of thousands. Simulation results
are presented to highlight the performance of COMBSS in comparison to existing
popular non-exhaustive methods such as Forward Stepwise and the Lasso, as well
as for exhaustive methods such as Mixed-Integer Optimization. Because of the
outstanding overall performance, framing the best subset selection challenge as
a continuous optimization problem opens new research directions for feature
extraction for a large variety of regression models.


abstract:
  Hierarchical clustering recursively partitions data at an increasingly finer
granularity. In real-world applications, multi-view data have become
increasingly important. This raises a less investigated problem, i.e.,
multi-view hierarchical clustering, to better understand the hierarchical
structure of multi-view data. To this end, we propose a novel neural
network-based model, namely Contrastive Multi-view Hyperbolic Hierarchical
Clustering (CMHHC). It consists of three components, i.e., multi-view alignment
learning, aligned feature similarity learning, and continuous hyperbolic
hierarchical clustering. First, we align sample-level representations across
multiple views in a contrastive way to capture the view-invariance information.
Next, we utilize both the manifold and Euclidean similarities to improve the
metric property. Then, we embed the representations into a hyperbolic space and
optimize the hyperbolic embeddings via a continuous relaxation of hierarchical
clustering loss. Finally, a binary clustering tree is decoded from optimized
hyperbolic embeddings. Experimental results on five real-world datasets
demonstrate the effectiveness of the proposed method and its components.


title:
Phase shifts of the light pseudoscalar meson and heavy meson scattering
  in heavy meson chiral perturbation theory

abstract:
  We calculate the complete $T$ matrices of the elastic light pseudoscalar
meson and heavy meson scattering to the third order in heavy meson chiral
perturbation theory. We determine the low-energy constants by fitting to the
phase shifts and scattering lengths from the lattice QCD simulations
simultaneously and predict the phase shifts at the physical meson masses. The
phase shifts in the $D\pi(I=1/2)$, $DK(I=0)$, $D\bar{K}(I=0)$, $D_s\bar{K}$,
$D\eta$ and $D_s\eta$ $S$ waves are so strong that bound states or resonances
may be generated dynamically in all these channels. The $DK(I=0)$ channel
corresponds to the well-known exotic state $D_{s0}^{*}(2317)$. The
coupled-channel $D\pi$, $D\eta$ and $D_s\bar{K}$ scattering corresponds to the
$D_{0}^{*}(2400)$. We also predict the scattering lengths and scattering
volumes and observe a good convergence in the $P$ waves and scattering volumes.
Our calculations provide a possibility to accurately investigate the exotic
state in the light pseudoscalar meson and heavy meson interactions.


title: Heterogeneous Semantic and Bit Communications: A Semi-NOMA Scheme

abstract:
  Multiple access (MA) design is investigated for facilitating the coexistence
of the emerging semantic transmission and the conventional bit-based
transmission in future networks. The semantic rate is considered for measuring
the performance of the semantic transmission. However, a key challenge is that
there is a lack of a closed-form expression for a key parameter, namely the
semantic similarity, which characterizes the sentence similarity between an
original sentence and the corresponding recovered sentence. To overcome this
challenge, we propose a data regression method, where the semantic similarity
is approximated by a generalized logistic function. Using the obtained
tractable function, we propose a heterogeneous semantic and bit communication
framework, where an access point simultaneously sends the semantic and bit
streams to one semantics-interested user (S-user) and one bit-interested user
(B-user). To realize this heterogeneous semantic and bit transmission in
multi-user networks, three MA schemes are proposed, namely orthogonal multiple
access (OMA), non-orthogonal multiple access (NOMA), and semi-NOMA. More
specifically, the bit stream in semi-NOMA is split into two streams, one is
transmitted with the semantic stream over the shared frequency sub-band and the
other is transmitted over the separate orthogonal frequency sub-band. To study
the fundamental performance limits of the three proposed MA schemes, the
semantic-versus-bit (SvB) rate region and the power region are defined. An
optimal resource allocation procedure is then derived for characterizing the
boundary of the SvB rate region and the power region achieved by each MA
scheme. The structures of the derived solutions demonstrate that semi-NOMA is
superior to both NOMA and OMA given its highly flexible transmission policy.
Our numerical results validate the analysis and show the superiority of
semi-NOMA.


title: MTBF Model for AVs -- From Perception Errors to Vehicle-Level Failures

abstract:
  The development of Automated Vehicles (AVs) is progressing quickly and the
first robotaxi services are being deployed worldwide. However, to receive
authority certification for mass deployment, manufactures need to justify that
their AVs operate safer than human drivers. This in turn creates the need to
estimate and model the collision rate (failure rate) of an AV taking all
possible errors and driving situations into account. In other words, there is
the strong demand for comprehensive Mean Time Between Failure (MTBF) models for
AVs. In this paper, we will introduce such a generic and scalable model that
creates a link between errors in the perception system to vehicle-level
failures (collisions). Using this model, we are able to derive requirements for
the perception quality based on the desired vehicle-level MTBF or vice versa to
obtain an MTBF value given a certain mission profile and perception quality.


title: Diverging current fluctuations in critical Kerr resonators

abstract:
  The parametrically pumped Kerr model describes a driven-dissipative nonlinear
cavity, whose nonequilibrium phase diagram features both continuous and
discontinuous quantum phase transitions. We consider the consequences of these
critical phenomena for the fluctuations of the photocurrent obtained via
continuous weak measurements on the cavity. Considering both direct
photodetection and homodyne detection schemes, we find that the current
fluctuations diverge exponentially at the discontinuous phase transition.
However, we find strikingly different current fluctuations for these two
detection schemes near the continuous transition, a behaviour which is
explained by the complementary information revealed by measurements in
different bases. To obtain these results, we develop formulas to efficiently
compute the diffusion coefficient -- which characterises the long-time current
fluctuations -- directly from the quantum master equation, thus connecting the
formalisms of full counting statistics and stochastic quantum trajectories. Our
findings highlight the rich features of current fluctuations near
nonequilibrium phase transitions in quantum-optical systems.


title:
Geometry Relaxation and Transition State Search throughout Chemical
  Compound Space with Quantum Machine Learning

abstract:
  We apply response operator based quantum machine learning (OQML) to the
problem of geometry optimization and transition state search throughout
chemical compound space. Using legacy optimizers for both applications, the
impact of including of OQML based atomic forces on optimization outcome has
been explored. Numerical results for randomly sampled small organic query
molecules indicate systematic improvement of equilibrium and transition state
geometries as training set sizes increase. For geometry optimizations, we have
considered 5'989 randomly chosen instances of relaxation paths of 5'500
constitutional isomers (sum formula: C$_7$H$_{10}$O$_2$) from the QM9-database.
Using the resulting OQML models with an LBFGS optimizer reproduces the minimum
geometry with an RMSD of 0.15\r{A}. Training on 3812 instances drawn at random
from 200 transition state search trajectories from the QMrxn20 data-set,
out-of-sample S$_\mathrm{N}$2 transition state geometries have been obtained
using OQML based forces within the QST2 algorithm with an RMSD of 0.3 \r{A}.
For the converged equilibrium and transition state geometries subsequent
vibrational normal mode frequency analysis deviates from MP2 reference results
on average 39 and 41 cm$^{-1}$, respectively. The number of steps until
convergence is typically larger for OQML than for DFT based forces. However,
the success rate for reaching convergence increases systematically with
training set size, indicating OQML's considerable potential applicability.


title: Correspondence Theory for Generalized Modal Algebras

abstract:
  In the present paper, we give a systematic study of the correspondence theory
of generalized modal algebras and generalized modal spaces. The special feature
of the present paper is that in the proof of the (right-handed) topological
Ackermann lemma, the admissible valuations are not the clopen valuations
anymore, but values in the set DK(X) which are only closed and satisfy
additional properties, not necessarily open. This situation is significantly
different from existing settings using Stone/Priestley-like dualities, where
all admissible valuations are clopen valuations.


title: GANimator: Neural Motion Synthesis from a Single Sequence

abstract:
  We present GANimator, a generative model that learns to synthesize novel
motions from a single, short motion sequence. GANimator generates motions that
resemble the core elements of the original motion, while simultaneously
synthesizing novel and diverse movements. Existing data-driven techniques for
motion synthesis require a large motion dataset which contains the desired and
specific skeletal structure. By contrast, GANimator only requires training on a
single motion sequence, enabling novel motion synthesis for a variety of
skeletal structures e.g., bipeds, quadropeds, hexapeds, and more. Our framework
contains a series of generative and adversarial neural networks, each
responsible for generating motions in a specific frame rate. The framework
progressively learns to synthesize motion from random noise, enabling
hierarchical control over the generated motion content across varying levels of
detail. We show a number of applications, including crowd simulation, key-frame
editing, style transfer, and interactive control, which all learn from a single
input sequence. Code and data for this paper are at
https://peizhuoli.github.io/ganimator.


title: Perron communicability and sensitivity of multilayer networks

abstract:
  Modeling complex systems that consist of different types of objects leads to
multilayer networks, where nodes in the different layers represent different
kind of objects. Nodes are connected by edges, which have positive weights. A
multilayer network is associated with a supra-adjacency matrix. This paper
investigates the sensitivity of the communicability in a multilayer network to
perturbations of the network by studying the sensitivity of the Perron root of
the supra-adjacency matrix. Our analysis sheds light on which edge weights to
make larger to increase the communicability of the network, and which edge
weights can be made smaller or set to zero without affecting the
communicability significantly.


