title: A Note on the Hardness of Problems from Cryptographic Group Actions

abstract:
  Given a cryptographic group action, we show that the Group Action Inverse
Problem (GAIP) and other related problems cannot be NP-hard unless the
Polynomial Hierarchy collapses. We show this via random self-reductions and the
design of interactive proofs. Since cryptographic group actions are the
building block of many security protocols, this result serves both as an upper
bound on the worst-case complexity of some cryptographic assumptions and as
proof that the hardness in the worst and in the average case coincide. We also
point out the link with Graph Isomorphism and other related NP intermediate
problems.


title:
Connection between the semiconductor--superconductor transition and the
  spin-polarized superconducting phase in the honeycomb lattice

abstract:
  The band structure of noninteracting fermions in the honeycomb lattice
exhibits the Dirac cones at the corners of the Brillouin zone. As a
consequence, fermions in this lattice manifest a semiconducting behavior below
some critical value of the onsite attraction, $U_{c}$. However, above $U_{c}$,
the superconducting phase can occur. We discuss an interplay between the
semiconductor--superconductor transition and the possibility of realization of
the spin-polarized superconductivity (the so-called Sarma phase). We show that
the critical interaction can be tuned by the next-nearest-neighbor (NNN)
hopping in the absence of the magnetic field. Moreover, a critical value of the
NNN hopping exists, defining a range of parameters for which the semiconducting
phase can emerge. In the weak coupling limit case, this quantum phase
transition occurs for the absolute value of the NNN hopping equal to one third
of the hopping between the nearest neighbors. Similarly, in the presence of the
magnetic field, the Sarma phase can appear, but only in a range of parameters
for which initially the semiconducting state is observed. Both of these aspects
are attributed to the Lifshitz transition, which is induced by the NNN hopping
as well as the external magnetic field.


title: TraceNet: Tracing and Locating the Key Elements in Sentiment Analysis

abstract:
  In this paper, we study sentiment analysis task where the outcomes are mainly
contributed by a few key elements of the inputs. Motivated by the two-streams
hypothesis, we propose a neural architecture, named TraceNet, to address this
type of task. It not only learns discriminative representations for the target
task via its encoders, but also traces key elements at the same time via its
locators. In TraceNet, both encoders and locators are organized in a layer-wise
manner, and a smoothness regularization is employed between adjacent
encoder-locator combinations. Moreover, a sparsity constraints are enforced on
locators for tracing purposes and items are proactively masked according to the
item weights output by locators.A major advantage of TraceNet is that the
outcomes are easier to understand, since the most responsible parts of inputs
are identified. Also, under the guidance of locators, it is more robust to
attacks due to its focus on key elements and the proactive masking training
strategy. Experimental results show its effectiveness for sentiment
classification. Moreover, we provide several case studies to demonstrate its
robustness and interpretability.


title: Dynamic Cournot-Nash Equilibrium: The Non-Potential Case

abstract:
  We consider a large population dynamic game in discrete time where players
are characterized by time-evolving types. It is a natural assumption that the
players' actions cannot anticipate future values of their types. Such games go
under the name of dynamic Cournot-Nash equilibria, and were first studied by
Acciaio et al., as a time/information dependent version of the games devised by
Blanchet and Carlier for the static situation, under an extra assumption that
the game is of potential type. The latter means that the game can be reduced to
the resolution of an auxiliary variational problem.
  In the present work we study dynamic Cournot-Nash equilibria in their natural
generality, namely going beyond the potential case. As a first result, we
derive existence and uniqueness of equilibria under suitable assumptions.
Second, we study the convergence of the natural fixed-point iterations scheme
in the quadratic case. Finally we illustrate the previously mentioned results
in a toy model of optimal liquidation with price impact, which is a game of
non-potential kind.


title: The Nonvanishing problem for varieties with nef anticanonical bundle

abstract:
  We prove that if $(X,\Delta)$ is a threefold pair with mild singularities
such that ${-}(K_X+\Delta)$ is nef, then the numerical class of
${-}(K_X+\Delta)$ is effective.


title: Doppler-Shifted Alkali D Absorption as Indirect Evidence for Exomoons

abstract:
  Sodium and potassium signatures in transiting exoplanets can be challenging
to isolate from the stellar absorption lines. Here, these challenges are
discussed in the framework of Solar System observations, and transits of
Mercury in particular. Radiation pressure is important for alkali gas dynamics
in close-orbiting exoplanets since the D lines are efficient at resonant
scattering. When the star-planet velocity is >10km/s, eccentric exoplanets
experience more than an order of magnitude higher radiation pressures, aiding
atmospheric escape and producing a larger effective cross-section for absorbing
starlight at the phase of transit. The Doppler shift also aids in isolating the
planetary signature from the stellar photosphere's absorption. Only one
transiting exoplanet, HD 80606b, is presently thought to have both this
requisite Doppler shift and alkali absorption. Radiation pressure on a
planetary exosphere naturally produces blue-shifted absorption, but at levels
insufficient to account for the extreme Doppler shifts that have been inferred
from potassium transit measurements of this system. In the absence of clear
mechanisms to generate such a strong wind, it is described how this
characteristic could arise from an exomoon-magnetosphere interaction, analogous
to Io-Jupiter. At low contrasts presented here, follow-up transit spectra of HD
80606b cannot rule out a potassium jet or atmospheric species with a broad
absorption structure. However, it is evident that line absorption within the
imaging passbands fails to explain the narrow-band photometry that has been
reported in-transit. New observations of energetic alkalis produced by the
Io-Jupiter interaction are also presented, which illustrate that energetic
sodium Doppler structure offers a more valuable marker for the presence of an
exomoon than potassium.


title:
Hodge-Riemann Relations for Schur Classes in the Linear and K\"ahler
  Cases

abstract:
  We prove a version of the Hodge-Riemann bilinear relations for Schur
polynomials of K\"ahler forms and for Schur polynomials of positive forms on a
complex vector space.


title: Robust Textual Embedding against Word-level Adversarial Attacks

abstract:
  We attribute the vulnerability of natural language processing models to the
fact that similar inputs are converted to dissimilar representations in the
embedding space, leading to inconsistent outputs, and propose a novel robust
training method, termed Fast Triplet Metric Learning (FTML). Specifically, we
argue that the original sample should have similar representation with its
adversarial counterparts and distinguish its representation from other samples
for better robustness. To this end, we adopt the triplet metric learning into
the standard training to pull the words closer to their positive samples (i.e.,
synonyms) and push away their negative samples (i.e., non-synonyms) in the
embedding space. Extensive experiments demonstrate that FTML can significantly
promote the model robustness against various advanced adversarial attacks while
keeping competitive classification accuracy on original samples. Besides, our
method is efficient as it only needs to adjust the embedding and introduces
very little overhead on the standard training. Our work shows the great
potential of improving the textual robustness through robust word embedding.


title: On the values taken by slice torus invariants

abstract:
  We study the space of slice-torus invariants. In particular we characterize
the set of values that slice-torus invariants may take on a given knot in terms
of the stable smooth slice genus. Our study reveals that the resolution of the
local Thom conjecture implies the existence of slice torus invariants without
having to appeal to any explicit construction from a knot homology theory.


title:
New limits from microlensing on Galactic Black Holes in the mass range
  10xMsun < M < 1000xMsun

abstract:
  We have searched for long duration microlensing events originating from
intermediate mass Black Holes (BH) in the halo of the Milky Way, using archival
data from EROS-2 and MACHO photometric surveys towards the Large Magellanic
Cloud. We combined data from these two surveys to create a common database of
light curves for 14.1 million objects in LMC, covering a total duration of 10.6
years, with flux series measured through 4 wide band filters. We have carried
out a microlensing search on these light curves, complemented by 22.7 million
light curves, observed by EROS-2 only or MACHO only over about 7 years, with
flux series measured through only 2 filters. A likelihood analysis, taking into
account LMC self lensing and Milky Way disk contributions allows us to conclude
that compact objects with masses in the range 10 - 100 Msun cannot make up more
than ~15% of a standard halo total mass (at 95% confidence level). Our analysis
sensitivity weakens for heavier objects, although we still exclude that ~ 50%
of the halo be made of ~ 1000xMsun BHs. Combined with previous EROS results, an
upper limit of ~ 15% of the total halo mass can be obtained for the
contribution of compact halo objects in the mass range 10^{-6} - 10^2 Msun.


title: Complete Visitation Statistics of 1d Random Walks

abstract:
  We develop a framework to determine the complete statistical behavior of a
fundamental quantity in the theory of random walks, namely, the probability
that $n_1$, $n_2$, $n_3$, . . . distinct sites are visited at times $t_1$,
$t_2$, $t_3$, ... . From this multiple-time distribution, we show that the
visitation statistics of 1d random walks are temporally correlated and we
quantify the non-Markovian nature of the process. We exploit these ideas to
derive unexpected results for the two-time trapping problem and also to
determine the visitation statistics of two important stochastic processes, the
run-and-tumble particle and the biased random walk.


title:
Three-dimensional discontinuous Galerkin based high-order gas-kinetic
  scheme and GPU implementation

abstract:
  In this paper, the discontinuous Galerkin based high-order gas-kinetic
schemes (DG-HGKS) are developed for the three-dimensional Euler and
Navier-Stokes equations. Different from the traditional discontinuous Galerkin
(DG) methods with Riemann solvers, the current method adopts a kinetic
evolution process, which is provided by the integral solution of
Bhatnagar-Gross-Krook (BGK) model. In the weak formulation of DG method, a
time-dependent evolution function is provided, and both inviscid and viscous
fluxes can be calculated uniformly. The temporal accuracy is achieved by the
two-stage fourth-order discretization, and the second-order gas-kinetic solver
is adopted for the fluxes over the cell interface and the fluxes inside a cell.
Numerical examples, including accuracy tests and Taylor-Green vortex problem,
are presented to validate the efficiency and accuracy of DG-HGKS. Both optimal
convergence and super-convergence are achieved by the current scheme. The
comparison between DG-HGKS and high-order gas-kinetic scheme with weighted
essential non-oscillatory reconstruction (WENO-HGKS) is also given, and the
numerical performances are comparable with the approximate number of degree of
freedom. To accelerate the computation, the DG-HGKS is implemented with the
graphics processing unit (GPU) using compute unified device architecture
(CUDA). The obtained results are also compared with those calculated by the
central processing units (CPU) code in terms of the computational efficiency.
The speedup of GPU code suggests the potential of high-order gas-kinetic
schemes for the large scale computation.


