title:
Structural invariants and semantic fingerprints in the "ego network" of
  words

abstract:
  Well-established cognitive models coming from anthropology have shown that,
due to the cognitive constraints that limit our "bandwidth" for social
interactions, humans organize their social relations according to a regular
structure. In this work, we postulate that similar regularities can be found in
other cognitive processes, such as those involving language production. In
order to investigate this claim, we analyse a dataset containing tweets of a
heterogeneous group of Twitter users (regular users and professional writers).
Leveraging a methodology similar to the one used to uncover the
well-established social cognitive constraints, we find regularities at both the
structural and semantic level. At the former, we find that a concentric layered
structure (which we call ego network of words, in analogy to the ego network of
social relationships) very well captures how individuals organise the words
they use. The size of the layers in this structure regularly grows
(approximately 2-3 times with respect to the previous one) when moving
outwards, and the two penultimate external layers consistently account for
approximately 60% and 30% of the used words, irrespective of the number of the
total number of layers of the user. For the semantic analysis, each ring of
each ego network is described by a semantic profile, which captures the topics
associated with the words in the ring. We find that ring #1 has a special role
in the model. It is semantically the most dissimilar and the most diverse among
the rings. We also show that the topics that are important in the innermost
ring also have the characteristic of being predominant in each of the other
rings, as well as in the entire ego network. In this respect, ring #1 can be
seen as the semantic fingerprint of the ego network of words.


title: Decomposition of idempotent 2-cocycles

abstract:
  Let L be a finite Galois extension of K with Galois group G. We decompose any
idempotent 2-cocycle f using finite sequences of descending two-sided ideals of
the corresponding weak crossed product algebra A:= (L/k, G, f). We specialise
the results in case f is the corresponding idempotent 2-cocycle for some
semilinear map from G to a multiplicative monoid with minimum element.


title:
Finite-time Correlations Boost Large Voltage-Angle Fluctuations in
  Electric Power Grids

abstract:
  Decarbonization in the energy sector has been accompanied by an increased
penetration of new renewable energy sources in electric power systems. Such
sources differ from traditional productions in that, first, they induce larger,
undispatchable fluctuations in power generation and second, they lack inertia.
Therefore, substituting new renewables for traditional generation induces
stronger and more frequent disturbances and modifies the way disturbances
propagate across AC electric power grids. Recent measurements have indeed
reported long, non-Gaussian tails in the distribution of local grid-frequency
data. Large frequency deviations may induce grid instabilities, leading in
worst-case scenarios to cascading failures and large-scale blackouts. In this
manuscript, we investigate how correlated noise disturbances, characterized by
the cumulants of their distribution, propagate through meshed, high-voltage
power grids. We show that for a single source of fluctuations,
non-Gaussianities in the form of finite skewness and positive kurtosis of the
noise distribution propagate over the entire network when the noise correlation
time is larger than the network's intrinsic time scales, but that they vanish
over short distances if the noise fluctuates rapidly. We furthermore show that
a Berry-Esseen theorem leads to the vanishing of non-Gaussianities as the
number of uncorrelated noise sources increases. Our results show that the
persistence of non-Gaussian fluctuations of feed-in power have a global impact
on power-grid dynamics when they fluctuate over time scales larger than the
intrinsic time scales of the system, which, we argue, is the relevant regime in
real power grids. Our predictions are corroborated by numerical simulations on
realistic models of power grids.

categories: nlin.AO cond-mat.dis-nn cs.SY eess.SY physics.soc-ph


title:
SwitchHit: A Probabilistic, Complementarity-Based Switching System for
  Improved Visual Place Recognition in Changing Environments

abstract:
  Visual place recognition (VPR), a fundamental task in computer vision and
robotics, is the problem of identifying a place mainly based on visual
information. Viewpoint and appearance changes, such as due to weather and
seasonal variations, make this task challenging. Currently, there is no
universal VPR technique that can work in all types of environments, on a
variety of robotic platforms, and under a wide range of viewpoint and
appearance changes. Recent work has shown the potential of combining different
VPR methods intelligently by evaluating complementarity for some specific VPR
datasets to achieve better performance. This, however, requires ground truth
information (correct matches) which is not available when a robot is deployed
in a real-world scenario. Moreover, running multiple VPR techniques in parallel
may be prohibitive for resource-constrained embedded platforms. To overcome
these limitations, this paper presents a probabilistic complementarity based
switching VPR system, SwitchHit. Our proposed system consists of multiple VPR
techniques, however, it does not simply run all techniques at once, rather
predicts the probability of correct match for an incoming query image and
dynamically switches to another complementary technique if the probability of
correctly matching the query is below a certain threshold. This innovative use
of multiple VPR techniques allow our system to be more efficient and robust
than other combined VPR approaches employing brute force and running multiple
VPR techniques at once. Thus making it more suitable for resource constrained
embedded systems and achieving an overall superior performance from what any
individual VPR method in the system could have by achieved running
independently.


title:
Tiny Autoscalers for Tiny Workloads: Dynamic CPU Allocation for
  Serverless Functions

abstract:
  In serverless computing, applications are executed under lightweight
virtualization and isolation environments, such as containers or micro virtual
machines. Typically, their memory allocation is set by the user before
deployment. All other resources, such as CPU, are allocated by the provider
statically and proportionally to memory allocations. This contributes to either
under-utilization or throttling. The former significantly impacts the provider,
while the latter impacts the client. To solve this problem and accommodate both
clients and providers, a solution is dynamic CPU allocation achieved through
autoscaling. Autoscaling has been investigated for long-running applications
using history-based techniques and prediction. However, serverless applications
are short-running workloads, where such techniques are not well suited. In this
paper, we investigate tiny autoscalers and how dynamic CPU allocation
techniques perform for short-running serverless workloads. We experiment with
Kubernetes as the underlying platform and implement using its vertical pod
autoscaler several dynamic CPU rightsizing techniques. We compare these
techniques using state-of-the-art serverless workloads. Our experiments show
that dynamic CPU allocation for short-running serverless functions is feasible
and can be achieved with lightweight algorithms that offer good performance.


title: Emergence of local geometric laws of step flow in homoepitaxial growth

abstract:
  Below the roughening transition, crystal surfaces exhibit nanoscale line
defects, steps, that move by exchanging atoms with their environment. In
homoepitaxy, we analytically show how the motion of a step train in vacuum
under strong desorption can be approximately described by nonlinear laws that
depend on local geometric features such as the curvature of each step, as well
as suitably defined effective terrace widths. We assume that each step edge, a
free boundary, can be represented by a smooth curve in a fixed reference plane
for sufficiently long times. Besides surface diffusion and evaporation, the
processes under consideration include kinetic step-step interactions in slowly
varying geometries, material deposition on the surface from above, attachment
and detachment of atoms at steps, step edge diffusion, and step permeability.
Our methodology relies on boundary integral equations for the adatom fluxes
responsible for step flow. By applying asymptotics, which effectively treat the
diffusive term of the free boundary problem as a singular perturbation, we
describe an intimate connection of universal character between step kinetics
and local geometry.

categories: cond-mat.mtrl-sci math-ph math.AP math.MP


title: Continuous quantum clock with high precision and long recurrence time

abstract:
  Continuous clocks, i.e. the clocks that measure time in a continuous manner,
are regarded as an essential component of sensing technology. Precision and
recurrence time are two basic features of continuous clocks. In this paper, in
the framework of quantum estimation theory various models for continuous
quantum clocks are proposed, where all tools of quantum estimation theory are
employed to seek the characteristics of clocks with high precision and long
recurrence time. Then, in a resource-based approach, the performance of the
proposed models is compared. It is shown that quantum clocks based on $n$
two-qubits system not only can have better precision than quantum clocks based
on $2n$ one-qubit system but also support long recurrence time. Finally, it is
shown that while employing the number of $n$ entangled qubits improves the
precision of clocks by a factor of $1/\sqrt n $, it inevitably worsens the
recurrence time of the clock.


title:
Parameter estimation for WMTI-Watson model of white matter using
  encoder-decoder recurrent neural network

abstract:
  Biophysical modelling of the diffusion MRI signal provides estimates of
specific microstructural tissue properties. Although nonlinear optimization
such as non-linear least squares (NLLS) is the most widespread method for model
estimation, it suffers from local minima and high computational cost. Deep
Learning approaches are steadily replacing NL fitting, but come with the
limitation that the model needs to be retrained for each acquisition protocol
and noise level. The White Matter Tract Integrity (WMTI)-Watson model was
proposed as an implementation of the Standard Model of diffusion in white
matter that estimates model parameters from the diffusion and kurtosis tensors
(DKI). Here we proposed a deep learning approach based on the encoder-decoder
recurrent neural network (RNN) to increase the robustness and accelerate the
parameter estimation of WMTI-Watson. We use an embedding approach to render the
model insensitive to potential differences in distributions between training
data and experimental data. This RNN-based solver thus has the advantage of
being highly efficient in computation and more readily translatable to other
datasets, irrespective of acquisition protocol and underlying parameter
distributions as long as DKI was pre-computed from the data. In this study, we
evaluated the performance of NLLS, the RNN-based method and a multilayer
perceptron (MLP) on synthetic and in vivo datasets of rat and human brain. We
showed that the proposed RNN-based fitting approach had the advantage of highly
reduced computation time over NLLS (from hours to seconds), with similar
accuracy and precision but improved robustness, and superior translatability to
new datasets over MLP.