title: WDV: A Broad Data Verbalisation Dataset Built from Wikidata

abstract:
  Data verbalisation is a task of great importance in the current field of
natural language processing, as there is great benefit in the transformation of
our abundant structured and semi-structured data into human-readable formats.
Verbalising Knowledge Graph (KG) data focuses on converting interconnected
triple-based claims, formed of subject, predicate, and object, into text.
Although KG verbalisation datasets exist for some KGs, there are still gaps in
their fitness for use in many scenarios. This is especially true for Wikidata,
where available datasets either loosely couple claim sets with textual
information or heavily focus on predicates around biographies, cities, and
countries. To address these gaps, we propose WDV, a large KG claim
verbalisation dataset built from Wikidata, with a tight coupling between
triples and text, covering a wide variety of entities and predicates. We also
evaluate the quality of our verbalisations through a reusable workflow for
measuring human-centred fluency and adequacy scores. Our data and code are
openly available in the hopes of furthering research towards KG verbalisation.


title:
Is there a trade-off relation between efficiency and power in a
  collisional Penrose process in an extreme Reissner-Nordstr\"{o}m spacetime?

abstract:
  We consider the power of an collisional Penrose process with an unbound
energy extraction from an extreme Reissner-Nordstr\"{o}m black hole. It takes
infinite time in a time coordinate at a constant radial coordinate outside of
the black hole in the process. For a practical use of black holes as a power
plant, the power of the process for an obaserver far away from the black hole
can be useful. We define the power as the energy gain from the extreme
Reissner-Nordstr\"{o}m black hole divided by time interval of the process in a
coordinate time and we find the upper bound of the power in a near-horizon
limit while the efficiency of the process can be arbitrary large in the limit.
Thus, we conclude that there is no trade-off relation between the efficiency
and power in the collisional Penrose process in the extreme
Reissner-Nordstr\"{o}m spacetime.


title: Efficient yet Competitive Speech Translation: FBK@IWSLT2022

abstract:
  The primary goal of this FBK's systems submission to the IWSLT 2022 offline
and simultaneous speech translation tasks is to reduce model training costs
without sacrificing translation quality. As such, we first question the need of
ASR pre-training, showing that it is not essential to achieve competitive
results. Second, we focus on data filtering, showing that a simple method that
looks at the ratio between source and target characters yields a quality
improvement of 1 BLEU. Third, we compare different methods to reduce the
detrimental effect of the audio segmentation mismatch between training data
manually segmented at sentence level and inference data that is automatically
segmented. Towards the same goal of training cost reduction, we participate in
the simultaneous task with the same model trained for offline ST. The
effectiveness of our lightweight training strategy is shown by the high score
obtained on the MuST-C en-de corpus (26.7 BLEU) and is confirmed in
high-resource data conditions by a 1.6 BLEU improvement on the IWSLT2020 test
set over last year's winning system.


title: Signatures of non-local conductivity in near-field microscopy

abstract:
  We propose and theoretically substantiate a new method to study the non-local
conductivity of two-dimensional electron systems (2DES) using the tools of
near-field microscopy. We show that the height dependence of induced dipole
moment of illuminated near-field probe is substantially different for various
transport regimes of charge carriers in 2DES. For hydrodynamic transport
regime, the induced dipole moment scales as $z_0^{-2}$, where $z_0$ is the
elevation of probe above the 2DES. Both for Drude and classical ballistic
regimes of conduction, the dipole moment scales as $z_0^{-3}$. In the former
case, the dipole moment is carrier density-independent, while in the latter it
largely depends on carrier density. More generally, we find that the induced
dipole moment of the probe is proportional to the Laplace transform of
wave-vector dependent conductivity and inverse dielectric function of 2DES over
the wave vectors $q$. Our results should provide a simple tool for studies of
non-local conductivity in solids that was challenging to address with other
techniques.


title:
Theory of non-integer high-harmonic generation in a topological surface
  state

abstract:
  High harmonic generation is a versatile experimental technique for probing
ultrafast electron dynamics. While in the past it has been employed typically
in dielectrics and semiconductors, recently high harmonic generation was also
observed from a topological surface [Schmid et al., Nature 593, 385 (2021)]. It
has been found that harmonic orders in the intermediate range of 13-18
continuously shift when the carrier envelope phase (CEP) is varied. In this
work, we adopt a minimal model of the topological surface state and calculate
analytically the high-harmonic spectrum. We derive formulae describing the
parametric dependencies of CEP shifts in high harmonics; in particular, we have
a transparent result for the shift of the (peak) frequency $\omega$ when
changing the CEP $\varphi$: $d\omega/d\varphi = -2 \bar{\mathfrak f}'
\omega/\omega_0$, where $\omega_0$ describes the fundamental driving frequency
and $\bar{\mathfrak f}'$ characterizes the chirp of the driving laser pulse. We
compare the analytical formula to full-fledged numerical simulations finding
only 17% average absolute deviation in $d\omega/d\varphi$. Our analytical
result is fully consistent with experimental observations. It therefore
provides the first understanding of the phenomenon of CEP shifts in solids
based on analytically derived parametric dependencies.


title: On the Chromatic Number of some generalized Kneser Graphs

abstract:
  We determine the chromatic number of the Kneser graph q{\Gamma}_{7,{3,4}} of
flags of vectorial type {3, 4} of a rank 7 vector space over the finite field
GF(q) for large q and describe the colorings that attain the bound. This result
relies heavily, not only on the independence number, but also on the structure
of all large independent sets. Furthermore, our proof is more general in the
following sense: it provides the chromatic number of the Kneser graphs
q{\Gamma}_{2d+1,{d,d+1}} of flags of vectorial type {d, d+1} of a rank 2d+1
vector space over GF(q) for large q as long as the large independent sets of
the graphs are only the ones that are known.


title: Affine Bruhat order and Demazure products

abstract:
  We give new descriptions of the Bruhat order and Demazure products of affine
Weyl groups in terms of the weight function of the quantum Bruhat graph. These
results can be understood to describe certain closure relations concerning the
Iwahori-Bruhat decomposition of an algebraic group. As an application towards
affine Deligne-Lusztig varieties, we present a new formula for generic Newton
points.


title: Some results on the super domination number of a graph II

abstract:
  Let $G=(V,E)$ be a simple graph. A dominating set of $G$ is a subset
$S\subseteq V$ such that every vertex not in $S$ is adjacent to at least one
vertex in $S$. The cardinality of a smallest dominating set of $G$, denoted by
$\gamma(G)$, is the domination number of $G$. A dominating set $S$ is called a
super dominating set of $G$, if for every vertex $u\in \overline{S}=V-S$, there
exists $v\in S$ such that $N(v)\cap \overline{S}=\{u\}$. The cardinality of a
smallest super dominating set of $G$, denoted by $\gamma_{sp}(G)$, is the super
domination number of $G$. In this paper, we obtain more results on the super
domination number of graphs which is modified by an operation on vertices.
Also, we present some sharp bounds for super domination number of chain and
bouquet of pairwise disjoint connected graphs.


title:
Reduced Density Matrix Functional Theory for Bosons: Foundations and
  Applications

abstract:
  Density functional theory constitutes the workhorse of modern electronic
structure calculations due to its favourable computational cost despite the
fact that it usually fails to describe strongly correlated systems. A
particularly promising approach to overcome those difficulties is reduced
density matrix functional theory (RDMFT): It abandons the complexity of the
$N$-particle wave function and at the same time explicitly allows for
fractional occupation numbers. It is the goal of this thesis to initiate and
establish a bosonic RDMFT for both ground state and excited state energy
calculations. Motivated by the Onsager and Penrose criterion which identifies
RDMFT as a particularly suitable approach to describe Bose-Einstein condensates
(BECs), we derive the universal functional for a homogeneous BEC in the
Bogoliubov regime. Remarkably, the gradient of the universal functional is
found to diverge repulsively in the regime of complete condensation. This
introduces the new concept of a BEC force, which provides a universal
explanation for quantum depletion since it is merely based on the geometry of
quantum states. In the second part of the thesis, we propose and work out an
ensemble RDMFT targeting excitations in bosonic quantum systems. This endeavour
further highlights the potential of convex analysis for the development of
functional theories in the future. Indeed by resorting to several concepts from
convex analysis, we succeeded to provide a comprehensive foundation of
$\boldsymbol{w}$-ensemble RDMFT for bosons which is further based on a
generalization of the Ritz variational principle and a constrained search
formalism. In particular, we solve the emerging $N$-representability problem
leading to a generalization of Pauli's famous exclusion principle to bosonic
mixed states.


title: Implementing Choreography Extraction

abstract:
  Choreographies are global descriptions of interactions among concurrent
components, most notably used in the settings of verification and synthesis of
correct-by-construction software. They require a top-down approach: programmers
first write choreographies, and then use them to verify or synthesize their
programs. However, most software does not come with choreographies yet, which
prevents their application. To attack this problem, previous work investigated
choreography extraction, which automatically constructs a choreography that
describes the behaviour of a given set of programs or protocol specifications.
  We propose a new extraction methodology that improves on the state of the
art: we can deal with programs that are equipped with state and internal
computation and time complexity is dramatically better. We also implement this
theory and show that, in spite of its theoretical exponential complexity, it is
usable in practice. We discuss the data structures needed for an efficient
implementation, introduce some optimisations, and perform a systematic
practical evaluation.