title:
Exploring hyper-parameter spaces of neuroscience models on high
  performance computers with Learning to Learn

abstract:
  Neuroscience models commonly have a high number of degrees of freedom and
only specific regions within the parameter space are able to produce dynamics
of interest. This makes the development of tools and strategies to efficiently
find these regions of high importance to advance brain research. Exploring the
high dimensional parameter space using numerical simulations has been a
frequently used technique in the last years in many areas of computational
neuroscience. High performance computing (HPC) can provide today a powerful
infrastructure to speed up explorations and increase our general understanding
of the model's behavior in reasonable times.


title: Automatic Test-Case Reduction in Proof Assistants: A Case Study in Coq

abstract:
  As the adoption of proof assistants increases, there is a need for efficiency
in identifying, documenting, and fixing compatibility issues that arise from
proof assistant evolution. We present the Coq Bug Minimizer, a tool for
reproducing buggy behavior with minimal and standalone files, integrated with
coqbot to trigger automatically on Coq reverse CI failures. Our tool eliminates
the overhead of having to download, set up, compile, and then explore and
understand large developments: enabling Coq developers to easily obtain modular
test-case files for fast experimentation. In this paper, we describe insights
about how test-case reduction is different in Coq than in traditional
compilers. We expect that our insights will generalize to other proof
assistants. We evaluate the Coq Bug Minimizer on over 150 CI failures. Our tool
succeeds in reducing failures to smaller test cases in roughly 75% of the time.
The minimizer produces a fully standalone test case 89% of the time, and it is
on average about one-third the size of the original test. The average reduced
test case compiles in 1.25 seconds, with 75% taking under half a second.


title:
Universality of the fully connected vertex in Laplacian continuous-time
  quantum walk problems

abstract:
  A fully connected vertex $w$ in a simple graph $G$ of order $N$ is a vertex
connected to all the other $N-1$ vertices. Upon denoting by $L$ the Laplacian
matrix of the graph, we prove that the continuous-time quantum walk (CTQW) --
with Hamiltonian $H=\gamma L$ -- of a walker initially localized at $\vert w
\rangle$ does not depend on the graph $G$. We also prove that for any
Grover-like CTQW -- with Hamiltonian $H=\gamma L +\sum_w \lambda_w \vert w
\rangle\langle w \vert$ -- the probability amplitude at the fully connected
marked vertices $w$ does not depend on $G$. The result does not hold for CTQW
with Hamiltonian $H=\gamma A$ (adjacency matrix). We apply our results to
spatial search and quantum transport for single and multiple fully connected
marked vertices, proving that CTQWs on any graph $G$ inherit the properties
already known for the complete graph of the same order, including the
optimality of the spatial search. Our results provide a unified framework for
several partial results already reported in literature for fully connected
vertices, such as the equivalence of CTQW and of spatial search for the central
vertex of the star and wheel graph, and any vertex of the complete graph.


title:
Sylow normalizers and irreducible characters with small cyclotomic field
  of values

abstract:
  In this paper, we prove the existence of a relation between the prime
divisors of the order of a Sylow normalizer and the degree of characters having
values in some small cyclotomic fields. This relation is stronger when the
group is solvable.


title: Magnitude-aware Probabilistic Speaker Embeddings

abstract:
  Recently, hyperspherical embeddings have established themselves as a dominant
technique for face and voice recognition. Specifically, Euclidean space vector
embeddings are learned to encode person-specific information in their direction
while ignoring the magnitude. However, recent studies have shown that the
magnitudes of the embeddings extracted by deep neural networks may indicate the
quality of the corresponding inputs. This paper explores the properties of the
magnitudes of the embeddings related to quality assessment and
out-of-distribution detection. We propose a new probabilistic speaker embedding
extractor using the information encoded in the embedding magnitude and leverage
it in the speaker verification pipeline. We also propose several quality-aware
diarization methods and incorporate the magnitudes in those. Our results
indicate significant improvements over magnitude-agnostic baselines both in
speaker verification and diarization tasks.


title: THz ratchet effect in HgTe interdigitated structures

abstract:
  The emergence of ratchet effects in two-dimensional materials is strongly
correlated with the introduction of asymmetry into the system. In general,
dual-grating-gate structures forming lateral asymmetric superlattices provide a
suitable platform for studying this phenomenon. Here, we report on the
observation of ratchet effects in HgTe-based dual-grating-gate structures
hosting different band structure properties. Applying polarized terahertz laser
radiation we detected linear and polarization independent ratchets, as well as
an radiation-helicity driven circular ratchet effect. Studying the ratchet
effect in devices made of quantum wells (QWs) of different thickness we
observed that the magnitude of the signal substantially increases with
decreasing QW width with a maximum value for devices made of QWs of critical
thickness hosting Dirac fermions. Furthermore, sweeping the gate voltage
amplitude we observed sign-alternating oscillations for gate voltages
corresponding to p-type conductivity. The amplitude of the oscillations is more
than two orders of magnitude larger than the signal for n-type conducting QWs.
The oscillations and the signal enhancement are shown to be caused by the
complex valence band structure of HgTe-based QWs. These peculiar features of
the ratchet currents make these materials an ideal platform for the development
of THz applications.


title: Detecting the power spectrum turnover with HI intensity mapping

abstract:
  A goal for pathfinder intensity mapping (IM) surveys will be detecting
features in the neutral hydrogen (HI) power spectrum, which serve as conclusive
evidence of cosmological signals. Observing such features at the expected
scales in HI IM auto-correlations, where contribution from systematics is
uncertain, will provide a more convincing cosmological detection. We
demonstrate how the turnover, i.e. the peak of the power spectrum at
ultra-large scales, can be detected with HI IM. We find that a MeerKAT
4,000$\,{\rm deg}^2$ survey using the UHF-band is capable of a $3.1\sigma$
detection of the turnover, relative to a null model power spectrum with no
turnover. This should exceed what is capable by current galaxy surveys in
optical and near-infrared. The detection significance falls to ${\sim}1\sigma$
in MeerKAT's L-band but can reach ${\sim}13\sigma$ with the SKAO, which should
easily surpass the constraints from future Stage-IV-like spectroscopic galaxy
surveys. We also propose a new model-independent methodology for constraining
the precise turnover scale ($k_0$) and our tests on UHF-band simulated data
achieved a precision of 10%. This improved to 2.4% when using the full SKAO. We
demonstrate how the results are robust to foreground contamination by using
transfer functions, even when an incorrect cosmology has been assumed in their
construction. Given that the turnover is related to the horizon scale at
matter-radiation equality, a sufficiently precise constraint of $k_0$ presents
the possibility for a novel probe of cosmology. We therefore present a
potential methodology for constructing a standard-ruler-based distance
measurement, independent of the sound horizon, using the turnover location in
the HI power spectrum.


title: How and what to learn:The modes of machine learning

abstract:
  We proposal a new approach, namely the weight pathway analysis (WPA), to
study the mechanism of multilayer neural networks. The weight pathways linking
neurons longitudinally from input neurons to output neurons are considered as
the basic units of a neural network. We decompose a neural network into a
series of subnetworks of weight pathways, and establish characteristic maps for
these subnetworks. The parameters of a characteristic map can be visualized,
providing a longitudinal perspective of the network and making the neural
network explainable. Using WPA, we discover that a neural network stores and
utilizes information in a "holographic" way, that is, the network encodes all
training samples in a coherent structure. An input vector interacts with this
"holographic" structure to enhance or suppress each subnetwork which working
together to produce the correct activities in the output neurons to recognize
the input sample. Furthermore, with WPA, we reveal fundamental learning modes
of a neural network: the linear learning mode and the nonlinear learning mode.
The former extracts linearly separable features while the latter extracts
linearly inseparable features. It is found that hidden-layer neurons
self-organize into different classes in the later stages of the learning
process. It is further discovered that the key strategy to improve the
performance of a neural network is to control the ratio of the two learning
modes to match that of the linear and the nonlinear features, and that
increasing the width or the depth of a neural network helps this ratio
controlling process. This provides theoretical ground for the practice of
optimizing a neural network via increasing its width or its depth. The
knowledge gained with WPA enables us to understand the fundamental questions
such as what to learn, how to learn, and how can learn well.

categories: cs.LG cond-mat.dis-nn physics.data-an stat.ML


title: Curb Your Self-Modifying Code

abstract:
  Self-modifying code has many intriguing applications in a broad range of
fields including software security, artificial general intelligence, and
open-ended evolution. Having control over self-modifying code, however, is
still an open challenge since it is a balancing act between providing as much
freedom as possible to not limit possible solutions and restriction to avoid
security issues and invalid code or solutions. In the present study, I provide
a prototype implementation of how one might curb self-modifying code by
introducing a control mechanisms for code modifications within specific regions
and for specific transitions between code and data. I show that this is
possible to achieve with the so-called allagmatic method - an approach to
formalise, model, implement, and interpret complex systems inspired by Gilbert
Simondon's philosophy of individuation and Alfred North Whitehead's philosophy
of organism. Thereby, the allagmatic method serves as guidance for
self-modification based on concepts defined in a metaphysical framework.


title:
Ultra-Efficient Resistance Switching between Charge Ordered Phases in
  1T-TaS$_2$ with a Single Picosecond Electrical Pulse

abstract:
  Progress in high-performance computing demands significant advances in memory
technology. Among novel memory technologies that promise efficient device
operation on a sub-ns timescale, resistance switching between charge ordered
phases of the 1T-TaS$_2$ has shown to be potentially useful for the development
of high-speed, energy efficient non-volatile memory device. While ultrafast
switching was previously reported with optical pulses, determination of the
intrinsic speed limits of actual devices that are triggered by electrical
pulses is technically challenging and hitherto still largely unexplored. A new
optoelectronic laboratory-on-a-chip, designed for measurements of ultrafast
memory switching, enables an accurate measurement of the electrical switching
parameters with 100 fs temporal resolution. A photoconductive response is used
for ultrashort electrical pulse generation, while its propagation along a
coplanar transmission line is detected using electro-optical sampling using a
purpose-grown highly-resistive electro-optic (Cd,Mn)Te crystal substrate. By
combining the transmission line and the 1T-TaS$_2$ device in a single
optoelectronic circuit a non-volatile resistance switching with a single 1.9 ps
electrical pulse is demonstrated, with an extremely small switching energy
density per unit area E$_A$ = 9.4 fJ/$\mu$m$^2$. The experiments demonstrate
ultrafast, energy-efficient circuits utilizing switching between non-volatile
charge-ordered states offers a new technological platform for cryogenic memory
devices.


