title: On-chip distribution of quantum information using traveling phonons

abstract:
  Distributing quantum entanglement on a chip is a crucial step towards
realizing scalable quantum processors. Using traveling phonons - quantized
guided mechanical wavepackets - as a medium to transmit quantum states is
currently gaining significant attention, due to their small size and low
propagation speed compared to other carriers, such as electrons or photons.
Moreover, phonons are highly promising candidates to connect heterogeneous
quantum systems on a chip, such as microwave and optical photons for
long-distance transmission of quantum states via optical fibers. Here, we
experimentally demonstrate the feasibility of distributing quantum information
using phonons, by realizing quantum entanglement between two traveling phonons
and creating a time-bin encoded traveling phononic qubit. The mechanical
quantum state is generated in an optomechanical cavity and then launched into a
phononic waveguide in which it propagates for around two hundred micrometers.
We further show how the phononic, together with a photonic qubit, can be used
to violate a Bell-type inequality.


title:
Radio-frequency manipulation of state populations in an entangled
  fluorine-muon-fluorine system

abstract:
  Entangled spin states are created by implanting muons into single crystal
LiY0.95Ho0.05F4 to form a cluster of correlated, dipole-coupled local magnetic
moments. The resulting states have well-defined energy levels allowing
experimental manipulation of the state populations by electromagnetic
excitation. Experimental control of the evolution of the muon spin polarization
is demonstrated through application of continuous, radio-frequency magnetic
excitation fields. A semiclassical model of quantum, dipole-coupled spins
interacting with a classical, oscillating magnetic field accounts for the muon
spin evolution. On application of the excitation field, this model shows how
changes in the state populations lead to the experimentally observed effects,
thus enabling a spectroscopic probe of entangled spin states with muons.


title:
HFT: Lifting Perspective Representations via Hybrid Feature
  Transformation

abstract:
  Autonomous driving requires accurate and detailed Bird's Eye View (BEV)
semantic segmentation for decision making, which is one of the most challenging
tasks for high-level scene perception. Feature transformation from frontal view
to BEV is the pivotal technology for BEV semantic segmentation. Existing works
can be roughly classified into two categories, i.e., Camera model-Based Feature
Transformation (CBFT) and Camera model-Free Feature Transformation (CFFT). In
this paper, we empirically analyze the vital differences between CBFT and CFFT.
The former transforms features based on the flat-world assumption, which may
cause distortion of regions lying above the ground plane. The latter is limited
in the segmentation performance due to the absence of geometric priors and
time-consuming computation. In order to reap the benefits and avoid the
drawbacks of CBFT and CFFT, we propose a novel framework with a Hybrid Feature
Transformation module (HFT). Specifically, we decouple the feature maps
produced by HFT for estimating the layout of outdoor scenes in BEV.
Furthermore, we design a mutual learning scheme to augment hybrid
transformation by applying feature mimicking. Notably, extensive experiments
demonstrate that with negligible extra overhead, HFT achieves a relative
improvement of 13.3% on the Argoverse dataset and 16.8% on the KITTI 3D Object
datasets compared to the best-performing existing method. The codes are
available at https://github.com/JiayuZou2020/HFT.


title: Simple derivations and their images

abstract:
  In the paper, we prove that the derivation
$D=y\partial_x+(a_2(x)y^2+a_1(x)y+a_0(x))\partial_y$ of $K[x,y]$ with
$a_2(x),a_1(x),a_0(x)\in K[x]$ is simple iff the following conditions hold:
$(1)$ $a_0(x)\in K^*$, $(2)$ $\deg a_1(x)\geq1$ or $\deg a_2(x)\geq1$, $(3)$
there exist no $l\in K^*$ such that $a_2(x)=la_1(x)-l^2a_0(x)$. In addition, we
prove that the image of the derivation $D=\partial_x+{\sum_{i=1}^n \gamma_i(x)
y_i^{k_i}}{\partial_i}$ is a Mathieu-Zhao space iff $D$ is locally finite.
Moreover, we prove that the image of the derivation $D={\sum_{i=1}^n \gamma_i
y_i^{k_i}}{\partial_i}$ of $K[y_1,\ldots,y_n]$ is a Mathieu-Zhao space iff
$k_i\leq 1$ for all $1\leq i\leq n$, $n\geq 2$.


title: Fine-grained Noise Control for Multispeaker Speech Synthesis

abstract:
  A text-to-speech (TTS) model typically factorizes speech attributes such as
content, speaker and prosody into disentangled representations.Recent works aim
to additionally model the acoustic conditions explicitly, in order to
disentangle the primary speech factors, i.e. linguistic content, prosody and
timbre from any residual factors, such as recording conditions and background
noise.This paper proposes unsupervised, interpretable and fine-grained noise
and prosody modeling. We incorporate adversarial training, representation
bottleneck and utterance-to-frame modeling in order to learn frame-level noise
representations. To the same end, we perform fine-grained prosody modeling via
a Fully Hierarchical Variational AutoEncoder (FVAE) which additionally results
in more expressive speech synthesis.


title: Heading-error-free optical atomic magnetometry in the Earth-field range

abstract:
  Alkali-metal atomic magnetometry is widely used due to its high sensitivity
and cryogen-free operation. However, when operating in geomagnetic field, it
suffers from heading errors originating from nonlinear Zeeman (NLZ) splittings
and magnetic resonance asymmetries, which lead to difficulties in
mobile-platform measurements. We demonstrate an alignment based $^{87}$Rb
magnetometer, which, with only a single magnetic resonance peak and
well-separated hyperfine transition frequencies, is insensitive or even immune
to NLZ-related heading errors. It is shown that the magnetometer can be
implemented for practical measurements in the geomagnetic environments and the
photon-shot-noise-limited sensitivity reaches $9\,{\rm{fT}}/\sqrt{\rm{Hz}}$ at
room temperature.


title: Few-Shot Object Detection in Unseen Domains

abstract:
  Few-shot object detection (FSOD) has thrived in recent years to learn novel
object classes with limited data by transfering knowledge gained on abundant
base classes. FSOD approaches commonly assume that both the scarcely provided
examples of novel classes and test-time data belong to the same domain.
However, this assumption does not hold in various industrial and robotics
applications (e.g., object grasping and manipulation), where a model can learn
novel classes from a source domain while inferring on classes from a different
target domain. In this work, we address the task of zero-shot domain
adaptation, also known as domain generalization, for FSOD. Specifically, we
assume that neither images nor labels of the novel classes in the target domain
are available during training. Our approach for solving the domain gap is
two-fold. First, we leverage a meta-training paradigm, where we learn
domain-invariant features on the base classes. Second, we propose various data
augmentations techniques on the few shots of novel classes to account for all
possible domain-specific information. To further constraint the network into
encoding domain-agnostic class-specific representations only, a contrastive
loss is proposed to maximize the mutual information between foreground
proposals and class prototypes, and to reduce the network's bias to the
background information. Our experiments on the T-LESS dataset show that the
proposed approach succeeds in alleviating the domain gap considerably without
utilizing labels or images of novel categories from the target domain.


title:
DFT calculation of intrinsic properties of magnetically hard phase
  L1$\mathrm{_0}$ FePt

abstract:
  Due to its strong magnetocrystalline anisotropy, FePt L1$\mathrm{_0}$ phase
is considered as a promising magnetic recording media material. Although the
magnetic properties of this phase have already been analyzed many times using
density functional theory (DFT), we decided to study it again, emphasizing on
full potential methods, including spin-polarized relativistic
Korringa-Kohn-Rostoker (SPR-KKR) and full-potential local-orbital (FPLO)
scheme. In addition to the determination of exact values of the
magnetocrystalline anisotropy constants K$\mathrm{_1}$ and K$\mathrm{_2}$, the
magnetic moments (m), the Curie temperature, and the magnetostriction
coefficient, we focused on the investigation of the magnetocrystalline
anisotropy energy (MAE) dependence on the magnetic moment values using the
fully relativistic fixed spin moment (FSM) method with various
exchange-correlation potentials. We present nearly identical MAE(m) curves near
the equilibrium point, along with different equilibrium values of MAE and
magnetic moments. For a magnetic moment reduced by about 10%, we determined a
theoretical MAE maximum in the ground state (0 K) equal to about 20.3 MJ
m$\mathrm{^{-3}}$ and independent of the choice of the exchange-correlation
potential form. These calculations allow us to understand the discrepancies
between the previous MAE results for different exchange-correlation potentials.


title:
Supercritical Site Percolation on the Hypercube: Small Components are
  Small

abstract:
  We consider supercritical site percolation on the $d$-dimensional hypercube
$Q^d$. We show that typically all components in the percolated hypercube,
besides the giant, are of size $O(d)$. This resolves a conjecture of
Bollob\'as, Kohayakawa, and {\L}uczak from 1994.


title:
Zero-phase angle asteroid taxonomy classification using unsupervised
  machine learning algorithms

abstract:
  We are in an era of large catalogs and, thus, statistical analysis tools for
large data sets, such as machine learning, play a fundamental role. One example
of such a survey is the Sloan Moving Object Catalog (MOC), which lists the
astrometric and photometric information of all moving objects captured by the
Sloan field of view. One great advantage of this telescope is represented by
its set of five filters, allowing for taxonomic analysis of asteroids by
studying their colors. However, until now, the color variation produced by the
change of phase angle of the object has not been taken into account. In this
paper, we address this issue by using absolute magnitudes for classification.
We aim to produce a new taxonomic classification of asteroids based on their
magnitudes that is unaffected by variations caused by the change in phase
angle. We selected 9481 asteroids with absolute magnitudes of Hg, Hi and Hz,
computed from the Sloan Moving Objects Catalog using the HG12 system. We
calculated the absolute colors with them. To perform the taxonomic
classification, we applied a unsupervised machine learning algorithm known as
fuzzy C-means. This is a useful soft clustering tool for working with {data
sets where the different groups are not completely separated and there are
regions of overlap between them. We have chosen to work with the four main
taxonomic complexes, C, S, X, and V, as they comprise most of the known
spectral characteristics. We classified a total of 6329 asteroids with more
than 60% probability of belonging to the assigned taxonomic class, with 162 of
these objects having been characterized by an ambiguous classification in the
past. By analyzing the sample obtained in the plane Semimajor axis versus
inclination, we identified 15 new V-type asteroid candidates outside the Vesta
family region.