title:
Towards Fast Simulation of Environmental Fluid Mechanics with
  Multi-Scale Graph Neural Networks

abstract:
  Numerical simulators are essential tools in the study of natural
fluid-systems, but their performance often limits application in practice.
Recent machine-learning approaches have demonstrated their ability to
accelerate spatio-temporal predictions, although, with only moderate accuracy
in comparison. Here we introduce MultiScaleGNN, a novel multi-scale graph
neural network model for learning to infer unsteady continuum mechanics in
problems encompassing a range of length scales and complex boundary geometries.
We demonstrate this method on advection problems and incompressible fluid
dynamics, both fundamental phenomena in oceanic and atmospheric processes. Our
results show good extrapolation to new domain geometries and parameters for
long-term temporal simulations. Simulations obtained with MultiScaleGNN are
between two and four orders of magnitude faster than those on which it was
trained.


title: ImPosIng: Implicit Pose Encoding for Efficient Camera Pose Estimation

abstract:
  We propose a novel learning-based formulation for camera pose estimation that
can perform relocalization accurately and in real-time in city-scale
environments. Camera pose estimation algorithms determine the position and
orientation from which an image has been captured, using a set of
geo-referenced images or 3D scene representation. Our new localization
paradigm, named Implicit Pose Encoding (ImPosing), embeds images and camera
poses into a common latent representation with 2 separate neural networks, such
that we can compute a similarity score for each image-pose pair. By evaluating
candidates through the latent space in a hierarchical manner, the camera
position and orientation are not directly regressed but incrementally refined.
Compared to the representation used in structure-based relocalization methods,
our implicit map is memory bounded and can be properly explored to improve
localization performances against learning-based regression approaches. In this
paper, we describe how to effectively optimize our learned modules, how to
combine them to achieve real-time localization, and demonstrate results on
diverse large scale scenarios that significantly outperform prior work in
accuracy and computational efficiency.


title:
Dancing in the dark: detecting a population of distant primordial black
  holes

abstract:
  Primordial black holes (PBHs) are compact objects proposed to have formed in
the early Universe from the collapse of small-scale over-densities. Their
existence may be detected from the observation of gravitational waves (GWs)
emitted by PBH mergers, if the signals can be distinguished from those produced
by the merging of astrophysical black holes. In this work, we forecast the
capability of the Einstein Telescope, a proposed third-generation GW
observatory, to identify and measure the abundance of a subdominant population
of distant PBHs, using the difference in the redshift evolution of the merger
rate of the two populations as our discriminant. We carefully model the merger
rates and generate realistic mock catalogues of the luminosity distances and
errors that would be obtained from GW signals observed by the Einstein
Telescope. We use two independent statistical methods to analyse the mock data,
finding that, with our more powerful, likelihood-based method, PBH abundances
as small as $f_\mathrm{PBH} \approx 7 \times 10^{-6}$ ($f_\mathrm{PBH} \approx
2\times10^{-6}$) would be distinguishable from $f_\mathrm{PBH} = 0$ at the
level of $3\sigma$ with a one year (ten year) observing run of the Einstein
Telescope. Our mock data generation code, darksirens, is fast, easily
extendable and publicly available on GitLab.


title:
Model-Based Deep Learning: On the Intersection of Deep Learning and
  Optimization

abstract:
  Decision making algorithms are used in a multitude of different applications.
Conventional approaches for designing decision algorithms employ principled and
simplified modelling, based on which one can determine decisions via tractable
optimization. More recently, deep learning approaches that use highly
parametric architectures tuned from data without relying on mathematical
models, are becoming increasingly popular. Model-based optimization and
data-centric deep learning are often considered to be distinct disciplines.
Here, we characterize them as edges of a continuous spectrum varying in
specificity and parameterization, and provide a tutorial-style presentation to
the methodologies lying in the middle ground of this spectrum, referred to as
model-based deep learning. We accompany our presentation with running examples
in super-resolution and stochastic control, and show how they are expressed
using the provided characterization and specialized in each of the detailed
methodologies. The gains of combining model-based optimization and deep
learning are demonstrated using experimental results in various applications,
ranging from biomedical imaging to digital communications.


title:
High-energy reconstruction for single and double cascades using the
  KM3NeT detector

abstract:
  The discovery of a high-energy cosmic neutrino flux has paved the way for the
field of neutrino astronomy. For a large part of the flux, the sources remain
unidentified. The KM3NeT detector, which is under construction in the
Mediterranean sea, is designed to determine their origin. KM3NeT will
instrument a cubic kilometre of seawater with photomultiplier tubes that detect
Cherenkov radiation from neutrino interaction products with nanosecond
precision. For single cascade event signatures, KM3NeT already showed that it
can reach degree-level resolutions, greatly increasing the use of these
neutrinos for astronomy. In this contribution, we further refine the cascade
reconstruction by making a more detailed model of the neutrinos events and
including additional information on the hit times. The arrival time of light
can be used to improve the identification of double cascade signatures from tau
neutrinos, and the angular resolution of both single and double cascade
signatures. Sub-degree resolution is achieved in both cases.


title:
A Monte Carlo study of the duality and BKT phase transitions of the
  two-dimensional $q$-state clock model in flow representations

abstract:
  The two-dimensional (2D) $q$-state clock model for $q \geq 5$ undergoes two
Berenzinskii-Kosterlitz-Thouless (BKT) phase transitions as temperature
decreases. Here we report an extensive worm-type simulation of the
square-lattice clock model for $q=5$ to 9 in a pair of flow representations,
respectively from the high- and low-temperature expansions. By finite-size
scaling analysis of susceptibility-like quantities, we determine the critical
points with a precision improving over the existing results. Thanks to the dual
flow representations, each point in the critical region is observed to
simultaneously exhibit a pair of anomalous dimensions, which are ($\eta_1=1/4$,
$\eta_2 = 4/q^2$) at the two BKT transitions. Further, the approximate
self-dual points $\beta_{\rm sd}(L)$, defined by the stringent condition that
the susceptibility-like quantities in both flow representations are identical,
are found to be nearly independent of system size $L$ and behave as $\beta_{\rm
sd} \simeq q/2\pi$ asymptotically at the large-$q$ limit. The exponent $\eta$
at $\beta_{\rm sd}$ is consistent with $1/q$ within statistical errors as long
as $q \geq 5$. Our work provides a vivid demonstration of rich phenomena
associated with the duality and self-duality of the clock model in two
dimensions.


title: Quantum Modular Forms from Real Quadratic Double Sums

abstract:
  In 2015, Lovejoy and Osburn discovered twelve $q$-hypergeometric series and
proved that their Fourier coefficients can be understood as counting functions
of ideals in certain quadratic fields. In this paper, we study their modular
and quantum modular properties and show that they yield three vector-valued
quantum modular forms on the group $\Gamma_0 (2)$.


title:
A fusion variant of the classical and dynamical Mordell-Lang conjectures
  in positive characteristic

abstract:
  We study an open question at the interplay between the classical and the
dynamical Mordell-Lang conjectures in positive characteristic. Let $K$ be an
algebraically closed field of positive characteristic, let $G$ be a finitely
generated subgroup of the multiplicative group of $K$, and let $X$ be a
(irreducible) quasiprojective variety defined over $K$. We consider $K$-valued
sequences of the form $a_n:=f(\varphi^n(x_0))$, where $\varphi\colon
X\rightarrow X$ and $f\colon X\rightarrow\mathbb{P}^1$ are rational maps
defined over $K$ and $x_0\in X$ is a point whose forward orbit avoids the
indeterminacy loci of $\varphi$ and $f$. We show that the set of $n$ for which
$a_n\in G$ is a finite union of arithmetic progressions along with a set of
upper Banach density zero. In addition, we show that if $a_n\in G$ for every
$n$ and the $\varphi$ orbit of $x$ is Zariski dense in $X$ then {there is} a
multiplicative torus $\mathbb{G}_m^d$ and maps $\Psi:\mathbb{G}_m^d \to
\mathbb{G}_m^d$ and $g:\mathbb{G}_m^d \to \mathbb{G}_m$ such that $a_n = g\circ
\Psi^n(y)$ for some $y\in \mathbb{G}_m^d$. We then describe various
applications of our results.


title:
PyDaddy: A Python package for discovering stochastic dynamical equations
  from timeseries data

abstract:
  Most real-world ecological dynamics, ranging from ecosystem dynamics to
collective animal movement, are inherently stochastic in nature. Stochastic
differential equations (SDEs) are a popular modelling framework to model
dynamics with intrinsic randomness. Here, we focus on the inverse question: If
one has empirically measured time-series data from some system of interest, is
it possible to discover the SDE model that best describes the data. Here, we
present PyDaddy (PYthon library for DAta Driven DYnamics), a toolbox to
construct and analyze interpretable SDE models based on time-series data. We
combine traditional approaches for data-driven SDE reconstruction with an
equation learning approach, to derive symbolic equations governing the
stochastic dynamics. The toolkit is presented as an open-source Python library,
and consists of tools to construct and analyze SDEs. Functionality is included
for visual examination of the stochastic structure of the data, guided
extraction of the functional form of the SDE, and diagnosis and debugging of
the underlying assumptions and the extracted model. Using simulated time-series
datasets, exhibiting a wide range of dynamics, we show that PyDaddy is able to
correctly identify underlying SDE models. We demonstrate the applicability of
the toolkit to real-world data using a previously published movement data of a
fish school. Starting from the time-series of the observed polarization of the
school, pyDaddy readily discovers the SDE model governing the dynamics of group
polarization. The model recovered by PyDaddy is consistent with the previous
study. In summary, stochastic and noise-induced effects are central to the
dynamics of many biological systems. In this context, we present an easy-to-use
package to reconstruct SDEs from timeseries data.