title: Monotonicity of the $p$-Green functions

abstract:
  On a complete $p$-nonparabolic $3$-dimensional manifold with non-negative
scalar curvature and vanishing second homology, we establish a sharp
monotonicity formula for the proper $p$-Green function along its level sets for
$1<p<3$. This can be viewed as a generalization of the recent result by
Munteanu-Wang \cite{MunteanuWang2021} in the case of $p=2$. No smoothness
assumption is made on the $p$-Green function when $1<p\leq 2$. Several rigidity
results are also proven.


title: Formally verified asymptotic consensus in robust networks

abstract:
  Distributed architectures are used to improve performance and reliability of
various systems. An important capability of a distributed architecture is the
ability to reach consensus among all its nodes. To achieve this, several
consensus algorithms have been proposed for various scenarii, and many of these
algorithms come with proofs of correctness that are not mechanically checked.
Unfortunately, those proofs are known to be intricate and prone to errors.
  In this paper, we formalize and mechanically check a consensus algorithm
widely used in the distributed controls community: the Weighted-Mean
Subsequence Reduced (W-MSR) algorithm proposed by Le Blanc et al. This
algorithm provides a way to achieve asymptotic consensus in a distributed
controls scenario in the presence of adversarial agents (attackers) that may
not update their states based on the nominal consensus protocol, and may share
inaccurate information with their neighbors. Using the Coq proof assistant, we
formalize the necessary and sufficient conditions required to achieve resilient
asymptotic consensus under the assumed attacker model. We leverage the existing
Coq formalizations of graph theory, finite sets and sequences of the mathcomp
library for our development. To our knowledge, this is the first mechanical
proof of an asymptotic consensus algorithm. During the formalization, we
clarify several imprecisions in the paper proof, including an imprecision on
quantifiers in the main theorem.


title: Convexity and uncertainty in operational quantum foundations

abstract:
  To find the essential nature of quantum theory has been an important problem
for not only theoretical interest but also applications to quantum
technologies. In those studies on quantum foundations, the notion of
uncertainty plays a primary role among several stunning features of quantum
theory. The purpose of this thesis is to investigate fundamental aspects of
uncertainty. In particular, we address this problem focusing on convexity,
which has an operational origin. We first try to reveal why in quantum theory
similar bounds are often obtained for two types of uncertainty relations,
namely, preparation and measurement uncertainty relations. To do this, we
consider uncertainty relations in the most general framework of physics called
generalized probabilistic theories (GPTs). It is proven that some geometric
structures of states connect those two types of uncertainty relations in GPTs
in terms of several expressions such as entropic one. Our result implies what
is essential for the close relation between those uncertainty relations. Then
we consider a broader expression of uncertainty in quantum theory called
quantum incompatibility. Motivated by an operational intuition, we propose and
investigate new quantifications of incompatibility which are related directly
to the convexity of states. It is also shown that there can be observed a
notable phenomenon for those quantities even in the simplest incompatibility
for a pair of mutually unbiased qubit observables. Finally, we study
thermodynamical entropy of mixing in quantum theory, which also can be seen as
a quantification of uncertainty. We consider its operationally natural
extension to GPTs, and then try to characterize how specific the entropy in
quantum theory is. It is shown that the operationally natural entropy is
allowed to exist only in classical and quantum-like theories among a class of
GPTs called regular polygon theories.


title:
The chemical nature of Orion protostars: Are ORANGES different from
  PEACHES? ORANGES II

abstract:
  Understanding the chemical past of our Sun and how life appeared on Earth is
no mean feat. The best strategy we can adopt is to study newborn stars located
in an environment similar to the one in which our Sun was born and assess their
chemical content. In particular, hot corinos are prime targets since recent
studies showed correlations between interstellar Complex Organic Molecules
(iCOMs) abundances from hot corinos and comets. The ORion ALMA New GEneration
Survey (ORANGES) aims to assess the number of hot corinos in the closest and
best analogue to our Sun's birth environment, the OMC-2/3 filament. In this
context, we investigated the chemical nature of 19 solar-mass protostars and
found that 26\% of our sample sources shows warm methanol emission indicative
of hot corinos. Compared to the Perseus low-mass star-forming region, where the
PErseus ALMA CHEmistry Survey (PEACHES) detected $\sim 60$\% of hot corinos,
the latter seem to be relatively scarce in the OMC-2/3 filament. While this
suggests that the chemical nature of protostars in Orion and Perseus is
different, improved statistics are needed in order to consolidate this result.
If the two regions are truly different, this would indicate that the
environment is likely playing a role in shaping the chemical composition of
protostars.


title: Relativistic topological molecular crystals

abstract:
  Topological phases usually are difficult to realize in molecular solids
because the intermolecular interactions are in general too weak to mediate band
inversion. In this work, however, we propose that nontrivial electronic
topology may ubiquitously emerge in a class of molecular crystals that contain
interstitial electron orbitals and heavy elements, whereby spin-orbit coupling
drives band inversion between the interstitial and the molecular orbitals. We
provide guidelines to hunt for such relativistic topological molecular
crystals, and utilize first-principles calculations to illustrate the
extraordinary characteristics of the representative candidate
$\mathrm{K}_4\mathrm{Ba}_2[\mathrm{Sn}\mathrm{Bi}_4]$: (1) It hosts cleavable
surfaces along multiple orientations, with pronounced topological boundary
states free from dangling bonds. (2) Strong response to external pressure or
strain, whereby topological phase transition may occur under relatively low
pressure. (3) High-efficiency thermoelectricity, with the Seebeck coefficient
as high as 240 $\mu$VK$^{-1}$, and the lattice thermal conductivity around 0.22
- 0.23 Wm$^{-1}$K$^{-1}$ at 300 K. (4) Ultralow work function (2.2 eV) caused
by the active interstitial orbitals. Our work suggests a pathway of realizing
topological phases in molecular materials.


abstract:
  We propose Fuse Local and Global Semantics in Representation Learning (FLAGS)
to generate richer representations. FLAGS aims at extract both global and local
semantics from images to benefit various downstream tasks. It shows promising
results under common linear evaluation protocol. We also conduct detection and
segmentation on PASCAL VOC and COCO to show the representations extracted by
FLAGS are transferable.


title:
Long-term Variations in Solar Activity: Predictions for Amplitude and
  North--South Asymmetry of Solar Cycle 25

abstract:
  We analysed the sunspot group data from Greenwich Photoheliographic Results
(GPR) during 1874-1976 and Debrecen Photoheliographic Data (DPD) during
1977-2017 and studied the cycle-to-cycle variations in the values of 13-month
smoothed monthly mean sunspot-group area in whole sphere (WSGA), northern
hemisphere (NSGA), and southern hemisphere (SSGA) at the epochs of maxima of
Sunspot Cycles 12-24, and at the epochs of maxima of WSGA, NSGA, and SSGA
Cycles 12-24. The cosine fits to the values of WSGA, NSGA, and SSGA at the
maxima of the sunspot number, WSGA, NSGA, and SSGA Cycles 12-24, and to the
values of the corresponding north-south asymmetry, suggest the existence of a
~132-year periodicity in the activity of northern hemisphere, a 54-66-year
periodicity in the activity of southern hemisphere, and a 50-66 year
periodicity in the north-south asymmetry in activity at all the aforementioned
epochs. By extrapolating the best-fit cosine curves we predicted the amplitudes
and the corresponding north-south asymmetry of the 25th WSGA, NSGA, and SSGA
cycles. We find that on average Solar Cycle 25 in sunspot-group area would be
to some extent smaller than Solar Cycle 24 in sunspot-group area. However, by
inputting the predicted amplitudes of the 25th WSGA, NSGA, and SSGA cycles in
the linear relationship between sunspot-group area and sunspot number we find
that the amplitude (130 +or- 12) of Sunspot Cycle 25 would be slightly larger
than that of reasonably small Sunspot Cycle 24. Still it confirms that the
beginning of the upcoming Gleissberg cycle would take place around Solar Cycle
25. We also find that except at the maximum of NSGA Cycle 25 where the strength
of activity in northern hemisphere would be dominant, the strength of activity
in the southern hemisphere would be dominant at the maximum epochs of the 25th
sunspot, WSGA, and SSGA cycles.


title:
Spectropolarimetry and spectral decomposition of high-accreting Narrow
  Line Seyfert 1 galaxies

abstract:
  Narrow Line Seyfert 1 (NLSy1) galaxies have been shown to have high Eddington
ratios and relatively small black hole masses. The measurement of the black
hole masses is based on the virial relation which is dependent on the
distribution of the line-emitting gas and the viewing angle to the source.
Spectropolarimetry enables us to probe the geometry of this line-emitting gas
and allows us to estimate independently the viewing angle of the source by
comparing the spectrum viewed under natural light and in the polarized light.
We performed spectropolarimetric observations of three NLSy1 - Mrk 1044, SDSS
J080101.41+184840.7, and IRAS 04416+1215 using the European Southern
Observatory's Very Large Telescope. We use ESO Reflex workflow to perform
standard data reduction and extract the natural and polarized spectra. We
estimate the Stokes parameters and the viewing angles of the three sources. We
model the Stokes parameters and infer the properties of the scattering media -
located in the equatorial and polar regions, and simulate the spectra observed
both in natural light and in polarized light using the polarization radiative
transfer code STOKES. We confirm that all three sources are high Eddington
ratio objects. We are successful in recovering the observed H$\alpha$ line
profile both in the natural and polarized light using the STOKES modelling. We
recover the polarization fractions of the order of 0.2-0.5% for the three
sources. Our principal component analysis shows that the sample of the 25
sources including our sources, Fairall 9 from Jiang et al. (2021), and sources
from Capetti et al. (2021) are mainly driven by the black hole mass and
Eddington ratio. We re-affirm the connection of the strength of the optical
FeII emission with the Eddington ratio. But, the dependence on the viewing
angle is moderate, more like a secondary effect.