categories: physics.med-ph cs.LG physics.bio-ph q-bio.QM


title: Weighted inequalities involving Hardy and Copson operators

abstract:
  We characterize a four-weight inequality involving the Hardy operator and the
Copson operator. More precisely, given $p_1, p_2, q_1, q_2 \in (0, \infty)$, we
find necessary and sufficient conditions on nonnegative measurable functions
$u_1, u_2, v_1, v_2$ on $(0,\infty)$ for which there exists a positive constant
$c$ such that the inequality \begin{align*} &\bigg(\int_0^{\infty}
\bigg(\int_0^t f(s)^{p_2} v_2(s)^{p_2} ds \bigg)^{\frac{q_2}{p_2}} u_2(t)^{q_2}
dt \bigg)^{\frac{1}{q_2}} \notag \\ & \hspace{3cm} \leq c \bigg(\int_0^{\infty}
\bigg(\int_t^{\infty} f(s)^{p_1} v_1(s)^{p_1} ds \bigg)^{\frac{q_1}{p_1}}
u_1(t)^{q_1} dt \bigg)^{\frac{1}{q_1}} \end{align*} holds for every
non-negative measurable function $f$ on $(0, \infty)$. The proof is based on
discretizing and antidiscretizing techniques. The principal innovation consists
in development of a new method which carefully avoids duality techniques and
therefore enables us to obtain the characterization in previously unavailable
situations, solving thereby a long-standing open problem. We then apply the
characterization of the inequality to the establishing of criteria for
embeddings between weighted Copson spaces $\operatorname{Cop}_{p_1,q_1} (u_1,
v_1)$ and weighted Ces\`{a}ro spaces $\operatorname{Ces}_{p_2, q_2} (u_2,
v_2)$, and also between spaces $S^q(w)$ equipped with the norm $\|f\|_{S^q(w)}=
\bigg(\int_0^\infty [f^{**}(t)-f^*(t)]^q w(t)\,dt\bigg)^{{1}/{q}}$ and
classical Lorentz spaces of type $\Lambda$.


title:
Path sampling of recurrent neural networks by incorporating known
  physics

abstract:
  Recurrent neural networks have seen widespread use in modeling dynamical
systems in varied domains such as weather prediction, text prediction and
several others. Often one wishes to supplement the experimentally observed
dynamics with prior knowledge or intuition about the system. While the
recurrent nature of these networks allows them to model arbitrarily long
memories in the time series used in training, it makes it harder to impose
prior knowledge or intuition through generic constraints. In this work, we
present a path sampling approach based on principle of Maximum Caliber that
allows us to include generic thermodynamic or kinetic constraints into
recurrent neural networks. We show the method here for a widely used type of
recurrent neural network known as long short-term memory network in the context
of supplementing time series collected from different application domains.
These include classical Molecular Dynamics of a protein and Monte Carlo
simulations of an open quantum system continuously losing photons to the
environment and displaying Rabi oscillations. Our method can be easily
generalized to other generative artificial intelligence models and to generic
time series in different areas of physical and social sciences, where one
wishes to supplement limited data with intuition or theory based corrections.

categories: cond-mat.dis-nn cond-mat.soft cs.LG physics.bio-ph physics.comp-ph


title: Tachyonic cosmology with cubic contractions of the Riemann tensor

abstract:
  A new cosmological theory is proposed in the theoretical framework of
modified gravity theories which is based on a tachyonic field non-minimally
coupled with a specific topological invariant constructed with third order
contractions of the Riemann tensor. After proposing the action of the
cosmological model, the modified Friedmann relations and the Klein-Gordon
equations are obtained, describing the corresponding geometrical corrections to
the Einstein-Hilbert action. The physical features of the cosmological system
are investigated by adopting the dynamical system analysis in the case of an
exponential function for the geometrical coupling term. The investigation
revealed that the cosmological system can explain the current accelerated
expansion of the Universe and the matter dominated epoch, showing a high
compatibility to the recent history of our Universe for various values of the
coupling coefficients.


title: Modeling the kinematics of globular cluster systems

abstract:
  Globular clusters (GCs) are old massive star clusters that serve as "fossils"
of galaxy formation. The advent of Gaia observatory has enabled kinematics
studies of the Galactic GCs with great detail and revolutionized our
understanding of the connections between GC properties and galaxy assembly.
However, lack of kinematic measurements of extragalactic GCs limits the sample
size of GC systems that we can fully study observationally. In this work, we
present a model for GC formation and evolution, which includes positional and
kinematic information of individual GCs by assigning them to particles in the
Illustris TNG50-1 simulation based on the age and location. The model
predictions are consistent with many observed properties of GC systems,
including the distributions of position, systemic velocity, velocity
dispersion, anisotropy parameter, orbital actions, and metallicities. We also
analyze the properties of GCs from different origins. In the outer galaxy,
ex-situ clusters are more dominant than the clusters formed in-situ. This leads
to the GC metallicities decreasing outwards due to the increasing abundance of
accreted, metal-poor clusters. We also find the ex-situ GCs to have greater
velocity dispersions and orbital actions, in agreement with their accretion
origin.


title: Dual Embodied-Symbolic Concept Representations for Deep Learning

abstract:
  Motivated by recent findings from cognitive neural science, we advocate the
use of a dual-level model for concept representations: the embodied level
consists of concept-oriented feature representations, and the symbolic level
consists of concept graphs. Embodied concept representations are modality
specific and exist in the form of feature vectors in a feature space. Symbolic
concept representations, on the other hand, are amodal and language specific,
and exist in the form of word / knowledge-graph embeddings in a concept /
knowledge space. The human conceptual system comprises both embodied
representations and symbolic representations, which typically interact to drive
conceptual processing. As such, we further advocate the use of dual
embodied-symbolic concept representations for deep learning. To demonstrate
their usage and value, we discuss two important use cases: embodied-symbolic
knowledge distillation for few-shot class incremental learning, and
embodied-symbolic fused representation for image-text matching. Dual
embodied-symbolic concept representations are the foundation for deep learning
and symbolic AI integration. We discuss two important examples of such
integration: scene graph generation with knowledge graph bridging, and
multimodal knowledge graphs.


title: Beyond Ans\"atze: Learning Quantum Circuits as Unitary Operators

abstract:
  This paper explores the advantages of optimizing quantum circuits on $N$
wires as operators in the unitary group $U(2^N)$. We run gradient-based
optimization in the Lie algebra $\mathfrak u(2^N)$ and use the exponential map
to parametrize unitary matrices. We argue that $U(2^N)$ is not only more
general than the search space induced by an ansatz, but in ways easier to work
with on classical computers. The resulting approach is quick, ansatz-free and
provides an upper bound on performance over all ans\"atze on $N$ wires.


title:
Humanoid Path Planning over Rough Terrain using Traversability
  Assessment

abstract:
  We present a planning framework designed for humanoid navigation over
challenging terrain. This framework is designed to plan a traversable, smooth,
and collision-free path using a 2.5D height map. The planner is comprised of
two stages. The first stage consists of an A* planner which reasons about
traversability using terrain features. A novel cost function is presented which
encodes the bipedal gait directly into the graph structure, enabling natural
paths that are robust to small gaps in traversability. The second stage is an
optimization framework which smooths the path while further improving
traversability. The planner is tested on a variety of terrains in simulation
and is combined with a footstep planner and balance controller to create an
integrated navigation framework, which is demonstrated on a DRC Boston Dynamics
Atlas robot.


title:
Active particles driven by competing spatially dependent self-propulsion
  and external force

abstract:
  We investigate how the competing presence of a nonuniform motility landscape
and an external confining field affects the properties of active particles. We
employ the active Ornstein-Uhlenbeck particle (AOUP) model with a periodic swim
velocity profile to derive analytical approximations for the steady-state
probability distribution of position and velocity, encompassing both the
Unified Colored Noise Approximation and the theory of potential-free active
particles with spatially dependent swim velocity recently developed. We test
the theory by confining an active particle in a harmonic trap, which gives rise
to interesting properties, such as a transition from a unimodal to a bimodal
(and, eventually multimodal) spatial density, induced by decreasing the spatial
period of the self propulsion. Correspondingly, the velocity distribution shows
pronounced deviations from the Gaussian shape, even displaying a bimodal
profile in the high-motility regions. Our results can be confirmed by
real-space experiments on active colloidal Janus particles in external fields.


title:
Meson masses and decay constants in holographic QCD consistent with ChPT
  and HQET

abstract:
  We focus on the chiral and heavy quark mass expansion of mesons masses and
decay constants. We propose a light-front QCD formalism for the evaluation of
these quantities, consistent with chiral perturbation theory and heavy quark
effective theory.


title: On the entropy numbers and the Kolmogorov widths

abstract:
  Direct estimates between linear or nonlinear Kolmogorov widths and entropy
numbers are presented. These estimates are derived using the recently
introduced Lipschitz widths. Applications for m-term approximation are
obtained.


title:
Multidimensional Fractional Wavelet Transforms and Uncertainty
  Principles

abstract:
  In this paper, we have given a new definition of continuous fractional
wavelet transform in $\mathbb{R}^N$, namely the multidimensional fractional
wavelet transform (MFrWT) and studied some of the basic properties along with
the inner product relation and the reconstruction formula. We have also shown
that the range of the proposed transform is a reproducing kernel Hilbert space
and obtain the associated kernel. We have obtained the uncertainty principle
like Heisenberg's uncertainty principle, logarithmic uncertainty principle and
local uncertainty principle of the multidimensional fractional Fourier
transform (MFrFT). Based on these uncertainty principles of the MFrFT we have
obtained the corresponding uncertainty principles i.e., Heisenberg's,
logarithmic and local uncertainty principles for the proposed MFrWT.


title:
The eccentric millisecond pulsar, PSR J0955$-$6150 I: Pulse profile
  analysis, mass measurements and constraints on binary evolution

abstract:
  PSR J0955$-$6150 is a member of a class of eccentric MSP+He WD systems
(eMSPs), whose binary evolution is poorly understood and believed to be
different to that of traditional MSP+He WD systems. Measuring the masses of the
stars in this system is important for testing hypotheses for the formation of
eMSPs. We have carried out observations of this pulsar with the Parkes and
MeerKAT radio telescopes. Our observations reveal a strong frequency evolution
of this pulsar's intensity, with a spectral index ($\alpha$) of $-3.13(2)$. The
sensitivity of MeerKAT has resulted in a $>10$-fold improvement in the timing
precision compared to older Parkes observations. Combined with the 8-year
timing baseline, it has allowed precise measurements of a proper motion and
three orbital "post-Keplerian" parameters: the rate of advance of periastron,
$\dot{\omega} = 0.00152(1) \, {\rm deg} \, yr^{-1}$ and the orthometric Shapiro
delay parameters, $h_3 = 0.89(7) \, \mu$s and $\varsigma = 0.88(2)$. Assuming
general relativity, we obtain $M_{p} = 1.71(2) \, M_{\odot}$ for the mass of
the pulsar and $M_{c} = 0.254(2) \, M_{\odot}$ for the mass of the companion;
the orbital inclination is 83.2(4) degrees. We find that the spin axis has a
misalignment relative to the orbital angular momentum of $> 4.8$ degrees at 99%
CI. While the value of $M_{\rm p}$ falls within the wide range observed in
eMSPs, $M_{\rm c}$ is significantly smaller than expected, allowing several
formation hypotheses being ruled out. $M_{\rm c}$ is also significantly
different from the expected value for an ideal low mass X-ray binary evolution
scenario. The putative misalignment between the spin axis of the pulsar and the
orbital angular momentum suggests that the unknown process that created the
orbital eccentricity of the binary was also capable of changing its orbital
orientation, an important evidence for understanding the origin of eMSPs.