title:
Fast Reconstruction of Three-Quarter Sampling Measurements Using
  Recurrent Local Joint Sparse Deconvolution and Extrapolation

abstract:
  Recently, non-regular three-quarter sampling has shown to deliver an
increased image quality of image sensors by using differently oriented L-shaped
pixels compared to the same number of square pixels. A three-quarter sampling
sensor can be understood as a conventional low-resolution sensor where one
quadrant of each square pixel is opaque. Subsequent to the measurement, the
data can be reconstructed on a regular grid with twice the resolution in both
spatial dimensions using an appropriate reconstruction algorithm. For this
reconstruction, local joint sparse deconvolution and extrapolation (L-JSDE) has
shown to perform very well. As a disadvantage, L-JSDE requires long computation
times of several dozen minutes per megapixel. In this paper, we propose a
faster version of L-JSDE called recurrent L-JSDE (RL-JSDE) which is a
reformulation of L-JSDE. For reasonable recurrent measurement patterns, RL-JSDE
provides significant speedups on both CPU and GPU without sacrificing image
quality. Compared to L-JSDE, 20-fold and 733-fold speedups are achieved on CPU
and GPU, respectively.


title: Spin imaging of Poiseuille flow of viscous electronic fluid

abstract:
  Recent progress in fabricating high-quality conductors with small densities
of defects has initiated the studies of the viscous electron fluid and has
motivated the search for the evidences of the hydrodynamic regime of electron
transport. In this work we come up with the spin imaging technique allowing us
to attest to the emergence of electron hydrodynamic flows. Based on numerical
calculations we demonstrate that the injected electron spin density is
inhomogeneous across the channel when the viscous electron fluid forms the
Poiseuille flow. We also argue that the Hanle curves at different positions
across the channel acquire relative phase shifts resulting from the variation
of the electron drift velocity in inhomogeneous hydrodynamic flows. The studied
effects can be employed to evidence and study the viscous electron fluid
non-invasively.


title:
Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential
  Privacy in Python

abstract:
  This paper introduces the \texttt{multi-freq-ldpy} Python package for
multiple frequency estimation under Local Differential Privacy (LDP)
guarantees. LDP is a gold standard for achieving local privacy with several
real-world implementations by big tech companies such as Google, Apple, and
Microsoft. The primary application of LDP is frequency (or histogram)
estimation, in which the aggregator estimates the number of times each value
has been reported. The presented package provides an easy-to-use and fast
implementation of state-of-the-art solutions and LDP protocols for frequency
estimation of: multiple attributes (i.e., multidimensional data), multiple
collections (i.e., longitudinal data), and both multiple
attributes/collections. Multi-Freq-LDPy is built on the well-established
\textit{Numpy} package -- a \textit{de facto} standard for scientific computing
in Python -- and the \textit{Numba} package for fast execution. These features
are illustrated in this demo paper with different tutorial-like case studies.
This package is open-source and publicly available under an MIT license via
GitHub https://github.com/hharcolezi/multi-freq-ldpy and can be installed via
PyPi.


title:
The structure of the periodic XXZ spin-$\frac{1}{2}$ chain as a module
  over the affine Temperley-Lieb algebra

abstract:
  The affine Temperley-Lieb algebra
$\mathsf{a}\hskip-1.8pt\mathsf{TL}_{N}(\beta)$ is an infinite-dimensional
algebra parametrized by a number $\beta \in \mathbb{C}$ and an integer $N\in
\mathbb{N}$. It naturally acts on the periodic XXZ chain with $N$
spin-$\frac{1}{2}$ to produce a family of representations labeled by a further
parameter $z\in\mathbb C^{\times}$. The structure of these representations,
which were first defined by Pasquier and Saleur, is here made explicit. They
share their composition factors with the cellular
$\mathsf{a}\hskip-1.8pt\mathsf{TL}_{N}(\beta)$-modules of Graham and Lehrer,
but differ from the latter representations by the direction of about half of
the arrows of their Loewy diagrams. The proof of this statement uses a morphism
introduced by Morin-Duchesne and Saint-Aubin as well as new maps that
intertwine various $\mathsf{a}\hskip-1.8pt\mathsf{TL}_{N}(\beta)$-actions on
the XXZ chain and generalize applications studied by Deguchi $\textit{et al}$
and after by Morin-Duchesne and Saint-Aubin.

categories: math.RT cond-mat.stat-mech hep-th math-ph math.MP


title:
Phenomenology and dynamics of competitive ecosystems beyond the
  niche-neutral regimes

abstract:
  Structure, composition and stability of ecological populations are shaped by
the inter- and intra-species interactions within these communities. It remains
to be fully understood how the interplay of these interactions with other
factors, such as immigration, control the structure, diversity and the long
term stability of ecological systems in the presence of noise and fluctuations.
We address this problem using a minimal model of interacting multi-species
ecological communities that incorporates competition, immigration and
demographic noise. We find that the complete phase diagram exhibits rich
behavior with multiple regimes that go beyond the classical 'niche' and
'neutral' regimes, extending and modifying the 'neutral-like' or 'niche-like'
dichotomy. In particular, we observe novel regimes that cannot be characterized
as either 'niche' or 'neutral' where a multimodal species abundance
distribution is observed. We characterize the transitions between the different
regimes and show how they arise from the underlying kinetics of the species
turnover, extinction and invasion. Our model serves as a minimal null model of
noisy competitive ecological systems, against which more complex models that
include factors such as mutations and environmental noise can be compared.


title:
Polynomial deceleration for a system of cubic nonlinear Schr\"{o}dinger
  equations in one space dimension

abstract:
  In this paper, we consider the initial value problem of a specific system of
cubic nonlinear Schr\"{o}dinger equations. Our aim of this research is to
specify the asymptotic profile of the solution in $L^{\infty}$ as $t \to
\infty$. It is then revealed that the solution decays slower than a linear
solution does. Further, the difference of the decay rate is a polynomial order.
This deceleration of the decay is due to an amplification effect by the
nonlinearity. This nonlinear amplification phenomena was previously known for
several specific systems, however the deceleration of the decay in these
results was by a logarithmic order. As far as we know, the system studied in
this paper is the first model in that the deceleration in a polynomial order is
justified.


title: Can collaborative learning be private, robust and scalable?

abstract:
  We investigate the effectiveness of combining differential privacy, model
compression and adversarial training to improve the robustness of models
against adversarial samples in train- and inference-time attacks. We explore
the applications of these techniques as well as their combinations to determine
which method performs best, without a significant utility trade-off. Our
investigation provides a practical overview of various methods that allow one
to achieve a competitive model performance, a significant reduction in model's
size and an improved empirical adversarial robustness without a severe
performance degradation.


title:
Analysis of the survival time of the SIS and SIRS process on stars and
  cliques

abstract:
  We study two continuous-time Markov chains modeling the spread of infections
on graphs, namely the SIS and the SIRS model. In the SIS model, vertices are
either susceptible or infected; each infected vertex becomes susceptible at
rate 1 and infects each of its neighbors independently at rate $\lambda$. In
the SIRS model, vertices are either susceptible, infected, or recovered; each
infected vertex becomes recovered at rate 1 and infects each of its susceptible
neighbors independently at rate $\lambda$; each recovered vertex becomes
susceptible at a rate $\varrho$, which we assume to be independent of the size
of the graph. The survival time of the SIS process, i.e., the time until no
vertex of the host graph is infected, is fairly well understood for a variety
of graph classes. Stars are an important graph class for the SIS model, as the
survival time of SIS on stars has been used to show that the process survives
on real-world graphs for a long time. For the SIRS model, however, to the best
of our knowledge, there are no rigorous results, even for simple graphs such as
stars.
  We analyze the survival time of the SIS and the SIRS process on stars and
cliques. We determine three threshold values for $\lambda$ such that when
$\lambda<\lambda_\ell$, the expected survival time of the process is at most
logarithmic, when $\lambda<\lambda_p$, it is at most polynomial, and when
$\lambda>\lambda_s$, it is at least super-polynomial in the number of vertices.
Our results show that the survival time of the two processes behaves
fundamentally different on stars, while it behaves fairly similar on cliques.
Our analyses bound the drift of potential functions with globally stable
equilibrium points. On the SIRS process, our two-state potential functions are
inspired by Lyapunov functions used in mean-field theory.


title:
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent
  DAGs with Applications

abstract:
  Counting and sampling directed acyclic graphs from a Markov equivalence class
are fundamental tasks in graphical causal analysis. In this paper we show that
these tasks can be performed in polynomial time, solving a long-standing open
problem in this area. Our algorithms are effective and easily implementable. As
we show in experiments, these breakthroughs make thought-to-be-infeasible
strategies in active learning of causal structures and causal effect
identification with regard to a Markov equivalence class practically
applicable.