title:
Text Smoothing: Enhance Various Data Augmentation Methods on Text
  Classification Tasks

abstract:
  Before entering the neural network, a token is generally converted to the
corresponding one-hot representation, which is a discrete distribution of the
vocabulary. Smoothed representation is the probability of candidate tokens
obtained from a pre-trained masked language model, which can be seen as a more
informative substitution to the one-hot representation. We propose an efficient
data augmentation method, termed text smoothing, by converting a sentence from
its one-hot representation to a controllable smoothed representation. We
evaluate text smoothing on different benchmarks in a low-resource regime.
Experimental results show that text smoothing outperforms various mainstream
data augmentation methods by a substantial margin. Moreover, text smoothing can
be combined with those data augmentation methods to achieve better performance.


title: On $B_{h}[1]$-sets which are asymptotic bases of order $2h$

abstract:
  Let $h,k \ge 2$ be integers. A set $A$ of positive integers is called
asymptotic basis of order $k$ if every large enough positive integer can be
written as the sum of $k$ terms from $A$. A set of positive integers $A$ is
said to be a $B_{h}[g]$-set if every positive integer can be written as the sum
of $h$ terms from $A$ at most $g$ different ways. In this paper we prove the
existence of $B_{h}[1]$ sets which are asymptotic bases of order $2h$ by using
probabilistic methods.


title:
Sub-harmonic parametric instability in nearly-brimful
  circular-cylinders: a weakly nonlinear analysis

abstract:
  In lab-scale Faraday experiments, meniscus waves respond harmonically to
small-amplitude forcing without threshold, hence potentially cloaking the
instability onset of parametric waves. Their suppression can be achieved by
resorting to a contact line pinned at the container brim with static contact
angle $\theta_s=90^{\circ}$ (brimful condition). However, tunable meniscus
waves are desired in some applications as those of liquid-based biosensors,
where they can be controlled adjusting the shape of the static meniscus by
slightly under/over-filling the vessel ($\theta_s\ne90^{\circ}$) while keeping
the contact line fixed at the brim. Here, we refer to this wetting condition as
nearly-brimful. Although classic inviscid theories based on Floquet analysis
have been reformulated for the case of a pinned contact line (Kidambi 2013),
accounting for (i) viscous dissipation and (ii) static contact angle effects,
including meniscus waves, makes such analyses practically intractable and a
comprehensive theoretical framework is still lacking. Aiming at filling this
gap, in this work we formalize a weakly nonlinear analysis via multiple
timescale method capable to predict the impact of (i) and (ii) on the
instability onset of viscous sub-harmonic standing waves in both brimful and
nearly-brimful circular-cylinders. Notwithstanding that the form of the
resulting amplitude equation is in fact analogous to that obtained by symmetry
arguments (Douady 1990), the normal form coefficients are here computed
numerically from first principles, thus allowing us to rationalize and
systematically quantify the modifications on the Faraday tongues and on the
associated bifurcation diagrams induced by the interaction of meniscus and
sub-harmonic parametric waves.


abstract:
  With the rapid progress of generation technology, it has become necessary to
attribute the origin of fake images. Existing works on fake image attribution
perform multi-class classification on several Generative Adversarial Network
(GAN) models and obtain high accuracies. While encouraging, these works are
restricted to model-level attribution, only capable of handling images
generated by seen models with a specific seed, loss and dataset, which is
limited in real-world scenarios when fake images may be generated by privately
trained models. This motivates us to ask whether it is possible to attribute
fake images to the source models' architectures even if they are finetuned or
retrained under different configurations. In this work, we present the first
study on Deepfake Network Architecture Attribution to attribute fake images on
architecture-level. Based on an observation that GAN architecture is likely to
leave globally consistent fingerprints while traces left by model weights vary
in different regions, we provide a simple yet effective solution named DNA-Det
for this problem. Extensive experiments on multiple cross-test setups and a
large-scale dataset demonstrate the effectiveness of DNA-Det.


title: All Graphs with at most 8 nodes are 2-interval-PCGs

abstract:
  A graph G is a multi-interval PCG if there exist an edge weighted tree T with
non-negative real values and disjoint intervals of the non-negative real
half-line such that each node of G is uniquely associated to a leaf of T and
there is an edge between two nodes in G if and only if the weighted distance
between their corresponding leaves in T lies within any such intervals. If the
number of intervals is k, then we call the graph a k-interval-PCG; in symbols,
G = k-interval-PCG (T, I1, . . . , Ik). It is known that 2-interval-PCGs do not
contain all graphs and the smallest known graph outside this class has 135
nodes. Here we prove that all graphs with at most 8 nodes are 2-interval-PCGs,
so doing one step towards the determination of the smallest value of n such
that there exists an n node graph that is not a 2-interval-PCG.


title:
Role of electronic excitation, relaxation and transport processes for
  X-ray induced ultrafast demagnetization within magnetic multilayer systems

abstract:
  We investigated the role of electronic excitation, relaxation and transport
processes in X-ray induced ultrafast demagnetization of magnetic multilayer
systems. In what follows, we report on the results obtained with the newly
developed modeling tool, XSPIN, which enables nanoscopic description of
electronic processes occurring in X-ray irradiated ferromagnetic materials.
With this tool, we have studied the specific response of Co/Pt multilayer
system irradiated by an ultrafast XUV pulse at the M-edge of Co (photon energy
~ 60 eV). It was previously studied experimentally at the FERMI
free-electron-laser facility, using the magnetic small-angle X-ray scattering
technique. The XSPIN simulations show that the magnetic scattering signal from
cobalt decreases - on the femtosecond timescales considered - due to electronic
excitation, relaxation and transport processes both in the cobalt and in the
platinum layers. The signal decrease scales with the increasing fluence of
incoming radiation, following the trend observed in the experimental data.
Confirmation of the predominant role of electronic processes for X-ray induced
demagnetization in the regime below the structural damage threshold, achieved
with our theoretical study, is a step towards quantitative control and
manipulation of X-ray induced magnetic processes on femtosecond timescales.


title: Improved bounds for acyclic coloring parameters

abstract:
  The acyclic chromatic number of a graph is the least number of colors needed
to properly color its vertices so that none of its cycles has only two colors.
The acyclic chromatic index is the analogous graph parameter for edge
colorings. We first show that the acyclic chromatic index is at most
$2\Delta-1$, where $\Delta$ is the maximum degree of the graph. We then show
that for all $\epsilon >0$ and for $\Delta$ large enough (depending on
$\epsilon$), the acyclic chromatic number of the graph is at most
$\lceil(2^{-1/3} +\epsilon) {\Delta}^{4/3} \rceil +\Delta+ 1$. Both results
improve long chains of previous successive advances. Both are algorithmic, in
the sense that the colorings are generated by randomized algorithms. However,
in contrast with extant approaches, where the randomized algorithms assume the
availability of enough colors to guarantee properness deterministically, and
use additional colors for randomization in dealing with the bichromatic cycles,
our algorithms may initially generate colorings that are not necessarily
proper; they only aim at avoiding cycles where all pairs of edges, or vertices,
that are one edge, or vertex, apart in a traversal of the cycle are
homochromatic (of the same color). When this goal is reached, they check for
properness and if necessary they repeat until properness is attained.


title:
TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo
  Camera for Automated Driving Vehicles with Uncertainty Analysis

abstract:
  In this paper, we present TEScalib, a novel extrinsic self-calibration
approach of LiDAR and stereo camera using the geometric and photometric
information of surrounding environments without any calibration targets for
automated driving vehicles. Since LiDAR and stereo camera are widely used for
sensor data fusion on automated driving vehicles, their extrinsic calibration
is highly important. However, most of the LiDAR and stereo camera calibration
approaches are mainly target-based and therefore time consuming. Even the newly
developed targetless approaches in last years are either inaccurate or
unsuitable for driving platforms.
  To address those problems, we introduce TEScalib. By applying a 3D mesh
reconstruction-based point cloud registration, the geometric information is
used to estimate the LiDAR to stereo camera extrinsic parameters accurately and
robustly. To calibrate the stereo camera, a photometric error function is
builded and the LiDAR depth is involved to transform key points from one camera
to another. During driving, these two parts are processed iteratively. Besides
that, we also propose an uncertainty analysis for reflecting the reliability of
the estimated extrinsic parameters. Our TEScalib approach evaluated on the
KITTI dataset achieves very promising results.


title:
Higher-order topological phases emerging from the Su-Schrieffer-Heeger
  stacking

abstract:
  In this work, we develop a systematical approach of constructing and
classifying the model Hamiltonians for two-dimensional (2D) higher-order
topological phase with corner zero energy states (CZESs). Our approach is based
on the direct construction of analytical solution of the CZESs in a series of
2D systems that stack the 1D extended Su-Schrieffer-Heeger (SSH) model, two
copies of the original SSH model, along two orthogonal directions.
Fascinatingly, our approach not only gives the celebrated
Benalcazar-Bernevig-Hughes and 2D SSH models but also reveals a novel model and
we refer it to crossed 2D SSH model. Although these three models exhibit
completely different bulk topology, we find that the CZESs can be universally
characterized by edge winding number for 1D edge states, attributing to their
unified Hamiltonian construction form and edge topology. Remarkably, our
principle of obtaining CZESs can be readily generalized to arbitrary dimension
and superconducting systems. Thus, our work sheds new light on the theoretical
understanding of the higher-order topological phase and paves the way to
looking for higher-order topological insulators and superconductors.


title:
Error-budgeting for a controlled-phase gate with strontium-88 Rydberg
  atoms

abstract:
  We study the implementation of a high fidelity controlled-phase gate in a
Rydberg quantum computer. The protocol is based on a symmetric gate with
respect to the two qubits as experimentally realized by Levine et al [Phys.
Rev. Lett. 123, 170503 (2019)], but allows for arbitrary pulse shapes with
time-dependent detuning. Optimizing the pulse shapes, we introduce laser pulses
which shorten the time spent in the Rydberg state by 10% and reduce the leading
contribution to the gate infidelity, i.e., the decay from the Rydberg state.
Remarkably, this reduction can be achieved for smooth pulses in detuning and
smooth turning on of the Rabi frequency as required in any experimental
realization. We carefully analyze the influence of fundamental error sources
such as the photon recoil, the microscopic interaction potential, as well as
the harmonic trapping of the atoms for an experimentally realistic setup based
on strontium-88 atoms. We find that an average gate fidelity above 99.9% is
possible for a very conservative estimation of experimental parameters.