title: End-to-End Speech Translation for Code Switched Speech

abstract:
  Code switching (CS) refers to the phenomenon of interchangeably using words
and phrases from different languages. CS can pose significant accuracy
challenges to NLP, due to the often monolingual nature of the underlying
systems. In this work, we focus on CS in the context of English/Spanish
conversations for the task of speech translation (ST), generating and
evaluating both transcript and translation. To evaluate model performance on
this task, we create a novel ST corpus derived from existing public data sets.
We explore various ST architectures across two dimensions: cascaded (transcribe
then translate) vs end-to-end (jointly transcribe and translate) and
unidirectional (source -> target) vs bidirectional (source <-> target). We show
that our ST architectures, and especially our bidirectional end-to-end
architecture, perform well on CS speech, even when no CS training data is used.


title: Learning Trajectories of Hamiltonian Systems with Neural Networks

abstract:
  Modeling of conservative systems with neural networks is an area of active
research. A popular approach is to use Hamiltonian neural networks (HNNs) which
rely on the assumptions that a conservative system is described with Hamilton's
equations of motion. Many recent works focus on improving the integration
schemes used when training HNNs. In this work, we propose to enhance HNNs with
an estimation of a continuous-time trajectory of the modeled system using an
additional neural network, called a deep hidden physics model in the
literature. We demonstrate that the proposed integration scheme works well for
HNNs, especially with low sampling rates, noisy and irregular observations.


title:
Can Caroli-de Gennes-Matricon and Majorana vortex states be
  distinguished in the presence of impurities?

abstract:
  Majorana zero modes states (MZMs) are predicted to appear as bound states in
vortices of topological superconductors. MZMs are pinned at zero energy and
have zero charge due to particle-hole symmetry. MZMs in vortices of topological
superconductors tend to coexist with other subgap states, named Caroli-de
Gennes-Matricon (CdGM) states. The distinction between MZMs and CdGM is limited
since current experiments rely on zero-bias peak measurements obtained via
scanning tunneling spectroscopy. In this work, we show that a local impurity
potential can push CdGM states to zero energy. Furthermore, the finite charge
in CdGM states can also drop to zero under the same mechanism. We establish,
through exploration of the impurity parameter space, that these two phenomena
generally happen in consonance. This means that energy and charge shifts in
CdGM may be enough to imitate spectroscopic signatures of MZMs.


title: Multiplicity in restricting minimal representations

abstract:
  We discuss the action of a subgroup on small nilpotent orbits, and prove a
bounded multiplicity property for the restriction of minimal representations of
real reductive Lie groups with respect to arbitrary reductive symmetric pairs.


title:
Semantic Exploration from Language Abstractions and Pretrained
  Representations

abstract:
  Continuous first-person 3D environments pose unique exploration challenges to
reinforcement learning (RL) agents because of their high-dimensional state and
action spaces. These challenges can be ameliorated by using semantically
meaningful state abstractions to define novelty for exploration. We propose
that learned representations shaped by natural language provide exactly this
form of abstraction. In particular, we show that vision-language
representations, when pretrained on image captioning datasets sampled from the
internet, can drive meaningful, task-relevant exploration and improve
performance on 3D simulated environments. We also characterize why and how
language provides useful abstractions for exploration by comparing the impacts
of using representations from a pretrained model, a language oracle, and
several ablations. We demonstrate the benefits of our approach in two very
different task domains -- one that stresses the identification and manipulation
of everyday objects, and one that requires navigational exploration in an
expansive world -- as well as two popular deep RL algorithms: Impala and R2D2.
Our results suggest that using language-shaped representations could improve
exploration for various algorithms and agents in challenging environments.


title: On the analytic functions

abstract:
  Let $\Omega$ be a connected bounded domain on the complex plane, $S$ be its
boundary, which is closed, star-shaped, $C^1$-smooth, and $H(\Omega)$ is the
set of analytic (holomorphic) in $\Omega$ functions. The aim of this paper is
to prove that an arbitrary $f\in L^1(S)$, satisfying the condition
$\int_Sf(s)ds=0$, can be boundary value of an $f\in H(\Omega)$.


title: An approach to improving sound-based vehicle speed estimation

abstract:
  We consider improving the performance of a recently proposed sound-based
vehicle speed estimation method. In the original method, an intermediate
feature, referred to as the modified attenuation (MA), has been proposed for
both vehicle detection and speed estimation. The MA feature maximizes at the
instant of the vehicle's closest point of approach, which represents a training
label extracted from video recording of the vehicle's pass by. In this paper,
we show that the original labeling approach is suboptimal and propose a method
for label correction. The method is tested on the VS10 dataset, which contains
304 audio-video recordings of ten different vehicles. The results show that the
proposed label correction method reduces average speed estimation error from
7.39 km/h to 6.92 km/h. If the speed is discretized into 10 km/h classes, the
accuracy of correct class prediction is improved from 53.2% to 53.8%, whereas
when tolerance of one class offset is allowed, accuracy is improved from 93.4%
to 94.3%.


title: Chiral spin symmetry and the QCD phase diagram

abstract:
  Lattice QCD simulations with chirally symmetric quarks have recently
established approximate $SU(2)_{CS}$ and $SU(2N_F)$ symmetries of the quantum
effective action in a temperature range above the chiral crossover
$T_\mathrm{ch}$, in which color-electric interactions between quarks dominate
the dynamics. We show that such an intermediate temperature range between the
chirally broken and plasma regimes is fully consistent with published screening
mass spectra, which demonstrate the breakdown of thermal perturbation theory at
the crossover between the partonic and the chiral spin symmetric regime at
$T_s\sim (2-3)T_\mathrm{ch}$. From the known behavior of screening masses with
baryon chemical potential, we deduce qualitatively how this chiral spin
symmetric band extends into the QCD phase diagram. In the cold and dense
region, we propose parity doubled baryons as possible candidates for chiral
spin symmetric matter. This represents a special case of quarkyonic matter with
confinement and restored chiral symmetry, and can smoothly transform to quark
matter at sufficiently high densities. Finally, we discuss the potential of
dilepton spectra to identify such matter forms.


title:
XMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font
  Generation

abstract:
  Generating a new font library is a very labor-intensive and time-consuming
job for glyph-rich scripts. Few-shot font generation is thus required, as it
requires only a few glyph references without fine-tuning during test. Existing
methods follow the style-content disentanglement paradigm and expect novel
fonts to be produced by combining the style codes of the reference glyphs and
the content representations of the source. However, these few-shot font
generation methods either fail to capture content-independent style
representations, or employ localized component-wise style representations,
which is insufficient to model many Chinese font styles that involve
hyper-component features such as inter-component spacing and
"connected-stroke". To resolve these drawbacks and make the style
representations more reliable, we propose a self-supervised cross-modality
pre-training strategy and a cross-modality transformer-based encoder that is
conditioned jointly on the glyph image and the corresponding stroke labels. The
cross-modality encoder is pre-trained in a self-supervised manner to allow
effective capture of cross- and intra-modality correlations, which facilitates
the content-style disentanglement and modeling style representations of all
scales (stroke-level, component-level and character-level). The pre-trained
encoder is then applied to the downstream font generation task without
fine-tuning. Experimental comparisons of our method with state-of-the-art
methods demonstrate our method successfully transfers styles of all scales. In
addition, it only requires one reference glyph and achieves the lowest rate of
bad cases in the few-shot font generation task 28% lower than the second best


title:
Electroweak Phase Transition in 2HDM under Higgs, Z-pole, and W
  precision measurements

abstract:
  In this work we revisit the existence of a strong first order electroweak
phase transition (SFOEWPT) and recent $m_W$ precision measurement in the Type-I
and Type-II 2HDMs. The O(100) GeV new scalars in 2HDMs are favored by SFOEWPT,
which is necessary for electroweak baryogenesis, and observed $m_W$ shift as
well. % We find that under current constraints, both Type-I and Type-II 2HDM
can explain the SFOEWPT, Z-pole, Higgs precision measurements and $m_W$
precision measurement of CDF-II at same time, and all these precision
measurements are sensitive to heavy Higgs mass splitting in 2HDM. The allowed
regions are $\Delta m_{A/C} \in (-400, 400) GeV, \tanb\in(1,50)$, and $\Delta
m_{A/C} \in (-200, 300) GeV, \tanb\in(1,12)$ for Type-I and Type-II 2HDM
respectively. Furthermore future lepton collider measurements on Higgs and $Z$
boson properties can explore this scenario in more detail or even rule out it.


title: Blind Orthogonal Least Squares based Compressive Spectrum Sensing

abstract:
  Compressive spectrum sensing (CSS) has been widely studied in wideband
cognitive radios, benefiting from the reduction of sampling rate via
compressive sensing (CS) technology. However, the sensing performance of most
existing CSS excessively relies on the prior information such as spectrum
sparsity or noise variance. Thus, a key challenge in practical CSS is how to
work effectively even in the absence of such information. In this paper, we
propose a blind orthogonal least squares based CSS algorithm (B-OLS-CSS), which
functions properly without the requirement of prior information. Specifically,
we develop a novel blind stopping rule for the OLS algorithm based on its
probabilistic recovery condition. This innovative rule gets rid of the need of
the spectrum sparsity or noise information, but only requires the
computational-feasible mutual incoherence property of the given measurement
matrix. Our theoretical analysis indicates that the signal-to-noise ratio
required by the proposed B-OLS-CSS for achieving a certain sensing accuracy is
relaxed than that by the benchmark CSS using the OMP algorithm, which is
verified by extensive simulation results.


title: Electromotive force in driven topological quantum circuits

abstract:
  Time-dependent control of superconducting quantum circuits is a prerequisite
for building scalable quantum hardware. The quantum description of these
circuits is complicated due to the electromotive force (emf) induced by
time-varying magnetic fields. Here, we examine how the emf modifies the
fractional Josephson effect. We show that a time-varying flux introduces a new
term that depends on the geometry of both the circuit and the applied magnetic
field. This term can be probed via current and charge measurements in
closed-loop and open-circuit geometries. Our results refine the current
understanding of how to properly describe time-dependent control of topological
quantum circuits.