title: Language Models Can See: Plugging Visual Controls in Text Generation

abstract:
  Generative language models (LMs) such as GPT-2/3 can be prompted to generate
text with remarkable quality. While they are designed for text-prompted
generation, it remains an open question how the generation process could be
guided by modalities beyond text such as images. In this work, we propose a
training-free framework, called MAGIC (iMAge-Guided text generatIon with CLIP),
for plugging in visual controls in the generation process and enabling LMs to
perform multimodal tasks (e.g., image captioning) in a zero-shot manner. MAGIC
is a simple yet efficient plug-and-play framework, which directly combines an
off-the-shelf LM (i.e., GPT-2) and an image-text matching model (i.e., CLIP)
for image-grounded text generation. During decoding, MAGIC influences the
generation of the LM by introducing a CLIP-induced score, called magic score,
which regularizes the generated result to be semantically related to a given
image while being coherent to the previously generated context. Notably, the
proposed decoding scheme does not involve any gradient update operation,
therefore being computationally efficient. On the challenging task of zero-shot
image captioning, MAGIC outperforms the state-of-the-art method by notable
margins with a nearly 27 times decoding speedup. MAGIC is a flexible framework
and is theoretically compatible with any text generation tasks that incorporate
image grounding. In the experiments, we showcase that it is also capable of
performing visually grounded story generation given both an image and a text
prompt.


title: Computing treedepth in polynomial space and linear fpt time

abstract:
  The treedepth of a graph $G$ is the least possible depth of an elimination
forest of $G$: a rooted forest on the same vertex set where every pair of
vertices adjacent in $G$ is bound by the ancestor/descendant relation. We
propose an algorithm that given a graph $G$ and an integer $d$, either finds an
elimination forest of $G$ of depth at most $d$ or concludes that no such forest
exists; thus the algorithm decides whether the treedepth of $G$ is at most $d$.
The running time is $2^{O(d^2)}\cdot n^{O(1)}$ and the space usage is
polynomial in $n$. Further, by allowing randomization, the time and space
complexities can be improved to $2^{O(d^2)}\cdot n$ and $d^{O(1)}\cdot n$,
respectively. This improves upon the algorithm of Reidl et al. [ICALP 2014],
which also has time complexity $2^{O(d^2)}\cdot n$, but uses exponential space.


title: On the matrix Cauchy-Schwarz inequality

abstract:
  The main goal of this work is to present new matrix inequalities of the
Cauchy-Schwarz type. In particular, we investigate the so-called Lieb
functions, whose definition came as an umbrella of Cauchy-Schwarz-like
inequalities, then we consider the mixed Cauchy-Schwarz inequality. This latter
inequality has been influential in obtaining several other matrix inequalities,
including numerical radius and norm results.


title: Scattering Hypervolume of Fermions in Two Dimensions

abstract:
  We define the three-body scattering hypervolume $D_F$ for identical
spin-polarized fermions in two dimensions, by considering the wave function of
three such fermions colliding at zero energy and zero orbital angular
momentum.We derive the asymptotic expansions of such a wave function when three
fermions are far apart or one pair and the third fermion are far apart, and
$D_F$ appears in the coefficients of such expansions. For weak pairwise
interaction potentials, we derive an approximate formula of $D_F$ by using the
Born expansion. We then study the shift of energy of three such fermions in a
large periodic area due to $D_F$. This shift is proportional to $D_F$ times the
square of the area of the triangle formed by the momenta of the fermions. We
also calculate the shifts of energy and of pressure of spin-polarized
two-dimensional Fermi gases due to a nonzero $D_F$ and the three-body
recombination rate of spin-polarized ultracold atomic Fermi gases in two
dimensions.


title:
Text Detection on Technical Drawings for the Digitization of Brown-field
  Processes

abstract:
  This paper addresses the issue of autonomously detecting text on technical
drawings. The detection of text on technical drawings is a critical step
towards autonomous production machines, especially for brown-field processes,
where no closed CAD-CAM solutions are available yet. Automating the process of
reading and detecting text on technical drawings reduces the effort for
handling inefficient media interruptions due to paper-based processes, which
are often todays quasi-standard in brown-field processes. However, there are no
reliable methods available yet to solve the issue of automatically detecting
text on technical drawings. The unreliable detection of the contents on
technical drawings using classical detection and object character recognition
(OCR) tools is mainly due to the limited number of technical drawings and the
captcha-like structure of the contents. Text is often combined with unknown
symbols and interruptions by lines. Additionally, due to intellectual property
rights and technical know-how issues, there are no out-of-the box training
datasets available in the literature to train such models. This paper combines
a domain knowledge-based generator to generate realistic technical drawings
with a state-of-the-art object detection model to solve the issue of detecting
text on technical drawings. The generator yields artificial technical drawings
in a large variety and can be considered as a data augmentation generator.
These artificial drawings are used for training, while the model is tested on
real data. The authors show that artificially generated data of technical
drawings improve the detection quality with an increasing number of drawings.


title: Region-Based Merging of Open-Domain Terminological Knowledge

abstract:
  This paper introduces a novel method for merging open-domain terminological
knowledge. It takes advantage of the Region Connection Calculus (RCC5), a
formalism used to represent regions in a topological space and to reason about
their set-theoretic relationships. To this end, we first propose a faithful
translation of terminological knowledge provided by several and potentially
conflicting sources into region spaces. The merging is then performed on these
spaces, and the result is translated back into the underlying language of the
input sources. Our approach allows us to benefit from the expressivity and the
flexibility of RCC5 while dealing with conflicting knowledge in a principled
way.


title: An introduction to sheaf quantization

abstract:
  The notion of sheaf quantization has many faces: an enhancement of the notion
of constructible sheaves, the Betti counterpart of Fukaya--Floer theory, a
topological realization of WKB-states in geometric quantization. The purpose of
this note is to give an introduction to the subject.


title:
Greedy Clustering-Based Algorithm for Improving Multi-point Robotic
  Manipulation Sequencing

abstract:
  The problem of optimizing a sequence of tasks for a robot, also known as
multi-point manufacturing, is a well-studied problem. Many of these solutions
use a variant of the Traveling Salesman Problem (TSP) and seek to find the
minimum distance or time solution. Optimal solution methods struggle to run in
real-time and scale for larger problems. In online planning applications where
the tasks being executed are fast, the computational time to optimize the
ordering can dominate the total execution time. The optimal solution in this
application is defined as the computational time for planning plus the
execution time. Therefore, the algorithm presented here balances the quality of
the solution with the total execution time by finding a locally optimal
sequence. The algorithm is comprised of waypoint generation, spatial
clustering, and waypoint optimization. Significant improvements in time
reduction were seen and validated against a base case algorithm in simulation
and on a real UR5 robot.


title: Zoo of holographic moving mirrors

abstract:
  We systematically study moving mirror models in two-dimensional conformal
field theory (CFT). By focusing on their late-time behavior, we separate the
mirror profiles into four classes, named type A (timelike) mirrors, type B
(escaping) mirrors, type C (chasing) mirrors, and type D (terminated) mirrors.
We analytically explore the characteristic features of the energy flux and
entanglement entropy for each type and work out their physical interpretation.
Moreover, we construct their gravity duals for which end-of-the-world (EOW)
branes play a crucial role. Depending on the mirror type, the profiles of the
EOW branes show distinct behaviors. In addition, we also provide a criterion
that decides whether the replica method in CFTs computes entanglement entropy
or pseudo entropy in moving mirror models.


title:
Interaction between two polyelectrolytes in monovalent aqueous salt
  solutions

abstract:
  We use the recently developed soft-potential-enhanced Poisson-Boltzmann (SPB)
theory to study the interaction between two parallel polyelectrolytes (PEs) in
monovalent ionic solutions in the weak-coupling regime. The SPB theory is
fitted to ion distributions from coarse-grained molecular dynamics (MD)
simulations and benchmarked against all-atom MD modelling for
poly(diallyldimethylammonium) (PDADMA). We show that the SPB theory is able to
accurately capture the interactions between two PEs at distances beyond the PE
radius. For PDADMA positional correlations between the charged groups lead to
locally asymmetric PE charge and ion distributions. This gives rise to small
deviations from the SPB prediction that appear as short-range oscillations in
the potential of mean force. Our results suggest that the SPB theory can be an
efficient way to model interactions in chemically specific complex PE systems.


title: Querying hippocampal replay with subcortical inputs

abstract:
  During sleep, the hippocampus recapitulates neuronal patterns corresponding
to behavioral trajectories during previous experiences. This hippocampal replay
supports the formation of long-term memories. Yet, whether replay originates
within the hippocampal circuitry or is initiated by extrahippocampal inputs is
unknown. Here, I review recent findings regarding the organization of neuronal
activity upstream to the hippocampus, in the head-direction (HD) and grid cell
networks. I argue that hippocampal activity is under the influence of primary
spatial signals, which originate from subcortical structures and set the stage
for memory replay. In turn, hippocampal replay resets the HD network activity
to select a new direction for the next replay event. This reciprocal
interaction between the HD network and the hippocampus may be essential in
providing meaning to hippocampal activity, specifically by training decoders of
hippocampal sequences. Neuronal dynamics in thalamo-hippocampal loops may thus
be instrumental for memory processes during sleep.