title:
Experimental study of the $^{40,48}$Ca+ $^{40,48}$Ca reactions at 35
  MeV/nucleon

abstract:
  In this article we investigate $^{40,48}$Ca+$^{40,48}$Ca peripheral and
semi-peripheral reactions at 35 MeV/nucleon. Data were obtained using the
unique coupling of the VAMOS high acceptance spectrometer and the INDRA charged
particle multidetector.The spectrometer allowed high resolution measurement of
charge, mass and velocity of the cold projectile-like fragment (PLF), while the
INDRA detector recorded coincident charged particles with nearly $4\pi$
acceptance.The measured isotopic composition of the PLF identified in VAMOS and
the average light charged particle (LCP) multiplicities are promising
observables to study the isospin diffusion.The detection of the PLF in
coincidence with LCP allows the reconstruction of the mass, charge and
excitation energy of the associated initial quasi-projectile nuclei (QP), as
well as the extraction of apparent temperatures.We investigate the suitability
of the isoscaling method with the PLF and the experimental reconstructed QP.The
extracted $\alpha$ and $\Delta$ isoscaling parameters present a dependence on
the considered system combination that could justify their use as a surrogate
for isospin asymmetry in isospin transport studies.The reconstruction of the QP
allows to observe an evolution of the $\alpha/\Delta$ with the size of the QP,
the latter being consistent with a strong surface contribution to the symmetry
energy term in finite nuclei. This leads to the conclusion that the
reconstruction of the primary source is mandatory for the study of the symmetry
energy term based on the isoscaling method for such reactions.


title: The Causal Marginal Polytope for Bounding Treatment Effects

abstract:
  Due to unmeasured confounding, it is often not possible to identify causal
effects from a postulated model. Nevertheless, we can ask for partial
identification, which usually boils down to finding upper and lower bounds of a
causal quantity of interest derived from all solutions compatible with the
encoded structural assumptions. One appealing way to derive such bounds is by
casting it in terms of a constrained optimization method that searches over all
causal models compatible with evidence, as introduced in the classic work of
Balke and Pearl (1994) for discrete data. Although by construction this
guarantees tight bounds, it poses a formidable computational challenge. To cope
with this issue, alternatives include algorithms that are not guaranteed to be
tight, or by introducing restrictions on the class of models. In this paper, we
introduce a novel alternative: inspired by ideas coming from belief
propagation, we enforce compatibility between marginals of a causal model and
data, without constructing a global causal model. We call this collection of
locally consistent marginals the causal marginal polytope. As global
independence constraints disappear when considering small dimensional tractable
marginals, this also leads to a rethinking of how to elicit and express causal
knowledge. We provide an explicit algorithm and implementation of this idea,
and assess its practicality with numerical experiments.


title: Hyperbolic Graph Neural Networks: A Review of Methods and Applications

abstract:
  Graph neural networks generalize conventional neural networks to
graph-structured data and have received widespread attention due to their
impressive representation ability. In spite of the remarkable achievements, the
performance of Euclidean models in graph-related learning is still bounded and
limited by the representation ability of Euclidean geometry, especially for
datasets with highly non-Euclidean latent anatomy. Recently, hyperbolic space
has gained increasing popularity in processing graph data with tree-like
structure and power-law distribution, owing to its exponential growth property.
In this survey, we comprehensively revisit the technical details of the current
hyperbolic graph neural networks, unifying them into a general framework and
summarizing the variants of each component. More importantly, we present
various HGNN-related applications. Last, we also identify several challenges,
which potentially serve as guidelines for further flourishing the achievements
of graph learning in hyperbolic spaces.


title:
Severity classification in cases of Collagen VI-related myopathy with
  Convolutional Neural Networks and handcrafted texture features

abstract:
  Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical
assessment of low-prevalence neuromuscular disorders. Automated diagnosis
methods might reduce the need for biopsies and provide valuable information on
disease follow-up. In this paper, three methods are proposed to classify target
muscles in Collagen VI-related myopathy cases, based on their degree of
involvement, notably a Convolutional Neural Network, a Fully Connected Network
to classify texture features, and a hybrid method combining the two feature
sets. The proposed methods was evaluated on axial T1-weighted Turbo Spin-Echo
MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy or
Bethlem Myopathy patients at different evolution stages. The best results were
obtained with the hybrid model, resulting in a global accuracy of 93.8\%, and
F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases,
respectively.


title: Gravitational instantons with conformally coupled scalar fields

abstract:
  We present novel regular Euclidean solutions to General Relativity in
presence of Maxwell and conformally coupled scalar fields. In particular, we
consider metrics of the Eguchi-Hanson and Taub-NUT families to solve the field
equations analytically. The solutions have nontrivial topology labeled by the
Hirzebruch signature and Euler characteristic that we compute explicitly. We
find that, although the solutions are locally inequivalent with the original
(anti-)self-dual Eguchi-Hanson metric, their asymptotically locally Euclidean
limit leads to the same global properties. We revisit the Taub-NUT solution
previously found in the literature, analyze their nuts and bolts structure, and
obtain the renormalized Euclidean on-shell action as well as their topological
invariants. Additionally, we discuss how the solutions get modified in presence
of higher-curvature corrections that respect conformal invariance. In the
conformally invariant case, we obtain novel Eguchi-Hanson and Taub-NUT
solutions and demonstrate that both Euclidean on-shell action and Noether-Wald
charges are finite without any reference to intrinsic boundary counterterms.


title:
Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles
  with Adaptive Truncated Signed Distance Function

abstract:
  The Large-scale 3D reconstruction, texturing and semantic mapping are
nowadays widely used for automated driving vehicles, virtual reality and
automatic data generation. However, most approaches are developed for RGB-D
cameras with colored dense point clouds and not suitable for large-scale
outdoor environments using sparse LiDAR point clouds. Since a 3D surface can be
usually observed from multiple camera images with different view poses, an
optimal image patch selection for the texturing and an optimal semantic class
estimation for the semantic mapping are still challenging.
  To address these problems, we propose a novel 3D reconstruction, texturing
and semantic mapping system using LiDAR and camera sensors. An Adaptive
Truncated Signed Distance Function is introduced to describe surfaces
implicitly, which can deal with different LiDAR point sparsities and improve
model quality. The from this implicit function extracted triangle mesh map is
then textured from a series of registered camera images by applying an optimal
image patch selection strategy. Besides that, a Markov Random Field-based data
fusion approach is proposed to estimate the optimal semantic class for each
triangle mesh. Our approach is evaluated on a synthetic dataset, the KITTI
dataset and a dataset recorded with our experimental vehicle. The results show
that the 3D models generated using our approach are more accurate in comparison
to using other state-of-the-art approaches. The texturing and semantic mapping
achieve also very promising results.


title: Dynamic Spatiotemporal ARCH Models

abstract:
  Geo-referenced data are characterized by an inherent spatial dependence due
to the geographical proximity. In this paper, we introduce a dynamic
spatiotemporal autoregressive conditional heteroscedasticity (ARCH) process to
describe the effects of (i) the log-squared time-lagged outcome variable, i.e.,
the temporal effect, (ii) the spatial lag of the log-squared outcome variable,
i.e., the spatial effect, and (iii) the spatial lag of the log-squared
time-lagged outcome variable, i.e., the spatiotemporal effect, on the
volatility of an outcome variable. Furthermore, our suggested process allows
for the fixed effects over time and space to account for the unobserved
heterogeneity. For this dynamic spatiotemporal ARCH model, we derive a
generalized method of moments (GMM) estimator based on the linear and quadratic
moment conditions of a specific transformation. We show the consistency and
asymptotic normality of the GMM estimator, and determine the best set of moment
functions. We investigate the finite-sample properties of the proposed GMM
estimator in a series of Monte-Carlo simulations with different model
specifications and error distributions. Our simulation results show that our
suggested GMM estimator has good finite sample properties. In an empirical
application, we use monthly log-returns of the average condominium prices of
each postcode of Berlin from 1995 to 2015 (190 spatial units, 240 time points)
to demonstrate the use of our suggested model. Our estimation results show that
the temporal, spatial and spatiotemporal lags of the log-squared returns have
statistically significant effects on the volatility of the log-returns.


title: Mott correlations in ABC graphene trilayer aligned with hBN

abstract:
  The nature of the correlated phases found in some graphene heterostructures
is under debate. We use dynamical mean-field theory (DMFT) to analyze the
effect of local correlations close to half-filling on one of such systems, the
ABC trilayer graphene aligned with hexagonal boron nitride (ABC/hBN), which
presents a moir\'e superlattice. This system has shown insulating phases at
integer fillings of the moir\'e lattice, precisely the fillings at which a
sufficiently strong Coulomb interaction (U$_{\rm Mott}$) may produce a
metal-insulator Mott transition. Our calculations show that the electronic
states are strongly affected by a significant spectral weight transfer at
interactions with magnitudes expected to be relevant in experiments. This
effect, which emerges at interactions considerably smaller than U$_{\rm Mott}$
and does not require symmetry breaking, impacts the electronic properties at
temperatures above the magnetic transitions producing anomalous temperature and
doping dependences not present without alignment to hBN. Close to the Mott
transition we find that onsite interactions promote an antiferromagnetic (AF)
state, probably breaking the C$_3$ symmetry, that will compete with the
ferromagnetism arising from intersite exchange interactions to determine the
ground state.


title:
Out-of-plane nonlinear optical responses of superconducting cuprates
  detected by Terahertz pump-Terahertz probe spectroscopy

abstract:
  We systemically investigated the out-of-plane transient responses of
high-temperature superconductor La$_{1.905}$Ba$_{0.095}$CuO$_4$ by THz pump-THz
probe spectroscopy. Our experiments present that there are explicit
long-lasting temporal oscillations in the pump-probe decay procedure after THz
excitations in the superconducting state, which result from the THz nonlinear
electrodynamics and Josephson plasmon mode (JPM) along $c$-axis of the crystal.
We also elaborated the dissimilarities of the oscillatory signals induced by
single-cycle broad-band THz pump pulses and multi-cycle narrow-band ones. For
broad-band THz pump, the oscillation central frequency scales linearly with the
energy detected by THz probe beam, rather than centering at an individual
frequency. That linear dependent relationship has not been observed before. In
contrast, the narrow-band THz pump centering at $\omega_{\rm pump}$ turns to
consistently drive JPM like alternating electric field, which leads to an
2$\omega_{\rm pump}$ oscillation once $\omega_{\rm pump}$ approaching the
energy scale of JPM. By comparing the resonant condition, we further confirm
the invisibility of Higgs mode in the out-of-plane transient responses.