title:
A Method Based on Deep Learning for the Detection and Characterization
  of Cybersecurity Incidents in Internet of Things Devices

abstract:
  Given the increased growing of Internet of Things networks and their presence
in critical aspects of human activities, the security of devices connected to
these networks becomes critical. Machine Learning approaches are becoming
prominent as enablers for security solutions in computer networks due to its
capacity to process traffic information in order to detect abnormal patterns
which might represent attacks targeting infrastructures. In this paper, we
propose to leverage Convolutional and Recurrent Neural Networks, two artifacts
that have been successfully used in contexts such as image processing for
pattern recognition, for the development of a security solution to be used in
the context of Internet of Things. Our results show that this approach, when
evaluated with a state-of-the-art data set, achieves around 99% of accuracy in
the binary classification of attacks (i.e. normal traffic vs attack traffic)
and 96% for multiclass classification (recognition of different types of
attacks) accuracy. These results outperform proposals available in literature,
showing a promising landscape for developing security solutions for IoT
infrastructures.


title: Models for digitally contact-traced epidemics

abstract:
  Contacts between people are the absolute drivers of contagious respiratory
infections. For this reason, limiting and tracking contacts is a key strategy
for the control of the COVID-19 epidemic. Digital contact tracing has been
proposed as an automated solution to scale up traditional contact tracing.
However, the required penetration of contact tracing apps within a population
to achieve a desired target in the control of the epidemic is currently under
discussion within the research community. In order to understand the effects of
digital contact tracing, several mathematical models have been proposed. In
this article, we survey the main ones and we propose a compartmental SEIR model
with which it is possible, differently from the models in the related
literature, to derive closed-form conditions regarding the control of the
epidemic as a function of the contact tracing apps penetration and the testing
efficiency. Closed-form conditions are crucial for the understandability of
models, and thus for decision makers (including digital contact tracing
designers) to correctly assess the dependencies within the epidemic. With our
model, we find that digital contact tracing alone can rarely tame an epidemic:
for unrestrained COVID-19, this would require a testing turnaround of around 1
day and app uptake above 80% of the population, which are very difficult to
achieve in practice. However, digital contact tracing can still be effective if
complemented with other mitigation strategies, such as social distancing and
mask-wearing.


title: The Transfer Student Experience: It's A Lot Like Buying a Used Car

abstract:
  The experience transfer students encounter as they navigate their journeys
from community college to university is similar to that of buying a used car.
We demonstrate this by showing how the information asymmetry in the market for
used cars also occurs in the market for transfer students, producing
inefficient markets in both cases, thereby increasing the chances of adverse
selection. We diagnose the underlying conditions that produce transfer
inefficiencies, identifying them as a structural inequity within the system of
higher education. Finally, recommendations for alleviating information
asymmetry in transfer processes, that would lead to better outcomes for
transfer students, are provided.


title:
Learning Intermediate Representations using Graph Neural Networks for
  NUMA and Prefetchers Optimization

abstract:
  There is a large space of NUMA and hardware prefetcher configurations that
can significantly impact the performance of an application. Previous studies
have demonstrated how a model can automatically select configurations based on
the dynamic properties of the code to achieve speedups. This paper demonstrates
how the static Intermediate Representation (IR) of the code can guide
NUMA/prefetcher optimizations without the prohibitive cost of performance
profiling. We propose a method to create a comprehensive dataset that includes
a diverse set of intermediate representations along with optimum
configurations. We then apply a graph neural network model in order to validate
this dataset. We show that our static intermediate representation based model
achieves 80% of the performance gains provided by expensive dynamic performance
profiling based strategies. We further develop a hybrid model that uses both
static and dynamic information. Our hybrid model achieves the same gains as the
dynamic models but at a reduced cost by only profiling 30% of the programs.


title: Photometric and Morphological Analysis of Fornax Galaxies through S-PLUS

abstract:
  The photometric and morphological analysis of galaxies in clusters provides
invaluable information regarding the evolutionary stage of the cluster itself.
In addition, it helps to understand how the environment affects the properties
of the galaxies and, as a consequence, their evolutionary path. In this
contribution we present the first steps on the photometric and morphological
analysis of galaxies in the Fornax cluster using S-PLUS data. We expect that
the S-PLUS novel filter set and wide field coverage allow us to obtain new
information about Fornax and its galaxy population.


title: Towards a Common Speech Analysis Engine

abstract:
  Recent innovations in self-supervised representation learning have led to
remarkable advances in natural language processing. That said, in the speech
processing domain, self-supervised representation learning-based systems are
not yet considered state-of-the-art. We propose leveraging recent advances in
self-supervised-based speech processing to create a common speech analysis
engine. Such an engine should be able to handle multiple speech processing
tasks, using a single architecture, to obtain state-of-the-art accuracy. The
engine must also enable support for new tasks with small training datasets.
Beyond that, a common engine should be capable of supporting distributed
training with client in-house private data. We present the architecture for a
common speech analysis engine based on the HuBERT self-supervised speech
representation. Based on experiments, we report our results for language
identification and emotion recognition on the standard evaluations NIST-LRE 07
and IEMOCAP. Our results surpass the state-of-the-art performance reported so
far on these tasks. We also analyzed our engine on the emotion recognition task
using reduced amounts of training data and show how to achieve improved
results.


title:
Side-effects of Learning from Low Dimensional Data Embedded in an
  Euclidean Space

abstract:
  The low dimensional manifold hypothesis posits that the data found in many
applications, such as those involving natural images, lie (approximately) on
low dimensional manifolds embedded in a high dimensional Euclidean space. In
this setting, a typical neural network defines a function that takes a finite
number of vectors in the embedding space as input. However, one often needs to
consider evaluating the optimized network at points outside the training
distribution. This paper considers the case in which the training data is
distributed in a linear subspace of $\mathbb R^d$. We derive estimates on the
variation of the learning function, defined by a neural network, in the
direction transversal to the subspace. We study the potential regularization
effects associated with the network's depth and noise in the codimension of the
data manifold. We also present additional side effects in training due to the
presence of noise.


title: Forcing constellations of Cicho\'n's diagram by using the Tukey order

abstract:
  We use known finite support iteration techniques to present various examples
of models where several cardinal characteristics of Cicho\'n's diagram are
pairwise different. We show some simple examples forcing the left-hand side of
Cicho\'n's diagram, and present the technique of restriction to models to force
Cicho\'n's maximum (original from Goldstern, Kellner, Shelah, and the second
author). We focus on how the values forced in all the constellations are
obtained via the Tukey order.


title:
Dimension Reduction of Two-Dimensional Persistence via Distance
  Deformations

abstract:
  This article grew out of the application part of my Master's thesis at the
Faculty of Mathematics and Information Science at Ruprecht-Karls-Universit\"at
Heidelberg under the supervision of PD Dr. Andreas Ott. In the context of time
series analyses of RNA virus datasets with persistent homology, this article
introduces a new method for reducing two-dimensional persistence to
one-dimensional persistence by transforming time information into distances.


title:
Driven Superconducting Vortex Dynamics in Systems with Two-Fold
  Anisotropy in the Presence of Pinning

abstract:
  We examine the dynamics of superconducting vortices with two-fold anisotropic
interaction potentials driven over random pinning and compare the behavior
under drives applied parallel and perpendicular to the anisotropy direction.
The number of topological defects reaches a maximum near depinning and then
drops with increasing driving force as the vortices form one-dimensional
chains. This coincides with a transition from a pinned nematic to a moving
smectic aligned with the soft direction of the anisotropy. The system is
generally more ordered when the drive is applied along the soft direction of
the anisotropy, while for driving along the hard direction, there is a critical
value of the anisotropy above which the system remains aligned with the soft
direction. We also observe hysteresis in the dynamics, with one-dimensional
aligned chains persisting during a decreasing drive sweep to drives below the
threshold for chain formation during the increasing drive sweep. More
anisotropic systems have a greater amount of structural disorder in the moving
state. For lower anisotropy, the system forms a moving smectic-A state, while
at higher anisotropy, a moving nematic state appears instead.


title:
The resilience of the multirelational structure of geopolitical treaties
  is critically linked to past colonial world order and offshore fiscal havens

abstract:
  The governance of the political and economic world order builds on a complex
architecture of international treaties at various geographical scales. In a
historical phase of high institutional turbulence, assessing the stability of
such architecture with respect to the unilateral defection of single countries
and to the breakdown of single treaties is important. We carry out this
analysis on the whole global architecture and find that the countries with the
highest disruption potential are not major world powers (with the exception of
Germany and France) but mostly medium-small and micro-countries. Political
stability is highly dependent on many former colonial overseas territories that
are today part of the global network of fiscal havens, as well as on emerging
economies, mostly from South-East Asia. Economic stability depends on medium
sized European and African countries. However, single global treaties have
surprisingly less disruptive potential, with the major exception of the WTO.
These apparently counter-intuitive results highlight the importance to a
nonlinear approach to international relations where the complex multilayered
architecture of global governance is analyzed using advanced network science
techniques. Our results suggest that the potential fragility of the world order
seem to be much more directly related to global inequality and fiscal injustice
than it is commonly believed, and that the legacy of the colonial world order
is still very strong in the current international relations scenario. In
particular, vested interests related to tax avoidance seem to have a structural
role in the political architecture of global governance


title: Perturbations in Non-Flat Cosmology for $f(T)$ gravity

abstract:
  The study of cosmological perturbation theory in $f(T)$ gravity is a topic of
great interest in teleparallel gravity since this is one of the simplest
generalizations of the theory that modifies the teleparallel equivalent of
general relativity. In this work, we explore the possibility of a non-flat FLRW
background solution and perform perturbations for positively as well as
negatively curved spatial geometries, together with a comparison to the flat
case. We determine the generalized behaviour of the perturbative modes for this
non-flat FLRW setting for arbitrary $f(T)$ models, when the most general
homogeneous and isotropic background tetrads are used. We also identify
propagating modes in this setup, and relate this with the case of a flat
cosmology.