title:
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum
  Neural Networks

abstract:
  Variational quantum algorithms (VQAs) have recently received significant
attention from the research community due to their promising performance in
Noisy Intermediate-Scale Quantum computers (NISQ). However, VQAs run on
parameterized quantum circuits (PQC) with randomly initialized parameters are
characterized by barren plateaus (BP) where the gradient vanishes exponentially
in the number of qubits. In this paper, we first review quantum natural
gradient (QNG), which is one of the most popular algorithms used in VQA, from
the classical first-order optimization point of view. Then, we proposed a
\underline{L}ook \underline{A}round \underline{W}arm-\underline{S}tart QNG
(LAWS) algorithm to mitigate the widespread existing BP issues. LAWS is a
combinatorial optimization strategy taking advantage of model parameter
initialization and fast convergence of QNG. LAWS repeatedly reinitializes
parameter search space for the next iteration parameter update. The
reinitialized parameter search space is carefully chosen by sampling the
gradient close to the current optimal. Moreover, we present a unified framework
(WS-SGD) for integrating parameter initialization techniques into the
optimizer. We provide the convergence proof of the proposed framework for both
convex and non-convex objective functions based on Polyak-Lojasiewicz (PL)
condition. Our experiment results show that the proposed algorithm could
mitigate the BP and have better generalization ability in quantum
classification problems.


title:
A Variable Metric and Nesterov Extrapolated Proximal DCA with
  Backtracking for A Composite DC Program

abstract:
  In this paper, we consider a composite difference-of-convex (DC) program,
whose objective function is the sum of a smooth convex function with Lipschitz
continuous gradient, a proper closed and convex function, and a continuous
concave function. This problem has many applications in machine learning and
data science. The proximal DCA (pDCA), a special case of the classical DCA, as
well as two Nesterov-type extrapolated DCA -- ADCA (Phan et al.
IJCAI:1369--1375, 2018) and pDCAe (Wen et al. Comput Optim Appl 69:297--324,
2018) -- can solve this problem. The algorithmic step-sizes of pDCA, pDCAe, and
ADCA are fixed and determined by estimating a prior the smoothness parameter of
the loss function. However, such an estimate may be hard to obtain or poor in
some real-world applications. Motivated by this difficulty, we propose a
variable metric and Nesterov extrapolated proximal DCA with backtracking
(SPDCAe), which combines the backtracking line search procedure (not
necessarily monotone) and the Nesterov's extrapolation for potential
acceleration; moreover, the variable metric method is incorporated for better
local approximation. Numerical simulations on sparse binary logistic regression
and compressed sensing with Poisson noise demonstrate the effectiveness of our
proposed method.


title: A Market for Trading Forecasts: A Wagering Mechanism

abstract:
  The ever-increasing interest in the collection of data by advancing technical
and social sectors makes it distributed in terms of ownership. Also, the
diverse expertise of these owners results in the extraction of varying quality
of predictive information. Thus, the platforms for pooling forecasts based on
distributed data and heterogeneous predictive skills allow gaining a collective
value for a prediction task. For this purpose, we design a wagering-based
forecast elicitation market platform, where a buyer intending to improve their
forecasts posts a prediction task, and sellers respond to it with their
forecast reports and wagers. This market delivers an aggregated forecast to the
buyer (pre-event) and allocates a payoff to the sellers (post-event) for their
contribution. We propose a payoff mechanism and prove that it satisfies several
desirable economic properties, including those specific to electronic
platforms. Furthermore, we discuss the properties of the scoring rules that
allow us to provide ex-ante theoretical support for showing that the quantile
averaging produces higher-quality aggregate forecasts in terms of scores,
compared to the widely used linear pooling methods. Finally, we provide
numerical examples to illustrate the structure and properties of the proposed
market platform.


title: Unveiling the mechanisms of motion of synchro-Shockley dislocations

abstract:
  In Laves phases, synchroshear is the dominant basal slip mechanism. It is
accomplished by the glide of synchro-Shockley dislocations. However, the
atomic-scale mechanisms of motion of such zonal dislocations are still not well
understood. In this work, using atomistic simulations, two 30{\deg}
synchro-Shockley dislocations with different Burgers vectors and core
structures and energies are identified. We demonstrate that nucleation and
propagation of kink pairs is the energetically favorable mechanism for the
motion of the synchro-Shockley dislocation (partial I). Vacancy hopping and
interstitial shuffling are identified as two key mechanisms related to kink
propagation and we investigated how vacancies and antisite defects assist kink
nucleation and propagation, which is crucial for kink mobility. Additionally,
we identified a mechanism of non-sequential atomic shuffling for the motion of
the synchro-Shockley dislocation (partial II). These findings provide insights
into the dependency on temperature and chemical composition of plastic
deformation induced by zonal dislocations in Laves phases and the many related
topologically close-packed phases.


title: Unsupervised Mismatch Localization in Cross-Modal Sequential Data

abstract:
  Content mismatch usually occurs when data from one modality is translated to
another, e.g. language learners producing mispronunciations (errors in speech)
when reading a sentence (target text) aloud. However, most existing alignment
algorithms assume the content involved in the two modalities is perfectly
matched and thus leading to difficulty in locating such mismatch between speech
and text. In this work, we develop an unsupervised learning algorithm that can
infer the relationship between content-mismatched cross-modal sequential data,
especially for speech-text sequences. More specifically, we propose a
hierarchical Bayesian deep learning model, named mismatch localization
variational autoencoder (ML-VAE), that decomposes the generative process of the
speech into hierarchically structured latent variables, indicating the
relationship between the two modalities. Training such a model is very
challenging due to the discrete latent variables with complex dependencies
involved. We propose a novel and effective training procedure which estimates
the hard assignments of the discrete latent variables over a specifically
designed lattice and updates the parameters of neural networks alternatively.
Our experimental results show that ML-VAE successfully locates the mismatch
between text and speech, without the need for human annotations for model
training.


title:
What is Right for Me is Not Yet Right for You: A Dataset for Grounding
  Relative Directions via Multi-Task Learning

abstract:
  Understanding spatial relations is essential for intelligent agents to act
and communicate in the physical world. Relative directions are spatial
relations that describe the relative positions of target objects with regard to
the intrinsic orientation of reference objects. Grounding relative directions
is more difficult than grounding absolute directions because it not only
requires a model to detect objects in the image and to identify spatial
relation based on this information, but it also needs to recognize the
orientation of objects and integrate this information into the reasoning
process. We investigate the challenging problem of grounding relative
directions with end-to-end neural networks. To this end, we provide GRiD-3D, a
novel dataset that features relative directions and complements existing visual
question answering (VQA) datasets, such as CLEVR, that involve only absolute
directions. We also provide baselines for the dataset with two established
end-to-end VQA models. Experimental evaluations show that answering questions
on relative directions is feasible when questions in the dataset simulate the
necessary subtasks for grounding relative directions. We discover that those
subtasks are learned in an order that reflects the steps of an intuitive
pipeline for processing relative directions.


title: Multi-Agent Deep Reinforcement Learning in Vehicular OCC

abstract:
  Optical camera communications (OCC) has emerged as a key enabling technology
for the seamless operation of future autonomous vehicles. In this paper, we
introduce a spectral efficiency optimization approach in vehicular OCC.
Specifically, we aim at optimally adapting the modulation order and the
relative speed while respecting bit error rate and latency constraints. As the
optimization problem is NP-hard problem, we model the optimization problem as a
Markov decision process (MDP) to enable the use of solutions that can be
applied online. We then relaxed the constrained problem by employing Lagrange
relaxation approach before solving it by multi-agent deep reinforcement
learning (DRL). We verify the performance of our proposed scheme through
extensive simulations and compare it with various variants of our approach and
a random method. The evaluation shows that our system achieves significantly
higher sum spectral efficiency compared to schemes under comparison.


title: On Disentangled and Locally Fair Representations

abstract:
  We study the problem of performing classification in a manner that is fair
for sensitive groups, such as race and gender. This problem is tackled through
the lens of disentangled and locally fair representations. We learn a locally
fair representation, such that, under the learned representation, the
neighborhood of each sample is balanced in terms of the sensitive attribute.
For instance, when a decision is made to hire an individual, we ensure that the
$K$ most similar hired individuals are racially balanced. Crucially, we ensure
that similar individuals are found based on attributes not correlated to their
race. To this end, we disentangle the embedding space into two representations.
The first of which is correlated with the sensitive attribute while the second
is not. We apply our local fairness objective only to the second, uncorrelated,
representation. Through a set of experiments, we demonstrate the necessity of
both disentangled and local fairness for obtaining fair and accurate
representations. We evaluate our method on real-world settings such as
predicting income and re-incarceration rate and demonstrate the advantage of
our method.


title:
Geometric interpretation of the CHSH inequality of nonmaximally
  entangled states

abstract:
  We show that for pure and mixed states the problem of maximizing the
correlation measure in the CHSH inequality reduces to maximizing the perimeter
of a parallelogram enclosed by an ellipse characterized by the entanglement
contained in the bipartite system. Since our geometrical description is also
valid for a non-maximally entangled state we can determine the corresponding
optimal measurements.