title:
The magnetic drivers of campfires seen by the Polarimetric and
  Helioseismic Imager (PHI) on Solar Orbiter

abstract:
  The Extreme Ultraviolet Imager (EUI) on board the Solar Orbiter (SO)
spacecraft observed small extreme ultraviolet (EUV) bursts, termed campfires,
that have been proposed to be brightenings near the apexes of low-lying loops
in the quiet-Sun atmosphere. The underlying magnetic processes driving these
campfires are not understood. During the cruise phase of SO and at a distance
of 0.523\,AU from the Sun, the Polarimetric and Helioseismic Imager on Solar
Orbiter (SO/PHI) observed a quiet-Sun region jointly with SO/EUI, offering the
possibility to investigate the surface magnetic field dynamics underlying
campfires at a spatial resolution of about 380~km.
  In 71\% of the 38 isolated events, campfires are confined between bipolar
magnetic features, which seem to exhibit signatures of magnetic flux
cancellation. The flux cancellation occurs either between the two main
footpoints, or between one of the footpoints of the loop housing the campfire
and a nearby opposite polarity patch. In one particularly clear-cut case, we
detected the emergence of a small-scale magnetic loop in the internetwork
followed soon afterwards by a campfire brightening adjacent to the location of
the linear polarisation signal in the photosphere, that is to say near where
the apex of the emerging loop lays. The rest of the events were observed over
small scattered magnetic features, which could not be identified as magnetic
footpoints of the campfire hosting loops. The majority of campfires could be
driven by magnetic reconnection triggered at the footpoints, similar to the
physical processes occurring in the burst-like EUV events discussed in the
literature. About a quarter of all analysed campfires, however, are not
associated to such magnetic activity in the photosphere, which implies that
other heating mechanisms are energising these small-scale EUV brightenings.


title:
Data-driven simulation of Fisher-Kolmogorov tumor growth models using
  Dynamic Mode Decomposition

abstract:
  The computer simulation of organ-scale biomechanistic models of cancer
personalized via routinely collected clinical and imaging data enables to
obtain patient-specific predictions of tumor growth and treatment response over
the anatomy of the patient's affected organ. These patient-specific
computational forecasts have been regarded as a promising approach to
personalize the clinical management of cancer and derive optimal treatment
plans for individual patients, which constitute timely and critical needs in
clinical oncology. However, the computer simulation of the underlying
spatiotemporal models can entail a prohibitive computational cost, which
constitutes a barrier to the successful development of clinically-actionable
computational technologies for personalized tumor forecasting. To address this
issue, here we propose to utilize Dynamic-Mode Decomposition (DMD) to construct
a low-dimensional representation of cancer models and accelerate their
simulation. DMD is an unsupervised machine learning method based on the
singular value decomposition that has proven useful in many applications as
both a predictive and a diagnostic tool. We show that DMD may be applied to
Fisher-Kolmogorov models, which constitute an established formulation to
represent untreated solid tumor growth that can further accommodate other
relevant cancer phenomena. Our results show that a DMD implementation of this
model over a clinically-relevant parameter space can yield impressive
predictions, with short to medium-term errors remaining under 1% and long-term
errors remaining under 20%, despite very short training periods. We posit that
this data-driven approach has the potential to greatly reduce the computational
overhead of personalized simulations of cancer models, thereby facilitating
tumor forecasting, parameter identification, uncertainty quantification, and
treatment optimization.


title:
Continuing the classification of homogeneous Kobayashi-hyperbolic
  manifolds with high-dimensional automorphism group

abstract:
  We classify all homogeneous Kobayashi-hyperbolic manifolds of dimension $n
\ge 2$ whose group of holomorphic automorphisms has dimension either $n^2 - 7$
or $n^2 - 8.$ This paper continues the work of A. Isaev, who classified all
such manifolds with automorphism group dimension $n^2 - 6$ and greater.


title: Variable Rate Compression for Raw 3D Point Clouds

abstract:
  In this paper, we propose a novel variable rate deep compression architecture
that operates on raw 3D point cloud data. The majority of learning-based point
cloud compression methods work on a downsampled representation of the data.
Moreover, many existing techniques require training multiple networks for
different compression rates to generate consolidated point clouds of varying
quality. In contrast, our network is capable of explicitly processing point
clouds and generating a compressed description at a comprehensive range of
bitrates. Furthermore, our approach ensures that there is no loss of
information as a result of the voxelization process and the density of the
point cloud does not affect the encoder/decoder performance. An extensive
experimental evaluation shows that our model obtains state-of-the-art results,
it is computationally efficient, and it can work directly with point cloud data
thus avoiding an expensive voxelized representation.


title:
Provably Efficient Convergence of Primal-Dual Actor-Critic with
  Nonlinear Function Approximation

abstract:
  We study the convergence of the actor-critic algorithm with nonlinear
function approximation under a nonconvex-nonconcave primal-dual formulation.
Stochastic gradient descent ascent is applied with an adaptive proximal term
for robust learning rates. We show the first efficient convergence result with
primal-dual actor-critic with a convergence rate of
$\mathcal{O}\left(\sqrt{\frac{\ln \left(N d G^2 \right)}{N}}\right)$ under
Markovian sampling, where $G$ is the element-wise maximum of the gradient, $N$
is the number of iterations, and $d$ is the dimension of the gradient. Our
result is presented with only the Polyak-\L{}ojasiewicz condition for the dual
variables, which is easy to verify and applicable to a wide range of
reinforcement learning (RL) scenarios. The algorithm and analysis are general
enough to be applied to other RL settings, like multi-agent RL. Empirical
results on OpenAI Gym continuous control tasks corroborate our theoretical
findings.


title:
A new face database simultaneously acquired in visible, near infrared
  and thermal spectrum

abstract:
  In this paper we present a new database acquired with three different sensors
(visible, near infrared and thermal) under different illumination conditions.
This database consists of 41 people acquired in four different acquisition
sessions, five images per session and three different illumination conditions.
The total amount of pictures is 7.380 pictures. Experimental results are
obtained through single sensor experiments as well as the combination of two
and three sensors under different illumination conditions (natural, infrared
and artificial illumination). We have found that the three spectral bands
studied contribute in a nearly equal proportion to a combined system.
Experimental results show a significant improvement combining the three
spectrums, even when using a simple classifier and feature extractor. In six of
the nine scenarios studied we obtained identification rates higher or equal to
98%, when using a trained combination rule, and two cases of nine when using a
fixed rule.


title: On the relevance of bandwidth extension for speaker identification

abstract:
  In this paper we discuss the relevance of bandwidth extension for speaker
identification tasks. Mainly we want to study if it is possible to recognize
voices that have been bandwith extended. For this purpose, we created two
different databases (microphonic and ISDN) of speech signals that were
bandwidth extended from telephone bandwidth ([300, 3400] Hz) to full bandwidth
([100, 8000] Hz). We have evaluated different parameterizations, and we have
found that the MELCEPST parameterization can take advantage of the bandwidth
extension algorithms in several situations.


title: N-dimensional nonlinear prediction with MLP

abstract:
  In this paper we propose a Non-Linear Predictive Vector quantizer (PVQ) for
speech coding, based on Multi-Layer Perceptrons. With this scheme we have
improved the results of our previous ADPCM coder with nonlinear prediction, and
we have reduced the bit rate up to 1 bit per sample.


title:
Unfolding collective AIS transmission behavior for vessel movement
  modeling on irregular timing data using noise-robust neural networks

abstract:
  This paper aims to model the Automatic Identification System (AIS) message
transmission behavior through neural networks for forecasting the upcoming AIS
messages' content for multiple vessels simultaneously in the face of messages'
irregular timing. We present a set of experiments comprising tens of algorithms
used for forecasting tasks with horizon sizes of varying lengths. Deep learning
models revealed themselves to adequately capture the temporal irregularity
while preserving the spatial awareness of different vessels. We show how a
multi-directional and multi-layer long-short-term memory network and a
convolution feature-extraction layer improve such a task by up to 20.01%.


title: A Real-World Implementation of Unbiased Lift-based Bidding System

abstract:
  In display ad auctions of Real-Time Bid-ding (RTB), a typical Demand-Side
Platform (DSP)bids based on the predicted probability of click and conversion
right after an ad impression. Recent studies find such a strategy is suboptimal
and propose a better bidding strategy named lift-based bidding.Lift-based
bidding simply bids the price according to the lift effect of the ad impression
and achieves maximization of target metrics such as sales. Despiteits
superiority, lift-based bidding has not yet been widely accepted in the
advertising industry. For one reason, lift-based bidding is less profitable for
DSP providers under the current billing rule. Second, thepractical usefulness
of lift-based bidding is not widely understood in the online advertising
industry due to the lack of a comprehensive investigation of its impact.We here
propose a practically-implementable lift-based bidding system that perfectly
fits the current billing rules. We conduct extensive experiments usinga
real-world advertising campaign and examine the performance under various
settings. We find that lift-based bidding, especially unbiased lift-based
bidding is most profitable for both DSP providers and advertisers. Our ablation
study highlights that lift-based bidding has a good property for currently
dominant first price auctions. The results will motivate the online


title:
Query Expansion and Entity Weighting for Query Reformulation Retrieval
  in Voice Assistant Systems

abstract:
  Voice assistants such as Alexa, Siri, and Google Assistant have become
increasingly popular worldwide. However, linguistic variations, variability of
speech patterns, ambient acoustic conditions, and other such factors are often
correlated with the assistants misinterpreting the user's query. In order to
provide better customer experience, retrieval based query reformulation (QR)
systems are widely used to reformulate those misinterpreted user queries.
Current QR systems typically focus on neural retrieval model training or direct
entities retrieval for the reformulating. However, these methods rarely focus
on query expansion and entity weighting simultaneously, which may limit the
scope and accuracy of the query reformulation retrieval. In this work, we
propose a novel Query Expansion and Entity Weighting method (QEEW), which
leverages the relationships between entities in the entity catalog (consisting
of users' queries, assistant's responses, and corresponding entities), to
enhance the query reformulation performance. Experiments on Alexa annotated
data demonstrate that QEEW improves all top precision metrics, particularly 6%
improvement in top10 precision, compared with baselines not using query
expansion and weighting; and more than 5% improvement in top10 precision
compared with other baselines using query expansion and weighting.