title:
M$^2$BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified
  Birds-Eye View Representation

abstract:
  In this paper, we propose M$^2$BEV, a unified framework that jointly performs
3D object detection and map segmentation in the Birds Eye View~(BEV) space with
multi-camera image inputs. Unlike the majority of previous works which
separately process detection and segmentation, M$^2$BEV infers both tasks with
a unified model and improves efficiency. M$^2$BEV efficiently transforms
multi-view 2D image features into the 3D BEV feature in ego-car coordinates.
Such BEV representation is important as it enables different tasks to share a
single encoder. Our framework further contains four important designs that
benefit both accuracy and efficiency: (1) An efficient BEV encoder design that
reduces the spatial dimension of a voxel feature map. (2) A dynamic box
assignment strategy that uses learning-to-match to assign ground-truth 3D boxes
with anchors. (3) A BEV centerness re-weighting that reinforces with larger
weights for more distant predictions, and (4) Large-scale 2D detection
pre-training and auxiliary supervision. We show that these designs
significantly benefit the ill-posed camera-based 3D perception tasks where
depth information is missing. M$^2$BEV is memory efficient, allowing
significantly higher resolution images as input, with faster inference speed.
Experiments on nuScenes show that M$^2$BEV achieves state-of-the-art results in
both 3D object detection and BEV segmentation, with the best single model
achieving 42.5 mAP and 57.0 mIoU in these two tasks, respectively.


title:
Kelvin-Helmholtz instability of the collisionless anisotropic space
  plasma

abstract:
  The linear MHD Kelvin-Helmholtz instability (KHI) in an anisotropic plasma
concerning the direction of an external magnetic field is examined in detail.
For this purpose, the MHD equations are used to describe the motion of plasma
as a fluid, which is derived from 16 moments of Boltzmann-Vlasov kinetic
equations for collisionless plasma. In addition, the heat flux along the
magnetic field is taken into account. The growing rates of KHI are calculated
as functions of the anisotropic plasma properties for a shear flow along the
magnetic field at supersonic velocities. On the other hand, the
quasi-transverse propagation of surface waves between flows with varying
velocities is thoroughly examined for both zero-width and finite-width
transition layers. In contrast to the tangential discontinuity, it is proved
that the limiting breadth of the transition layer constrains the KHI excitation
as the wavenumber grows. The instability under investigation could be one of
the main ways of dissipation of large-scale low-frequency Alf\'en wave
turbulence existing in the solar wind plasma.


title:
Direct 3D observation and unraveling of electroconvection phenomena
  during concentration polarization at ion-exchange membranes

abstract:
  A decade ago, two-dimensional microscopic flow visualization proved the
theoretically predicted existence of electroconvection roles as well as their
decisive role in destabilizing the concentration polarization layer at
ion-selective fluid/membrane interfaces. Electroconvection induces chaotic flow
vortices injecting volume having bulk concentration into the ion-depleted
diffusion layer at the interface. Experimental quantification of these
important flow patterns have so far only been carried out in 2D. Numerical
direct simulations suggest 3D features, yet experimental proof is lacking. 3D
simulations are also limited in covering extended spacial and temporal scales.
  This study presents a new comprehensive experimental method for the
time-resolved recording of the 3D electroconvective velocity field near a
cation-exchange membrane. For the first time, the spatio-temporal velocity
field can be visualized in 3D at multiples of the overlimiting current density.
In contrast to today's simulations, these experiments cover length and time
scales typical for actual electrodialytic membrane processes.
  We visualize coherent vortex structures and reveal the changes in the
velocity field and its statistics during the transition from vortex rolls to
vortex rings with increasing current density. The transition is characterized
by changes in the rotational direction, mean square velocity, and temporal
energy spectrum with only little influence on the spatial spectrum. These
findings indicate a more significant impact of EC's structural change on the
mean square velocities and temporal spectra than on the spatial spectra. This
knowledge is a prerequisite for engineering ion-selective surfaces that will
enable the operation of electrically driven processes beyond the
diffusion-limited Nernst regime.


title: Linguistic communication as (inverse) reward design

abstract:
  Natural language is an intuitive and expressive way to communicate reward
information to autonomous agents. It encompasses everything from concrete
instructions to abstract descriptions of the world. Despite this, natural
language is often challenging to learn from: it is difficult for machine
learning methods to make appropriate inferences from such a wide range of
input. This paper proposes a generalization of reward design as a unifying
principle to ground linguistic communication: speakers choose utterances to
maximize expected rewards from the listener's future behaviors. We first extend
reward design to incorporate reasoning about unknown future states in a linear
bandit setting. We then define a speaker model which chooses utterances
according to this objective. Simulations show that short-horizon speakers
(reasoning primarily about a single, known state) tend to use instructions,
while long-horizon speakers (reasoning primarily about unknown, future states)
tend to describe the reward function. We then define a pragmatic listener which
performs inverse reward design by jointly inferring the speaker's latent
horizon and rewards. Our findings suggest that this extension of reward design
to linguistic communication, including the notion of a latent speaker horizon,
is a promising direction for achieving more robust alignment outcomes from
natural language supervision.


title: Efficient Geometric Linearization of Moving-Base Rigid Robot Dynamics

abstract:
  The linearization of the equations of motion of a robotics system about a
given state-input trajectory, including a controlled equilibrium state, is a
valuable tool for model-based planning, closed-loop control, gain tuning, and
state estimation. Contrary to the case of fixed based manipulators with
prismatic or rotary joints, the state space of moving-base robotic systems such
as humanoids, quadruped robots, or aerial manipulators cannot be globally
parametrized by a finite number of independent coordinates. This impossibility
is a direct consequence of the fact that the state of these systems includes
the system's global orientation, formally described as an element of the
special orthogonal group SO(3). As a consequence, obtaining the linearization
of the equations of motion for these systems is typically resolved, from a
practical perspective, by locally parameterizing the system's attitude by means
of, e.g., Euler or Cardan angles. This has the drawback, however, of
introducing artificial parameterization singularities and extra derivative
computations. In this contribution, we show that it is actually possible to
define a notion of linearization that does not require the use of a local
parameterization for the system's orientation, obtaining a mathematically
elegant, recursive, and singularity-free linearization for moving-based robot
systems. Recursiveness, in particular, is obtained by proposing a nontrivial
modification of existing recursive algorithms to allow for computations of the
geometric derivatives of the inverse dynamics and the inverse of the mass
matrix of the robotic system. The correctness of the proposed algorithm is
validated by means of a numerical comparison with the result obtained via
geometric finite difference.


title:
When is Good Good Enough? Context Factors for Good Remote Work of Agile
  Software Development Teams. The Otto Case

abstract:
  The Covid-19 pandemic led to several challenges in everybody working life.
Many companies worldwide enabled comprehensive remote work settings for their
employees. Agile Software Development Teams are affected by the switch to
remote work as agile methods setting communication and collaboration in focus.
The well-being and motivation of software engineers and developers, which
impacting their performance, are influenced by specific context factors. This
paper aims to analyze identify specific context factors for a good remote work
setting. We designed a single case study at a German ecommerce company and
conducted an experiment using a gamification approach including eight
semi-structured interviews. Our results show, that the agile software
development team members to their health. Furthermore, most the team members
value the gamification approach to put more focus on physical activities and
the health well-being. We discuss several practical implications and provide
recommendations for other teams and companies.


title:
In-situ investigation of wetting patterns in polymeric multibore
  membranes via magnetic resonance imaging

abstract:
  Wetting of the membrane to displace air or conditioning liquids is important
to exploit the complex porosity of a filtration membrane. This study reveals
the details of wetting in multibore membrane-based filtration modules. Using
magnetic resonance imaging (MRI), we quantify the fluid distribution patterns
during initial membrane wetting in dead-end permeation mode. The
spatio-temporal evolution of aqueous copper sulfate solution wetting the
membrane fibers was investigated as a function of the applied flux, packing
density, and position along the membrane module length. Three initial wetting
conditions were examined: delivery-state membranes, ethanol-washed and dried
(air-filled) membranes, and ethanol-filled membranes. Significant changes in
wetting patterns were observed due to interfacial and polymer swelling effects.
This in-situ investigation reveals a slow wetting progression over six hours
and more to obtain complete wetting, even at high fluxes of 200 LMH. However,
an increased flux leads to faster wetting kinetics as the evolving wetting
patterns are flux dependent. The packing density of the multibore fibers
additionally impacts the wetting kinetics by shifting the prevalent pressure
conditions. Although in dead-end mode, the wetting progression is non-uniform
along the membrane module length. In addition to this parameter study,
different pre-wetting agents' effect on the displacement behavior was
investigated in depth. This study helps to understand (a) complex wetting
phenomena inside multibore membranes in dead-end filtration, (b) the membranes'
interaction with their surroundings due to neighboring membranes, and (c) the
effect of the used fluid system for displacement on the resulting wetting
patterns.


title: Physics is the New Data

abstract:
  The rapid development of machine learning (ML) methods has fundamentally
affected numerous applications ranging from computer vision, biology, and
medicine to accounting and text analytics. Until now, it was the availability
of large and often labeled data sets that enabled significant breakthroughs.
However, the adoption of these methods in classical physical disciplines has
been relatively slow, a tendency that can be traced to the intrinsic
differences between correlative approaches of purely data-based ML and the
causal hypothesis-driven nature of physical sciences. Furthermore, anomalous
behaviors of classical ML necessitate addressing issues such as explainability
and fairness of ML. We also note the sequence in which deep learning became
mainstream in different scientific disciplines - starting from medicine and
biology and then towards theoretical chemistry, and only after that, physics -
is rooted in the progressively more complex level of descriptors, constraints,
and causal structures available for incorporation in ML architectures. Here we
put forth that over the next decade, physics will become a new data, and this
will continue the transition from dot-coms and scientific computing concepts of
the 90ies to big data of 2000-2010 to deep learning of 2010-2020 to
physics-enabled scientific ML.