title:
High order geometric methods with splines: an analysis of discrete
  Hodge--star operators

abstract:
  A new kind of spline geometric method approach is presented. Its main
ingredient is the use of well established spline spaces forming a discrete de
Rham complex to construct a primal sequence $\{X^k_h\}^n_{k=0}$, starting from
splines of degree $p$, and a dual sequence $\{\tilde{X}^k_h\}_{k=0}^n$,
starting from splines of degree $p-1$. By imposing homogeneous boundary
conditions to the spaces of the primal sequence, the two sequences can be
isomorphically mapped into one another. Within this setup, many familiar second
order partial differential equations can be finally accommodated by explicitly
constructing appropriate discrete versions of constitutive relations, called
Hodge--star operators. Several alternatives based on both global and local
projection operators between spline spaces will be proposed. The appeal of the
approach with respect to similar published methods is twofold: firstly, it
exhibits high order convergence. Secondly, it does not rely on the geometric
realization of any (topologically) dual mesh. Several numerical examples in
various space dimensions will be employed to validate the central ideas of the
proposed approach and compare its features with the standard Galerkin approach
in Isogeometric Analysis.


title: Revealing the Cosmic History with Gravitational Waves

abstract:
  The characteristics of the cosmic microwave background provide circumstantial
evidence that the hot radiation-dominated epoch in the early universe was
preceded by a period of inflationary expansion. Here, we show how a measurement
of the stochastic gravitational wave background can reveal the cosmic history
and the physical conditions during inflation, subsequent pre- and reheating,
and the beginning of the hot big bang era. This is exemplified with a
particularly well-motivated and predictive minimal extension of the Standard
Model which is known to provide a complete model for particle physics -- up to
the Planck scale, and for cosmology -- back to inflation.


title:
Descriptors for mechanical strength and slip-induced crack-blunting in
  refractory ceramics

abstract:
  Understanding the competition between brittleness and plasticity in
refractory ceramics is of importance for aiding design of hard materials with
enhanced fracture resistance. We carry out ab initio and classical molecular
dynamics (AIMD and CMD) investigations at temperatures between 300 and 1200 K
to identify atomic-scale mechanisms responsible for brittle fracture vs
slip-induced crack-blunting in Ti-N ceramics. AIMD simulations of
single-crystal and notched TiN lattices (1100 atoms) subject to tensile and
shear deformation serve to verify predictions of mechanical properties
separately obtained by CMD. Benchmarked by AIMD results, CMD is thus
confidently used to probe the mechanical response of large (40000 atoms)
notched TiN and TiNx models under mode-I tension. Although crack growth occurs
in most cases, CMD simulations reveal that typically-brittle TiN and TiNx
ceramics can - for comparable rates of accumulation of tensile and shear stress
around a flaw - prevent fracture via nucleation and emission of dislocations
from the notch tip. Furthermore, we identify descriptors based on properties
calculated for ideal single-crystals which reproduce trends in mechanical
behavior of flawed lattices. Specifically: (i) the probability of notched
ceramics to resist fracture via slip-induced plasticity exhibits linear
relationship with ideal tensile-to-shear strength ratios (Iplasticity), (ii) at
parity of Iplasticity values, the effective strength (fracture stress) of
defective systems ranks according to the tensile strength of corresponding
single-crystal phases. The descriptors proposed in this work pave the way for
high-throughput screening of ceramics that may combine high strength to
superior fracture resistance at room and elevated temperature.


title:
Prospects for distinguishing dynamical tides in inspiralling binary
  neutron stars with third generation gravitational-wave detectors

abstract:
  Tidal effects in gravitational-wave (GW) observations from binary neutron
star mergers have the potential to probe ultra-dense matter and shed light on
the unknown nuclear equation of state of neutron stars. Tidal effects in
inspiralling neutron star binaries become relevant at GW frequencies of a few
hundred Hz and require detectors with exquisite high-frequency sensitivity.
Third generation GW detectors such as the Einstein Telescope or Cosmic Explorer
will be particularly sensitive in this high-frequency regime, allowing us to
probe neutron star tides beyond the adiabatic approximation. Here we assess
whether dynamical tides can be measured from a neutron star inspiral. We find
that the measurability of dynamical tides depends strongly on the neutron star
mass and equation of state. For a semi-realistic population of 10,000
inspiralling binary neutron stars, we conservatively estimate that on average
$\mathcal{O}(50)$ binaries will have measurable dynamical tides. As dynamical
tides are characterised not only by the star's tidal deformability but also by
its fundamental ($f$-) mode frequency, they present a possibility of probing
higher-order tidal effects and test consistency with quasi-universal relations.
For a GW170817-like signal in a third generation detector network, we find that
the stars' $f$-mode frequencies can be measured to within a few hundred Hz.


title:
A unified 3D framework for Organs at Risk Localization and Segmentation
  for Radiation Therapy Planning

abstract:
  Automatic localization and segmentation of organs-at-risk (OAR) in CT are
essential pre-processing steps in medical image analysis tasks, such as
radiation therapy planning. For instance, the segmentation of OAR surrounding
tumors enables the maximization of radiation to the tumor area without
compromising the healthy tissues. However, the current medical workflow
requires manual delineation of OAR, which is prone to errors and is
annotator-dependent. In this work, we aim to introduce a unified 3D pipeline
for OAR localization-segmentation rather than novel localization or
segmentation architectures. To the best of our knowledge, our proposed
framework fully enables the exploitation of 3D context information inherent in
medical imaging. In the first step, a 3D multi-variate regression network
predicts organs' centroids and bounding boxes. Secondly, 3D organ-specific
segmentation networks are leveraged to generate a multi-organ segmentation map.
Our method achieved an overall Dice score of $0.9260\pm 0.18 \%$ on the
VISCERAL dataset containing CT scans with varying fields of view and multiple
organs.


title: Three new high-mass X-ray binaries in the Large Magellanic Cloud

abstract:
  The Magellanic Clouds host a large population of high-mass X-ray binary
(HMXB) systems, but although the Large Magellanic Cloud (LMC) is an order of
magnitude more massive than the Small Magellanic Cloud, significantly fewer
HMXBs are known. We conducted a search for new HMXBs in XMM-Newton
observations, which we performed to investigate supernova remnant candidates in
the supergiant shells LMC5 and LMC7. The three observed fields are located in
regions, which were little explored in X-rays before. We analysed the
XMM-Newton data to look for sources with hard X-ray spectrum and counterparts
with optical colours and brightness typical for HMXBs. We report the discovery
of three new Be/X-ray binaries, two of them showing pulsations in their X-ray
flux. With a luminosity of 6.5e34 erg/s, XMMU J045315.1-693242 in LMC7 was
relatively X-ray faint. The long-term OGLE I-band light curve of the V = 15.5
mag counterpart suggests a 49.6 day or 24.8 day orbital period for the binary
system. XMMU J045736.9-692727, also located in LMC7 was brighter with a
luminosity of 5.6e35 erg/s and hard spectrum with a power-law photon index of
0.63. The X-ray flux revealed clear pulsations with a period of 317.7 s. We
obtained optical high resolution spectra from the V = 14.2 mag counterpart
using the SALT-HRS spectrograph. Halpha and Hbeta were observed in emission
with complex line profiles and equivalent widths of -8.0 A and -1.3 A,
respectively. The I-band light curve obtained from OGLE shows a series of four
strong outbursts followed by a sudden drop in brightness by more than 1 mag
within 73-165 days and a recovery to the level before the outbursts. RX
J0524.2-6620, previously classified as X-ray binary candidate, is located at
the eastern part of LMC5. We report the discovery of 360.7 s pulsations.
(abridged)


title:
On the conjectures of Vojta and Campana over function fields with
  explicit exceptional sets

abstract:
  We prove new cases of Vojta's conjectures for surfaces in the context of
function fields, with truncation equal to one and providing an effective
explicit description of the exceptional set. We also prove a general and
explicit result towards Campana's conjecture over complex function fields of
curves. Our methods rely on a local study of $\omega$-integral varieties.


title:
Convergence for the fractional $p$-Laplacian and its corresponding
  extended Nirenberg problem

abstract:
  The main objective of this paper is to establish the convergence for the
fractional $p$-Laplacian of nonnegative sequence of functions with $p>2$.
Further, we show the blow-up phenomena for solutions to the extended Nirenberg
problem modeled by fractional $p$-Laplacian with the prescribed negative
functions.


title:
A Neural Ordinary Differential Equation Model for Visualizing Deep
  Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation

abstract:
  Purpose: To develop a neural ordinary differential equation (ODE) model for
visualizing deep neural network (DNN) behavior during multi-parametric MRI
(mp-MRI) based glioma segmentation as a method to enhance deep learning
explainability. Methods: By hypothesizing that deep feature extraction can be
modeled as a spatiotemporally continuous process, we designed a novel deep
learning model, neural ODE, in which deep feature extraction was governed by an
ODE without explicit expression. The dynamics of 1) MR images after
interactions with DNN and 2) segmentation formation can be visualized after
solving ODE. An accumulative contribution curve (ACC) was designed to
quantitatively evaluate the utilization of each MRI by DNN towards the final
segmentation results. The proposed neural ODE model was demonstrated using 369
glioma patients with a 4-modality mp-MRI protocol: T1, contrast-enhanced T1
(T1-Ce), T2, and FLAIR. Three neural ODE models were trained to segment
enhancing tumor (ET), tumor core (TC), and whole tumor (WT). The key MR
modalities with significant utilization by DNN were identified based on ACC
analysis. Segmentation results by DNN using only the key MR modalities were
compared to the ones using all 4 MR modalities. Results: All neural ODE models
successfully illustrated image dynamics as expected. ACC analysis identified
T1-Ce as the only key modality in ET and TC segmentations, while both FLAIR and
T2 were key modalities in WT segmentation. Compared to the U-Net results using
all 4 MR modalities, Dice coefficient of ET (0.784->0.775), TC (0.760->0.758),
and WT (0.841->0.837) using the key modalities only had minimal differences
without significance. Conclusion: The neural ODE model offers a new tool for
optimizing the deep learning model inputs with enhanced explainability. The
presented methodology can be generalized to other medical image-related deep
learning applications.