title: Simulating Network Paths with Recurrent Buffering Units

abstract:
  Simulating physical network paths (e.g., Internet) is a cornerstone research
problem in the emerging sub-field of AI-for-networking. We seek a model that
generates end-to-end packet delay values in response to the time-varying load
offered by a sender, which is typically a function of the previously output
delays. We formulate an ML problem at the intersection of dynamical systems,
sequential decision making, and time-series generative modeling. We propose a
novel grey-box approach to network simulation that embeds the semantics of
physical network path in a new RNN-style architecture called Recurrent
Buffering Unit, providing the interpretability of standard network simulator
tools, the power of neural models, the efficiency of SGD-based techniques for
learning, and yielding promising results on synthetic and real-world network
traces.


title: Wastewater Pipe Rating Model Using Natural Language Processing

abstract:
  Closed-circuit video (CCTV) inspection has been the most popular technique
for visually evaluating the interior status of pipelines in recent decades.
Certified inspectors prepare the pipe repair document based on the CCTV
inspection. The traditional manual method of assessing sewage structural
conditions from pipe repair documents takes a long time and is prone to human
mistakes. The automatic identification of necessary texts has received little
attention. By building an automated framework employing Natural Language
Processing (NLP), this study presents an effective technique to automate the
identification of the pipe defect rating of the pipe repair documents. NLP
technologies are employed to break down textual material into grammatical units
in this research. Further analysis entails using words to discover pipe defect
symptoms and their frequency and then combining that information into a single
score. Our model achieves 95.0% accuracy,94.9% sensitivity, 94.4% specificity,
95.9% precision score, and 95.7% F1 score, showing the potential of the
proposed model to be used in large-scale pipe repair documents for accurate and
efficient pipeline failure detection to improve the quality of the pipeline.
Keywords: Sewer pipe inspection, Defect detection, Natural language processing,
Text recognition


title: Von Neumann equivalence and group exactness

abstract:
  We will show that the group exactness is a von Neumann equivalence invariant.
This result generalizes the previously known fact stating that the group
exactness is invariant under measure equivalence and W*-equivalence.


title:
Stability analysis of RBF-FD and WLS based local strong form meshless
  methods on scattered nodes

abstract:
  The popularity of local meshless methods in the field of numerical
simulations has increased greatly in recent years. This is mainly due to the
fact that they can operate on scattered nodes and that they allow a direct
control over the approximation order and basis functions. In this paper we
analyse two popular variants of local strong form meshless methods, namely the
radial basis function-generated finite differences (RBF-FD) using polyharmonic
splines (PHS) augmented with monomials, and the weighted least squares (WLS)
approach using only monomials. Our analysis focuses on the accuracy and
stability of the numerical solution computed on scattered nodes in a two- and
three-dimensional domain. We show that while the WLS variant is a better choice
when lower order approximations are sufficient, the RBF-FD variant exhibits a
more stable behavior and a higher accuracy of the numerical solution for higher
order approximations, but at the cost of higher computational complexity.


title: Time Series Analysis of Blockchain-Based Cryptocurrency Price Changes

abstract:
  In this paper we apply neural networks and Artificial Intelligence (AI) to
historical records of high-risk cryptocurrency coins to train a prediction
model that guesses their price. This paper's code contains Jupyter notebooks,
one of which outputs a timeseries graph of any cryptocurrency price once a CSV
file of the historical data is inputted into the program. Another Jupyter
notebook trains an LSTM, or a long short-term memory model, to predict a
cryptocurrency's closing price. The LSTM is fed the close price, which is the
price that the currency has at the end of the day, so it can learn from those
values. The notebook creates two sets: a training set and a test set to assess
the accuracy of the results.
  The data is then normalized using manual min-max scaling so that the model
does not experience any bias; this also enhances the performance of the model.
Then, the model is trained using three layers -- an LSTM, dropout, and dense
layer-minimizing the loss through 50 epochs of training; from this training, a
recurrent neural network (RNN) is produced and fitted to the training set.
Additionally, a graph of the loss over each epoch is produced, with the loss
minimizing over time. Finally, the notebook plots a line graph of the actual
currency price in red and the predicted price in blue. The process is then
repeated for several more cryptocurrencies to compare prediction models. The
parameters for the LSTM, such as number of epochs and batch size, are tweaked
to try and minimize the root mean square error.


title: Lectures on classical and quantum cosmology

abstract:
  These lecture notes introduce the reader to the hot big bang model,
cosmological perturbations, gravitational waves, the cosmic microwave
background, inflation, the singularity problem, the cosmological constant
problem and the cosmology of quantum gravity.


title:
PMC-Patients: A Large-scale Dataset of Patient Notes and Relations
  Extracted from Case Reports in PubMed Central

abstract:
  We present PMC-Patients, a dataset consisting of 167k patient notes with 3.1M
relevant article annotations and 293k similar patient annotations. The patient
notes are extracted by identifying certain sections from case reports in PubMed
Central, and those with at least CC BY-NC-SA license are re-distributed.
Patient-article relevance and patient-patient similarity are defined by
citation relationships in PubMed. We also perform four tasks with PMC-Patients
to demonstrate its utility, including Patient Note Recognition (PNR),
Patient-Patient Similarity (PPS), Patient-Patient Retrieval (PPR), and
Patient-Article Retrieval (PAR). In summary, PMC-Patients provides the
largest-scale patient notes with high quality, diverse conditions, easy access,
and rich annotations.


title:
Negative reflection of polaritons at the nanoscale in a low-loss natural
  medium

abstract:
  Negative reflection occurs when light is reflected towards the same side of
the normal to the boundary from which it is incident. This exotic optical
phenomenon, which provides a new avenue towards light manipulation, is not only
yet to be visualized in real space but remains largely unexplored both at the
nanoscale and in natural media. Here, we directly visualize nanoscale-confined
polaritons negatively reflecting on subwavelength mirrors fabricated in a
low-loss van der Waals crystal. Our near-field nanoimaging results unveil an
unconventional and broad tunability of both the polaritonic wavelength and
direction of propagation upon negative reflection. Based on these findings, we
introduce a novel device in nano-optics: a hyperbolic nanoresonator, in which
hyperbolic polaritons with different momenta reflect back to a common point
source, enhancing its intensity. These results pave the way to realize
nanophotonics in low-loss natural media, providing a novel and efficient route
to confine and control the flow of light at the nanoscale, key for future
optical on-chip nanotechnologies.


title:
Proceedings of the XI International Workshop on Locational Analysis and
  Related Problems

abstract:
  The International Workshop on Locational Analysis and Related Problems will
take place during January 31-February 1, 2022 in Elche (Spain). It is organized
by the Spanish Location Network and the Location Group GELOCA from the Spanish
Society of Statistics and Operations Research (SEIO). The Spanish Location
Network is a group of more than 140 researchers from several Spanish
universities organized into 7 thematic groups. The Network has been funded by
the Spanish Government since 2003. This edition of the conference is organized
in collaboration with project PROMETEO/2021/063 funded by the Valencian
government.
  One of the main activities of the Network is a yearly meeting aimed at
promoting the communication among its members and between them and other
researchers, and to contribute to the development of the location field and
related problems. The last meetings have taken place in Sevilla (January 23-24,
2020), C\'adiz (January 20-February 1, 2019), Segovia (September 27-29, 2017),
M\'alaga (September 14-16, 2016), Barcelona (November 25-28, 2015), Sevilla
(October 1-3, 2014), Torremolinos (M\'alaga, June 19-21, 2013), Granada (May
10-12, 2012), Las Palmas de Gran Canaria (February 2-5, 2011) and Sevilla
(February 1-3, 2010).
  The topics of interest are location analysis and related problems. This
includes location models, networks, transportation, logistics, exact and
heuristic solution methods, and computational geometry, among others.


title:
Topological transitions in fluid lipid vesicles: activation energy and
  force fields

abstract:
  Lipid bilayer membranes are the fundamental biological barriers that permit
life.
  The bilayer dynamics largely participates in orchestrating cellular workings
and is characterized by substantial stability together with extreme plasticity
that allows controlled morphological/topological changes.
  Modeling and understanding the topological change of vesicle-like membrane at
the scale of a full cell has proved an elusive aim.
  We propose and discuss here a continuum model able to encompass the
fusion/fission transition of a bilayer membrane at the scale of a Large
Unilamellar Vesicle and evaluate the minimal free energy path across the
transition, inspired by the idea that fusion/fission-inducing proteins have
evolved in Nature towards minimal work expenditure.
  The results predict the correct height for the energetic barrier and provide
the force field that, by acting on the membrane, can induce the transition.
They are found in excellent agreement, in terms of intensity, scale, and
spatial localization with experimental data on typical protein systems at play
during the transition.
  The model may pave the way for the development of more complete models of the
process where the dynamics is coupled to the fluid internal and external
environment and to other applications where the topological changes do either
occur or should be prevented.


title:
Allocating Duplicate Copies for IoT Data in Cloud Computing Based on
  Harmony Search Algorithm

abstract:
  The Internet of things (IoT) generates a plethora of data nowadays, and cloud
computing has been introduced as an efficient solution to IoT data management.
A cloud resource administrator usually adopts the replication strategy to
guarantee the reliability of IoT data. This mechanism can significantly reduce
data access time, and evidently, more replicas of data increase the data
storage cost. Furthermore, the process of selecting mini clouds for replica
allocation and sorting replicas in mini clouds is considered an NP-hard
problem. Therefore, this paper proposes an approach based on the harmony search
(HS) algorithm to allocate replicas to the IoT data in the cloud computing
environment in order to mitigate the data access cost. The HS algorithm was
employed in the proposed approach to determine the best location for data
replication in the cloud computing environment. According to the implementation
results, the proposed approach outperformed the other methods and managed to
significantly decrease data access time and delay as well as energy
consumption.