title:
Block-Segmentation Vectors for Arousal Prediction using Semi-supervised
  Learning

abstract:
  To handle emotional expressions in computer applications, Russell's circum-
plex model has been useful for representing emotions according to valence and
arousal. In SentiWordNet, the level of valence is automatically assigned to a
large number of synsets (groups of synonyms in WordNet) using semi-supervised
learning. However, when assigning the level of arousal, the existing method
proposed for SentiWordNet reduces the accuracy of sentiment prediction. In this
paper, we propose a block-segmentation vector for predicting the arousal levels
of many synsets from a small number of labeled words using semi-supervised
learning. We analyze the distribution of arousal and non-arousal words in a
corpus of sentences by comparing it with the distribution of valence words. We
address the problem that arousal level prediction fails when arousal and
non-arousal words are mixed together in some sentences. To capture the features
of such arousal and non-arousal words, we generate word vectors based on
inverted indexes by block IDs, where the corpus is divided into blocks in the
flow of sentences. In the evaluation experiment, we show that the results of
arousal prediction with the block-segmentation vectors outperform the results
of the previous method in SentiWordNet.


title: Universality of political corruption networks

abstract:
  Corruption crimes demand highly coordinated actions among criminal agents to
succeed. But research dedicated to corruption networks is still in its infancy
and indeed little is known about the properties of these networks. Here we
present a comprehensive investigation of corruption networks related to
political scandals in Spain and Brazil over nearly three decades. We show that
corruption networks of both countries share universal structural and dynamical
properties, including similar degree distributions, clustering and
assortativity coefficients, modular structure, and a growth process that is
marked by the coalescence of network components due to a few recidivist
criminals. We propose a simple model that not only reproduces these empirical
properties but reveals also that corruption networks operate near a critical
recidivism rate below which the network is entirely fragmented and above which
it is overly connected. Our research thus indicates that actions focused on
decreasing corruption recidivism may substantially mitigate this type of
organized crime.


title: Reference Energies for Cyclobutadiene: Automerization and Excited States

abstract:
  Cyclobutadiene is a well-known playground for theoretical chemists and is
particularly suitable to test ground- and excited-state methods. Indeed, due to
its high spatial symmetry, especially at the $D_{4h}$ square geometry but also
in the $D_{2h}$ rectangular arrangement, the ground and excited states of
cyclobutadiene exhibit multi-configurational characters and single-reference
methods, such as adiabatic time-dependent density-functional theory (TD-DFT) or
equation-of-motion coupled cluster (EOM-CC), are notoriously known to struggle
in such situations. In this work, using a large panel of methods and basis
sets, we provide an extensive computational study of the automerization barrier
(defined as the difference between the square and rectangular ground-state
energies) and the vertical excitation energies at $D_{2h}$ and $D_{4h}$
equilibrium structures. In particular, selected configuration interaction
(SCI), multi-reference perturbation theory (CASSCF, CASPT2, and NEVPT2), and
coupled-cluster (CCSD, CC3, CCSDT, CC4, and CCSDTQ) calculations are performed.
The spin-flip formalism, which is known to provide a qualitatively correct
description of these diradical states, is also tested within TD-DFT (combined
with numerous exchange-correlation functionals) and the algebraic diagrammatic
construction [ADC(2)-s, ADC(2)-x, and ADC(3)] schemes. A theoretical best
estimate is defined for the automerization barrier and for each vertical
transition energy.


title: Oscillation estimates for truncated singular Radon operators

abstract:
  In this paper we prove uniform oscillation estimates on $L^p$, with
$p\in(1,\infty)$, for truncated singular integrals of the Radon type associated
with Calder\'on-Zygmund kernel, both in continuous and discrete settings. In
the discrete case we use the Ionescu-Wainger multiplier theorem and the
Rademacher-Menshov inequality to handle the number-theoretic nature of the
discrete singular integral. The result we obtained in the continuous setting
can be seen as a generalisation of the results of Campbell, Jones, Reinhold and
Wierdl for the continuous singular integrals of the Calder\'on-Zygmund type.


title: Non-symplectic automorphisms of order multiple of seven on K3 surfaces

abstract:
  In this paper we present a classification of non-symplectic automorphisms of
K3 surfaces whose order is a multiple of seven by describing the topological
type of their fixed locus. In the case of purely non-symplectic automorphisms,
we provide new results for order 14 and alternative proofs for orders 21, 28
and 42. For each of these orders we also consider not purely non-symplectic
automorphisms and obtain a complete characterization of their fixed loci.
Several results of our paper were obtained independently from the results of a
recent paper by Brandhorst and Hofmann; the methods we use are also different.


title:
Concept Drift Adaptation for CTR Prediction in Online Advertising
  Systems

abstract:
  Click-through rate (CTR) prediction is a crucial task in web search,
recommender systems, and online advertisement displaying. In practical
application, CTR models often serve with high-speed user-generated data
streams, whose underlying distribution rapidly changing over time. The concept
drift problem inevitably exists in those streaming data, which can lead to
performance degradation due to the timeliness issue. To ensure model freshness,
incremental learning has been widely adopted in real-world production systems.
However, it is hard for the incremental update to achieve the balance of the
CTR models between the adaptability to capture the fast-changing trends and
generalization ability to retain common knowledge. In this paper, we propose
adaptive mixture of experts (AdaMoE), a new framework to alleviate the concept
drift problem by adaptive filtering in the data stream of CTR prediction. The
extensive experiments on the offline industrial dataset and online A/B tests
show that our AdaMoE significantly outperforms all incremental learning
frameworks considered.


title:
Convolutional autoencoders for spatially-informed ensemble
  post-processing

abstract:
  Ensemble weather predictions typically show systematic errors that have to be
corrected via post-processing. Even state-of-the-art post-processing methods
based on neural networks often solely rely on location-specific predictors that
require an interpolation of the physical weather model's spatial forecast
fields to the target locations. However, potentially useful predictability
information contained in large-scale spatial structures within the input fields
is potentially lost in this interpolation step. Therefore, we propose the use
of convolutional autoencoders to learn compact representations of spatial input
fields which can then be used to augment location-specific information as
additional inputs to post-processing models. The benefits of including this
spatial information is demonstrated in a case study of 2-m temperature
forecasts at surface stations in Germany.


title: Transformer-Based Self-Supervised Learning for Emotion Recognition

abstract:
  In order to exploit representations of time-series signals, such as
physiological signals, it is essential that these representations capture
relevant information from the whole signal. In this work, we propose to use a
Transformer-based model to process electrocardiograms (ECG) for emotion
recognition. Attention mechanisms of the Transformer can be used to build
contextualized representations for a signal, giving more importance to relevant
parts. These representations may then be processed with a fully-connected
network to predict emotions. To overcome the relatively small size of datasets
with emotional labels, we employ self-supervised learning. We gathered several
ECG datasets with no labels of emotion to pre-train our model, which we then
fine-tuned for emotion recognition on the AMIGOS dataset. We show that our
approach reaches state-of-the-art performances for emotion recognition using
ECG signals on AMIGOS. More generally, our experiments show that transformers
and pre-training are promising strategies for emotion recognition with
physiological signals.


title: Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation

abstract:
  Domain adaptation (DA) tries to tackle the scenarios when the test data does
not fully follow the same distribution of the training data, and multi-source
domain adaptation (MSDA) is very attractive for real world applications. By
learning from large-scale unlabeled samples, self-supervised learning has now
become a new trend in deep learning. It is worth noting that both
self-supervised learning and multi-source domain adaptation share a similar
goal: they both aim to leverage unlabeled data to learn more expressive
representations. Unfortunately, traditional multi-task self-supervised learning
faces two challenges: (1) the pretext task may not strongly relate to the
downstream task, thus it could be difficult to learn useful knowledge being
shared from the pretext task to the target task; (2) when the same feature
extractor is shared between the pretext task and the downstream one and only
different prediction heads are used, it is ineffective to enable inter-task
information exchange and knowledge sharing. To address these issues, we propose
a novel \textbf{S}elf-\textbf{S}upervised \textbf{G}raph Neural Network (SSG),
where a graph neural network is used as the bridge to enable more effective
inter-task information exchange and knowledge sharing. More expressive
representation is learned by adopting a mask token strategy to mask some domain
information. Our extensive experiments have demonstrated that our proposed SSG
method has achieved state-of-the-art results over four multi-source domain
adaptation datasets, which have shown the effectiveness of our proposed SSG
method from different aspects.


title: Describing Sen's Transitivity Condition in Inequalities and Equations

abstract:
  In social choice theory, Sen's value restriction condition is a sufficiency
condition restricted to individuals' ordinal preferences so as to obtain a
transitive social preference under the majority decision rule. In this article,
Sen's transitivity condition is described by use of inequality and equation.
First, for a triple of alternatives, an individual's preference is represented
by a preference map, whose entries are sets containing the ranking position or
positions derived from the individual's preference over that triple of those
alternatives. Second, by using the union operation of sets and the cardinality
concept, Sen's transitivity condition is described by inequalities. Finally, by
using the membership function of sets, Sen's transitivity condition is further
described by equations.


title:
Monolayer MnX and Janus XMnY (X, Y= S, Se, Te): A New Family of 2D
  Antiferromagnetic Semiconductors

abstract:
  We present first-principles results on the structural, electronic, and
magnetic properties of a new family of two-dimensional antiferromagnetic (AFM)
manganese chalcogenides, namely monolayer MnX and Janus XMnY (X, Y= S, Se, Te),
among which monolayer MnSe was recently synthesized in experiments
[\href{https://pubs.acs.org/doi/abs/10.1021/acsnano.1c05532}{ACS Nano 15
(8),13794 (2021)}]. By carrying out calculations of the phonon dispersion and
\textit{ab-initio} molecular dynamics simulations, we first confirmed that
these systems, characterized by an unconventional strongly coupled bilayer
atomic structure (consisting of Mn atoms buckled to chalcogens forming top and
bottom ferromagnetic (FM) planes with antiparallel spin orientation) are
dynamically and thermally stable. The analysis of the the magnetic properties
shows that these materials have robust AFM order, retaining a much lower energy
than the FM state even for under strain. Our electronic structure calculations
reveal that pristine MnX and their Janus counterparts are indirect-gap
semiconductors, covering a wide energy range and displaying tunable band gaps
by the application of biaxial tensile and compressive strain. Interestingly,
owing to the absence of inversion and time-reversal symmetry, and the presence
of an asymmetrical potential in the out-of-plane direction, Janus XMnY become
spin-split gapped systems, presenting a rich physics yet to be explored. Our
findings provide novel insights in this physics, and highlight the potential
for these two-dimensional manganese chalcogenides in AFM spintronics.