title: Full RGB Just Noticeable Difference (JND) Modelling

abstract:
  Just Noticeable Difference (JND) has many applications in multimedia signal
processing, especially for visual data processing up to date. It's generally
defined as the minimum visual content changes that the human can perspective,
which has been studied for decades. However, most of the existing methods only
focus on the luminance component of JND modelling and simply regard chrominance
components as scaled versions of luminance. In this paper, we propose a JND
model to generate the JND by taking the characteristics of full RGB channels
into account, termed as the RGB-JND. To this end, an RGB-JND-NET is proposed,
where the visual content in full RGB channels is used to extract features for
JND generation. To supervise the JND generation, an adaptive image quality
assessment combination (AIC) is developed. Besides, the RDB-JND-NET also takes
the visual attention into account by automatically mining the underlying
relationship between visual attention and the JND, which is further used to
constrain the JND spatial distribution. To the best of our knowledge, this is
the first work on careful investigation of JND modelling for full-color space.
Experimental results demonstrate that the RGB-JND-NET model outperforms the
relevant state-of-the-art JND models. Besides, the JND of the red and blue
channels are larger than that of the green one according to the experimental
results of the proposed model, which demonstrates that more changes can be
tolerated in the red and blue channels, in line with the well-known fact that
the human visual system is more sensitive to the green channel in comparison
with the red and blue ones.


title: Traces for Hilbert Complexes

abstract:
  We study a new notion of trace operators and trace spaces for abstract
Hilbert complexes. We introduce trace spaces as quotient spaces/annihilators.
We characterize the kernels and images of the related trace operators and
discuss duality relationships between trace spaces. We elaborate that many
properties of the classical boundary traces associated with the Euclidean de
Rham complex on bounded Lipschitz domains are rooted in the general structure
of Hilbert complexes. We arrive at abstract trace Hilbert complexes that can be
formulated using quotient spaces/annihilators. We show that, if a Hilbert
complex admits stable "regular decompositions" with compact lifting operators,
then the associated trace Hilbert complex is Fredholm. Incarnations of abstract
concepts and results in the concrete case of the de Rham complex in
three-dimensional Euclidean space will be discussed throughout.


title:
Asymmetry-Based Quantum Backaction Suppression in Quadratic
  Optomechanics

abstract:
  As the field of optomechanics advances, quadratic dispersive coupling (QDC)
promise an increasingly feasible class of qualitatively new functionality.
However, the leading QDC geometries also generate linear dissipative coupling,
and an associated quantum radiation force noise that is detrimental to QDC
applications. Here, we propose a simple modification that dramatically reduces
this noise without altering the QDC strength. We identify optimal regimes of
operation, and discuss advantages within the examples of optical levitation and
nondestructive phonon measurement.


title:
RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting
  Cooperative Nonlinear Interaction

abstract:
  The spatial resolution of magnetic resonance imaging (MRI) is fundamentally
limited by the width of Lorentzian point spread functions (PSF) associated with
the exponential decay rate of transverse magnetization (1/T2*). Here we show a
different contrast mechanism in MRI by establishing RASER (Radio-frequency
Amplification by Stimulated Emission of Radiation) in imaged media. RASER
imaging bursts emerge out of noise and without applying (Radio Frequency) RF
pulses when placing spins with sufficient population inversion in a weak
magnetic field gradient. A small difference in initial population inversion
density creates a stronger image contrast than conventional MRI. This contrast
is based on the cooperative nonlinear interaction between all slices. On the
other hand, the cooperative nonlinear interaction gives rise to imaging
artifacts, such as amplitude distortions and side lobes outside of the imaging
domain. Both the contrast and the artifacts are demonstrated experimentally and
predicted by simulations based on a proposed theory. This theory of RASER MRI
is strongly connected to many other distinct fields related to synergetics and
non-linear dynamics.


title:
Transformer Grammars: Augmenting Transformer Language Models with
  Syntactic Inductive Biases at Scale

abstract:
  Transformer language models that are trained on vast amounts of data have
achieved remarkable success at various NLP benchmarks. Intriguingly, this
success is achieved by models that lack an explicit modeling of hierarchical
syntactic structures, which were hypothesized by decades of linguistic research
to be necessary for good generalization. This naturally leaves a question: to
what extent can we further improve the performance of Transformer language
models, through an inductive bias that encourages the model to explain the data
through the lens of recursive syntactic compositions? Although the benefits of
modeling recursive syntax have been shown at the small data and model scales,
it remains an open question whether -- and to what extent -- a similar design
principle is still beneficial in the case of powerful Transformer language
models that work well at scale. To answer these questions, we introduce
Transformer Grammars -- a novel class of Transformer language models that
combine: (i) the expressive power, scalability, and strong performance of
Transformers, and (ii) recursive syntactic compositions, which here are
implemented through a special attention mask. We find that Transformer Grammars
outperform various strong baselines on multiple syntax-sensitive language
modeling evaluation metrics, in addition to sentence-level language modeling
perplexity. Nevertheless, we find that the recursive syntactic composition
bottleneck harms perplexity on document-level modeling, providing evidence that
a different kind of memory mechanism -- that works independently of syntactic
structures -- plays an important role in the processing of long-form text.


title: Decoherence and quantum steering of accelerated qubit-qutrit system

abstract:
  The bidirectional steerability between different-size subsystems is discussed
for a single parameter accelerated qubit-qutrit system. The decoherence due to
the mixing and acceleration parameters is investigated, where for the total
system and the qutrit, it increases as the mixing parameter increases, while it
decreases for the qubit. The non-classical correlations are quantified by using
the local quantum uncertainty, where it increases at large values of the
acceleration parameter. The possibility that each subsystem steers each other
is studied, where the behavior of the steering inequality predicts that the
qubit has a large ability to steer the qutrit. The degree of steerability
decays gradually when the qubit is accelerated. However, it decays suddenly
when the qutrit or both subsystems are accelerated. The degree of steerability
is shown for the qutrit/qubit vanishes at small/large values of the
acceleration. The difference between the degrees of steerability depends on the
initial state settings and the size of the accelerated subsystem.


title: Efficient Simulation of $p$-Tempered $\alpha$-Stable OU Processes

abstract:
  We develop efficient methods for simulating processes of Ornstein-Uhlenbeck
type related to the class of $p$-tempered $\alpha$-stable ($\ts$)
distributions. Our results hold for both the univariate and multivariate cases
and we consider both the case where the $\ts$ distribution is the stationary
law and where it is the distribution of the background driving L\'evy process
(BDLP). In the latter case, we also derive an explicit representation for the
transition law as this was previous known only in certain special cases and
only for $p=1$ and $\alpha\in[0,1)$. Simulation results suggest that our
methods work well in practice.


title:
Distributional Reinforcement Learning for Scheduling of Chemical
  Production Processes

abstract:
  Reinforcement Learning (RL) has recently received significant attention from
the process systems engineering and control communities. Recent works have
investigated the application of RL to identify optimal scheduling decision in
the presence of uncertainty. In this work, we present a RL methodology tailored
to efficiently address production scheduling problems in the presence of
uncertainty. We consider commonly imposed restrictions on these problems such
as precedence and disjunctive constraints which are not naturally considered by
RL in other contexts. Additionally, this work naturally enables the
optimization of risk-sensitive formulations such as the conditional
value-at-risk (CVaR), which are essential in realistic scheduling processes.
The proposed strategy is investigated thoroughly in a parallel batch production
environment, and benchmarked against mixed integer linear programming (MILP)
strategies. We show that the policy identified by our approach is able to
account for plant uncertainties in online decision-making, with expected
performance comparable to existing MILP methods. Additionally, the framework
gains the benefits of optimizing for risk-sensitive measures, and identifies
online decisions orders of magnitude faster than the most efficient
optimization approaches. This promises to mitigate practical issues and ease in
handling realizations of process uncertainty in the paradigm of online
production scheduling.


title: Signature Correction Attack on Dilithium Signature Scheme

abstract:
  Motivated by the rise of quantum computers, existing public-key cryptosystems
are expected to be replaced by post-quantum schemes in the next decade in
billions of devices. To facilitate the transition, NIST is running a
standardization process which is currently in its final Round. Only three
digital signature schemes are left in the competition, among which Dilithium
and Falcon are the ones based on lattices. Classical fault attacks on signature
schemes make use of pairs of faulty and correct signatures to recover the
secret key which only works on deterministic schemes. To counter such attacks,
Dilithium offers a randomized version which makes each signature unique, even
when signing identical messages.
  In this work, we introduce a novel Signature Correction Attack which not only
applies to the deterministic version but also to the randomized version of
Dilithium and is effective even on constant-time implementations using AVX2
instructions. The Signature Correction Attack exploits the mathematical
structure of Dilithium to recover the secret key bits by using faulty
signatures and the public-key. It can work for any fault mechanism which can
induce single bit-flips. For demonstration, we are using Rowhammer induced
faults. Thus, our attack does not require any physical access or special
privileges, and hence could be also implemented on shared cloud servers. We
perform a thorough classical and quantum security analysis of Dilithium and
successfully recover 1,851 bits out of 3,072 bits of secret key $s_1$ for
security level 2. The lattice strength against quantum attackers is reduced
from $2^{128}$ to $2^{81}$ while the strength against classical attackers is
reduced from $2^{141}$ to $2^{89}$. Hence, the Signature Correction Attack may
be employed to achieve a practical attack on Dilithium (security level 2) as
proposed in Round 3 of the NIST post-quantum standardization process.