title: Resource Allocation for Single Carrier Massive MIMO Systems

abstract:
  Resource allocation in orthogonal frequency division multiplexing (OFDM)
systems is performed through allocating blocks of subcarriers to each user.
Even though OFDM is the primary waveform for 5G NR systems, research reports
have noted that single carrier modulation (SCM) offers several advantages over
OFDM in massive multiple input multiple output (MIMO) systems, making it a
preferred candidate for some future applications such as massive machine type
communications (mMTC). This paper presents a method for SCM resource allocation
and the relevant information recovery algorithms at the receiver. Our emphasis
is on cyclic prefixed SCM, where highly flexible and efficient frequency domain
detection algorithms enable the operation of many simultaneous users in a
massive MIMO uplink scenario. The proposed resource allocation method allows
the number of users to exceed the number of antennas at the base station (BS).
Each single carrier transmission is partitioned into $L$ interleaved streams,
and each user is allocated a number of such streams. One major benefit of SCM
is that each data symbol is spread over the entire bandwidth. As such, the
receiver performance is dictated by the average channel gain across the
transmission band rather than the channel gain at a given frequency bin or a
small group of frequencies. In the proposed setup, each stream may be thought
of as a resource block in SCM, analogous to resource blocks in OFDM. Hence, in
the context of this paper, the terms resource blocks and streams may be used
interchangeably.


title: Effective field theory of waterfall in hybrid inflation

abstract:
  We examine the validity of the classical approximation of the waterfall phase
transition in hybrid inflation from an effective field theory (EFT) point of
view. The EFT is constructed by integrating out the waterfall field
fluctuations, up to one-loop order in the perturbative expansion. Assuming
slow-roll conditions are obeyed, right after the onset of the waterfall phase,
we find the backreaction of the waterfall field fluctuations to the evolution
of the system can be dominant. In this case the classical approximation is
completely spoiled. We derive the necessary constraint that ensures the
validity of the EFT.


title:
EdgeMixup: Improving Fairness for Skin Disease Classification and
  Segmentation

abstract:
  Skin lesions can be an early indicator of a wide range of infectious and
other diseases. The use of deep learning (DL) models to diagnose skin lesions
has great potential in assisting clinicians with prescreening patients.
However, these models often learn biases inherent in training data, which can
lead to a performance gap in the diagnosis of people with light and/or dark
skin tones. To the best of our knowledge, limited work has been done on
identifying, let alone reducing, model bias in skin disease classification and
segmentation. In this paper, we examine DL fairness and demonstrate the
existence of bias in classification and segmentation models for subpopulations
with darker skin tones compared to individuals with lighter skin tones, for
specific diseases including Lyme, Tinea Corporis and Herpes Zoster. Then, we
propose a novel preprocessing, data alteration method, called EdgeMixup, to
improve model fairness with a linear combination of an input skin lesion image
and a corresponding a predicted edge detection mask combined with color
saturation alteration. For the task of skin disease classification, EdgeMixup
outperforms much more complex competing methods such as adversarial approaches,
achieving a 10.99% reduction in accuracy gap between light and dark skin tone
samples, and resulting in 8.4% improved performance for an underrepresented
subpopulation.


title:
Numeric Lyndon-based feature embedding of sequencing reads for machine
  learning approaches

abstract:
  Feature embedding methods have been proposed in literature to represent
sequences as numeric vectors to be used in some bioinformatics investigations,
such as family classification and protein structure prediction. Recent
theoretical results showed that the well-known Lyndon factorization preserves
common factors in overlapping strings. Surprisingly, the fingerprint of a
sequencing read, which is the sequence of lengths of consecutive factors in
variants of the Lyndon factorization of the read, is effective in preserving
sequence similarities, suggesting it as basis for the definition of novels
representations of sequencing reads. We propose a novel feature embedding
method for Next-Generation Sequencing (NGS) data using the notion of
fingerprint. We provide a theoretical and experimental framework to estimate
the behaviour of fingerprints and of the k-mers extracted from it, called
k-fingers, as possible feature embeddings for sequencing reads. As a case study
to assess the effectiveness of such embeddings, we use fingerprints to
represent RNA-Seq reads and to assign them to the most likely gene from which
they were originated as fragments of transcripts of the gene. We provide an
implementation of the proposed method in the tool lyn2vec, which produces
Lyndon-based feature embeddings of sequencing reads.


title: Uniform boundedness for algebraic groups and Lie groups

abstract:
  Let $G$ be a semisimple linear algebraic group over a field $k$ and let
$G^+(k)$ be the subgroup generated by the subgroups $R_u(Q)(k)$, where $Q$
ranges over all the minimal $k$-parabolic subgroups $Q$ of $G$. We prove that
if $G^+(k)$ is bounded then it is uniformly bounded. Under extra assumptions we
get explicit bounds for $\Delta(G^+(k))$: we prove that if $k$ is algebraically
closed then $\Delta(G^+(k))\leq 4\, {\rm rank}\,G$, and if $G$ is split over
$k$ then $\Delta(G^+(k))\leq 28\, {\rm rank}\,G$. We deduce some analogous
results for real and complex semisimple Lie groups.


title:
The reverse H\"older inequality for matrix-valued stochastic
  exponentials and applications to quadratic BSDE systems

abstract:
  In this paper, we study the connections between three concepts - the reverse
H\"older inequality for matrix-valued martingales, the well-posedness of linear
BSDEs with unbounded coefficients, and the well-posedness of quadratic BSDE
systems. In particular, we show that a linear BSDE with bmo (bounded mean
oscillation) coefficients is well-posed if and only if the stochastic
exponential of a related matrix-valued martingale satisfies a reverse H\"older
inequality. Furthermore, we give structural conditions under which these two
equivalent conditions are satisfied. Finally, we apply our results on linear
equations to obtain global well-posedness results for two new classes of
non-Markovian quadratic BSDE systems with special structure.


title:
Probing the Robustness of Trained Metrics for Conversational Dialogue
  Systems

abstract:
  This paper introduces an adversarial method to stress-test trained metrics to
evaluate conversational dialogue systems. The method leverages Reinforcement
Learning to find response strategies that elicit optimal scores from the
trained metrics. We apply our method to test recently proposed trained metrics.
We find that they all are susceptible to giving high scores to responses
generated by relatively simple and obviously flawed strategies that our method
converges on. For instance, simply copying parts of the conversation context to
form a response yields competitive scores or even outperforms responses written
by humans.


title: Several Remarks on the Numerical Integrator in Lagrangian Monte Carlo

abstract:
  Riemannian manifold Hamiltonian Monte Carlo (RMHMC) is a powerful method of
Bayesian inference that exploits underlying geometric information of the
posterior distribution in order to efficiently traverse the parameter space.
However, the form of the Hamiltonian necessitates complicated numerical
integrators, such as the generalized leapfrog method, that preserve the
detailed balance condition. The distinguishing feature of these numerical
integrators is that they involve solutions to implicitly defined equations.
Lagrangian Monte Carlo (LMC) proposes to eliminate the fixed point iterations
by transitioning from the Hamiltonian formalism to Lagrangian dynamics, wherein
a fully explicit integrator is available. This work makes several contributions
regarding the numerical integrator used in LMC. First, it has been claimed in
the literature that the integrator is only first-order accurate for the
Lagrangian equations of motion; to the contrary, we show that the LMC
integrator enjoys second order accuracy. Second, the current conception of LMC
requires four determinant computations in every step in order to maintain
detailed balance; we propose a simple modification to the integration procedure
in LMC in order to reduce the number of determinant computations from four to
two while still retaining a fully explicit numerical integration scheme. Third,
we demonstrate that the LMC integrator enjoys a certain robustness to human
error that is not shared with the generalized leapfrog integrator, which can
invalidate detailed balance in the latter case. We discuss these contributions
within the context of several benchmark Bayesian inference tasks.


title: CGAL Made More Accessible

abstract:
  We introduce bindings that enable the convenient, efficient, and reliable use
of software modules of CGAL (Computational Geometry Algorithm Library), which
are written in C++, from within code written in Python. There are different
tools that facilitate the creation of such bindings. We present a short study
that compares three main tools, which leads to the tool of choice. The
implementation of algorithms and data structures in computational geometry
presents tremendous difficulties, such as obtaining robust software despite the
use of (inexact) floating point arithmetic, found in standard hardware, and
meticulous handling of all degenerate cases, which typically are in abundance.
The code of CGAL extensively uses function and class templates in order to
handle these difficulties, which implies that the programmer has to make many
choices that are resolved during compile time (of the C++ modules). While
bindings take effect at run time (of the Python code), the type of the C++
objects that are bound must be known when the bindings are generated, that is,
when they are compiled. The types of the bound objects are instances
(instantiated types) of C++ function and class templates. The number of object
types that can potentially be bound, in implementation of generic
computational-geometry algorithms, is enormous; thus, the generation of the
bindings for all these types in advance is practically impossible. Often there
are several choices to make, resulting in a prohibitively large number of
combinations. We present a system that rapidly generates bindings for desired
object types according to user prescriptions, which enables the convenient use
of any subset of bound object types concurrently. The introduction of the
bindings made them easily accessible to newcomers and practitioners in
non-computing fields, as we report in the paper.


title:
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards
  Optimal Sample Complexity

abstract:
  Offline or batch reinforcement learning seeks to learn a near-optimal policy
using history data without active exploration of the environment. To counter
the insufficient coverage and sample scarcity of many offline datasets, the
principle of pessimism has been recently introduced to mitigate high bias of
the estimated values. While pessimistic variants of model-based algorithms
(e.g., value iteration with lower confidence bounds) have been theoretically
investigated, their model-free counterparts -- which do not require explicit
model estimation -- have not been adequately studied, especially in terms of
sample efficiency. To address this inadequacy, we study a pessimistic variant
of Q-learning in the context of finite-horizon Markov decision processes, and
characterize its sample complexity under the single-policy concentrability
assumption which does not require the full coverage of the state-action space.
In addition, a variance-reduced pessimistic Q-learning algorithm is proposed to
achieve near-optimal sample complexity. Altogether, this work highlights the
efficiency of model-free algorithms in offline RL when used in conjunction with
pessimism and variance reduction.


title:
Dust resurgence in protoplanetary disks due to planetesimal-planet
  interactions

abstract:
  Observational data on the dust content of circumstellar disks show that the
median dust content in disks around pre-main sequence stars in nearby star
forming regions seem to increase from about 1 Myr to about 2 Myr, and then
decline with time. This behaviour challenges the models where the small dust
grains steadily decline by accumulating into larger bodies and drifting inwards
on a short timescale (less than about 1 Myr). In this Letter we explore the
possibility to reconcile this discrepancy in the framework of a model where the
early formation of planets dynamically stirs the nearby planetesimals and
causes high energy impacts between them, resulting in the production of
second-generation dust. We show that the observed dust evolution can be
naturally explained by this process within a suite of representative
disk-planet architectures.