title:
Challenges in implementing DDR3 memory interface on PCB systems: a
  methodology for interfacing DDR3 SDRAM DIMM to an FPGA

abstract:
  Undoubtedly faster, larger and lower power per bit, but just how do you go
about interfacing a DDR3 SDRAM DIMM to an FPGA? The DDR3 standard addresses the
faster, more bandwidth and lower power per bit need, but it introduces new
design challenges in addition to challenges introduced by DDR2 ODT, slew rate
derating, etc. The DDR3 fly-by topology requirement means customers designing
DDR3 memories must now account for write leveling and read de-skew on the PCB.
This paper will cover modeling, simulation, and physical layout approaches
required to meet JEDEC-defined termination and tight timing requirements for
designing DDR3 memory interfaces on PCB systems.


title:
Improved Training of Physics-Informed Neural Networks with Model
  Ensembles

abstract:
  Learning the solution of partial differential equations (PDEs) with a neural
network (known in the literature as a physics-informed neural network, PINN) is
an attractive alternative to traditional solvers due to its elegancy, greater
flexibility and the ease of incorporating observed data. However, training
PINNs is notoriously difficult in practice. One problem is the existence of
multiple simple (but wrong) solutions which are attractive for PINNs when the
solution interval is too large. In this paper, we propose to expand the
solution interval gradually to make the PINN converge to the correct solution.
To find a good schedule for the solution interval expansion, we train an
ensemble of PINNs. The idea is that all ensemble members converge to the same
solution in the vicinity of observed data (e.g., initial conditions) while they
may be pulled towards different wrong solutions farther away from the
observations. Therefore, we use the ensemble agreement as the criterion for
including new points for computing the loss derived from PDEs. We show
experimentally that the proposed method can improve the accuracy of the found
solution.


title:
Temporal and spatial evolution of power-law in the numbers of COVID-19
  pandemic

abstract:
  This work systematically conducts a data analysis based on the numbers of
both cumulative and daily confirmed COVID-19 cases and deaths in a time span
through March 2020 to March 2022 for over 200 countries around the world. Such
research feature aims to reveal the temporal and spatial evolution of power-law
and scaling behaviour observed in COVID-19 pandemic, and obtains some
interesting results as follows. (1) The country-level distributions of the
numbers for the 4 metrics across 2-year period exhibit similar power-law
behaviour. At the early course of the COVID-19 spreading, the distributions in
whole range follow power-law. However, the power-law is narrowing to tail at
the subsequent course. (2) The country-level distributions of the normalized
numbers for each metric show a temporal scaling behaviour of power-law in
2-year period. The indexes of power-law for such 4 metrics are consistent with
each other. The observation of power-law and scaling behaviour suggests
underlying intrinsic dynamics of a virus spreading process in the human
interconnected society, and thus is important for understanding and
mathematically modeling the COVID-19 pandemic.

categories: q-bio.PE physics.data-an physics.soc-ph stat.AP


title:
Comparative Survey of Multigraph Integration Methods for Holistic Brain
  Connectivity Mapping

abstract:
  One of the greatest scientific challenges in network neuroscience is to
create a representative map of a population of heterogeneous brain networks,
which acts as a connectional fingerprint. The connectional brain template
(CBT), also named network atlas, presents a powerful tool for capturing the
most representative and discriminative traits of a given population while
preserving its topological patterns. The idea of a CBT is to integrate a
population of heterogeneous brain connectivity networks, derived from different
neuroimaging modalities or brain views (e.g., structural and functional), into
a unified holistic representation. Here we review current state-of-the-art
methods designed to estimate well-centered and representative CBT for
populations of single-view and multi-view brain networks. We start by reviewing
each CBT learning method, then we introduce the evaluation measures to compare
CBT representativeness of populations generated by single-view and multigraph
integration methods, separately, based on the following criteria: centeredness,
biomarker-reproducibility, node-level similarity, global-level similarity, and
distance-based similarity. We demonstrate that the deep graph normalizer (DGN)
method significantly outperforms other multi-graph and all single-view
integration methods for estimating CBTs using a variety of healthy and
disordered datasets in terms of centeredness, reproducibility (i.e.,
graph-derived biomarkers reproducibility that disentangle the typical from the
atypical connectivity variability), and preserving the topological traits at
both local and global graph-levels.


title: Irregularity of the Bergman projection on smooth unbounded worm domains

abstract:
  In this work we consider smooth unbounded worm domains $\mathcal Z_\lambda$
in $\mathbb C^2$ and show that the Bergman projection, densely defined on the
Sobolev spaces $H^{s,p}(\mathcal Z_\lambda)$, $p\in(1,\infty)$, $s\ge0$, does
not extend to a bounded operator $P_\lambda:H^{s,p}(\mathcal Z_\lambda)\to
H^{s,p}(\mathcal Z_\lambda)$ when $s>0$ or $p\neq2$. The same irregularity was
known in the case of the non-smooth unbounded worm. This improved result shows
that the irregularity of the projection is not a consequence of the
irregularity of the boundary but instead of the infinite windings of the worm
domain.


title:
FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and
  Support-Vector Machines

abstract:
  Neural Networks and related Deep Learning methods are currently at the
leading edge of technologies used for classifying objects. However, they
generally demand large amounts of time and data for model training; and their
learned models can sometimes be difficult to interpret. In this paper, we
re-introduce FastMapSVM, an interpretable Machine Learning framework for
classifying complex objects. FastMapSVM combines the strengths of FastMap and
Support-Vector Machines. FastMap is an efficient linear-time algorithm that
maps complex objects to points in a Euclidean space, while preserving pairwise
non-Euclidean distances between them. We demonstrate the efficiency and
effectiveness of FastMapSVM in the context of classifying seismograms. We show
that its performance, in terms of precision, recall, and accuracy, is
comparable to that of other state-of-the-art methods. However, compared to
other methods, FastMapSVM uses significantly smaller amounts of time and data
for model training. It also provides a perspicuous visualization of the objects
and the classification boundaries between them. We expect FastMapSVM to be
viable for classification tasks in many other real-world domains.


title:
ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less
  Neural Networks

abstract:
  Multiplication-less neural networks significantly reduce the time and energy
cost on the hardware platform, as the compute-intensive multiplications are
replaced with lightweight bit-shift operations. However, existing bit-shift
networks are all directly transferred from state-of-the-art convolutional
neural networks (CNNs), which lead to non-negligible accuracy drop or even
failure of model convergence. To combat this, we propose ShiftNAS, the first
framework tailoring Neural Architecture Search (NAS) to substantially reduce
the accuracy gap between bit-shift neural networks and their real-valued
counterparts. Specifically, we pioneer dragging NAS into a shift-oriented
search space and endow it with the robust topology-related search strategy and
custom regularization and stabilization. As a result, our ShiftNAS breaks
through the incompatibility of traditional NAS methods for bit-shift neural
networks and achieves more desirable performance in terms of accuracy and
convergence. Extensive experiments demonstrate that ShiftNAS sets a new
state-of-the-art for bit-shift neural networks, where the accuracy increases
(1.69-8.07)% on CIFAR10, (5.71-18.09)% on CIFAR100 and (4.36-67.07)% on
ImageNet, especially when many conventional CNNs fail to converge on ImageNet
with bit-shift weights.


title:
PetroGAN: A novel GAN-based approach to generate realistic, label-free
  petrographic datasets

abstract:
  Deep learning architectures have enriched data analytics in the geosciences,
complementing traditional approaches to geological problems. Although deep
learning applications in geosciences show encouraging signs, the actual
potential remains untapped. This is primarily because geological datasets,
particularly petrography, are limited, time-consuming, and expensive to obtain,
requiring in-depth knowledge to provide a high-quality labeled dataset. We
approached these issues by developing a novel deep learning framework based on
generative adversarial networks (GANs) to create the first realistic synthetic
petrographic dataset. The StyleGAN2 architecture is selected to allow robust
replication of statistical and esthetical characteristics, and improving the
internal variance of petrographic data. The training dataset consists of 10070
images of rock thin sections both in plane- and cross-polarized light. The
algorithm trained for 264 GPU hours and reached a state-of-the-art Fr\'echet
Inception Distance (FID) score of 12.49 for petrographic images. We further
observed the FID values vary with lithology type and image resolution. Our
survey established that subject matter experts found the generated images were
indistinguishable from real images. This study highlights that GANs are a
powerful method for generating realistic synthetic data, experimenting with the
latent space, and as a future tool for self-labelling, reducing the effort of
creating geological datasets.


title: Quadratic Forms and their Number Systems and Geometry

abstract:
  Quaternions, split quaternions, and hybrid numbers are very well-known number
systems. These number systems are used to make geometry in Euclidean and
Lorentz spaces. These number systems can be obtained with the help of a
quadratic form. This article examines how to find the corresponding number
system for any quadratic form. As a result of this examination, bilinear form,
vector product, skew-symmetric matrix, and rotation matrices corresponding to
any number system were obtained.


title:
IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG
  Classification

abstract:
  Early detection of cardiovascular diseases is crucial for effective treatment
and an electrocardiogram (ECG) is pivotal for diagnosis. The accuracy of Deep
Learning based methods for ECG signal classification has progressed in recent
years to reach cardiologist-level performance. In clinical settings, a
cardiologist makes a diagnosis based on the standard 12-channel ECG recording.
Automatic analysis of ECG recordings from a multiple-channel perspective has
not been given enough attention, so it is essential to analyze an ECG recording
from a multiple-channel perspective. We propose a model that leverages the
multiple-channel information available in the standard 12-channel ECG
recordings and learns patterns at the beat, rhythm, and channel level. The
experimental results show that our model achieved a macro-averaged ROC-AUC
score of 0.9216, mean accuracy of 88.85\%, and a maximum F1 score of 0.8057 on
the PTB-XL dataset. The attention visualization results from the interpretable
model are compared against the cardiologist's guidelines to validate the
correctness and usability.