title:
PaSca: a Graph Neural Architecture Search System under the Scalable
  Paradigm

abstract:
  Graph neural networks (GNNs) have achieved state-of-the-art performance in
various graph-based tasks. However, as mainstream GNNs are designed based on
the neural message passing mechanism, they do not scale well to data size and
message passing steps. Although there has been an emerging interest in the
design of scalable GNNs, current researches focus on specific GNN design,
rather than the general design space, limiting the discovery of potential
scalable GNN models. This paper proposes PasCa, a new paradigm and system that
offers a principled approach to systemically construct and explore the design
space for scalable GNNs, rather than studying individual designs. Through
deconstructing the message passing mechanism, PasCa presents a novel Scalable
Graph Neural Architecture Paradigm (SGAP), together with a general architecture
design space consisting of 150k different designs. Following the paradigm, we
implement an auto-search engine that can automatically search well-performing
and scalable GNN architectures to balance the trade-off between multiple
criteria (e.g., accuracy and efficiency) via multi-objective optimization.
Empirical studies on ten benchmark datasets demonstrate that the representative
instances (i.e., PasCa-V1, V2, and V3) discovered by our system achieve
consistent performance among competitive baselines. Concretely, PasCa-V3
outperforms the state-of-the-art GNN method JK-Net by 0.4\% in terms of
predictive accuracy on our large industry dataset while achieving up to
$28.3\times$ training speedups.


title:
Variance-based sensitivity analysis: The quest for better estimators and
  designs between explorativity and economy

abstract:
  Variance-based sensitivity indices have established themselves as a reference
among practitioners of sensitivity analysis of model outputs. A variance-based
sensitivity analysis typically produces the first-order sensitivity indices
$S_j$ and the so-called total-effect sensitivity indices $T_j$ for the
uncertain factors of the mathematical model under analysis.
  The cost of the analysis depends upon the number of model evaluations needed
to obtain stable and accurate values of the estimates. While efficient
estimation procedures are available for $S_j$, this availability is less the
case for $T_j$. When estimating these indices, one can either use a
sample-based approach whose computational cost depends on the number of factors
or use approaches based on meta modelling/emulators.
  The present work focuses on sample-based estimation procedures for $T_j$ and
tests different avenues to achieve an algorithmic improvement over the existing
best practices. To improve the exploration of the space of the input factors
(design) and the formula to compute the indices (estimator), we propose
strategies based on the concepts of economy and explorativity. We then discuss
how several existing estimators perform along these characteristics.
  We conclude that: a) sample-based approaches based on the use of multiple
matrices to enhance the economy are outperformed by designs using fewer
matrices but with better explorativity; b) among the latter, asymmetric designs
perform the best and outperform symmetric designs having corrective terms for
spurious correlations; c) improving on the existing best practices is fraught
with difficulties; and d) ameliorating the results comes at the cost of
introducing extra design parameters.


title:
Impact of chemical disorder on magnetic exchange interactions in
  L1$_0$-FeNi (tetrataenite)

abstract:
  We investigate the effect of chemical disorder on the magnetic exchange
couplings and the Curie temperature ($T_{\text{c}}$) in L1$_0$-ordered FeNi
using first-principles-based calculations. We use supercells to model chemical
disorder, to account for the specific symmetry-broken local chemical
environments around the individual atoms. We find a very strong variation of
the most dominant first-nearest neighbor Fe-Fe interaction for different
inequivalent Fe-Fe pairs, ranging from around 5 meV to 37 meV, compared to a
coupling strength of 27 meV in the ordered state. To estimate the influence of
such strong variations of the magnetic coupling constants on the Curie
temperature of the disordered or partially ordered state, we study a simple
Heisenberg model with random Gaussian-distributed nearest neighbor couplings on
an fcc lattice. Our Monte Carlo simulations for this model indicate that
strongly varying exchange couplings, such as those obtained for FeNi, can lead
to a reduction of $T_{\text{c}}$ of around 10% relative to the one obtained
using only the average coupling.


title: Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images

abstract:
  Background and Objectives: Predicting patient response to treatment and
survival in oncology is a prominent way towards precision medicine. To that
end, radiomics was proposed as a field of study where images are used instead
of invasive methods. The first step in radiomic analysis is the segmentation of
the lesion. However, this task is time consuming and can be physician
subjective. Automated tools based on supervised deep learning have made great
progress to assist physicians. However, they are data hungry, and annotated
data remains a major issue in the medical field where only a small subset of
annotated images is available. Methods: In this work, we propose a multi-task
learning framework to predict patient's survival and response. We show that the
encoder can leverage multiple tasks to extract meaningful and powerful features
that improve radiomics performance. We show also that subsidiary tasks serve as
an inductive bias so that the model can better generalize. Results: Our model
was tested and validated for treatment response and survival in lung and
esophageal cancers, with an area under the ROC curve of 77% and 71%
respectively, outperforming single task learning methods. Conclusions: We show
that, by using a multi-task learning approach, we can boost the performance of
radiomic analysis by extracting rich information of intratumoral and
peritumoral regions.


title: Relaxed virtual memory in Armv8-A (extended version)

abstract:
  Virtual memory is an essential mechanism for enforcing security boundaries,
but its relaxed-memory concurrency semantics has not previously been
investigated in detail. The concurrent systems code managing virtual memory has
been left on an entirely informal basis, and OS and hypervisor verification has
had to make major simplifying assumptions.
  We explore the design space for relaxed virtual memory semantics in the
Armv8-A architecture, to support future system-software verification. We
identify many design questions, in discussion with Arm; develop a test suite,
including use cases from the pKVM production hypervisor under development by
Google; delimit the design space with axiomatic-style concurrency models; prove
that under simple stable configurations our architectural model collapses to
previous "user" models; develop tooling to compute allowed behaviours in the
model integrated with the full Armv8-A ISA semantics; and develop a hardware
test harness.
  This lays out some of the main issues in relaxed virtual memory bringing
these security-critical systems phenomena into the domain of
programming-language semantics and verification with foundational architecture
semantics.
  This document is an extended version of a paper in ESOP 2022, with additional
explanation and examples in the main body, and appendices detailing our litmus
tests, models, proofs, and test results.


title: A Lattice-Theoretic Perspective on the Persistence Map

abstract:
  We provide a naturally isomorphic description of the persistence map from
merge trees to barcodes in terms of a monotone map from the partition lattice
to the subset lattice. Our description is local, which offers the potential to
speed up inverse computations, and brings classical tools in combinatorics to
bear on an active area of research in topological data analysis (TDA).


title:
Tello Leg: The Study of Design Principles and Metrics for Dynamic
  Humanoid Robots

abstract:
  To be useful tools in real scenarios, humanoid robots must realize tasks
dynamically. This means that they must be capable of applying substantial
forces, rapidly swinging their limbs, and also mitigating impacts that may
occur during the motion. Towards creating capable humanoids, this letter
presents the leg of the robot TELLO and demonstrates how it embodies two new
fundamental design concepts for dynamic legged robots. The limbs follows the
principles of: (i) Cooperative Actuation (CA), by combining motors in
differential configurations to increase the force capability of the limb. We
demonstrate that the CA configuration requires half the motor torque to perform
a jump in comparison to conventional serial design configurations. And (ii)
proximal actuation, by placing heavy motors near the body to reduce the inertia
of the limb. To quantify the effect of motor placement on the robot's dynamics,
we introduce a novel metric entitle Centroidal Inertia Isotropy (CII). We show
that the design of state-of-the-art dynamic legged robots empirically increase
the CII to improve agility and facilitate model-based control. We hope this
metric will enable a quantifiable way to design these machines in the future.


title: Variational Autoencoders Without the Variation

abstract:
  Variational autoencdoers (VAE) are a popular approach to generative
modelling. However, exploiting the capabilities of VAEs in practice can be
difficult. Recent work on regularised and entropic autoencoders have begun to
explore the potential, for generative modelling, of removing the variational
approach and returning to the classic deterministic autoencoder (DAE) with
additional novel regularisation methods. In this paper we empirically explore
the capability of DAEs for image generation without additional novel methods
and the effect of the implicit regularisation and smoothness of large networks.
We find that DAEs can be used successfully for image generation without
additional loss terms, and that many of the useful properties of VAEs can arise
implicitly from sufficiently large convolutional encoders and decoders when
trained on CIFAR-10 and CelebA.


title:
Is the number of subrings of index $p^e$ in $\mathbb{Z}^n$ polynomial in
  $p$?

abstract:
  It is well-known that for each fixed $n$ and $e$, the number of subgroups of
index $p^e$ in $\mathbb{Z}^n$ is a polynomial in $p$. Is this true for
\emph{subrings} in $\mathbb{Z}^n$ of index $p^e$? Let $f_n(k)$ denote the
number of subrings of index $k$ in $\mathbb{Z}^n$. We can define the subring
zeta function over $\mathbb{Z}^n$ to be $\zeta_{\mathbb{Z}^n}^R(s) = \sum_{k
\ge 1} f_n(k)k^{-s}$. Is this zeta function uniform? These two questions are
closely related.
  In this paper, we describe what is known about these questions, and we make
progress toward answering them in a couple ways. First, we describe the
connection between counting subrings of index $p^e$ in $\mathbb{Z}^n$ and
counting the solutions to a corresponding set of equations modulo various
powers of $p$. We then show that the number of solutions to certain subsets of
these equations is a polynomial in $p$ for any fixed $n$. On the other hand, we
give an example for which the number of solutions to a certain subset of
equations is not polynomial. Finally, we give an explicit polynomial formula
for the number of `irreducible' subrings of index $p^{n+2}$ in $\mathbb{Z}^n$.


title:
Coexistence of Age Sensitive Traffic and High Throughput Flows: Does
  Prioritization Help?

abstract:
  We study the coexistence of high throughput traffic flows with status update
flows that require timely delivery of updates. A mix of these flows share an
end-to-end path that includes a WiFi access network followed by paths over the
Internet to a server in the cloud. Using real-world experiments, we show that
commonly used methods of prioritization (DSCP at the IP layer and EDCA at the
802.11 MAC layer) in networks are highly effective in isolating status update
flows from the impact of high throughput flows in the absence of WiFi access
contention, say when all flows originate from the same WiFi client.
Prioritization, however, isn't as effective in the presence of contention that
results from the throughput and status update flows sharing WiFi. This results
in prioritized status update flows suffering from a time-average age of
information at the destination server that is about the same as when all flows
have the same priority.