title:
Parity Violation in Spin-Precessing Binaries: Gravitational Waves from
  the Inspiral of Black Holes in Dynamical Chern-Simons Gravity

abstract:
  Spin precession in compact binaries is intricately tuned to the multipole
structure of the underlying bodies. For black holes, violations of the no-hair
theorems induced by modifications to general relativity correct the precession
dynamics, which in turn imprints onto the amplitude and phase modulations of
the gravitational waves emitted by the binary. Recently, the spin precession
equations were derived up to second order in spin for dynamical Chern-Simons
gravity, a parity violating modified theory of gravity. We here solve these
equations and construct, for the first time, analytic expressions for the time-
and frequency-domain gravitational waves emitted in the quasi-circular inspiral
of spin-precessing black hole binaries in a modified theory of gravity using
the post-Newtonian approximation. Working within the small coupling
approximation and using multiple scale analysis, we show that the corrections
to the nutation phase enter at relative 1PN order, and the corrections to the
precession angle and Thomas phase enter at relative 0PN order. Making use of
the stationary phase approximation and shifted uniform asymptotics, we find
that the Fourier phase of the waveform is characterized by three modifications,
two due to the back-reaction of the precession dynamics onto the spin-orbit and
spin-spin couplings that enter at 1.5PN and 2PN orders, and a 2PN modification
due to the emission of dipole radiation. We also find that back-reaction of the
precession dynamics forces the dCS corrections to the Fourier amplitude to
enter at 0PN order, as opposed to 2PN order, as expected for spin-aligned
binaries. Our work lays the first foundational stones to build an
inspiral-merger-ringdown phenomenological model for spin-precessing binaries in
a modified theory of gravity.


title:
Quantum thermalization and thermal entanglement in the open quantum Rabi
  model

abstract:
  We study quantum thermalization and thermal entanglement in the open quantum
Rabi model (QRM), in which a two-level system and a single-mode bosonic field
are coupled to either two individual heat baths or a common heat bath. By
treating the QRM as an effective multilevel system and deriving global quantum
master equations in the eigenstate representation of the QRM, we study the
physical conditions for quantum thermalization of the open QRM. It is found
that, in the individual heat-bath case, the QRM can only be thermalized when
either the two heat baths have the same temperature or the QRM is only coupled
to one of the two baths. In the common heat-bath case, differently, the QRM can
always be thermalized. We also study thermal entanglement of the QRM in both
the resonant- and non-resonant coupling cases. The logarithmic negativity for
the thermal state of the QRM is obtained in a wide parameter space, ranging
from the low- to high-temperature limits, and from the weak- to
deep-strong-coupling regimes. This work paves the way towards the study of
quantum effects in nonequilibrium ultrastrongly-coupled light-matter systems.


title: Bifurcations and patterns in the Kuramoto model with inertia

abstract:
  In this work, we analyze the Kuramoto model (KM) with inertia on a convergent
family of graphs. It is assumed that the intrinsic frequencies of the
individual oscillators are sampled from a probability distribution. In
addition, a given graph, which may also be random, assigns network
connectivity. As in the original KM, in the model with inertia, the weak
coupling regime features mixing, the state of the network when the phases (but
not velocities) of all oscillators are distributed uniformly around the unit
circle. We study patterns, which emerge when mixing loses stability under the
variation of the strength of coupling. We identify a pitchfork (PF) and an
Andronov-Hopf (AH) bifurcations in the model with multimodal intrinsic
frequency distributions. To this effect, we use a combination of the linear
stability analysis and Penrose diagrams, a geometric technique for studying
stability of mixing. We show that the type of a bifurcation and a nascent
spatiotemporal pattern depend on the interplay of the qualitative properties of
the intrinsic frequency distribution and network connectivity.


title: Chemoreception and chemotaxis of a three-sphere swimmer

abstract:
  The coupled problem of hydrodynamics and solute transport for the
Najafi-Golestanian three-sphere swimmer is studied, with the Reynolds number
set to zero and P\'eclet numbers (Pe) ranging from 0.06 to 60. The adopted
method is the numerical simulation of the problem with a finite element code
based upon the FEniCS library. For the swimmer executing the optimal locomotion
gait, we report the Sherwood number as a function of Pe in homogeneous fluids
and confirm that little gain in solute flux is achieved by swimming unless Pe
is significantly larger than 10. We also consider the swimmer as an learning
agent moving inside a fluid that has a concentration gradient. The outcomes of
Q-learning processes show that learning locomotion (with the displacement as
reward) is significantly easier than learning chemotaxis (with the increase of
solute flux as reward). The chemotaxis problem, even at low Pe, has a varying
environment that renders learning more difficult. Further, the learning
difficulty increases severely with the P\'eclet number. The results demonstrate
the challenges that natural and artificial swimmers need to overcome to migrate
efficiently when exposed to chemical inhomogeneities.


title:
KnitCity: a machine learning-based, game-theoretical framework for
  prediction assessment and seismic risk policy design

abstract:
  Knitted fabric exhibits avalanche-like events when deformed: by analogy with
eathquakes, we are interested in predicting these "knitquakes". However, as in
most analogous seismic models, the peculiar statistics of the corresponding
time-series severely jeopardize this endeavour, due to the time intermittence
and scale-invariance of these events. But more importantly, such predictions
are hard to {\it assess}: depending on the choice of what to predict, the
results can be very different and not easily compared. Furthermore, forecasting
models may be trained with various generic metrics which ignore some important
specificities of the problem at hand, in our case seismic risk. Finally, these
models often do not provide a clear strategy regarding the best way to use
these predictions in practice. Here we introduce a framework that allows to
design, evaluate and compare not only predictors but also decision-making
policies: a model seismically active {\it city} subjected to the crackling
dynamics observed in the mechanical response of knitted fabric. We thus proceed
to study the population of KnitCity, introducing a policy through which the
mayor of the town can decide to either keep people in, which in case of large
events cause human loss, or evacuate the city, which costs a daily fee. The
policy only relies on past seismic observations. We construct efficient
policies using a reinforcement learning environment and various time-series
predictors based on artificial neural networks. By inducing a physically
motivated metric on the predictors, this mechanism allows quantitative
assessment and comparison of their relevance in the decision-making process.

categories: cs.LG cond-mat.dis-nn cond-mat.stat-mech physics.soc-ph


title:
Absorption spectra from galactic wind models: a framework to link PLUTO
  simulations to TRIDENT

abstract:
  Galactic winds probe how feedback regulates the mass and metallicity of
galaxies. Galactic winds have cold gas, which is mainly observable with
absorption and emission lines. Theoretically studying how absorption lines are
produced requires numerical simulations and realistic starburst UV backgrounds.
We use outputs from a suite of 3D PLUTO simulations of wind-cloud interactions
to first estimate column densities and temperatures. Then, to create synthetic
spectra, we developed a python interface to link our PLUTO simulations to
TRIDENT via the YT-package infrastructure. First we produce UV backgrounds
accounting for the star formation rate of starbursts. For this purpose, we use
fluxes generated by STARBURST99, which are then processed through CLOUDY to
create customised ion tables. Such tables are subsequently read into TRIDENT to
generate absorption spectra. We explain how the various packages and tools
communicate with each other to create ion spectra consistent with spectral
energy distributions of starburst systems.


title:
The next-to-leading order Higgs impact factor in the infinite top-mass
  limit

abstract:
  We calculate the next-to-leading order correction to the impact factor
(vertex) for the production of a forward Higgs boson, obtained in the infinite
top-mass limit. We present the result both in the momentum representation and
as superposition of the eigenfunctions of the leading-order BFKL kernel. This
impact factor is a necessary ingredient for the description of the inclusive
hadroproduction of a forward Higgs in the limit of small Bjorken $x$, as well
as for the study of inclusive forward emissions of a Higgs boson in association
with a backward identified object.


title:
Temporally and Spatially variant-resolution illumination patterns in
  computational ghost imaging

abstract:
  Conventional computational ghost imaging (CGI) uses light carrying a sequence
of patterns with uniform-resolution to illuminate the object, then performs
correlation calculation based on the light intensity value reflected by the
target and the preset patterns to obtain object image. It requires a large
number of measurements to obtain high-quality images, especially if
high-resolution images are to be obtained. To solve this problem, we developed
temporally variable-resolution illumination patterns, replacing the
conventional uniform-resolution illumination patterns with a sequence of
patterns of different imaging resolutions. In addition, we propose to combine
temporally variable-resolution illumination patterns and spatially
variable-resolution structure to develop temporally and spatially
variable-resolution (TSV) illumination patterns, which not only improve the
imaging quality of the region of interest (ROI) but also improve the robustness
to noise. The methods using proposed illumination patterns are verified by
simulations and experiments compared with CGI. For the same number of
measurements, the method using temporally variable-resolution illumination
patterns has better imaging quality than CGI, but it is less robust to noise.
The method using TSV illumination patterns has better imaging quality in ROI
than the method using temporally variable-resolution illumination patterns and
CGI under the same number of measurements. We also experimentally verify that
the method using TSV patterns have better imaging performance when applied to
higher resolution imaging. The proposed methods are expected to solve the
current computational ghost imaging that is difficult to achieve
high-resolution and high-quality imaging.


title:
Low-complexity Beam Selection algorithms based on SVD for MmWave Massive
  MIMO Systems

abstract:
  To realize mmWave massive MIMO systems in practice, Beamspace MIMO with beam
selection provides an attractive solution at a considerably reduced number of
radio frequency (RF) chains. We propose low-complexity beam selection
algorithms based on singular value decomposition (SVD). We first diagonalize
the channel matrix by SVD, and the appropriate beams are selected one-by-one in
a decremental or incremental order based on the criterion of sum-rate
maximization. To reduce the complexity of the proposed algorithms
significantly, we make use of SVD in the last iteration to aviod SVD from
scratch again. Meanwhile, our proposed algorithms naturally obtain the
precoding matrix, which can eliminate the multiusers interference. Simulation
results demonstrate that our proposed algorithms can outperform the competing
algorithms, including the fully digital zero-precoding.