title:
ReservoirComputing.jl: An Efficient and Modular Library for Reservoir
  Computing Models

abstract:
  We introduce ReservoirComputing.jl, an open source Julia library for
reservoir computing models. The software offers a great number of algorithms
presented in the literature, and allows to expand on them with both internal
and external tools in a simple way. The implementation is highly modular, fast
and comes with a comprehensive documentation, which includes reproduced
experiments from literature. The code and documentation are hosted on Github
under an MIT license https://github.com/SciML/ReservoirComputing.jl.


title:
Progress, challenges and perspectives of computational studies on glassy
  superionic conductors for solid-state batteries

abstract:
  Sulfide-based glasses and glass-ceramics showing high ionic conductivities
and excellent mechanical properties are considered as promising solid-state
electrolytes. Nowadays, the computational material techniques with the
advantage of low research cost are being widely utilized for understanding,
effectively screening and discovering of battery materials. In consideration of
the rising importance and contributions of computational studying on the glassy
SSE materials, here, this work summarizes the common computational methods
utilized for studying the amorphous inorganic materials, review the recent
progress in computational investigations of the lithium and sodium sulfide-type
glasses for solid-state batteries, and outlines our understandings of the
challenges and future perspective on them. This review would facilitate and
accelerate the future computational screening and discovering more glassy-state
SSE materials for the solid-state batteries.


title: The Lindel\"of Condition for Charge Distribution and Balayage

abstract:
  Let $\nu$ be a charge distribution on the complex plane $\mathbb C$, i.e. the
real Radon measure on $\mathbb C$ with total variation $|\nu|$. The charge
distribution $\nu$ is of finite upper density under order of $1$ if $$
\limsup_{0<r\to+\infty} \frac{1}{t}|\nu|\Bigl(\bigl\{z\in \mathbb C \bigm|
|z|\leq r\bigr\}\Bigr)<+\infty. $$ The charge distribution $\nu$ satisfies the
Lindel\"of condition of genus $1$ if $$ \limsup_{1\leq r\to
+\infty}\biggl|\int_{1\leq |z|\leq r}
\frac{1}{z}\operatorname{d}\!\nu(z)\biggr|<+\infty. $$ These concepts play a
key role in the study of entire functions of exponential type and subharmonic
functions of finite type under order of $1$, as well as in their applications.
In our previous works, a technique was developed for balayage of finite genus
$q=0,1,\dots$ of the charge distribution $\nu$ from the half-plane. We show
that balayage of genus $q=1$ of the charge distribution $\nu$ from the
half-plane preserves this pair of properties under some condition on $\nu$.


title:
Influence of the Environment on the Effect of Super Resonance in
  Mesoscale Dielectric Spheres

abstract:
  Dielectric mesoscale spheres have aroused strong interest because of their
potential to localize light at deep subwavelength volume and to yield extremal
internal magnetic and/or electric field enhancements. Recently, we showed that
such particle could support high-order Mie resonance modes with giant field
localization and enhancement. Optimizing the internal fields appears as a key
challenge for enhancing wave matter interactions in dielectric mesoscale
particles. However, a dielectric particle is always located in some medium, and
not in a vacuum. Moreover, the question is how much the environment medium
affects the internal field intensities enhancement in the super-resonance
effect. Based on Mie theory we show for the first time that the presence of the
environment leads to a significant decrease in the intensity of the field in
the particle. Thus, the study of the effect of super-resonance becomes
meaningless without taking into account the environment. However, a greater
enhancement of the internal field is found for the blue-shifted Mie size
parameter of the sphere when the particle, for example, is in air rather than
in vacuum.


title:
Small ionic radii limit time step in Martini 3 molecular dynamics
  simulations

abstract:
  Among other improvements, the Martini 3 coarse-grained force field provides a
more accurate description of the solvation of protein pockets and channels
through the consistent use of various bead types and sizes. Here, we show that
the representation of Na$^+$ and Cl$^-$ ions as "tiny" (TQ5) beads limits the
accessible time step to 25 fs. By contrast, with Martini 2, time steps of 30-40
fs were possible for lipid bilayer systems without proteins. This limitation is
relevant for, e.g., phase separating lipid mixtures that require long
equilibration times. We derive a quantitative kinetic model of time-integration
instabilities in molecular dynamics (MD) as a function of time step, ion
concentration and mass, system size, and simulation time. With this model, we
demonstrate that ion-water interactions are the main source of instability at
physiological conditions, followed closely by ion-ion interactions. We show
that increasing the ionic masses makes it possible to use time steps up to 40
fs with minimal impact on static equilibrium properties and on dynamical
quantities such as lipid and ion diffusion coefficients. Increasing the size of
the bead representing the ions (and thus changing their hydration) also permits
longer time steps. The use of larger time steps in Martini 3 simulations
results in a more efficient exploration of configuration space. The kinetic
model of MD simulation crashes can be used to determine the maximum allowed
time step whenever sampling efficiency is critical.


title: Measuring and Mitigating the Risk of IP Reuse on Public Clouds

abstract:
  Public clouds provide scalable and cost-efficient computing through resource
sharing. However, moving from traditional on-premises service management to
clouds introduces new challenges; failure to correctly provision, maintain, or
decommission elastic services can lead to functional failure and vulnerability
to attack. In this paper, we explore a broad class of attacks on clouds which
we refer to as cloud squatting. In a cloud squatting attack, an adversary
allocates resources in the cloud (e.g., IP addresses) and thereafter leverages
latent configuration to exploit prior tenants. To measure and categorize cloud
squatting we deployed a custom Internet telescope within the Amazon Web
Services us-east-1 region. Using this apparatus, we deployed over 3 million
servers receiving 1.5 million unique IP addresses (56% of the available pool)
over 101 days beginning in March of 2021. We identified 4 classes of cloud
services, 7 classes of third-party services, and DNS as sources of exploitable
latent configurations. We discovered that exploitable configurations were both
common and in many cases extremely dangerous; we received over 5 million cloud
messages, many containing sensitive data such as financial transactions, GPS
location, and PII. Within the 7 classes of third-party services, we identified
dozens of exploitable software systems spanning hundreds of servers (e.g.,
databases, caches, mobile applications, and web services). Lastly, we
identified 5446 exploitable domains spanning 231 eTLDs-including 105 in the top
10,000 and 23 in the top 1000 popular domains. Through tenant disclosures we
have identified several root causes, including (a) a lack of organizational
controls, (b) poor service hygiene, and (c) failure to follow best practices.
We conclude with a discussion of the space of possible mitigations and describe
the mitigations to be deployed by Amazon in response to this study.


title:
The Physicalization of Metamathematics and Its Implications for the
  Foundations of Mathematics

abstract:
  Both metamathematics and physics are posited to emerge from samplings by
observers of the unique ruliad structure that corresponds to the entangled
limit of all possible computations. The possibility of higher-level mathematics
accessible to humans is posited to be the analog for mathematical observers of
the perception of physical space for physical observers. A physicalized
analysis is given of the bulk limit of traditional axiomatic approaches to the
foundations of mathematics, together with explicit empirical metamathematics of
some examples of formalized mathematics. General physicalized laws of
mathematics are discussed, associated with concepts such as metamathematical
motion, inevitable dualities, proof topology and metamathematical
singularities. It is argued that mathematics as currently practiced can be
viewed as derived from the ruliad in a direct Platonic fashion analogous to our
experience of the physical world, and that axiomatic formulation, while often
convenient, does not capture the ultimate character of mathematics. Among the
implications of this view is that only certain collections of axioms may be
consistent with inevitable features of human mathematical observers. A
discussion is included of historical and philosophical connections, as well as
of foundational implications for the future of mathematics.


title:
Hybrid Au-Si microspheres produced by laser ablation in liquid (LAL) for
  temperature-feedback optical nano-sensing and anti-counterfeit labeling

abstract:
  Recent progress in hybrid nanomaterials composed of dissimilar constituents
permitted to improve performance and functionality of novel devices developed
for optoelectronics, catalysis, medical diagnostic and sensing. However, the
rational combination of such contrasting materials as noble metals and
semiconductors within individual hybrid nanostructures $via$ a ready-to-use and
lithography-free fabrication approach is still a standing challenge. Here, we
report on a two-step synthesis of Au-Si microspheres generated by laser
ablation of silicon in isopropanol followed by laser irradiation of the
produced Si nanoparticles in presence of HAuCl$_4$. Thermal reduction of
[AuCl$_4$]$^-$ species to metallic gold phase, along with its subsequent mixing
with silicon under laser irradiation creates a nanostructured material with a
unique composition and morphology revealed by electron microscopy, tomography
and elemental analysis. Combination of such basic plasmonic and nanophotonic
materials as gold and silicon within a single microsphere allows for efficient
light-to-heat conversion, as well as single-particle SERS sensing with
temperature feedback modality and expanded functionality. Moreover, the
characteristic Raman signal and hot-electron induced nonlinear
photoluminescence coexisting within Au-Si hybrids, as well as commonly
criticized randomness of the nanomaterials prepared by laser ablation in
liquid, were proved useful for realization of anti-counterfeiting labels based
on physically unclonable function approach.


title:
ESCM$^2$: Entire Space Counterfactual Multi-Task Model for Post-Click
  Conversion Rate Estimation

abstract:
  Accurate estimation of post-click conversion rate is critical for building
recommender systems, which has long been confronted with sample selection bias
and data sparsity issues. Methods in the Entire Space Multi-task Model (ESMM)
family leverage the sequential pattern of user actions, i.e.
$impression\rightarrow click \rightarrow conversion$ to address data sparsity
issue. However, they still fail to ensure the unbiasedness of CVR estimates. In
this paper, we theoretically demonstrate that ESMM suffers from the following
two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of
ESMM is inherently higher than the ground truth; (2) Potential Independence
Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM
overlooks the causality from click to conversion. To this end, we devise a
principled approach named Entire Space Counterfactual Multi-task Modelling
(ESCM$^2$), which employs a counterfactual risk miminizer as a regularizer in
ESMM to address both IEB and PIP issues simultaneously. Extensive experiments
on offline datasets and online environments demonstrate that our proposed
ESCM$^2$ can largely mitigate the inherent IEB and PIP issues and achieve
better performance than baseline models.