title:
Measuring the Impact of Individual Domain Factors in Self-Supervised
  Pre-Training

abstract:
  Human speech data comprises a rich set of domain factors such as accent,
syntactic and semantic variety, or acoustic environment. Previous work explores
the effect of domain mismatch in automatic speech recognition between
pre-training and fine-tuning as a whole but does not dissect the contribution
of individual factors. In this paper, we present a controlled study to better
understand the effect of such factors on the performance of pre-trained
representations. To do so, we pre-train models either on modified natural
speech or synthesized audio, with a single domain factor modified, and then
measure performance on automatic speech recognition after fine tuning. Results
show that phonetic domain factors play an important role during pre-training
while grammatical and syntactic factors are far less important. To our
knowledge, this is the first study to better understand the domain
characteristics in self-supervised pre-training for speech.


title: Lodestar: An Integrated Embedded Real-Time Control Engine

abstract:
  In this work we present Lodestar, an integrated engine for rapid real-time
control system development. Using a functional block diagram paradigm, Lodestar
allows for complex multi-disciplinary control software design, while
automatically resolving execution order, circular data-dependencies, and
networking. In particular, Lodestar presents a unified set of control, signal
processing, and computer vision routines to users, which may be interfaced with
external hardware and software packages using interoperable user-defined
wrappers. Lodestar allows for user-defined block diagrams to be directly
executed, or for them to be translated to overhead-free source code for
integration in other programs. We demonstrate how our framework departs from
approaches used in state-of-the-art simulation frameworks to enable real-time
performance, and compare its capabilities to existing solutions in the realm of
control software. To demonstrate the utility of Lodestar in real-time control
systems design, we have applied Lodestar to implement two real-time
torque-based controller for a robotic arm. In addition, we have developed a
novel autofocus algorithm for use in thermography-based localization and
parameter estimation in electrosurgery and other areas of robot-assisted
surgery. We compare our algorithm design approach in Lodestar to a classical
ground-up approach, showing that Lodestar considerably eases the design
process. We also show how Lodestar can seamlessly interface with existing
simulation and networking framework in a number of simulation examples.


title: Reduced fluctuations for bosons in a double well

abstract:
  We review two recent results on the ground state properties of bosonic
systems trapped by a double well external potential. In the limit of large
population and large separation between the wells we prove that fluctuations in
the number of particles occupying each single-well low energy mode occur at a
reduced scale with respect to $ \sqrt{N}$, the latter being the typical
prediction of the central limit theorem. This signals the breakdown of the
independent and uncorrelated particles picture of standard Bose-Einstein
condensation and the emergence of an interaction-driven correlated phase in the
ground state.


title: Gaussian zonoids and the determinant of a non-centered Gaussian matrix

abstract:
  We study the Vitale zonoids associated to a non-centered Gaussian vector. We
compute two ellipsoids in between which each zonoid is trapped. This allows to
estimate from above and below the expectation of the absolute value of the
determinant of a non--centered Gaussian matrix with independent columns with
the mixed volume of ellipsoid.


title:
Parsing Randomness: Unifying and Differentiating Parsers and Random
  Generators

abstract:
  "A generator is a parser of randomness." This perspective on generators for
random data structures is well established as folklore in the programming
languages community, but it has apparently never been formalized, nor have its
consequences been deeply explored.
  We present free generators, which unify parsing and generation using a common
structure that makes the relationship between the two concepts precise. Free
generators lead naturally to a proof that a large class of generators can be
factored into a parser plus a distribution over choice sequences. Further, free
generators support a notion of derivative, analogous to familiar Brzozowski
derivatives of formal languages, that allows analysis tools to "preview" the
effect of a particular generator choice. This, in turn, gives rise to a novel
algorithm for generating data structures satisfying user-specified
preconditions.


title:
ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in
  Temporal Networks

abstract:
  In network analysis, the betweenness centrality of a node informally captures
the fraction of shortest paths visiting that node. The computation of the
betweenness centrality measure is a fundamental task in the analysis of modern
networks, enabling the identification of the most central nodes in such
networks. Additionally to being massive, modern networks also contain
information about the time at which their events occur. Such networks are often
called temporal networks. The temporal information makes the study of the
betweenness centrality in temporal networks (i.e., temporal betweenness
centrality) much more challenging than in static networks (i.e., networks
without temporal information). Moreover, the exact computation of the temporal
betweenness centrality is often impractical on even moderately-sized networks,
given its extremely high computational cost. A natural approach to reduce such
computational cost is to obtain high-quality estimates of the exact values of
the temporal betweenness centrality. In this work we present ONBRA, the first
sampling-based approximation algorithm for estimating the temporal betweenness
centrality values of the nodes in a temporal network, providing rigorous
probabilistic guarantees on the quality of its output. ONBRA is able to compute
the estimates of the temporal betweenness centrality values under two different
optimality criteria for the shortest paths of the temporal network. In
addition, ONBRA outputs high-quality estimates with sharp theoretical
guarantees leveraging on the \emph{empirical Bernstein bound}, an advanced
concentration inequality. Finally, our experimental evaluation shows that ONBRA
significantly reduces the computational resources required by the exact
computation of the temporal betweenness centrality on several real world
networks, while reporting high-quality estimates with rigorous guarantees.


title: Deconvolution of spherical data corrupted with unknown noise

abstract:
  We consider the deconvolution problem for densities supported on a
$(d-1)$-dimensional sphere with unknown center and unknown radius, in the
situation where the distribution of the noise is unknown and without any other
observations. We propose estimators of the radius, of the center, and of the
density of the signal on the sphere that are proved consistent without further
information. The estimator of the radius is proved to have almost parametric
convergence rate for any dimension $d$. When $d=2$, the estimator of the
density is proved to achieve the same rate of convergence over Sobolev
regularity classes of densities as when the noise distribution is known.


title:
A hardware-software co-design approach to minimize the use of memory
  resources in multi-core neuromorphic processors

abstract:
  Both in electronics and biology, physical implementations of neural networks
have severe energy and memory constraints. We propose a hardware-software
co-design approach for minimizing the use of memory resources in multi-core
neuromorphic processors, by taking inspiration from biological neural networks.
We use this approach to design new routing schemes optimized for small-world
networks and to provide guidelines for designing novel application-specific
multi-core neuromorphic chips. Starting from the hierarchical routing scheme
proposed, we present a hardware-aware placement algorithm that optimizes the
allocation of resources for arbitrary network models. We validate the algorithm
with a canonical small-world network and present preliminary results for other
networks derived from it.


title: Trilinear birational maps in dimension three

abstract:
  A 3-dimensional trilinear birational map is a rational map $\phi:
(\mathbb{P}_\mathbb{C}^1)^3 \dashrightarrow \mathbb{P}_\mathbb{C}^3$ whose
entries are trilinear polynomials in three distinct pairs of variables, which
admits an inverse rational map. In this work, we prove that the set of classes
of trilinear birational maps, up to composition with an automorphism of
$\mathbb{P}_\mathbb{C}^3$, is an algebraic subset of some Grassmannian, and we
describe its irreducible components. Additionally, we provide the complete list
of possible base loci for $\phi$, up to isomorphism. Finally, we describe the
syzygies of the entries that define $\phi$, and prove that one can decide
birationality by looking exclusively at the first syzygies.


title: Some Min-max Theorems for Matroids Intersection

abstract:
  In this paper we will study the intersection of more than two matroids and
deduce some analogous results for Edmonds' Min-max theorems for matroids
intersection .


title:
Nature of many-body scars in a Rydberg chain: Gutzwiller projection of a
  large precessing pseudospin

abstract:
  We unveil the nature of the ergodicity-breaking "quantum many-body scar"
states that appear in the PXP model describing constrained Rabi oscillations in
a chain of Rydberg atoms. We show that the quasi-periodic motion ensuing from
certain initial states is simply the projection onto the Rydberg-constraint
subspace of the precession of a large pseudospin that we explicitly construct,
together with explicit approximate expressions for the scar states. The nearly
energy-equidistant tower of these states is shown to arise from the system's
close proximity to a Hamiltonian with a structure common to all known models
hosting quantum many-body scars. We construct a non-Hermitean, but strictly
local extension of the PXP model hosting exact quantum scars, and show how
various Hermitean scar-enhancing extensions from the literature can be
understood within our framework.


title: Generalized Hanson-Wright Inequality for Random Tensors

abstract:
  The Hanson-Wright inequality is an upper bound for tails of real quadratic
forms in independent random variables. In this work, we extend the
Hanson-Wright inequality for the Ky Fan k-norm for the polynomial function of
the quadratic sum of random tensors under Einstein product. We decompose the
quadratic tensors sum into the diagonal part and the coupling part. For the
diagonal part, we can apply the generalized tensor Chernoff bound directly.
But, for the coupling part, we have to apply decoupling method first, i.e.,
decoupling inequality to bound expressions with dependent random tensors with
independent random tensors, before applying generalized tensor Chernoff bound
again to get the the tail probability of the Ky Fan $k$-norm of the coupling
part sum of independent random tensors. At the end, the generalized
Hanson-Wright inequality for the Ky Fan k-norm for the polynomial function of
the quadratic sum of random tensors can be obtained by the combination of the
bound from the diagonal sum part and the bound from the coupling sum part.


title: On symbol correspondences for quark systems

abstract:
  We present the characterization of symbol correspondences for mechanical
systems that are symmetric under $SU(3)$, which we refer to as quark systems.
The quantum systems are the unitary irreducible representations of $SU(3)$,
denoted by $Q(p,q)$, $p,q\in\mathbb N_0$, together with their operator
algebras. We study the cases when the classical phase space is a coadjoint
orbit: either the complex projective plane $\mathbb CP^2$ or the flag manifold
that is the total space of fiber bundle $\mathbb CP^1\hookrightarrow \mathcal
E\to \mathbb CP^2$. In the first case, we refer to pure-quark systems and the
characterization of their correspondences is given in terms of characteristic
numbers, similarly to the case of spin systems, cf. [22]. In the second case,
we refer to generic quark systems and the characterization of their
correspondences is given in terms of characteristic matrices, which introduces
various novel features. Furthermore, we present the $SU(3)$ decomposition of
the product of quantum operators and their corresponding twisted products of
classical functions, for both pure and generic quark systems. In preparation
for asymptotic analysis of these twisted products, we also present the $SU(3)$
decomposition of the pointwise product of classical functions.