title:
RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced
  Labour

abstract:
  Forced labour is the most common type of modern slavery, and it is
increasingly gaining the attention of the research and social community. Recent
studies suggest that artificial intelligence (AI) holds immense potential for
augmenting anti-slavery action. However, AI tools need to be developed
transparently in cooperation with different stakeholders. Such tools are
contingent on the availability and access to domain-specific data, which are
scarce due to the near-invisible nature of forced labour. To the best of our
knowledge, this paper presents the first openly accessible English corpus
annotated for multi-class and multi-label forced labour detection. The corpus
consists of 989 news articles retrieved from specialised data sources and
annotated according to risk indicators defined by the International Labour
Organization (ILO). Each news article was annotated for two aspects: (1)
indicators of forced labour as classification labels and (2) snippets of the
text that justify labelling decisions. We hope that our data set can help
promote research on explainability for multi-class and multi-label text
classification. In this work, we explain our process for collecting the data
underpinning the proposed corpus, describe our annotation guidelines and
present some statistical analysis of its content. Finally, we summarise the
results of baseline experiments based on different variants of the
Bidirectional Encoder Representation from Transformer (BERT) model.


title:
The Lipschitz-free space over length space is locally almost square but
  never almost square

abstract:
  We prove that the Lipschitz-free space over a metric space M is locally
almost square whenever M is a length space. Consequently, the Lipschitz-free
space is locally almost square if and only if it has the Daugavet property. We
also show that a Lipschitz-free space is never almost square.


title:
Indirect optical manipulation of the antiferromagnetic order of
  insulating NiO by ultrafast interfacial energy transfer

abstract:
  We report the ultrafast, (sub)picosecond reduction of the antiferromagnetic
order of the insulating NiO thin film in a Pt/NiO bilayer. This reduction of
the antiferromagnetic order is not present in pure NiO thin films after a
strong optical excitation. This ultrafast phenomenon is attributed to an
ultrafast and highly efficient energy transfer from the optically excited
electron system of the Pt layer into the NiO spin system. We propose that this
energy transfer is mediated by a stochastic exchange scattering of hot Pt
electrons at the Pt/NiO interface.


title: Intuitive method for constructing effective field theories

abstract:
  We derive a novel method for constructing effective field theories.
Physically, the method is very close to the intuition behind effective field
theories: One can integrate out the heavier scale directly from the path
integral. We give a detailed recipe for the effective field theory
construction, which is nearly identical to the construction of the so-called
1PI effective action. We also demonstrate the equivalence of the novel method
to the commonly used procedure of matching Green's functions.


title: Intrinsically H\"older sections in metric spaces

abstract:
  We introduce a notion of intrinsically H\"older graphs in metric spaces.
Following a recent paper of Le Donne and the author, we prove some relevant
results as the Ascoli-Arzel\`a compactness Theorem, Ahlfors-David regularity
and the Extension Theorem for this class of sections. In the first part of this
note, we define suitable sets in order to obtain a vector space over
$\mathbb{R}$ or $\mathbb{C},$ a convex set and an equivalence relation for
intrinsically H\"older graphs. These last three properties are new also in the
Lipschitz case. Throughout the paper, we use basic mathematical tools.


title:
Hardware System Implementation for Human Detection using HOG and SVM
  Algorithm

abstract:
  Human detection is a popular issue and has been widely used in many
applications. However, including complexities in computation, leading to the
human detection system implemented hardly in real-time applications. This paper
presents the architecture of hardware, a human detection system that was
simulated in the ModelSim tool. As a co-processor, this system was built to
off-load to Central Processor Unit (CPU) and speed up the computation timing.
The 130x66 RGB pixels of static input image attracted features and classify by
using the Histogram of Oriented Gradient (HOG) algorithm and Support Vector
Machine (SVM) algorithm, respectively. As a result, the accuracy rate of this
system reaches 84.35 percent. And the timing for detection decreases to 0.757
ms at 50MHz frequency (54 times faster when this system was implemented in
software by using the Matlab tool).


title:
Study for a model-independent pole determination of overlapping
  resonances

abstract:
  We apply a model-independent reconstruction method to experimental data in
order to identify complex poles of overlapping resonances. The algorithm is
based on the Schlessinger Point Method where data points are interpolated using
a continued-fraction expression. Statistical uncertainties of the experimental
data are propagated with resampling. In order to demonstrate the feasibility of
this method, we apply it to the $J/\psi \to \gamma \pi^0\pi^0$ decay. We
benchmark the method on known analytic models, which allows us to estimate the
deviation from the true value. We then perform the pole extraction from BESIII
data, and identify the $f_0(1500)$, $f_0(1710)$, and $f_0(2020)$ scalar states.
Our results are in reasonable agreement with recent results, which suggests the
proposed method as a promising model-independent alternative for the
determination of resonance poles solely based on available experimental data.


title:
The Batch Artifact Scanning Protocol: A new method using computed
  tomography (CT) to rapidly create three-dimensional models of objects from
  large collections en masse

abstract:
  Within anthropology, the use of three-dimensional (3D) imaging has become
increasingly standard and widespread since it broadens the available avenues
for addressing a wide range of key issues. The ease with which 3D models can be
shared has had major impacts for research, cultural heritage, education,
science communication, and public engagement, as well as contributing to the
preservation of the physical specimens and archiving collections in widely
accessible data bases. Current scanning protocols have the ability to create
the required research quality 3D models; however, they tend to be time and
labor intensive and not practical when working with large collections. Here we
describe a streamlined, Batch Artifact Scanning Protocol we have developed to
rapidly create 3D models using a medical CT scanner. Though this method can be
used on a variety of material types, we use a large collection of
experimentally broken ungulate limb bones. Using the Batch Artifact Scanning
Protocol, we were able to efficiently create 3D models of 2,474 bone fragments
at a rate of less than $3$ minutes per specimen, as opposed to an average of 50
minutes per specimen using structured light scanning.


title: Gait Recognition in the Wild: A Benchmark

abstract:
  Gait benchmarks empower the research community to train and evaluate
high-performance gait recognition systems. Even though growing efforts have
been devoted to cross-view recognition, academia is restricted by current
existing databases captured in the controlled environment. In this paper, we
contribute a new benchmark for Gait REcognition in the Wild (GREW). The GREW
dataset is constructed from natural videos, which contains hundreds of cameras
and thousands of hours streams in open systems. With tremendous manual
annotations, the GREW consists of 26K identities and 128K sequences with rich
attributes for unconstrained gait recognition. Moreover, we add a distractor
set of over 233K sequences, making it more suitable for real-world
applications. Compared with prevailing predefined cross-view datasets, the GREW
has diverse and practical view variations, as well as more natural challenging
factors. To the best of our knowledge, this is the first large-scale dataset
for gait recognition in the wild. Equipped with this benchmark, we dissect the
unconstrained gait recognition problem. Representative appearance-based and
model-based methods are explored, and comprehensive baselines are established.
Experimental results show (1) The proposed GREW benchmark is necessary for
training and evaluating gait recognizer in the wild. (2) For state-of-the-art
gait recognition approaches, there is a lot of room for improvement. (3) The
GREW benchmark can be used as effective pre-training for controlled gait
recognition. Benchmark website is https://www.grew-benchmark.org/.


title: Coherence-protected nonadiabatic geometric quantum computation

abstract:
  Because of using geometric phases, nonadiabatic geometric gates have the
robustness against control errors. On the other hand, decoherence still affects
nonadiabatic geometric gates, which is a key factor in reducing their
fidelities. In this paper, we show that based on the system Hamiltonian that
realizes a nonadiabatic geometric gate, one may construct a new system
Hamiltonian, by using which not only the geometric feature of the nonadiabatic
geometric gate is preserved, but also the system's coherence is protected. As a
result, a coherence-protected nonadiabatic geometric gate is realized with the
new system Hamiltonian and this gate has the robustness against both control
errors and decoherence. We further implement our scheme with nitrogen-vacancy
centers and show that a universal set of coherence-protected nonadiabatic
geometric gates can be realized. Our scheme does not need auxiliary systems or
the encoding of logical qubits with physical qubits, which saves resources for
the implementation. Due to the robustness against both control errors and
decoherence, our scheme provides a promising way to realize high-fidelity
quantum gates.


title: Quantifying Language Variation Acoustically with Few Resources

abstract:
  Deep acoustic models represent linguistic information based on massive
amounts of data. Unfortunately, for regional languages and dialects such
resources are mostly not available. However, deep acoustic models might have
learned linguistic information that transfers to low-resource languages. In
this study, we evaluate whether this is the case through the task of
distinguishing low-resource (Dutch) regional varieties. By extracting
embeddings from the hidden layers of various wav2vec 2.0 models (including new
models which are pre-trained and/or fine-tuned on Dutch) and using dynamic time
warping, we compute pairwise pronunciation differences averaged over 10 words
for over 100 individual dialects from four (regional) languages. We then
cluster the resulting difference matrix in four groups and compare these to a
gold standard, and a partitioning on the basis of comparing phonetic
transcriptions. Our results show that acoustic models outperform the
(traditional) transcription-based approach without requiring phonetic
transcriptions, with the best performance achieved by the multilingual XLSR-53
model fine-tuned on Dutch. On the basis of only six seconds of speech, the
resulting clustering closely matches the gold standard.