title:
General Hamiltonian Representation of ML Detection Relying on the
  Quantum Approximate Optimization Algorithm

abstract:
  The quantum approximate optimization algorithm (QAOA) conceived for solving
combinatorial optimization problems has attracted significant interest since it
can be run on the existing noisy intermediate-scale quantum (NISQ) devices. A
primary step of using the QAOA is the efficient Hamiltonian construction based
on different problem instances. Hence, we solve the maximum likelihood (ML)
detection problem for general constellations by appropriately adapting the
QAOA, which gives rise to a new paradigm in communication systems. We first
transform the ML detection problem into a weighted minimum $N$-satisfiability
(WMIN-$N$-SAT) problem, where we formulate the objective function of the
WMIN-$N$-SAT as a pseudo Boolean function. Furthermore, we formalize the
connection between the degree of the objective function and the Gray-labelled
modulation constellations. Explicitly, we show a series of results exploring
the connection between the coefficients of the monomials and the patterns of
the associated constellation points, which substantially simplifies the
objective function with respect to the problem Hamiltonian of the QAOA. In
particular, for an M-ary Gray-mapped quadrature amplitude modulation (MQAM)
constellation, we show that the specific qubits encoding the in-phase
components and those encoding the quadrature components are independent in the
quantum system of interest, which allows the in-phase and quadrature components
to be detected separately using the QAOA. Furthermore, we characterize the
degree of the objective function in the WMIN-$N$-SAT problem corresponding to
the ML detection of multiple-input and multiple-output (MIMO) channels.
Finally, we evaluate the approximation ratio of the QAOA for the ML detection
problem of quadrature phase shift keying (QPSK) relying on QAOA circuits of
different depths.


title:
On the solution of a conformal mapping problem by means of Weierstrass
  functions

abstract:
  The conformal mapping problem for the section of a channel filled with porous
material under a rectangular dam onto the upper half-plane is considered.
Similar problems arise in computing of fluid flow in hydraulic structures. As a
solution method, the representation of Christoffel-Schwartz elliptic integral
in terms of Weierstrass functions is used. The calculation is based on Taylor
series for the sigma function, the coefficients of which are determined
recursively. A simple formula for a conformal mapping is obtained, which
depends on four parameters and uses the sigma function. A numerical experiment
was carried out for a specific area. The degeneration of the region, which
consists in the dam width tending to zero, is considered, and it is shown that
the resulting formula has a limit that implements the solution of the limiting
problem. A refined proof of Weierstrass recursive formula for the coefficients
of Taylor series of the sigma function is presented.


title:
Linking Scientific Instruments and HPC: Patterns, Technologies,
  Experiences

abstract:
  Powerful detectors at modern experimental facilities routinely collect data
at multiple GB/s. Online analysis methods are needed to enable the collection
of only interesting subsets of such massive data streams, such as by explicitly
discarding some data elements or by directing instruments to relevant areas of
experimental space. Such online analyses require methods for configuring and
running high-performance distributed computing pipelines--what we call
flows--linking instruments, HPC (e.g., for analysis, simulation, AI model
training), edge computing (for analysis), data stores, metadata catalogs, and
high-speed networks. In this article, we review common patterns associated with
such flows and describe methods for instantiating those patterns. We also
present experiences with the application of these methods to the processing of
data from five different scientific instruments, each of which engages HPC
resources for data inversion, machine learning model training, or other
purposes. We also discuss implications of these new methods for operators and
users of scientific facilities.


title:
Tunable ferroelectric topological defects on 2D topological surfaces:
  strain engineering skyrmion-like polar structures in 2D materials

abstract:
  Polar topological structures in ferroelectric thin films have recently drawn
significant interest due to their fascinating physical behaviors and promising
applications in high-density nonvolatile memories. However, most polar
topological patterns are only observed in the perovskites superlattices. Here,
we report the discovery of the tunable ferroelectric polar topological
defective structures designed and achieved by strain engineering in
two-dimensional PbX (X=S, Se, and Te) materials using multiscale computational
simulations. First, the first-principles calculations demonstrate the
strain-induced recoverable ferroelectric phase transition in such 2D materials.
The unique polar topological vortex pattern is then induced by applied
mechanical indentation, evidenced by molecular dynamics simulations based on a
developed deep-learning potential. According to the strain phase diagram and
applied complex strain loadings, the diverse polar topological structures,
including antivortex structure and flux-closure structure, are predicted to be
emergent through the finite-element simulations. We conclude that strain
engineering is promising to tailor various designed reversible polar topologies
in ultra-flexible 2D materials, which provide excellent opportunities for
next-generation nanoelectronics and sensor devices.


title: Collective dynamics in a glass-former with Mari-Kurchan interactions

abstract:
  We numerically study the equilibrium relaxation dynamics of a two-dimensional
Mari-Kurchan glass model. The tree-like structure of particle interactions
forbids both non-trivial structural motifs and the emergence of a complex
free-energy landscape leading to a thermodynamic glass transition, while the
finite-dimensional nature of the model prevents the existence of a
mode-coupling singularity. Nevertheless, the equilibrium relaxation dynamics is
shown to be in excellent agreement with simulations performed in conventional
glass-formers. Averaged time-correlation functions display a phenomenology
typical of supercooled liquids, including the emergence of an excess signal in
relaxation spectra at intermediate frequencies. We show that this evolution is
accompanied by strong signatures of collective and heterogeneous dynamics which
cannot be interpreted in terms of single particle hopping and emerge from
dynamic facilitation. Our study demonstrates that an off-lattice interacting
particle model with extremely simple structural correlations displays
quantitatively realistic glassy dynamics.


title: Charge transport in monolayers of metal nanoparticles

abstract:
  Two-dimensional (2D) nanoparticle films are a new class of materials with
interesting physical properties and applications ranging from nanoelectronics
to sensing and photonics. The importance of conducting nanoparticle films makes
the fundamental understanding of their charge transport extremely important for
materials and process design. Various hopping and transport mechanisms have
been proposed and the nanoparticle monolayer is consistent with the electrical
equivalent RC circuit, but their theoretical methods are limited to the model
of the single electron tunneling between capacitively coupled nanoparticles
with a characteristic time constant RC and the conductivity of thin film is the
experimental conductivity, which cannot be deduced from these theoretical
models. It is also unclear that how the specific process of electron transpot
is affected by temperature. So, nowadays the electron dynamics of thin film
cannot be understood fundamentally. Here, we develop an analytical theory based
on the model of Sommerfeld, backed up by Monte-Carlo simulations, that predicts
the process of charge transport and the effect of temperature on the electron
transport in the thin film. In this paper two different nanoparticle models
were built to cope with different types of morphology: triangular array and
rectangular array. The transport properties of these different kinds of arrays
including 2D ordered nanoparticle arrays with/without local structural disorder
and 2D gradient nanoparticle arrays were investigated at different
temperatures. For 2D well-ordered nanoparticle array without local structural
disorder, the I-V curves are non-linear and highly symmetric.


title:
A Spiking Neural Network based on Neural Manifold for Augmenting
  Intracortical Brain-Computer Interface Data

abstract:
  Brain-computer interfaces (BCIs), transform neural signals in the brain into
in-structions to control external devices. However, obtaining sufficient
training data is difficult as well as limited. With the advent of advanced
machine learning methods, the capability of brain-computer interfaces has been
enhanced like never before, however, these methods require a large amount of
data for training and thus require data augmentation of the limited data
available. Here, we use spiking neural networks (SNN) as data generators. It is
touted as the next-generation neu-ral network and is considered as one of the
algorithms oriented to general artifi-cial intelligence because it borrows the
neural information processing from bio-logical neurons. We use the SNN to
generate neural spike information that is bio-interpretable and conforms to the
intrinsic patterns in the original neural data. Ex-periments show that the
model can directly synthesize new spike trains, which in turn improves the
generalization ability of the BCI decoder. Both the input and output of the
spiking neural model are spike information, which is a brain-inspired
intelligence approach that can be better integrated with BCI in the future.


title:
On the link between conscious function and general intelligence in
  humans and machines

abstract:
  In popular media, there is often a connection drawn between the advent of
awareness in artificial agents and those same agents simultaneously achieving
human or superhuman level intelligence. In this work, we explore the validity
and potential application of this seemingly intuitive link between
consciousness and intelligence. We do so by examining the cognitive abilities
associated with three contemporary theories of conscious function: Global
Workspace Theory (GWT), Information Generation Theory (IGT), and Attention
Schema Theory (AST). We find that all three theories specifically relate
conscious function to some aspect of domain-general intelligence in humans.
With this insight, we turn to the field of Artificial Intelligence (AI) and
find that, while still far from demonstrating general intelligence, many
state-of-the-art deep learning methods have begun to incorporate key aspects of
each of the three functional theories. Given this apparent trend, we use the
motivating example of mental time travel in humans to propose ways in which
insights from each of the three theories may be combined into a unified model.
We believe that doing so can enable the development of artificial agents which
are not only more generally intelligent but are also consistent with multiple
current theories of conscious function.


title:
How does porosity heterogeneity affect the transport properties of
  multibore filtration membranes?

abstract:
  The prediction of pressure and flow distributions inside porous membranes is
important if the geometry deviates from single-bore tubular geometries. This
task remains challenging, especially when considering local porosity variations
caused by lumen- and shell-side membrane skins and macro- and micro-void
structures, all of them present in multibore membranes.
  This study analyzes pure water forward and reverse permeation and backwashing
phenomena for a polymeric multibore membrane with spatially-varying porosity
and permeability properties using computational fluid dynamics simulations. The
heterogeneity of porosity distribution is experimentally characterized by
scanning electron microscopy scans and reconstructed cuboids of X-ray
micro-computed tomography scans. The reconstructed cuboids are used to
determine porosity, pore size distribution, and intrinsic permeability in the
membrane's porous structure in all spatial directions. These position-dependent
properties are then applied to porous media flow simulations of the whole
membrane domain with different properties for separation layer, support
structure, and outside skin layer. Various cases mimicking the pure water
permeation, fouling, and backwashing behavior of the membrane are simulated and
compared to previously obtained MRI measurements.
  This work reveals (a) anisotropic permeability values and isoporosity in all
directions and (b) differing contributions of each lumen channel to the total
membrane performance, depending on the membrane-skin's properties. This study
encourages to pertain the quest of understanding the interaction of spatially
distributed membrane properties and the overall membrane module performance of
multibore membranes.