title: $2+1$ Einstein-Klein-Gordon black holes by gravitational decoupling

abstract:
  In this work we study the 2+1 Einstein-Klein-Gordon system in the framework
of Gravitational Decoupling. We associate the generic matter decoupling sector
with a real scalar field so we can obtain a constraint which allows to close
the system of differential equations. The constraint corresponds to a
differential equation involving the decoupling functions and the metric of the
seed sector and will be independent of the scalar field itself. We show that
when the equation admits analytical solutions, the scalar field and the
self-interacting potential can be obtained straightforwardly. We found that, in
the cases under consideration, it is possible to express the potential as an
explicit function of the scalar field only for certain particular cases
corresponding to limiting values of the parameters involved.


title: In-memory Associative Processors: Tutorial, Potential, and Challenges

abstract:
  In-memory computing is an emerging computing paradigm that overcomes the
limitations of exiting Von-Neumann computing architectures such as the
memory-wall bottleneck. In such paradigm, the computations are performed
directly on the data stored in the memory, which highly reduces the
memory-processor communications during computation. Hence, significant speedup
and energy savings could be achieved especially with data-intensive
applications. Associative processors (APs) were proposed in the seventies and
recently were revived thanks to the high-density memories. In this tutorial
brief, we overview the functionalities and recent trends of APs in addition to
the implementation of each content-addressable memory with different
technologies. The AP operations and runtime complexity are also summarized. We
also explain and explore the possible applications that can benefit from APs.
Finally, the AP limitations, challenges, and future directions are discussed.


title:
Iterative Residual Policy: for Goal-Conditioned Dynamic Manipulation of
  Deformable Objects

abstract:
  This paper tackles the task of goal-conditioned dynamic manipulation of
deformable objects. This task is highly challenging due to its complex dynamics
(introduced by object deformation and high-speed action) and strict task
requirements (defined by a precise goal specification). To address these
challenges, we present Iterative Residual Policy (IRP), a general learning
framework applicable to repeatable tasks with complex dynamics. IRP learns an
implicit policy via delta dynamics -- instead of modeling the entire dynamical
system and inferring actions from that model, IRP learns delta dynamics that
predict the effects of delta action on the previously-observed trajectory. When
combined with adaptive action sampling, the system can quickly optimize its
actions online to reach a specified goal. We demonstrate the effectiveness of
IRP on two tasks: whipping a rope to hit a target point and swinging a cloth to
reach a target pose. Despite being trained only in simulation on a fixed robot
setup, IRP is able to efficiently generalize to noisy real-world dynamics, new
objects with unseen physical properties, and even different robot hardware
embodiments, demonstrating its excellent generalization capability relative to
alternative approaches. Video is available at https://youtu.be/7h3SZ3La-oA


title: Deformation of pairs and Noether-Lefschetz loci in toric varieties

abstract:
  We continue our study of the Noether-Lefschetz loci in toric varieties and
investigate deformation of pairs (V,X) where V is a complete intersection
subvariety and X a quasi-smooth hypersurface in a odd dimensional simplicial
projective toric variety, with V\subset X. Under some assumptions, we prove
that the cohomological class in H^{k,k}(X) associated to V remains of type
(k,k) under an infinitesimal deformation if and only if V remains algebraic.
Actually we prove that locally the Noether-Lefschetz locus is an irreducible
component of a suitable Hilbert scheme. This generalizes Theorem 4.2 in our
previous work [4] and the main theorem proved by Dan in [10].


title:
Characteristics of Chromospheric Oscillation Periods in Magnetic Bright
  Points (MBPs)

abstract:
  In this investigation oscillation periods in Mg II k line intensity,
brightness temperature, and Doppler velocity obtained above Magnetic Bright
Points (MBPs) are investigated. For that purpose, data from the Interface
Region Imaging Spectrometer (IRIS) observing the higher chromosphere and
transition region (TR) were analysed together with imaging and magnetogram data
obtained by Solar Dynamics Observatory (SDO). The MBPs were identified in
combining Si IV 1403 A Slit Jaw Images (SJIs) with the magnetogram information
from the Heliospheric and Magnetic Imager (HMI). A time-slice analysis followed
by a wavelet inspection were carried out on the Mg II k (2796 A and 10,000 K)
resonance lines for the detection of the oscillation period. Finally, a power
spectrum analysis was performed to characterise the oscillations with the
result that network points feature a typical intensity, temperature, and
velocity oscillation period of about 300 seconds; The internetwork points have
a mean intensity oscillation period of about 180 seconds, mean temperature
oscillation period of about 202, and mean velocity oscillation period of about
202 seconds. In addition, one BP was analysed in detail, which demonstrates
intensity oscillation periods with a value of 500 seconds, obviously not
related to the common 3- or 5-minute oscillations found typically elsewhere in
chromospheric/photospheric structures.


title: Temporal increments of the KPZ equation with general initial data

abstract:
  We consider the Cole-Hopf solution of the (1+1)-dimensional KPZ equation
$\mathcal{H}^f(t,x)$ started with initial data $f$. In this article, we study
the sample path properties of the KPZ temporal process $\mathcal{H}_t^f :=
\mathcal{H}^f(t,0)$. We show that for a very large class of initial data which
includes any deterministic continuous initial data that grows slower than
parabola ($f(x) \ll 1 + x^2$) as well as narrow wedge and stationary initial
data, temporal increments of $\mathcal{H}_t^f$ are well approximated by
increments of a fractional Brownian motion of Hurst parameter $\frac14$. As a
consequence, we obtain several sample path properties of KPZ temporal
properties including variation of the temporal process, law of iterated
logarithms, modulus of continuity and Hausdorff dimensions of the set of points
with exceptionally large increments.


title: Generative Adversarial Networks

abstract:
  Generative Adversarial Networks (GANs) are very popular frameworks for
generating high-quality data, and are immensely used in both the academia and
industry in many domains. Arguably, their most substantial impact has been in
the area of computer vision, where they achieve state-of-the-art image
generation. This chapter gives an introduction to GANs, by discussing their
principle mechanism and presenting some of their inherent problems during
training and evaluation. We focus on these three issues: (1) mode collapse, (2)
vanishing gradients, and (3) generation of low-quality images. We then list
some architecture-variant and loss-variant GANs that remedy the above
challenges. Lastly, we present two utilization examples of GANs for real-world
applications: Data augmentation and face images generation.


title:
Information back-flow in quantum non-Markovian dynamics and its
  connection to teleportation

abstract:
  A quantum process is called non-Markovian when memory effects take place
during its evolution. Quantum non-Markovianity is a phenomenon typically
associated with the information back-flow from the environment to the principal
system, however it has been shown that such an effect is not necessary. In
particular, maximum quantum non-Markovianity can be achieved without any
physical transmission of information. In this work, it is shown that
time-homogeneity is a sufficient condition for a non-Markovian quantum process
to originate from an information back-flow effect. As a characteristic example,
the protocol of measurement-free teleportation is suggested as a
time-homogeneous maximally non-Markovian quantum process, in both discrete and
continuous-variable systems. Finally, given the resource-like role of
entanglement in teleportation protocol, the relationship between this property
and non-Markovianity is elucidated.


title: AI Planning Annotation for Sample Efficient Reinforcement Learning

abstract:
  AI planning and Reinforcement Learning (RL) both solve sequential
decision-making problems under the different formulations. AI Planning requires
operator models, but then allows efficient plan generation. RL requires no
operator model, instead learns a policy to guide an agent to high reward
states. Planning can be brittle in the face of noise whereas RL is more
tolerant. However, RL requires a large number of training examples to learn the
policy. In this work, we aim to bring AI planning and RL closer by showing that
a suitably defined planning model can be used to improve the efficiency of RL.
Specifically, we show that the options in the hierarchical RL can be derived
from a planning task and integrate planning and RL algorithms for training
option policy functions. Our experiments demonstrate an improved sample
efficiency on a variety of RL environments over the previous state-of-the-art.


title: How Big are the Stable Homotopy Groups of Spheres?

abstract:
  In this article we show that the $p$-torsion exponent of the stable stems
grows sublinearly in $n$ and the $p$-rank of the $E_2$-page of the Adams
spectral sequence grows as $\exp(\Theta( \log(n)^3))$. Together these bounds
provide the first subexponential bound on the size of the stable stems.
Conversely, we prove that a certain, precise, version of the failure of the
telescope conjecture would imply that the upper bound provided by the Adams
$E_2$-page is essentially sharp -- answering the titular question: As big as
the fate of the telescope conjecture demands.
  In an appendix joint with Andrew Senger we consider the unstable analog of
this question. Bootstrapping from the stable bounds we prove that the size of
the $p$-local homotopy groups of spheres grows like $\exp(O(\log(n)^3))$,
providing the first subexponential bound on the size of the unstable stems.


title: Maximum Flow and Minimum-Cost Flow in Almost-Linear Time

abstract:
  We give an algorithm that computes exact maximum flows and minimum-cost flows
on directed graphs with $m$ edges and polynomially bounded integral demands,
costs, and capacities in $m^{1+o(1)}$ time. Our algorithm builds the flow
through a sequence of $m^{1+o(1)}$ approximate undirected minimum-ratio cycles,
each of which is computed and processed in amortized $m^{o(1)}$ time using a
new dynamic graph data structure.
  Our framework extends to algorithms running in $m^{1+o(1)}$ time for
computing flows that minimize general edge-separable convex functions to high
accuracy. This gives almost-linear time algorithms for several problems
including entropy-regularized optimal transport, matrix scaling, $p$-norm
flows, and $p$-norm isotonic regression on arbitrary directed acyclic graphs.