title: Exergetic Port-Hamiltonian Systems: Navier-Stokes-Fourier Fluid

abstract:
  The Exergetic Port-Hamiltonian Systems modeling language combines a graphical
syntax inspired by bond graphs with a port-Hamiltonian semantics akin to the
GENERIC formalism. The syntax enables the modular and hierarchical
specification of the composition pattern of lumped and distributed-parameter
models. The semantics reflects the first and second law of thermodynamics as
structural properties. Interconnected and hierarchically defined models of
multiphysical thermodynamic systems can thus be expressed in a formal language
accessible to humans and computers alike. We discuss a composed model of the
Navier-Stokes-Fourier fluid on a fixed spatial domain as an example of an open
distributed-parameter system. At the top level, the system comprises five
subsystems which model kinetic energy storage, internal energy storage, thermal
conduction, bulk viscosity, and shear viscosity.

categories: eess.SY cs.SY physics.comp-ph physics.flu-dyn


title:
SoK: Privacy Preserving Machine Learning using Functional Encryption:
  Opportunities and Challenges

abstract:
  With the advent of functional encryption, new possibilities for computation
on encrypted data have arisen. Functional Encryption enables data owners to
grant third-party access to perform specified computations without disclosing
their inputs. It also provides computation results in plain, unlike Fully
Homomorphic Encryption. The ubiquitousness of machine learning has led to the
collection of massive private data in the cloud computing environment. This
raises potential privacy issues and the need for more private and secure
computing solutions. Numerous efforts have been made in privacy-preserving
machine learning (PPML) to address security and privacy concerns. There are
approaches based on fully homomorphic encryption (FHE), secure multiparty
computation (SMC), and, more recently, functional encryption (FE). However,
FE-based PPML is still in its infancy and has not yet gotten much attention
compared to FHE-based PPML approaches. In this paper, we provide a
systematization of PPML works based on FE summarizing state-of-the-art in the
literature. We focus on Inner-product-FE and Quadratic-FE-based machine
learning models for the PPML applications. We analyze the performance and
usability of the available FE libraries and their applications to PPML. We also
discuss potential directions for FE-based PPML approaches. To the best of our
knowledge, this is the first work to systematize FE-based PPML approaches.


title: NeoRS: a neonatal resting state fMRI data preprocessing pipeline

abstract:
  Resting state fMRI (rsfMRI) has been shown to be a promising tool to study
intrinsic functional connectivity and assess its integrity in cerebral
development. In neonates, where fMRI is limited to few paradigms, rsfMRI was
shown to be a relevant tool to explore regional interactions of brain networks.
However, to identify the resting state networks, data needs to be carefully
processed. Because of the non-collaborative nature of the neonates, the
differences in brain size and the reversed contrast compared to adults,
neonates can't be processed with the existing adult pipelines. Therefore, we
developed NeoRS. The main processing steps include atlas registration, skull
tripping, segmentation, slice timing and head motion correction and confounds
regression. To address the specificity of neonatal brain imaging, particular
attention was given to registration including neonatal atlas type and
parameters, such as brain size variations, and contrast differences compared to
adults. Furthermore, head motion was scrutinized and optimized, as it is a
major issue when processing neonatal data. The pipeline includes visual quality
control assessment checkpoints. To assess its effectiveness, we used the data
from the Baby Connectome Project including 10 neonates. NeoRS was designed to
work on both multi-band and single-band acquisitions and is applicable on
smaller datasets. It also includes popular functional connectivity analysis
features such as seed based correlations. Language, default mode, dorsal
attention, visual, ventral attention, motor and fronto parietal networks were
evaluated. The different analyzed networks were in agreement with previously
published studies in the neonate. NeoRS is coded in Matlab, it is open-source
and available on https://github.com/venguix/NeoRS. NeoRS allows robust image
processing of the neonatal rsfMRI data that can be readily customized to
different datasets.


title:
Artificial Intelligence Software Structured to Simulate Human Working
  Memory, Mental Imagery, and Mental Continuity

abstract:
  This article presents an artificial intelligence (AI) architecture intended
to simulate the human working memory system as well as the manner in which it
is updated iteratively. It features several interconnected neural networks
designed to emulate the specialized modules of the cerebral cortex. These are
structured hierarchically and integrated into a global workspace. They are
capable of temporarily maintaining high-level patterns akin to the
psychological items maintained in working memory. This maintenance is made
possible by persistent neural activity in the form of two modalities: sustained
neural firing (resulting in a focus of attention) and synaptic potentiation
(resulting in a short-term store). This persistent activity is updated
iteratively resulting in incremental changes to the content of the working
memory system. As the content stored in working memory gradually evolves,
successive states overlap and are continuous with one another. The present
article will explore how this architecture can lead to gradual shift in the
distribution of coactive representations, ultimately leading to mental
continuity between processing states, and thus to human-like cognition.


title: On unsupervised projections and second order signals

abstract:
  Linear projections are widely used in the analysis of high-dimensional data.
In unsupervised settings where the data harbour latent classes/clusters, the
question of whether class discriminatory signals are retained under projection
is crucial. In the case of mean differences between classes, this question has
been well studied. However, in many contemporary applications, notably in
biomedicine, group differences at the level of covariance or graphical model
structure are important. Motivated by such applications, in this paper we ask
whether linear projections can preserve differences in second order structure
between latent groups. We focus on unsupervised projections, which can be
computed without knowledge of class labels. We discuss a simple theoretical
framework to study the behaviour of such projections which we use to inform an
analysis via quasi-exhaustive enumeration. This allows us to consider the
performance, over more than a hundred thousand sets of data-generating
population parameters, of two popular projections, namely random projections
(RP) and Principal Component Analysis (PCA). Across this broad range of
regimes, PCA turns out to be more effective at retaining second order signals
than RP and is often even competitive with supervised projection. We complement
these results with fully empirical experiments showing 0-1 loss using simulated
and real data. We study also the effect of projection dimension, drawing
attention to a bias-variance trade-off in this respect. Our results show that
PCA can indeed be a suitable first-step for unsupervised analysis, including in
cases where differential covariance or graphical model structure are of
interest.


title:
Diffusion of elastic waves in a continuum solid with a random array of
  pinned dislocations

abstract:
  The propagation of incoherent elastic energy in a three-dimensional solid due
to the scattering by many, randomly placed and oriented, pinned dislocation
segments, is considered in a continuum mechanics framework. The scattering
mechanism is that of an elastic string of length L that re-radiates as a
response to an incoming wave. The scatterers are thus not static but have their
own dynamics. A Bethe-Salpeter (BS) equation is established, and a
Ward-Takahashi Identity (WTI) is demonstrated. The BS equation is written as a
spectral problem that, using the WTI, is solved in the diffusive limit. To
leading order a diffusion behavior indeed results, and an explicit formula for
the diffusion coeffcient is obtained. It can be evaluated in an Independent
Scattering Approximation (ISA) in the absence of intrinsic damping. It depends
not only on the bare longitudinal and transverse wave velocities but also on
the renormalized velocities, as well as attenuation coeffcients, of the
coherent waves. The influence of the length scale given by L, and of the
resonant behavior for frequencies near the resonance frequency of the strings,
can be explicitly identified. A Kubo representation for the diffusion constant
can be identified. Previous generic results, obtained with an energy transfer
formalism, are recovered when the number of dislocations per unit volume is
small. This includes the equipartition of diffusive energy density which,
however, does not hold in general. The formalism bears a number of similarities
with the behavior of electromagnetic waves in a medium with a random
distribution of dielectric scatterers; the elastic interaction, however, is
momentum dependent.


title: Learning Object-Centered Autotelic Behaviors with Graph Neural Networks

abstract:
  Although humans live in an open-ended world and endlessly face new
challenges, they do not have to learn from scratch each time they face the next
one. Rather, they have access to a handful of previously learned skills, which
they rapidly adapt to new situations. In artificial intelligence, autotelic
agents, which are intrinsically motivated to represent and set their own goals,
exhibit promising skill adaptation capabilities. However, these capabilities
are highly constrained by their policy and goal space representations. In this
paper, we propose to investigate the impact of these representations on the
learning capabilities of autotelic agents. We study different implementations
of autotelic agents using four types of Graph Neural Networks policy
representations and two types of goal spaces, either geometric or
predicate-based. We show that combining object-centered architectures that are
expressive enough with semantic relational goals enables an efficient transfer
between skills and promotes behavioral diversity. We also release our
graph-based implementations to encourage further research in this direction.


title: Parabolic subgroups inside parabolic subgroups of Artin groups

abstract:
  We prove that a parabolic subgroup $P$ contained in another parabolic
subgroup $P'$ of an Artin group $A$ is a parabolic subgroup of $P'$. This
answers a question of Godelle which is not obvious despite appearances. In
order to achieve our result we construct a set-retraction $A \to P$ of the
inclusion map from a parabolic subgroup $P$ into $A$. This retraction was
implicitly constructed in a previous paper by Charney and the second author.


title:
Re-weighting dark matter in disc galaxies: a new general relativistic
  observational test

abstract:
  A recent analysis of data from the ESA Gaia mission demonstrated that the
kinematics of stars in the Milky Way can be modelled without invoking the
presence of dark matter whatsoever. Indeed, the higher-than-Keplerian
velocities observed in outer stars can be ascribed to the properties of the
general relativistic (GR) metric assumed to describe the Galaxy. Here we
generalize the concept, and derive the most general exact GR model for a
stationary axi-symmetric dynamically cold dust structure.
  We explicitly show how deviations from the commonly adopted Newtonian
dynamics are indeed manifest even at low velocities and low densities. We
provide for the first time a detailed description of the frequency shift
experienced by photons travelling from any emission site within an external
disc galaxy to the observer, relating the outcome of the shift measurement to
the gravitational properties of the galaxy. Finally, we devise a novel,
groundbreaking observational test potentially able to fully characterize the GR
metric under the minimal set of assumptions mentioned above. The proposed
experiment exploits the effects non-diagonal GR terms have on the frequency
shift of photons, ultimately providing a test to evaluate whether dark matter
is actually required by disc galaxy kinematics.